{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8VXhc5Gm3EdB"
   },
   "source": [
    "**Chapter 9 – Unsupervised Learning**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XBrrAa183Gdn"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kGDgIPUR3EdD"
   },
   "source": [
    "_This notebook contains all the sample code and solutions to the exercises in chapter 9._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fwwnxwae3EdD"
   },
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/ageron/handson-ml3/blob/main/09_unsupervised_learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml3/blob/main/09_unsupervised_learning.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YH8bhPg63EdD",
    "tags": []
   },
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ogToN5ZA3EdD"
   },
   "source": [
    "This project requires Python 3.7 or above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "gySIuwNM3EdE"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "assert sys.version_info >= (3, 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YWSvUZUX3EdE"
   },
   "source": [
    "It also requires Scikit-Learn ≥ 1.0.1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "x-EdO-pI3EdF"
   },
   "outputs": [],
   "source": [
    "from packaging import version\n",
    "import sklearn\n",
    "import numpy as np\n",
    "\n",
    "assert version.parse(sklearn.__version__) >= version.parse(\"1.0.1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FcW1tlVP3EdF"
   },
   "source": [
    "As we did in previous chapters, let's define the default font sizes to make the figures prettier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "e-arqPmQ3EdF"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rc('font', size=14)\n",
    "plt.rc('axes', labelsize=14, titlesize=14)\n",
    "plt.rc('legend', fontsize=14)\n",
    "plt.rc('xtick', labelsize=10)\n",
    "plt.rc('ytick', labelsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8MPYMVy13EdF"
   },
   "source": [
    "And let's create the `images/unsupervised_learning` folder (if it doesn't already exist), and define the `save_fig()` function which is used through this notebook to save the figures in high-res for the book:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "9zMllHSA3EdF"
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "IMAGES_PATH = Path() / \"images\" / \"unsupervised_learning\"\n",
    "IMAGES_PATH.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = IMAGES_PATH / f\"{fig_id}.{fig_extension}\"\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "haLGLZ6M3EdF"
   },
   "source": [
    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jDOV_JQc3EdG"
   },
   "source": [
    "# Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Emd-xURD3EdG"
   },
   "source": [
    "**Introduction – Classification _vs_ Clustering**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 345
    },
    "id": "ILoIwsRv3EdG",
    "outputId": "2edb5dd4-b9f4-46f2-cd67-d350f9213336"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates and saves Figure 9–1\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "data = load_iris()\n",
    "X = data.data\n",
    "y = data.target\n",
    "data.target_names\n",
    "\n",
    "plt.figure(figsize=(9, 3.5))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(X[y==0, 2], X[y==0, 3], \"yo\", label=\"Iris setosa\")\n",
    "plt.plot(X[y==1, 2], X[y==1, 3], \"bs\", label=\"Iris versicolor\")\n",
    "plt.plot(X[y==2, 2], X[y==2, 3], \"g^\", label=\"Iris virginica\")\n",
    "plt.xlabel(\"Petal length\")\n",
    "plt.ylabel(\"Petal width\")\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.scatter(X[:, 2], X[:, 3], c=\"k\", marker=\".\")\n",
    "plt.xlabel(\"Petal length\")\n",
    "plt.tick_params(labelleft=False)\n",
    "plt.gca().set_axisbelow(True)\n",
    "plt.grid()\n",
    "\n",
    "save_fig(\"classification_vs_clustering_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VtBGSRCD3EdG"
   },
   "source": [
    "**Note**: the next cell shows how a Gaussian mixture model (explained later in this chapter) can actually separate these clusters pretty well using all 4 features: petal length & width, and sepal length & width. This code maps each cluster to a class. Instead of hard coding the mapping, the code picks the most common class for each cluster using the `scipy.stats.mode()` function:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8vY_2BvW3EdG"
   },
   "source": [
    "## K-Means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZmY-qs0n3EdG"
   },
   "source": [
    "**Fit and predict**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9Cx9wAjl3EdH"
   },
   "source": [
    "Let's train a K-Means clusterer on a dataset if blobs. It will try to find each blob's center and assign each instance to the closest blob:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "6429zr1U3EdH"
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.datasets import make_blobs\n",
    "\n",
    "# extra code – the exact arguments of make_blobs() are not important\n",
    "blob_centers = np.array([[ 0.2,  2.3], [-1.5 ,  2.3], [-2.8,  1.8],\n",
    "                         [-2.8,  2.8], [-2.8,  1.3]])\n",
    "blob_std = np.array([0.4, 0.3, 0.1, 0.1, 0.1])\n",
    "X, y = make_blobs(n_samples=2000, centers=blob_centers, cluster_std=blob_std,\n",
    "                  random_state=7)\n",
    "\n",
    "k = 5\n",
    "kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "y_pred = kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C0ZUsGc-3EdH"
   },
   "source": [
    "Now let's plot them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 395
    },
    "id": "HLhECMz23EdH",
    "outputId": "59f162ac-8183-4b30-ae54-680d31944a0c"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates and saves Figure 9–2\n",
    "\n",
    "def plot_clusters(X, y=None):\n",
    "    plt.scatter(X[:, 0], X[:, 1], c=y, s=1)\n",
    "    plt.xlabel(\"$x_1$\")\n",
    "    plt.ylabel(\"$x_2$\", rotation=0)\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "plot_clusters(X)\n",
    "plt.gca().set_axisbelow(True)\n",
    "plt.grid()\n",
    "save_fig(\"blobs_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cWShxcc23EdH"
   },
   "source": [
    "Each instance was assigned to one of the 5 clusters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "igzo_QCH3EdH",
    "outputId": "b9f3cd80-6495-49ae-e47d-0642aacad577"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 4, ..., 1, 4, 2], shape=(2000,), dtype=int32)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5pZMKAC_3EdH",
    "outputId": "b16036fe-5c15-490a-8a4e-a2a22d06274d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred is kmeans.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mZJAtEy43EdH"
   },
   "source": [
    "And the following 5 _centroids_ (i.e., cluster centers) were estimated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rpLJnKpW3EdI",
    "outputId": "60fcc027-c726-48cc-8ab9-b9ecbe3a3706"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.066884  ,  2.10378803],\n",
       "       [-2.79290307,  2.79641063],\n",
       "       [-2.80214068,  1.55162671],\n",
       "       [-1.47468607,  2.28399066],\n",
       "       [ 0.47042841,  2.41380533]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F4YC6nzo3EdI"
   },
   "source": [
    "Note that the `KMeans` instance preserves the labels of the instances it was trained on. Somewhat confusingly, in this context, the _label_ of an instance is the index of the cluster that instance gets assigned to (they are not targets, they are predictions):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jHSorVw33EdI",
    "outputId": "eb442d8c-e0ee-44ff-f13e-e6dde2f9faa7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 4, ..., 1, 4, 2], shape=(2000,), dtype=int32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ac6yYQBR3EdI"
   },
   "source": [
    "Of course, we can predict the labels of new instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b6SmNAlp3EdI",
    "outputId": "111165d8-fe6c-4eef-91dc-8b8228558352"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 4, 1, 1], dtype=int32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "X_new = np.array([[0, 2], [3, 2], [-3, 3], [-3, 2.5]])\n",
    "kmeans.predict(X_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fg-WxD1L3EdI"
   },
   "source": [
    "**Decision Boundaries**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OZxNqKC53EdI"
   },
   "source": [
    "Let's plot the model's decision boundaries. This gives us a _Voronoi diagram_:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 395
    },
    "id": "6TOmO2833EdI",
    "outputId": "e12f7f93-9a44-4c00-fa78-0dca46f0632f"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates and saves Figure 9–3\n",
    "\n",
    "def plot_data(X):\n",
    "    plt.plot(X[:, 0], X[:, 1], 'k.', markersize=2)\n",
    "\n",
    "def plot_centroids(centroids, weights=None, circle_color='w', cross_color='k'):\n",
    "    if weights is not None:\n",
    "        centroids = centroids[weights > weights.max() / 10]\n",
    "    plt.scatter(centroids[:, 0], centroids[:, 1],\n",
    "                marker='o', s=35, linewidths=8,\n",
    "                color=circle_color, zorder=10, alpha=0.9)\n",
    "    plt.scatter(centroids[:, 0], centroids[:, 1],\n",
    "                marker='x', s=2, linewidths=12,\n",
    "                color=cross_color, zorder=11, alpha=1)\n",
    "\n",
    "def plot_decision_boundaries(clusterer, X, resolution=1000, show_centroids=True,\n",
    "                             show_xlabels=True, show_ylabels=True):\n",
    "    mins = X.min(axis=0) - 0.1\n",
    "    maxs = X.max(axis=0) + 0.1\n",
    "    xx, yy = np.meshgrid(np.linspace(mins[0], maxs[0], resolution),\n",
    "                         np.linspace(mins[1], maxs[1], resolution))\n",
    "    Z = clusterer.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "\n",
    "    plt.contourf(Z, extent=(mins[0], maxs[0], mins[1], maxs[1]),\n",
    "                cmap=\"Pastel2\")\n",
    "    plt.contour(Z, extent=(mins[0], maxs[0], mins[1], maxs[1]),\n",
    "                linewidths=1, colors='k')\n",
    "    plot_data(X)\n",
    "    if show_centroids:\n",
    "        plot_centroids(clusterer.cluster_centers_)\n",
    "\n",
    "    if show_xlabels:\n",
    "        plt.xlabel(\"$x_1$\")\n",
    "    else:\n",
    "        plt.tick_params(labelbottom=False)\n",
    "    if show_ylabels:\n",
    "        plt.ylabel(\"$x_2$\", rotation=0)\n",
    "    else:\n",
    "        plt.tick_params(labelleft=False)\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "plot_decision_boundaries(kmeans, X)\n",
    "save_fig(\"voronoi_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7QEaxMCd3EdJ"
   },
   "source": [
    "Not bad! Some of the instances near the edges were probably assigned to the wrong cluster, but overall it looks pretty good."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z6sInQXK3EdJ"
   },
   "source": [
    "**Hard Clustering _vs_ Soft Clustering**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PoCKDTft3EdJ"
   },
   "source": [
    "Rather than arbitrarily choosing the closest cluster for each instance, which is called _hard clustering_, it might be better to measure the distance of each instance to all 5 centroids. This is what the `transform()` method does:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-FpbHDpW3EdJ",
    "outputId": "8c09e214-b0f9-4d71-e007-53653ec7f6d2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.12, 2.9 , 2.84, 1.5 , 0.63],\n",
       "       [3.07, 5.85, 5.82, 4.48, 2.56],\n",
       "       [3.07, 0.29, 1.46, 1.69, 3.52],\n",
       "       [2.96, 0.36, 0.97, 1.54, 3.47]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.transform(X_new).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8pPyEe4E3EdU"
   },
   "source": [
    "You can verify that this is indeed the Euclidian distance between each instance and each centroid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4OGFKmD73EdU",
    "outputId": "7c28a494-9d4c-42a7-ba66-85d9142157c4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.12, 2.9 , 2.84, 1.5 , 0.63],\n",
       "       [3.07, 5.85, 5.82, 4.48, 2.56],\n",
       "       [3.07, 0.29, 1.46, 1.69, 3.52],\n",
       "       [2.96, 0.36, 0.97, 1.54, 3.47]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code\n",
    "np.linalg.norm(np.tile(X_new, (1, k)).reshape(-1, k, 2)\n",
    "               - kmeans.cluster_centers_, axis=2).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KOjGgzZZ3EdU"
   },
   "source": [
    "### The K-Means Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yEDRohun3EdU"
   },
   "source": [
    "The K-Means algorithm is one of the fastest clustering algorithms, and also one of the simplest:\n",
    "* First initialize $k$ centroids randomly: e.g., $k$ distinct instances are chosen randomly from the dataset and the centroids are placed at their locations.\n",
    "* Repeat until convergence (i.e., until the centroids stop moving):\n",
    "    * Assign each instance to the closest centroid.\n",
    "    * Update the centroids to be the mean of the instances that are assigned to them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HhchwPY53EdV"
   },
   "source": [
    "The `KMeans` class uses an optimized initialization technique by default. To get the original K-Means algorithm (for educational purposes only), you must set `init=\"random\"` and `n_init=1`. More on this later in this chapter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "41nTn2rS3EdV"
   },
   "source": [
    "Let's run the K-Means algorithm for 1, 2 and 3 iterations, to see how the centroids move around:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 794
    },
    "id": "bjm9PYv93EdV",
    "outputId": "494e3b3b-1848-4928-9174-55d6ebd2f961"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates and saves Figure 9–4\n",
    "\n",
    "kmeans_iter1 = KMeans(n_clusters=5, init=\"random\", n_init=1, max_iter=1,\n",
    "                      random_state=5)\n",
    "kmeans_iter2 = KMeans(n_clusters=5, init=\"random\", n_init=1, max_iter=2,\n",
    "                      random_state=5)\n",
    "kmeans_iter3 = KMeans(n_clusters=5, init=\"random\", n_init=1, max_iter=3,\n",
    "                      random_state=5)\n",
    "kmeans_iter1.fit(X)\n",
    "kmeans_iter2.fit(X)\n",
    "kmeans_iter3.fit(X)\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "plt.subplot(321)\n",
    "plot_data(X)\n",
    "plot_centroids(kmeans_iter1.cluster_centers_, circle_color='r', cross_color='w')\n",
    "plt.ylabel(\"$x_2$\", rotation=0)\n",
    "plt.tick_params(labelbottom=False)\n",
    "plt.title(\"Update the centroids (initially randomly)\")\n",
    "\n",
    "plt.subplot(322)\n",
    "plot_decision_boundaries(kmeans_iter1, X, show_xlabels=False,\n",
    "                         show_ylabels=False)\n",
    "plt.title(\"Label the instances\")\n",
    "\n",
    "plt.subplot(323)\n",
    "plot_decision_boundaries(kmeans_iter1, X, show_centroids=False,\n",
    "                         show_xlabels=False)\n",
    "plot_centroids(kmeans_iter2.cluster_centers_)\n",
    "\n",
    "plt.subplot(324)\n",
    "plot_decision_boundaries(kmeans_iter2, X, show_xlabels=False,\n",
    "                         show_ylabels=False)\n",
    "\n",
    "plt.subplot(325)\n",
    "plot_decision_boundaries(kmeans_iter2, X, show_centroids=False)\n",
    "plot_centroids(kmeans_iter3.cluster_centers_)\n",
    "\n",
    "plt.subplot(326)\n",
    "plot_decision_boundaries(kmeans_iter3, X, show_ylabels=False)\n",
    "\n",
    "save_fig(\"kmeans_algorithm_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RcokfD5a3EdV"
   },
   "source": [
    "**K-Means Variability**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RNTIqykb3EdV"
   },
   "source": [
    "In the original K-Means algorithm, the centroids are just initialized randomly, and the algorithm simply runs a single iteration to gradually improve the centroids, as we saw above.\n",
    "\n",
    "However, one major problem with this approach is that if you run K-Means multiple times (or with different random seeds), it can converge to very different solutions, as you can see below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 314
    },
    "id": "95xlhuVW3EdV",
    "outputId": "838e2ef8-89c2-4034-9cb8-4dbe65b34ae9"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9IAAAEpCAYAAAB/WRotAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAA8CtJREFUeJzsnXdYFNfXx7+7S12KgL1gr4mKXbA3xK5RNLETY14VSUyiqKARTRRBYoy/KJZoQjRqjGg00RiCsSvYxWgsMTY0YgNcYKm78/6xzji7O7s7s33hfp7HR5hy75lhd+587zn3HBFFURQIBAKBQCAQCAQCgUAg8EJsawMIBAKBQCAQCAQCgUBwJIiQJhAIBAKBQCAQCAQCQQBESBMIBAKBQCAQCAQCgSAAIqQJBAKBQCAQCAQCgUAQABHSBAKBQCAQCAQCgUAgCIAIaQKBQCAQCAQCgUAgEARAhDSBQCAQCAQCgUAgEAgCIEKaQCAQCAQCgUAgEAgEARAhTSAQCAQCgUAgEAgEggCIkCYQKjC9evWCSCSyaB9JSUkQiURISkqyaD8EAoFgLORZaB6++uoruLi44N69e2Zr8+jRoxCJRFi8eLFFz7ElixcvhkgkwtGjR5lt9+7dg0gkQlhYmNbx58+fR3BwMKpWrQqRSIQ2bdrw2kewLVx/Z3tFJBKhV69eJrVhzPfw5s2bcHJyQmJiokl9WwsipAkEO6agoACxsbFo164dPD094erqijp16qB79+6IiorCv//+a2sT9Q729ohcLsfKlSsxbtw4NG/eHGKxGCKRyKwvfgQCwbyQZ6H5uXz5Mj799FMEBgaiWrVqcHV1RcOGDREeHo5Hjx4Jbi8nJweff/45pkyZgvr165vfYA3q169vlX7sDZlMhsGDB+Ps2bN4++23ERMTg+nTpxvc52hYY3KLYBv0fXebNWuGsWPHYsmSJcjLy7OuYUbgZGsDCAQCN3l5eejWrRuuXLmCxo0bY8KECahcuTKeP3+Os2fPIi4uDo0aNUKjRo1sbape3nrrLQQGBqJmzZq2NgUA8PTpU8yZMwcAUK9ePfj6+iI7O9vGVhEIBF2QZ6FlmD59Os6cOYNOnTrhnXfegaurK86cOYN169Zh165dOHHiBJo3b867vVWrViE7OxuRkZFmtbNTp064fv06qlSpYtZ27Z3atWvj+vXrqFSpktr2s2fP4unTp1i2bBmio6N57yMQhHD9+nVIpVKT2jD2uzt37lz88MMP+N///ocFCxaYZIOlIUKaQLBTvvrqK1y5cgVTp07Fxo0btWZm7969i+LiYhtZx59KlSppvQjYkipVquCPP/5A+/bt4efnhwEDBiAlJcXWZhEIBB2QZ6FlGD9+PH744Qc0btxYbXt8fDzmz5+P2bNn48CBA7zaKisrw6ZNm9C1a1ezT2hIpVJBgr684OzszHnd//33HwCgVq1agvYRCEIwx3fO2O9uq1at0Lp1a3zzzTeIioqCWGy/AdT2axmBUMFJS0sDAMycOZMzvKlBgwacD6irV69izJgxTKhegwYN8NFHH+HFixe8+tW3hkdzjV9SUhIaNGgAAPj+++8hEomYf/T5+tYFnjp1CoMHD4afnx/c3NzQvHlzxMTEQC6Xax1Lr9d58uQJJk+ejCpVqsDd3R2BgYGC1ht5enoiODgYfn5+vM8hEAi2gzwL1THXs/CDDz7QEtEAMGfOHLi7u+PYsWO82/r999/x+PFjjB49Wm17RkYGRCIRIiIi1Lbv3bsXIpEIrq6uWtdYv3595l4C2uss6RD6+/fv4/79+2r3mmstJr1m2MvLC5UqVcJbb70laCnPhQsXEBERgZYtW6JSpUpwd3dHq1atEBcXh9LSUt7tAEBmZibGjh0LPz8/eHp6omfPnjh+/DjnsVxLBUQiESZPngwAePfdd5nrpj9buvbR5OXlISYmBm+++Sbc3d3h4+ODkJAQnDx5Uqt/OrS6qKgICxcuRKNGjeDs7Kx2j+/evYupU6eibt26cHV1Rc2aNREWFob79+9rtSfkcysSiZjPH/vvy2fZBB02nJubi4iICPj7+8PJyYm5D0L/nnR7+fn5mDVrFmrVqgVXV1e0bt0aycnJnDYI+TvTfPfdd+jcuTM8PT3h6emJzp07cz4r2N+H06dPo3fv3vDy8kLVqlURHh6OwsJCAMCBAwcQFBQEDw8PVK9eHXPnzkVZWZnB+0fDtUY6LCwMIpEId+/exf/+9z80b94crq6uqFevHpYsWQKlUqnTVkDYd3fMmDG4f/8+jhw5wttmW0A80gSCnVK5cmUAwK1bt3gnCzl58iRCQkJQUlKC0NBQ1K9fH2lpaVi9ejX279+P9PR0s4bHtWnTBrNmzcLq1asREBCAESNGMPsMrV3btWsXxo4dC1dXV7z99tuoVq0a/vjjD3z22WdISUnB0aNH4ebmpnZObm4uunXrhkqVKmHixIl4+vQpdu7ciZCQEFy4cAEtW7Y027URCAT7gDwLrfssFIlEcHZ2FrQ+9c8//wQABAYGqm1v3bo1KleurPUyTP9eUlKCU6dOITg4GIBKmN2/fx/vvvuuzr58fHwQExODr776CgDw0UcfMfs0X/zPnTuHFStWoHfv3pg2bRouXbqEvXv34q+//sLVq1e17isX33zzDX799Vf06NEDgwYNglwux9GjRxEVFYVz585h9+7dBtsAgMePHyMoKAiPHj1CSEgI2rVrh+vXryM4OBi9e/fm1UZMTAwuX76Mffv2Yfjw4cz3oU2bNnr3AUB2djZ69OiBa9euoWvXrpg+fTpkMhn27duH3r17Y9euXWqfW5pRo0YhIyMDAwYMgI+PDzPJcebMGYSEhKCgoABDhgxBkyZNcO/ePWzbtg0HDx5EWloaGjZsqNYW389tTEwMkpKScP/+fcTExDDn8/3+FxcXo0+fPsjPz8ewYcPg5OSE6tWrAzDu71laWor+/fsjJycHo0aNglwux48//ogxY8bg999/R//+/Zljjfk7f/jhh/j6669Ru3ZtvPfeewCA3bt3491338WlS5ewevVqrXPOnDmD+Ph4hISEYNq0aThy5AjWrVsHmUyGoUOHIiwsDMOHD0dQUBAOHDiAhIQEeHp6YtGiRbzuoT4iIyNx7NgxDBkyBCEhIdi7dy8WL16MkpISLFu2TOd5Qr67QUFBAFTPlr59+5pss8WgCASCXbJv3z4KAOXl5UXNnj2bSklJoZ4/f67zeIVCQTVq1IgCQP3+++9q+yIjIykA1JQpU9S29+zZk9J8DMTExFAAqCNHjmj18d1331EAqO+++47ZdvfuXQoANXnyZE67uM55+fIlValSJcrV1ZXKyMhQu4a3336bAkB99tlnau0AoABQ4eHhlEKhYLZv2rSJAkBNmzaNs39DhISEUACou3fvGnU+gUCwLORZaJ1nIc3OnTspANTo0aN5n9OxY0dKLBZTRUVFWvtGjhxJAaCysrKYba1ataK6d+9Oubi4UFFRUcz2zZs3UwCoLVu2MNuOHDlCAaBiYmLU2q1Xrx5Vr149TnvocwBQP/74o9q+iRMnUgCoHTt28Lq2+/fvU2VlZWrblEolNWXKFAoAdfLkSV7tTJ48mQJALV26VG37hg0bGFvZnzVdnyeuzxGffePGjaMAUN98843a9idPnlD+/v5U1apVqcLCQmY7/Z1o06YN9eLFC7VzSkpKqPr161NeXl7UxYsX1fadOHGCkkgk1JAhQ9S2C/3ccn0n+VCvXj0KABUSEkLJ5XKt/UL/nnR7w4cPp4qLi5nthw4dYvphI/TvfOzYMQoA1aJFCyo3N5fZnp2dTTVt2pQCQB0/fpzZzv5s7927l9leUlJCtW7dmhKJRFSVKlWos2fPMvtkMhlVrVo1ys/PjyopKdF3+xgAUD179uS8tgYNGlD//fcfs/3Zs2eUj48P5eXlpXaPjPnu0rx8+ZICQPXo0YOXvbaChHYTCHbKsGHDsHLlSlAUhZUrVyIkJARVqlRB48aNERERgX/++Uft+FOnTuHff//FwIEDERISorZv0aJF8PPzw/bt21FSUmLNy+Bk3759ePnyJaZMmYLWrVsz28ViMVasWKEWhsXGw8MD8fHxautlJk+eDCcnJ5w7d84aphMIBCtDnoVJWudZ6lmYmZmJDz/8EO7u7vj88895n/fw4UP4+PjA1dVVax/thaO90M+fP8fVq1cxaNAgBAYG4vDhw8yx9DGmlt2h6dGjB95++221bVOmTAEA3vepbt26kEgkattEIhFmzpwJADh06JDBNkpKSrBz505Uq1YNs2fPVts3depUNGnShJctxvL8+XPs3LkTffr0wdSpU9X2VatWDZGRkXj27BnntSxZskRrKdT+/ftx7949REZGom3btmr7unXrhuHDh+O3336DTCZT22fNMXzFihVwd3fX2m7s33PVqlVwcXFhfu/bty/q1aunZrcxf+fvv/8egGopCTuHgq+vL+ON53oG9O7dG8OHD2d+d3Z2RmhoKCiKwtChQ9GxY0dmn5eXF4YMGYLs7Gw8fPiQ8/qE8Omnn6olTaxSpQqGDx+OvLw83Lx50+T2AcDb2xtubm5msdeSkNBuAsGO+eSTT/D+++/j999/x+nTp3H+/HmcOXMGa9euxebNm7Fz504MGzYMAHDp0iUA3C8gnp6e6NChA/744w/cvHkTrVq1suZlaKHP1rp166Jhw4a4desW8vLy4OXlxexr2rQpPD091Y6nQ7Zyc3MtaTKBQLAh5Flo+WfhixcvMGjQIDx9+hRbtmxBs2bNBJ1bp04dzn1sIf3OO+/g6NGjoCgKffr0QVFREZYtW8Zc35EjR9CoUSP4+/sbdQ2atG/fXmsbbSff+1RSUoI1a9bgxx9/xI0bN5Cfnw+Kopj9dIIvfdy8eRNFRUXo06ePVji5WCxG165dtSaEzMm5c+egUChQXFzMuY6c7vvGjRsYMmSI2r5OnTppHZ+eng5AdV1c7WVlZUGpVOLWrVvo0KEDs91aY7ibm5vO77Yxf092SDubOnXqMDkcAOP+zvqeAfR35/Lly1r7uMLcaXGrb99///3HeS1CMMf3ig9+fn54/vy52dqzBERIEwh2jpeXF0aPHs0kcXn58iWio6ORmJiI9957D48ePYKLiwsz80uvA9KEfohqzhDbAj623rp1CzKZTO3l0dvbm/N4JycnKBQK8xtKIBDsBvIstNyz8MWLF+jbty+uXbuGdevWYcKECYLOd3d3R1FREee+N998E9WqVWO8zUeOHIG3tzfat2+PwsJCLFmyBCdOnECTJk3w6NEjLY+pKXDdJycn1asv3/sUGhqKX3/9FU2bNmXWsDs7OyM3NxerV6/mlTH+5cuXAFTeXy50/f3NBV3i8dSpUzh16pTO4woKCrS2cdlGt7dt2za9/Wq2Z60xvFq1ajrX+Bvz99SVbd/JyUktwZYxf2eZTAaxWIyqVatyHi8SiTifVfo+2/r2CU2Qx4U5vld8KCwsNLkEl6UhQppAcDAqVaqENWvW4MCBA7h//z7++usvtG/fnnmwPXnyhPO8rKwsALoHMho65IoruyM9SJiKuWwlEAgVF/IsNA+0iM7IyMDatWsxbdo0wW1UrVpVbwhmr1698NNPP+HRo0c4evQoevToAYlEgsDAQLi7u+PIkSN49OgRAPBOvGUNzp07h19//RUhISE4cOCAWkhweno6ZxIoLmgh9vTpU879uv7+5oL+/MyePRtffPGFoHO5BCnd3q+//qrlwbYHdIloc/09dWHM39nb2xtKpRLPnj3TEuBPnz4FRVEV8l1IqVTi5cuXePPNN21til7IGmkCwQERiUTw8PBQ20avU+Iqf1JQUIDz58/D3d3dYLier68vADAvNWzoECQ29EAkZBZSn62ZmZn4999/0bBhQzUPDIFAIGhCnoWmwRbRX3/9NcLDw41qp1WrVigqKsKDBw8499PieMeOHfj777/Rp08fAICrqyu6dOmCw4cPC14fLZFILB6J9O+//wIABg8erLWu9sSJE7zbadq0Kdzc3HD+/Hktz71SqcTp06dNN1YPHTt2hEgkUgtDNoXOnTsDgNna48KY75MhzPX31IUxf2d9zwB6G99s5Y4Cn+/uP//8A6VSafPlN4YgQppAsFM2bNigM/nG3r17cf36dfj4+DDlIrp27YpGjRrh4MGDWskyli5dihcvXmDs2LFqyTK4oBNUbNmyRS1kKS0tjTOMy9fXFyKRCJmZmbyvbfjw4ahUqRK+++47XLt2jdlOURTmzZuHsrIyXvUiCQRC+Yc8C8N4tyeE7Oxs9OvXDxkZGVi9erVWrWch9OzZE4CqJA8XtJBesWIFADBCmt53+fJl/PHHH2jatClq1arFq096/aSukHJzUK9ePQDQqrN87do1LF++nHc7rq6uGDNmDJ4+fYqVK1eq7du0aRNu3bplurF6qFGjBsaMGYPTp08jISFBbU0wzZkzZzjrlnMxfPhw1K1bF19++SVnfeTS0lLO2tRCoBOcCfk+GcJcf09dGPN3pmt/L1myRC2E++XLl1iyZInaMeUFPt9d+llCP1vsFRLaTSDYKQcPHsT06dPRuHFjdO3aFbVq1UJBQQEuXbqEEydOQCwWIzExkcmSKhaLkZSUhJCQEAwaNAijR49GvXr1kJaWhqNHj6JRo0aIi4sz2G9gYCC6du2Kw4cPIygoCD169MD9+/exb98+DB06FD///LPa8Z6enujYsSOOHz+OiRMnokmTJhCLxZg4cSIzaGni7e2Nb775BmPHjkXnzp3x9ttvo2rVqjh06BAuXLiATp06ITIy0vSbqIM5c+YwCSz++usvZhudBGXq1Kno1q2bxfonEAj8Ic9CyzwLR44cicuXL6N58+bIzs7mTBr10UcfwcfHx2Bbw4cPxyeffILU1FRmDTubZs2aoWbNmnj8+DEqV66slqG8d+/eUCqVePHiBUJDQ3nb36dPH5w/fx4DBw5E9+7d4eLigh49eqBHjx682zBEp06d0KlTJ/z00094/PgxAgMD8eDBA/zyyy8YPHgwkpOTebcVFxeHP//8EwsXLsTJkyfRtm1bXL9+Hb/99hv69++PP/74w2x2c5GYmIibN29i7ty52Lp1K4KCguDj44PMzEycP38e//zzDx4/fsxrTaqrqyuSk5MxcOBA9OzZE3369EGrVq0gEolw//59nDhxApUrV8aNGzeMtrdPnz5ITk7GqFGjMHDgQLi5uSEgIABDhw41uk1z/j11IfTv3KNHD3zwwQf4+uuv0bJlS4waNQoURWH37t14+PAhPvzwQ7N+pu0BPt/d1NRUODk52eXSATVsVHaLQCAY4MaNG9SKFSuo4OBgqkGDBpSbmxvl5uZGNWrUiJo8eTJ1/vx5zvOuXLlChYaGUlWqVKGcnZ2pevXqUbNmzaKePXumdayuOo3Pnz+nJk2aRPn5+VHu7u5UYGAglZKSorNG5c2bN6lBgwZRPj4+lEgkUquTqK+u5fHjx6mBAwdSPj4+lIuLC9W0aVPq008/pfLz87WOBUdNQxo+NQk1j8erOoxc/7hsJRAItoE8C9Ux17PQ0HMQAHX37l1ebVEURQ0cOJDy9fXlrCVNUa/rGI8aNUpte0lJCeXp6amztrOuWrR5eXnU+++/T9WsWZOSSCRqx+g6h6IM1/vW5OnTp9SUKVOoWrVqUW5ublSrVq2otWvXUnfu3BHUDkWpahi//fbblI+PDyWVSqnu3btTx44d46xZbu460hRFUXK5nFqxYgXVvn17ysPDg3J3d6caNGhAjRgxgtqyZQtVWlrKHMunjvPDhw+pWbNmUU2aNKFcXV0pb29vqkWLFtTUqVOpP//8U+1YoZ/b0tJSau7cuVTdunUpJycn3vfa0HdA6N9TX3u67pGQvzPNt99+S3Xs2JGSSqWUVCqlOnbsSH377bdax+n7bOv7++vrmwuuvxddR5rrucDVvjHfXYqiqIKCAsrT05MaMWIEL1ttiYiiOOI7CAQCgUAgEAgEnvz555/o168ffvjhB4wfP97W5hAIBAdl06ZNeP/993Hs2DG798YTIU0gEAgEAoFAMJmBAwfi3r17uHbtGpP1nEAgEPhSVlaGpk2bolWrVti3b5+tzTEIecoRCAQCgUAgEExm9erVePvttzkznRMIBIIhHjx4gEmTJuHLL7+0tSm8IB5pAoFAIBAIBAKBQCAQBEA80gQCgUAgEAgEAoFAIAiACGkCgUAgEAgEAoFAIBAEUCHqSCuVSvz333/w8vKCSCSytTkEAoFAIJgViqKQl5eHWrVqWTTJExlPCQQCgVCeETKeVggh/d9//8Hf39/WZhAIBAKBYFEyMzNRp04di7VPxlMCgUAgVAT4jKcVQkh7eXkBAO7/tgHeHu42toZga17k5qHx8JkAgC0X/4Crq6uNLap4vP1mTwDA0aNHbWsIgVBOKCgowODBg5nxzlKQ8ZTAplb/91BYXAoXFxf88ccftjaHYCILFy7EyZMn4ePrjb93n7G1OQSCTcgryEezoR15jacVQkjT4WfeHu7w9pTa2BqCrZn/v60AgPa9u8K3SmUbW1OxSU1NxVtvvWVrMwiEcoOlw63JeEqgKSoqRmFxKQAgPj4enp6eNraIYCpKpRIAMOud6fD2tOykHIFg7/AZT0myMUKForS0DN/9cgQAEBG30MbWVFzEEtWjJzMz08aWEAgEAsEYuk99PYZ2797dhpYQCASCbSBCmlCh2HLgGEpKy1Czvj+q1Kxma3MqLBKJKhhmx44dNraEQCAQCMZw8fpdAMCYMWNsbAmBQCDYBiKkCRWKjxK+BQCEL4+2sSUVm//7bC4AoLS01MaWEAgEAkEoyzYnMz/PnTvXhpYQCASC7SBCmlBh+P3URciLiuFXvSradO1ka3MqNO16BjE/EzFNIBAIjkXMuh8BAA0aNLCxJQQCgWA7iJAmVBgi4jcBAMKiPrSxJQS/6lWYn2/dumVDSwgEAoEghFv3H4GiVD9v3brVtsYQCASCDSFCmlAhuP3gMe4+egpnFxf0GNbf1uZUeJycXhcMIELacUhOTsaQIUOQnJxs+GACgVAu6ThhPgDA1dUVbm5uNraGQHBMNu3eihbDOmPTbjIZ5cgQIU2oEHywQuWNDn5nOCROFaLqm93j5OwMQFU2heAYJCUlISsrC0lJSbY2hUAg2ACFQoF8eSEAYN26dTa2hkBwXFZ+vwaZWY+w8vs1tjaFYAJESBPKPXkFhUhNzwAATIwMt7E1BJqpMbMBvK5bSbB/wsLCUKNGDYSFhdnaFAKBYAN6vvcp83Pr1q1taAmB4NjMnhwB/xq1MXtyhK1NIZgAEdKEck/Cln2gKKB5hwB4VvK2tTmEV/hWVa2TViqVREw7CKGhodi/fz9CQ0Nt0j8JLScQbEvaX6qlOKTkFYFgGlNHTcT1X85g6qiJNumfhJabByKkCeUapVKJr374FQDwQfxCG1tDYNNlYG/mZyKkCXwgoeUEgu34fONPzM+RkZE2tIRAIJgKCS03D0RI2ynrk1PQYMgMrE9OsbUpDs3+ExdQ8KrkVd0mDW1tDkEHy5Yts7UJBAHYyjNMQssJxkDGU/OwZMMuAECdOnUgEolsbA2BUD6wlWeYhJabByKk7ZT4pL148PgZ4pP22toUh+bjL74DALy74CPbGkLgxN3LEwBw7Ngxo9sg4b7Wx1aeYVuHlhMcEzKemk5axg1QUNW82rFjh42tIViKrKwsAMD5a5dsbEnFwVaeYVuHlpcXiJC2U+aFjUDdmlUxL2yErU1xWB5mPce9/57C1dkJ3YcG29ocAgct2rcCAOTl5RndRkUN97XlBALxDBMcCTKems6giKUAAGdnZ7i7u9vYGoKlePToEQDg5MUzNrbEuthyvTDxDDs2REjbKdNDQ3B3/zpMDw2xtSkOy3ufJQIA+k8cDYlEYmNrCFwEjx4OAKAoyug2Kqqos+UEAvEMExwJMp6ahixfDpm8CACwdu1aG1tDsCS1a9cGAHRr19nGllgXW64XJp5hx4YIaUK5pLCoGIfOXAEAjPvo/2xsDUEX3ViRAsYmHKuooq6iTiCUN8jSBIK9Exy+hPm5Xbt2NrSEYGlq1KgBAOjwZlsbW2JdiFe4fGCLyAIipAnlks+/USVFeaNTW3j6eOPg1mRMCRqCg1vJy6q9MnfuXJPOr2iCpKJOIJQ3KurSBIJjQFEUzl/7FwDw1ltvAah4z9qKyPlrlypUaSTiFS4f2CKygAhpB4JkHuUHRVFYs/MgACB82XwAwK7EJDx79Bi7EpNsaBmBC28/HwBATk6OSe0QQUJwREhkgW0g4yk/Yta9TixGT3aSZ2355+TFM6Q0EsHhsEVkARHSNsDYAZxkHuXHwZMXUVBYDL8aVVGvWWMAwOjwMFStXROjw8NsaxxBC1c3VwBARkaGSe0QQUJwREhkgWmQ8dSyLNv8MwDV2llnZ2cA5FlbEejWrjMJdSY4HLaILCBC2kqwB3tjB3CSeZQfs774FgDw/qezmW0DJ4bi27T9GDiRvKzaGyv3myd0jAgSAqFiQMZT63D03F/Aq5JXGzduZLaTZ235p8ObbUmoM4HAA6sK6XXr1qF169bw9vaGt7c3goKCcPDgQb3n7Nq1C82bN4ebmxtatWqF3377zUrWmhf2YG/sAM6VeZSEp6nz+Fk27jx8AhcnJ7VEVgT7xbdKZebnx48f29ASAoHgCJDx1DoM+zgegKrkVfXq1W1sDcEa/PPPP7Y2gUBwKKwqpOvUqYO4uDhcuHAB58+fR58+fTB8+HBcu3aN8/jTp09j7NixeO+993Dp0iWMGDECI0aMwNWrV61ptknQA3OX1k2Zwd6cpThIeJo6UxarSnMMCnvHxpYQjMFRJ8oIBILlIeOp9XiRK0NBoarkVVxcnI2tIViL58+fAwD6dO5pY0sIBMfAqkJ66NChGDRoEJo0aYKmTZti2bJl8PT0RHp6Oufxq1evxoABAxAZGYkWLVrg888/R7t27bBmjeMkP6AH5tNXblmkjiUJT3uNUqnEH+mqdbbj50y3sTWWpzxlIhc7OwEA0tLSbGxJxYVk4iXYO2Q8tR4DZ37O/NyzZ/kXVeT59xoRgPZvBNjaDIfFFiWYCLbDZmukFQoFfvzxRxQUFCAoKIjzmLS0NPTr109tW0hIiMGX7eLiYshkMrV/5oZvCJjmwMx1ninhZOacjXd0lmz4CQDQMrAd3KTuNrbG8pSnTOTVa6tqV16+fNm2htgJtnipozPxJiYmkhdKAgMZTyseSqUSF27cBQCMGDHCtsZYCZKJvHxiC1HLLsFERHX5x+pC+q+//oKnpydcXV0xffp0/Pzzz3jjjTc4j83KytJal1O9enVkZWXp7WP58uWoVKkS88/f399s9tPwDQHTHJg/XbsDDx4/w6drd2B9cgqq9g5DRNwmvW0JeTGoyGu8Vm37FQDwwYpFNrbEOpSnTOSLvlttaxPsCnO/1PER5nQmXgB28UJJPET2ARlPK954Sk9KA8C8efNsaIn1IJnIyyeWqCtsSByzSzDZoq4xF0TQWw6rC+lmzZrh8uXLOHPmDGbMmIHJkyfj77//NmsfUVFRePnyJfMvMzPTrO0D+kPANAdf+vfx0auQk1cAQJUHMz5pL7Jl+aAoChKxGF1aN2WOY5+/8NXLwsK1O7T60qSirvH64/RlFBQWo3KNaqhV3/wverbAUOh2ecpE7uHtxfz89OlTG1rCD0uLPHO/1PER5nQm3vDwcLt4oSQeIvuAjKcVbzxdtkn1XPP392dKXjk6hp7ZJBO57bCkyLNEXWFD4phdgskWdY25sBdBXx6xupB2cXFB48aN0b59eyxfvhwBAQFYvZrbG1WjRg08efJEbduTJ08Yr4kuXF1dmczg9D9zoy8ETHPwpX/flZrGDPIhQQHIKyiE1M0Vvt6eaNu8Pn5MOcUcxz6/uKRU7X8artnyirrG64MVmwAA70Z/aFI79rTuuDyFbhvCt+rrzN15eXk2tIQflhZ5xrzU6XtRFCLM7eWFkniI7AMynlas8fT837dfFbwCEhMTTWrLnqJKyMSc/WJJkWdsXWF94l6IOLZFXWMu7EXQl0dsXkdaqVSiuLiYc19QUBD+/PNPtW2pqak611TbC5qDL/376OAg1K1ZFf+b9x5OX7mFHFk+qvh64/mRJFy6cY8539XFGVI3VzzPkaFK7zBme2FxidogzzVbXhHXeD188gK3M7Pg5OKMniMGmtSWPYnX8hS6LYRPPvnE1iYYxB5Fnr4XRXsRx0JwRJsJ5oeMp9alz/uqpVEuLi6oWbOmSW3Zk3i1x2c2QYU9ijx94t5exLEQHNFmR8GqQjoqKgrHjx/HvXv38NdffyEqKgpHjx7F+PHjAQCTJk1CVFQUc/ysWbPw+++/Y+XKlbhx4wYWL16M8+fPIyLCfr5sXGgOvvTv22I/ZrZ3ad2UCT8DgNHBQZCIxZC6uUJeVIziklLIi4qRI8tHUXEJAICiKLVwtIo4W87FzOUbAQADxo5U226Md9mexGt5Ct3mQ8vO7QHAYA4Ee8AeRR55USSUR8h4aj1keQUoKFLdn88//1xtnzHeZXt6JtnjM5ugwh5Fnj2Ke4J9YlUh/fTpU0yaNAnNmjVD3759ce7cOaSkpCA4OBgA8ODBAzx+/Jg5vkuXLti+fTs2btyIgIAAJCcnY+/evWjZsqU1zRaMrgQl7O2nr9yCQqnE6Su3mN9HB6s87SKRCLWr+UH06jwlRTFt5MuLmJ+nh4ZgXtgIxCftLffJUHTd05LSUuw/cQEAMDVG3ZNpjHe5oolXeyJwQC8Aqoz+BG3IGj9CRcTU8ZSGjKev0XVP+0xbzPzct29ftX3GeJfJM4lgrxhal22P4p5gn1hVSG/evBn37t1DcXExnj59ikOHDjEiGgCOHj2q9ZAePXo0bt68ieLiYly9ehWDBg2ypsm80ByU6BCxhWt3qG1nZxhlz36z13zJi4pBURQeZD1n1imJWH2VvRIZdJ904pTyngxFV9KXFa9+b96+NSROTmr77Mm7bE/Y0zpwNiHj3rK1CXaNPYVJEgiWwtzjKQAolEpQUI2lUjdXpi8ynu5ltlEUhUs3VSWv3npL+1lsT95le8Ke1oET+EOSbxHMhc3XSJcHNAclOswsX16EB4+fIWL5N6jaOwxFr5KbUHgdngYAeQWF8HuVIKVmjRpo2LChWtkvitXX2/27qPWZI8t/dRBVrst06Aq7o+/5R1/EaJ1j795lWwlae1oHzsbN/XXt76NHj9rOEDuF74ssebEjODLGjqe0iO7Suinq1qzKOZ5SUK2NpiHj6Qhm27Jv9zA/L1iwQOsce/cu2+q5RyY4HRO+odukbBTBEERImwHNQYkOM6NnuykA2bJ8Zna8UZ1q8Oo6Hs4dRiNy1RY0f7MVvlr9PyTt3IfzFy7gxIkTuHjxIjIyMrBu3Tp0eZVczc/bEwDg1GE0sp7nQCR67at+kPVc9ZIRt6lcDv5cSV8OnroIeVEx/KpXRe1G9W1nnJGYU9AKEeX27KkXiVWf6djYWBtbwh99L3DmfLnj+yJLXuwIjoyx4+nM5d/gweNnKHaphHsZaTrH06DAQABkPNUcTxevU60Xr1evnq3MMglzPveEPLeJp9686BOu5hS1fEO3ieeaYAgipM2A5qBEvwi83b8LfL091ULJAOD833cgLypG7Tp1sHHTZiQnJ6P/gIHw9fNTO65KlSoYNmwYdiUn44cffoBnJV/8mHIKFEWhpLQMIgDOTk5qod8UReFTVgIVXWuhygOzVmwGALy7wLSSV7bCnIJWiCi3Z0/9Gx3bAAAKCgpsa4gOuF6w9L3A2ULUGvNiZ+jFkXi5CdbC2PHU398fP/zwA9Zu+AbPKU8yngrg3wePQS8d//bbb21rjJGYU9AKeW7bu6fenuESxvqEqy1ErTFJx6w1GUCwD4iQNhPjo1fBpeMYjI9exYSZnb5yC0tnjkXeqW3o8EZDtePbtGmDlJQU1K9fH4BqDReNs5MEIpEIIhFAT5LXr18fv6ekoE2bNsxxSoqCl9QN/jWrqrXNDgXXtbbY0bnzMAv/PnwCZxdndB/qmKVJzCloaVHeon1ru1z/zJfGrd4AAJ0l8WwN1wuWvhc4a3oraLELQPCLnaEXR1t5uYmAr5iYMp5SFFBa9jphoWo8ff0zQMZTTdqNjwSgqhleqVIlG1tjHOYUtPRzOyAggDx/LAiXMNYnXK2ZSZsWvAAEJx2zt8kAGiLiLQMR0ibAnp3elZoGhVKJXalpALQH3DsPnzLn+fv7Y/v27UhPT0fv3r2xds0aZoAHAKWSgreHOygKoChgzZo16N27N9LT07F9+3b4+/szxxaVlDIz9u+EdIXUzRUv8wowPnoVgPJb0mNWgmrWvO/oYZBIJAaOtiymrHU21zppWpRfv3DFLtc/82XqIvuuIc0ljPW9wFnTW2FI7NKidMGCBVovh4YEv63CF0mYesVA09Nr7vGUTtlZWqbgPZ52eKMhRCIRiktKGbvK43iqUCiY7OWrVq2ysTWmTZ6Za+KNfm5nZGSQ548F4RLG+kKurZlJ25DgpUXpuwsjtMSpvUwGaELC1C2DiKIoyvBhjo1MJkOlSpWQc2wLvD2lZmu3wZAZePD4GerWrIourZtiV2oaRgcHYVvsxxgfvUrt9yq9w5hEJj/88APq16+P3r17o7RUlTBl0acLMX3GDGj+NdasWYPly5cDAJydnXHkyBHcu3cPEyZMAAD4envi+ZEk5niXjmOgUCohEYtRcu4ns12rPZEvL4RPj4mgKGDn38ch9fSwqT1Tgobg2aPHqFq7Jr5N22+1c7k4uDUZuxKTMDo8zC5Dt/kwtK6qnvT777+PadOm2dgaxyE5ORlJSUkICwvjFO5DhgxBVlYWxGIxlEolatSogf37Tf/MWRJD10R4TX5+Pnr16oWXL1/C29vbYv1YYjxlj6V396/TGj9tMZ7SNgFg7CqPBE2aj7PXbgMAzp8/b2NrXj+njHk+mXIuFxXx+dOxY0eAopB39qGtTbEpm3Zvxcrv12D25AhO4d5iWGdkZj2CRCyBQqmAf43auP7LGRtYyh9D10R4jSw/D7X6tOA1nhKPtB4MrYeis4l2ad0U3du9gUqeUqSkZajVtfzl2Hk4dRjNDPpBQUHo3bs3GjRogDlz5jBtffb5UqxZsxZSqRReXl6QSqVYu3YtM+gDwJw5c9CgQQP07dMH/fv2ga+3J7ykbpC0D0XniXMBAKODgyARi7VqaJantV1fbPkFFAU0a9fK5iIaMG2tsz0n/rIVEmdVGbOMjAwbW2LfaHpfDHm/aa9ycHCwXSbH4fImkfWH5Qd9Y9C8sBHw8/ZEXkEh1ien2MV4Oi9sBHy9PeHn7anlgS5P4yktokePHm1jS1SYEv1CEn8RjIEr5NmQ95v2LI/sN9RmHmZ9GHNNBOMgQloPhtZD0YP76Su3EJ+0F9myfOTI8hGftJcJASssLgHb6U/PfANAREQEoqKimN9jY2ORmJgIqVSKxMREtczFUVFRiIhQfVFdXZzx+46NeH4kCQ+yngNQJVwBgO7t3kDt6pXRvd0baoN9eVnbRVEUvvzhFwDAxyuX2NgaFXzWOusK4TZ34i97LW0lBI9XkyMXL160sSX2jWbYs6GwRlqUtm3bVm275nmkjAzBEugbg6aHhsDTw50ZP+1hPJ0eGoKlM8fC00NVlq88jqcrvt/L/Dxv3jzbGcKCz+SZrmeUuSfeyDOpYsAV8mxoPTEtSru27ay2XfM8W61LJmHc1oMIaT0YWg/F3k/PqPu+mr2mE6Sw12oBQLdu3QCokog5O0kQERGB6OhoZv+CBQtQpUoVtTqO0dHRzKAPqOpgXsuSY3z0KojFqj9h3RpVsD45BR/Gb2YGePZg7whru/jM8h88dQkFhcXwrVYFtRs5TpkOawlcYzzcbJEvZM22pepgh0WrsrCXlZWZtd3yhqb3he9Ln+Zxhn43FqGCnHiTyjeWHE+dnSRwd3Uh4ykLPuNp9NfbAEBtnbgjYC2Ba2oFBCHPQJJY0XZwrVvmK0Q1jzP0u7EIFeS2XItd0SBCWg+aZTg0ByZ6cKdnpZ8dScLSmWMRn7SXmbUuKVWJAZFIhDq1a6NKlSoAALFI/CoJChAZGYlly5Yx/ebm5jI/L1u2DJGRkVq2+VWujGMZ/0JJZ/sWiRCftJdZH02/jNCDPVfdSHuDzyz/rIRXJa+i7LfkFZfAbNG+NcQSMVq0b83reGMxxsPNFvlCBL+lJgf6jh7G/Gyv2bvtAU3vC9+XPs3jDP1uLEJfdkkYd/nGkuOpUkkx+8h4qsLQeHrln/uMd/+nn+w3nwqXwAwICIBYLEZAQACv443FmGcS+7kn5BlIvN+2gyvkma8Q1TzO0O/GIlSQkzBu60GSjQlAMyEK1zbNBGQ/ppwCoEpiMnHkQMxb9hVEIsDNxQVFJaWgKIpZw1WlShW1Qd/HxwfPnz+HXC5HXl6elj1vjxyOv6/fAAVg6cyxAMDMltvzAK8L+mVJl/1pV26i27sL4Ozqgl3XT0Di5GR2G8yRrIsrgRjXNrqvwoIC5OfKzJZwTCjsawbA+/otldiMoigMq9cBgCoEc9SoUWZru6JiStIcY8+tiIl6bImjJRsTMp76ensCAHJl+aBAxlM+GBpP/XpMwssCOZydnZGWlmYRG8zxDOBKIMa1je5LLpdDJpPZLJki+5oB8L5+e3lekmRjhjElaZex55JEYdaFJBuzEFzhXOwkKeOjVyG/oJAJR/sj7XWypJCgAJy6+DcAQASR2lovhUKB+Ph4tUEfUM2kx8fHQ6FQgItbdx+gqKQUS2eOxfTQEIeYJdeHIfsjln8DAJgYOdMiIhowj5eVK7xac9vBrclYvygezx49BgCTE46Z4tVme7HpnwGV+E+IiNbZrrnXd9OIRCKIJapH04sXL8zatib6vBf2EmpnDjtM8XYIXYtNQzzMBH0IGU9FAJNgTCwSkfGUB/rsLy4pxcsCOQAgMTHRYjaYw8vKFSGjuS05ORkrVqxAVlYWAJgcUWPKM5f93KN/BoA+ffqgT58+BnNY2PJ5WVpaClN8a/rCj+2lhrE57DAlXFvzXL72EA+z/UKEtAC4BiZ2kpRdqWnIluVDlq8aoNiPo9NXbmHK4C7Iyc6GUuNBlZCQoLaGy8fHh/l5wYIFSEhI0LLl2bNnePr0KeRFxUzoW3nJIspF1vMcXL51D2KxGEMmj7FYP7rWGAsRqlwCU3PbrsQkKBVKiCViTIqcabIgpScA1i+K12mjkGug2zt5INUmycucnV0AAN98841F+9H3omcvoXZC7ND1AmjOTLj2cl8Ijo2Q8bR/UAAkYjEoAEqKIuOpifSdFsP8rJl80Jzoeu4IEapcAlNzW1JSEpRKJcRiMcLDw00WpPQzbsWKFTptFHINSUlJkMlkkMlkdv3c3LFjBwBAopGLgC/6BKa9JL8SYocukWtKuLbmufZyXwjGQ4S0CdCDbZfWTVG3ZlWm9JRCqURE3CYUFBYBAERQlcoCgOMnTjDni0SqupbsbKLLli3D8+fP1dZ4xcbGYs0a9S/ZyZMnmZ+7tG5abrKI6mLeatWDLDCkF5xdXbT2mytJli4vq7nXA9OCffpn88zi0R0dHgaxRAylQqnTRkPXwL4vtH3dBgfbpDxX7K6NAGDS7Dgf9AlMe0l+JcQOXSJXqLeD/ZKoby22vXjtCY6PvvF05x+nIXkVpULGU9NJu3ILADBixAjO/eZKkqXruWPuyTj6mTR37lyzeHTDwsIgFouhVCp12mjoGtj3JSwsDN7e3vD29rb5eKIPOidJ3849jTpfn8C0l+RXQuzQJXKFeofZglzzXLY99uK1JwiDrJE2gL51RlxrvOhMnwo6aQkLqZsrAtq2Q3JyMkQi4Ouv16jVtYyOjkZkZCQkEgkUCgUSEhJ0luwYNWoU0tPTAahCYd/u3wW/HDuPouISjOnfBdtiPza4RspRKC4phXe38ShTKLHlQgp8q1bROoZrDbIuhBxLY6n1wObEkI2G9htzXyzFg3/uYGZfVV3T48ePQyo1fi1mRYLPOjs+x3CtQTT2OFuu/bOXdYfWwBHWSJPx1PYs3vAjPt+oEr1nzpyBRKLtfeT7/Rd6LI0jfC8N2WhovzH3xdZ888032LBhA0K69sHuVVtsbY7N4bMumc8xLYZ1RmbWI/jXqI3rv5zR2R+f42y5VroirdMma6TNiL6Zaa41XtNDQ/C/ee9B6uaqdXxhcQke3buNE8eP486du/jiiy+YfVFRUZg5cyaTCEUul2PmzJlqdTG/+OIL3L17F4cPH0Z6ejrEIhEAldfuj7QMFJeUQklR2JWahvXJKYiI24QHj5/h07U7zHdDbMA3ew6hTKFE3aYNOUU0IKzskzElosy1HpiP59zY9c6GbDS039B9sVS5Ky78Gzdgfrb0Oml7wFyeXT6eZz7eIGMzgBvbn6UgYej2BRlPbc/SjbsBALVq1eIU0YCwKBhjInfMtR6Yz3PT2GerIRsN7Td0X0g0j+Uwl2eXj+eZT2i2sRnAje3PUpAwdG6IkDaAvnqRupJ5TA8NQRXf1zMYErGYmeW+u38dln6+BH5+ftiwYQOcnZ3VZsY1iYiIQFRUFJydnbFhwwb4+fkhOjoaUjdXfD1/KnNcUUkpEwo3OjgI8Ul7mbBYRw85WLJxJwBgZtxCnccIEbrmTJIlVFzyCRG3Vs1pTQzdF2vaJXr1UgsA69evt0gf9vQiI1TwGbJd3376BS8gIEBwSKYxx9kyRN5ewvMJKsh4aluOXbgG6tUV6HvWCBG65kySJfSZzOe5aavJNEP3pbxN8tlTWLJQwWfIdn37afEb2LqjzmP4hoLzOc6WIfL2Ep5vbxAhbQBjM3fSLwxro97H/+a9B/8aVdC93RsAgJb+lTFu3DgEBgbiyJEjaoO+WCQCS0MAUA3+R44cQWBgIMaNG4fMzEwkfDwJ00ND4PeqLEhRcQm6t3sD/5v3Hk5fuYUurZvC19sTft6eTCkPNo6STOXctX+Q/TIfHt5eeKODds1IWyNUXPLxhhvjMbcG1rbL288HAHD48GGLtG9PLzJCBZ8+29kZbLn20y94GRkZFrt+fWusrYk9ZMIlvMba46nmWApU7PF0+EdxAABnZ2f4+fnZ2BpthD6T+Tw37XUyzV7tMhZ78lYKFXz6bN+0eytmJyzUuZ8Wv+lXzlnk+jVFvC2zd5PM4dwQIW0i9AA6PnqV2kDKfmFgh7OtT07BrtQ0XL58GSEhIbh37x7Tlkikmu2mKNWsuzsrqda9e/cQ+cH7+OvKFbwT0pV5EekfpBKXSorCp2t3MH2dvnILz48k4dmRJM6XFkdJpvJ/n6u8kW/PmmrgSH6YOzxZqLjk4w23VFkpU7G2XYMmqtZIl5WVWaR9Pp5ZayFU8Ol7CUtMTIRSqYRIJDL4gunt7Q25XG72a7fEJIU9RRAQLIO5x1NVajIyngJAzss85MkLAYAzc7kxmPs7KVRc8nlu2utkmr3aZSx8PLPWQqjg0ye8l6yLh0KpgEgk0ivMZ0+OgK+3D/LlBWa9dktNUNhTBIGjQ4S0idAD6K7UNGb9lObMdJfWTSERi5lsoHTilMzMTEyYMAGjRo3Czz//jKdPn4EOHFMolXjw8BF+/vlnjBo1Cvu3rsMvKz5GybmfmMQnDYbMQAqrVjUF/aFzbPgeZ0ue58hw5Z/7kIjFGPxKVJmKucOTTRWX1lx37Gi80UlVlsVS+RCt4Zm1FHxewho1aoRx48bBycmJ84U3NDQUUqlUZ0kWY1+Sk5OTIZfLzZ6h1p4iCAiWgYynlmNgxFLm527dupmlTXN/J00Vl2SyzXZY2jNrSfgIbx+vSsx+LhE6ddREeEo9kCPL1enZNka4BrbuCIlYgsDWHQWdZwh7iiBwdIiQNhF6AB0dHIS6NauCArRmpk9fuQWFUsmEiGmSnp6OiIgItGnTBm3atEX37t3Rtm1btGnTBhEREUhPT8fOP06jau8wrE9OYTKZPnj8DCIAft6e8H0VcsY3dM7YEDtr8uGKzQCAroOD4cKRbMYYzBGebE7xa6v10ID9i/ise5lW6cdeQuzM8RLo4eGBlJQUXLt2DRcuXECzZs3QokULTJw4EZ9++imuXr2qdry+azf2JZmumSqVSs36QmytvxN5GbcdZDy1DBRF4dzf/wIAhg0bZrZ2zfGdNOf3zVaTbY7wzLCWjfayjtZcHteYGfPgX6M2YmbMY7bpEqH6rt1Y4Zp+5RwUSgXSr5wz7gLAfS+s9XeqCJ5vUv7KzHCVyGBvo2fcRdCftETq5ooyhQKlpaqwVvrYujWrAlC9XEjEYvxv3nt2O3gbQl85kZLSUnh1VZW8Sjp7EJVrVLORldrQZaLEErHJdaAtUVaLb5vmvA5LQNsHAAEBAdi8ebNN7bF0yRZTyqU4Ozujdu3a8PLy4txfVFQEuVwOqVSK0tJSPHr0CKWlpTrbS05ORmJiIgAgPDxc0PXS55aUlMDFxcWo81esWAGlUmmT0jHG/h1sXdLHEcpfCYWMp/zRN54uWb8Tn32zCwCQlpYGZ2dnW5jICf19E4vFJteBtsR3UEi5QG9vb0ilUrss68XnuWbN8leWLqXEt+SUMQi1fdPurViyLh6ASpgLuV763OKSYri6uBp1/uyEhVAoFRa5F4Yw9u9g61JbpPyVDeGamWZvo2fc3w7pCj9vT0jdXCF1c4WvtyecnZyYcxI+noSRfTpDLBaj/RsNmUQn88JGMG048qAP6F9XtnF3KsoUStRr1giVa1SzK+/p6PAwiCViKBVKkz3JpoSGc92Tg1uTsX5RPC8vtzmvwxKo7FOVZ/nvv/9sbI3lvR1CvDts74K7uzuaNGmCJ0+ecB7LFtFubm548uQJmjRpAnd3d53tm+JVpkPGi4qKdIaN6yMpKQlKpRJisdihMn2T0HPzw2c89X3lQX4npCt8X42pZDxV5/NXIrp27dpwdna2Kw9qWFgYxGIxlEqlyd8dU0LDue6JocSNNPQzA4DdPgPsJfKKxtKhxUI8rkK8psYIvJXfr0GOLBeeUg/BopAOGZcXFeoMGzfUt0KpgEQscahM344Uek6EtBGYkqGTfgk4feUWsmX5qOLrjbxT2/D8SBKcnV7XdIxP2otdqWlQKJW4dOMenh9JwuczxzKDJP0i4SjZQrnQta6Moih8tlE18M9YNh8AvxBoY8W20PMGTgzF9M/mWTyDtSG7uO7JrsQkKBVKiCViTtvYbVrrOoxl4MRQNG37JgDg2bNnNrZG/4uIOV5MhbwE0qItNTUVDRo0wIEDB9CmTRvOJEJyuRxKpRJyuRwJCQlo06YNDhw4gAYNGuj0TLGv1ZhroxOZGbNOmu5bl3fK0iLA2Jdxe3tRdRRMHU+9PNyRLctnEoKR8XSE2vbTGTcYD/y6desA8Jv0MSVHgpDzQkNDMXfuXIt/dwzZxXVPDE3q0W0CwP79+xEeHm63zwB7S26mS2BZswY0jRDRZozAY1+rMddHJzLz9fYRLEjpvldGLuW8F5YOvTY207e9LBHgAwntNoIGQ2bgweNnqFuzKu7uX8frnPXJKfh07Q5QAFM+QzMMy6vreMiLigEAa6Pex4mLf2NXahpGBwdhW+zHqNI7DDmyfIhEIqyZPxXTQ0ME26Iv/MteOHv1FoImR8PD2ws/Xj0KgF+4Mh0KXLV2TXybxj8k09jzLI2mXZr3gOueGLpPY1v3Rn6uDJ4+3thx5QgvO3S1aYmwdE3+u/sA03q+BQA4f/68RfrgQmiIoClh2cawYMECpKamYt++fahcuTJ69uzJhGovXboUkZGRzLG0RzoxMRGff/45AFUo+OXLl1G9enW1TMdc123tazOEvdljLzhqaLep42lIUABOX7mlNaaR8VRFpe7jkS8vhrOzM9LS0gAIC1cW+j2z1++npl2a94Drnhi6T3369IFMJoO3tzfvMo262rT10hDAcqHdQry4lgzJ1sW7CyOw59CvCGjWEs9ynuu1k76WwNYdcSj9KADd4dpc122L69OHvdljL5DQbgtjTIbO+KS9yJblI0eWj/ikvZwha24uKu+Q1M0V8Ul70b3dG0xWUYAu5KHy2C5cu8MoWxyhTMf0ZRsBAOM+mcZs4xMCLSSRGNsza+x5lkCfXZoeaK57MnBiKEaHh2FXYhJvGzWvif27vlBxayRKq9WgLvPzw4cPLdYPoO6xsEQ9U1Ps0SQjIwOdO3dGu3btUK9ePcyZM4fZt3DhQiQkJEAsFkMikUAqlWLz5s2MiAaAyMhING7cGF5eXvDw8GC2c10317XZMjSUeH7LF6aOp6ev3OJM9EXGUyA7Nw/5ctVkAvv7z8c7aexSE2PPswT67NJ81nHdk9DQUISFhSEpKYm3jZrXxP5dX6h4eVsawvZ0CvHiWsoTqc/zSif0unTjCjKzHjHrmblgZyfPkeXqDbfmum6u67NlQi5H8vzaK8QjbSU0PdJcs9fjo1dhV2oaXF2cIS8qhp+3Jzw93JnZ7vXJKYiI2wSKouDn7YlnR5KMssOeZ9DlhUXw6jYBIpEIP/+bDglrnZs5sab3WojnVl/7QpOI8W1D83j27wB0JiSzhkcaAIbWbQ8A6NmzJ1auXGmxftgeC/rlKSAgABkZGcz/1vQW6PPsJCcno1mzZggJCWHKg23cuBFLlixhjomNjcX8+fMRFxeH6OhoZntUVBTmz58PNzc3AEBubi4yMzOZdvl4S+zV61SRcVSPtDGQ8ZQfnSbMxYXrdwBYNqLHmt5rIZ5bQ89QPu0IbUPzePbvAHQmVitvHmm2p3P25AjGi5t+5RzzvzUTSenzvNJi/0VuNuRFhRCJRFg1N1avbe8ujEBy6j64u7ohdtYi3h5pru3EK2x/EI+0HTI9NATPjiTh+ZEknQMuXdbD1cWZs/TH9NAQrJk/FXVrVsXnr8LDjbHDnst0TFu2AQDQY/gAi4lowPgyWMactyVhLZ49eowtCWtNap/LA83lIRfahubx7N/pn7myeptaQ5svHl4qj+njx48t2g/bY6FZYzo1NdXq3gJ9np3Q0FCMGTNGzZs8ffp0LF36ulZsdHQ0/Pz81ER0dHQ0IiLUZ549PT3V2uXyVGl6S4hXmGBLyHhqGKVSyYjooUOHWrQvY58HxpyXmJiIrKwspqqAse1zPeuElt3jakPzePbv+vI/2NsaZlNhezo1a0zvOfSr1RNJ6fO80vbFzloEiVgCiqK0bNP0GqdfOQeKolBcUqKzT13rgzU91cQr7NgQj7QdoTm7be+z3eaGoii4dnobCqUSWy+mwqeKn61NMgvGrEvmgmuN9PpF8VAqlGZZ331wazIj9idFzrSIQDbGix0R/Dbu37wNwLrrpIHXXgJre6YNeSecnJzQokULAOpZuaVSKRISEtTEM01sbCxmzJgBuVwOsVgMP7/X36/r16+jrKzMaHuA1+u2g4ODsWzZMqGXbHbswcNjTZ4/f44BAwZUCI80Hyr6ePr5pmQsXvcjAODMmTOQSCQGznAMjFmXzAXXGmlzlt0zpYSgkD7M+YyzdPkr9vpia3qmhazR1nWsptfYlLJSfOyh122P7DcU3y21feZqW5ejsjbEI+0AcGUHpct5xCftxfrkFLue7bYE63b9DoVSifrNG5cbEQ2oRGnV2jUxKXKmzmP0rVOm0VyTbChDt1B2JSYhP1eG/FyZxdY9G7OuetWBHwT3Y661d7SXYNmyZWoealM803xs41ovxz5PLH796HZzc4Ofnx/c3NwgEokwf/58+Pj4qLXn4+OD+fPnw93dHWKxGFKpugBit8cFH29JamoqlEolUlNT9bbFxpJrJMvbmkM2FEUhMzMTGzduxJQpU9CzZ08MGDDA1mbZDD7ZtivaeLpkvUpE+/v7lxsRDYDJjB0eHq7zGH3rlGk0nw/mLrtHlxA0pgSgkD6s8Ywzdybt75auUfNQm+KZ5mMb11plXefp8iJreo2njpqIlZFLjfIk88lkvefQr1AoFdhz6Ffe7VpyrbUjlaOyNkRI2whdSUrsPXmJJVmy4ScAwMzlC21siWXhK5I1BaeuEGyusGtjGB0eBk8fb3j6eBslzPkkYjMmNF4sFjE/X716ldc5lnrB0Bfmp08UCk1kRvcTEBDAeZ5SqeQ8j6IoxMXFITc3V217bm4u4uLiQFEUKIpCQUEBioqKmP262hNCcHAwxGIxgoODeZ9DX9OKFSvMLqbLU/i5UqnE5cuXERcXh9GjR6NLly546623sHHjRly5cgXOzs7o1b2lrc20GVzjZkUeS6/fyQQda7hhwwbbGmNh+IpkQ0kU9YVdG0OYCSUAAX6TjNZ6xllKROkLadYnCoUmMqP7CWzd0agEaIBu8ZsvL8CSdfFmF68j+w2FRCzByH78l2XQ1zQ7YaHZ7SHh57qxqpBevnw5OnbsCC8vL1SrVg0jRozAzZs39Z6TlJQEkUik9o9OkuPI6MoOOi9sBHy9PZFfUIj1ySk6Z9odud4lF5dv3sXz3Dx4VPJC8/atrN6/Mdm4+Z5jrEj29PFGYUEBkz1bMyRac42yqRnFB04MxY4rR7DjyhGjhDkfb7Mx66rZa+Vv377N6xxLvWDo88zqE8jsfXzEOAAtDzj9YiaXy/Hjjz9yhmLHx8erhXWzPdPR0dFYunQpI6blcjkAoKysTG9YN1+WLVuGs2fPCgrrDgsLg1gshlKpZDLjmstDzXcNpD1Ce5vfffdd9OzZE506dcLUqVORnJyM+/fvo0a1ynh7ZBckb41E8fOdyHnwLfbtmG9rs20G13hKb+vSuikzVlaU8bTzJNVnwcXFBdWqVbN6/8Z8z/ieY6xIpp+ddPZszZBozeeFqc+K0NBQHD58GIcPHzZKmPOZcLXWumpLiSh9nll9Qpe9j48YB6DlAadrM+fLC3iJTi5hv/L7NQYzdxvLd0vX4GX6fUFh3bMnR0AilkChVGDl92vM6qHm+lvZMtu4PWHVNdIDBgzAO++8g44dO6KsrAzR0dG4evUq/v77b7WkOWySkpIwa9YsNcEtEolQvXp13v06ypouGnYtSwCcdS2Nqb1pz3ScMBcXr9/B1JjZGP7eOKv2bexaY74ZvDXXHgMQnH0bgMG+6OO5Mmxr2mOJbNuWzOI9vH5HKJVKVKtWDb/99ptZ2zYX+tarGZshVl+27IyMDDWhnJCQgIULX0dzLFu2DPPmzUN8fDwWLFjAbI+KisIHH3wADw8PuLm5qWXttgXstYQlJSUoKiqCm5sbfHx8zL6+2R6zjdPe5pSUFFy8eBEPHz5kaoIDqsmQdgH+eKO5P8aN7o5O7RtzhurKZHJUqjOBrJHWoKKNp8XFJZB2UY2hK1euRM+ePa3av7Frjfl+NzXXHgMQ/GwFYLAv+niuDNua9lgiF4MtcjxYeo20EPStyeW7XpdrXbOx2bK5jt20eytTKktXLWlrw7apuKQY8qJCSN3cUdnHz+zrm8tztnG7XSP9+++/IywsDG+++SYCAgKQlJSEBw8e4MKFC3rPE4lEqFGjBvNPiIh2RNiz6/o810Jrb9oDXDP/uXkFuHj9DkQiEYZNMS57qinoW2usz8vbon1riCVitGjfmrNd+lwAcPfwYNYe8/XKcmXP1mVfQkQ0CgsKAABKhVIrQzj7OixV/1nXdZmj9nb7Pl0BAM+ePTPJRk0s7QHls4+NpidF8zz2/uzsbOY8TRG9YMECzJo1C4WFhfjkk0+wePFiZt/y5cuxZs3rWe7du3fb1EsbGhoKqVQKmUyG4mJVzdvi4mKrh+Zbi8LCQqSkpGDevHkYPHgwAgMD8X//93/YvXs37t+/j5rVq+Cd0G7YvW0uCrK2I+fBt/jz1yX4OmEqgjo1K1frXa1BRRtPe72/iPnZ2iIa0L/WWN/zNiAgAGKxGAEBAZztsqN16OdFUlKSUc9WXc8Buo8FCxYwETtKpVIrQ7jQpTrGoOu6LBlVY2oyT0t7QPnsY8O1rpl9Hh9PO31Nga07MseyPd0xM+bBU+qhdbytvLRTR02Ep9QDObJcFBarlm8VFhdZPTS/ImHTrN23b99GkyZN8Ndff6FlS+71XUlJSZg6dSpq164NpVKJdu3aITY2Fm+++abOdouLi5kXMkA1g+7v7+8wM+jlGa6Z//eWrEXSL0fQbUg/zEuMt7pN+jypml5n9rFbEtaqZePWbId97ujwMIt4a+k+RCIR2F9lzQzhpthiqqfZ2JrdbK6du4T5o6YCMG/mbkNeEGt6G4zpq379+njy5AnatGnDeDGjo6Mxc+ZMtczc2dnZ+N///ofly5cDAJydnXH06FE8fvwYU6ZMgUwms6mX1lbZ0a3Bixcv8Ntvv+HkyZP4559/IJPJmH0uLi5o1MAffXs1w/gx3dG5Q1OIRCI9renGUh5pMp7aL1zjqaS96vsycuRIzuz9lkbfc0xfxE1iYqJaNm590ThhYWEWeS7TfWiOp5oZwk2xxdQxxZJRNUFBQSgtLcX6T1diwtC3BZ9vyENpqczPXO2aqy+ua2JvA4DMrEfw9faBp9QD+fIC5MhybeqltVV29PKE3Xqk2SiVSnz00Ufo2rWrThENAM2aNcO3336Lffv24YcffoBSqUSXLl3w8OFDnecsX74clSpVYv75+/tb4hIIRsA1879l/1EAQETcQrN4LwFhXlB9HmJNT7A+by5dL3rz0lWYEjQELdq3NqpWtRDotdT0oO/q7saZIZx9Hezr5XOf6GtevyjeqL+LoQRjfGzwb9SA+ZntiTUVQx5KS3kbuNo1pq9Hjx6hQYMG2L59O5ydnbF06VLMmzdPKzO3VCrFhx9+iJiYGDg7O2Pjxo3w8/PDxx9/jJKSEpt7aTWzo9P/O5qIpigKN27cwFdffYXx48ejW7duCAkJwerVq3HhwgWUFBfD1cUJwwZ1xL2r61H8/Ef8fS4BXydMRWDHZkaLaEtCxlP7RXM8/eL7n5l90dHRZvNeCmlHn4dY83mr75lH14v+6quvMGTIEAQEBFj8OUWvpabHUzc3N84M4ezrYF+vkIoMxiZYNDRmmfI3p6/bGBENGPZQWippGVe75uqL65rY2+ifi0uKkZn1CMUlxTb30mpmR6f/L28i2tbefxqbeaRnzJiBgwcP4uTJk6hTpw7v80pLS9GiRQuMHTsWn3/+OecxjjKDXtHqWnKxee8h/N/n61G3aUOsPbTLLN5LwDxeUC7Y3llAfa1zaLOuKC58nQmZ3bc5aknr8gzzXRvNBZ/7ZO561cbYAABD67YHAOzdu1fQM8MULFUbWahHWt8+d3d3NGjQAHfv3kXjxo2Z7ez60uwEjbdv30aDBg3Qv39/nDx50uR6rHwpj3WdFQoF0tPT8ccffyAjIwOPHz+GQqEAoFqSVLlyZQR1rI8RQzpjzFtd0LLzR7if+Qz1/Kvi3jXzZlOu6B5pMp4Czh1CoaSAunXrYs+ePWbzXlrKC8p+JgDqa527deumVlmA3bc5aknreh7xXRvNBZ/7ZO561cbYoIvAwECUlZUh/6xuR5UpWKo2slCPtDHeakPn+PdriRxZLny9fZB5iF91EVOoaHWdubDkGm2790hHRERg//79OHLkiOAXYmdnZ7Rt21Zv9l5XV1em7AD9zx6pyOU5aKK+3gYAmLlcFYJmTHkkLszVjiZsb64+T7bQ2s5CPMOa3nC+ZbC4+uBznwZODMX0z+ZZzLsu9G81YcIEs9ugi4yMDCiVSmRkZJjcFttTwOW1MTYjeGFhIf755x+t3BFyuRxKpZJZ60dTvXp1/PPPP/Dw8IBYLEZQUJDJ18YHS3n3rcnNmzexZs0azJo1CyEhIQgMDMSsWbNw4MABZGVloWH9anh3Qh/8sXcRSrN/wrM7G/HLzmhMmdgXnp7umP/JSNTzr4r5n4y09aXwhoynjsG/Dx5D+cotsn37dgDmywlgjSoImp5d9uSN0NrOQjzDms8j+loNiWiuPvjcp9DQUMydO9di3nV7yAOhi/Qr56BQKpB+5ZzJbbG9kVxrpo3NCK4LQ+f0C+wFiViCfoG9BF+LMZC6zvazRtuqHmmKovDBBx/g559/xtGjR9GkSRPBbSgUCrz55psYNGgQvvzyS17n2GuW0Yo+g372r38QFBYFD28v7PjrCO+wRktmhzalH9rrLBKJMGPpfLVz2G1dPXMRJw+kotvgYESuiQWg2yurzwMuFEt56a1FYtQyHNy2B05OTkhPT+c8xhyZs009Rxe0p8Db2xtSqVTQ+mjNNcS6jvPw8ICfnx88PT1RVlbGeKSdnJyQn5+P7OxsFLxKSmftDNaO5pEuLS1Feno6UlNTceXKFTVvM6CKBHizeS307dkaE9/piTda+Ns0NLuiZ+2u6OOpX4+JeFlQCBcXF5w+fZr3edb6Xgrph71W2cvLC+Hh4Vpl7Oi2Ll26pBU1pOvZps8DLhR7rABgKpoeaWt7dYVAeyPptclC1kdrriHmY4+hNq2dwZp4pC2LEI+0VYV0eHg4tm/fjn379qFZs2bM9kqVKsHd3R0AMGnSJNSuXZtJivPZZ58hMDAQjRs3Rm5uLhISErB3715cuHABb7zxBq9+7XXgr+h0GB+JSzfuIiz6Q4yaPpn3eaYIQiHiWGg/mmWuuLJX70pMwvP/skBRFMQSMfbdPafXLnOEhGv2b+kJCEv198vmH/HNkgQAuhOO6UtmQ3sgbPXiQ9sil8u1knvxfSkT8vLm5OTE1GjmqhNtjhdoQy+0xrZliRd6Q+3LZDIcOnQIx44dw/Xr19XW4js5OaF+3Vro2bUJ3gnthg5tG8PHh7tko62o6EK6IqNUKuHccQwAYMOGDWjfvj3vc00RhMaIYz798Jk4pI958uSJajwVi3H27Fm9dpkjJFyzf2tNDFqjP00hra98FO0RtVVSLdoWruRefEWtOcWvOYQtu41Tl86YFAZvaaFd3oW83YZ2r1u3Di9fvkSvXr1Qs2ZN5t/OnTuZYx48eIDHjx8zv+fk5OD9999HixYtMGjQIMhkMpw+fZq3iCbYJ0+zX+LSjbsQiUWC60YbKgVlTHi0kH50MXBiqFqZK119u7i5QiwRo+GbzRl76TBxAGZJtqbLPj5lt8yFuctsDXvvHYPH6EtmY+uQNzp0MTw8XMsOvglkdCXc4QozLCsrQ0lJCaeIZttjyksZ+/6mpqZCqVQiNTXV5LaEwDe5jmb7Dx8+xIYNG/Duu++iZ8+e6NOnD2JjY3Hq1CkUFhaiQ9tGmP/JSFw/9z+UZv+Efy5/hU1rZ6Jf7wC7E9GEik1vVskrISIa0P3sMSU8Wkg/XNDPpoyMDJ3t0327urpCLBajefPmWktnAFisVJQ5np9CsMXSGM3QWXY4sa3DaunQ7ZgZ8/QmA+OCq6QV134hSaz4luTSB/v+7jn0KxRKBfYc+tXktoTA99pJaPlrrCqkKYri/Md+sB49elTtQbFq1Srcv3+fqSt64MABtG3b1ppmEyxA1Nc/AAA69+8FJ2dnQefqEoR8hBud5bqwoMCgWDVGeOoT33Td6c7BPbHv7jm8fJGjZa/mNUyKnKmWhdtcWc2tgaXWqQPAypUrObfrq7tsrhcffS+YfF4+Ne3g42mgM9impaVxXoOpmWCNhX1/g4ODIRaLERwcLLid5ORkyOVyeHt783rRFlrHVaFQoG/fvpBKpSgrK0NQUBBGjBiBb775Bn/99RfcXJ0R3DsA/1vxHp7c/hbyJ9tw7lgCli+egObNrJPYjkAwlpOXbwAA3nrrLcHn6nou8vle0Vmu5XK5weeOMc9ffeKbrjvds2dPnD17FtnZ2QYrIdCTmHQWbkvWZDY3tpgI1ld32RzCEdAv3PiIOk07+HhKl6yLR2bWIxxKP8p5DbRInJ2w0KoZodn3d2S/oZCIJRjZb6jgdjbt3op8eQF8vX0MTnRo3mO+AtnWEyn2hM3KXxEqLqWlZfjhwDEAwAfLF+g9Vohw5Js4S5/XWGifmsfrE9/XL1yBUqHE9QtXdNqrKfQHTgxl6j7TodLm9PJaEkt4wJ3dXAAAx44d43U835c3IS9UtKhNTEzU2meM18AcnoawsDAmjNuYdox9oWR7fjIyMjB37lyjwrqTkpIgk8kglUp5vWgbijQoLi5GSkoK5s6di8GDByMoKAjbtm2DXC5HdnY2alSrjLdHdcWuLXNQ/Hwnnt/7Fn/si8EH0wejWjUfwfYTCLYi7ts9zM+G6kYL+Z7zTZwllUohk8l0PneEPlsMJWWk0UwEyWWvptAPDQ1llvnwnYSzF6ztAeeCr3gW4tGlRe2SdfFa+yyRFIwPsydHQCKWQKFUGNWOsWWZ6PsLqBKzrYxcalRY98rv1yBHlgtPqYfBv5Xm/eIrkM01kVIeIEKaYFXWJ6egVv+pKFMo4d+kAbwr++o9nks46hK6fIWbPsFNl3oSIlb5ilvNfml7R0x+B1KJC1zFTlpCn23PloS1KCwogKePN9OGMR7qg1uTMbZ1b4xt3dshPNtsatevCwDIysoya7vmeqEyxmvA5xzakxIUFMT5UkpnguXrHdJE1/UbGzYt9OVZ6H3TjDRISkpCUVERZsyYgb59+6Jr165YsGABDh8+jJycHLzZvA4+nD4I6YfjUJr9Ex5cT8SP381G6IgucHHRjohZvzkF9d+chvWbU3jZQyDYgvXJKViwVpWhu1atWgaT3XF9z3V9V/kKN33fXbrUk5BnK99nsWa/tL3vvPMOXFxc4OTkpCX02fYkJiZqRcEYM6GYnJyMPn36oE+fPg7h2bYGlqzhbI5z6HDwfoG9OAXv1FETsTJyKXy9fZAvLxAsiHVdv7Fh00KFuZD7pnmsJQSyvdR7thRESBOsSnzSXmTL8gEAM5ZF6T324NZkLeEI6BeufISlPsG9KzEJSoWSV/kqWpDKsnMYG/X1z+7X18UDLX390aN6C3Sv0RxdqjdF9xrN0aN6CyTv24NBw4YynmjaHgDIz5Uxdhrrod6VmIT8XJler7y9MuTdsQBUyXXMiZB1gprhgcZCtw2A84WVq++0tDSdL5l8vEO60HX9xr7UCp2YEOJtKSwshKenJ9544w1899136Nq1KwYMGICvvvoK586dg1KpRLfA5lj66Vjcu7oexc9/xJX0r7B6xVR07tAUYrHhYS/uyz24n/kMcV/uMXgsgWArPt+4i/l5x44deo/VtXxC33fVmKUqbJKSkqBUKnmVr6IFaW5uLmOjvv7Z/Xp4eMDf3x8tWrRAixYt0KxZM+bnn3/+GUOGDGE80bQ9gCpxHm2nsR5qOprGmOdueUWXkOMSVLSojZkxz6Q+6bYBaAlBXULuUPpRnYJ/6qiJ8JR6IEeWK3hCQNf1Gxs2LXRiQogYtoZnubyvpyZCmmBVxoZ0BQC4uLmiZed2eo+lBZ+7h4ea6NXnUTY29JkWwC3at+ZVk5ltX3FhEWOjZv+awtpN4oy2fvXRvkoD1HCvBBeJhGmvSF4EefZLtG76BnbvSUb0rDn4v/mzGHsmRc6Ep483Cl7mMX0Ysw6ZDh/XnKCwN7gmJULGjmB+NqeYFrJOUF8iGyEvYoaOZa97psPJAej13LIFrRDviq7r5+sp1jyfXr8YEBBgsG9DPHv2DFu3bsW0adPQp08fdO/eHQsXLsThw4fx5MkTSN1dMWxQR3ybOBOyR9sge/Q9TvwRiwWRo1GvbjWj+jRU75l4rAn2QG6eqpSdWCyGh4f+BHi6lk/o+44bG6mjmRzRUE1mtn1FRUWMjYYiXZydnVG/fn00bNgQPj4+cHJyYtorKiqCTCbDG2+8gd27d2POnDmYNWsWY09QUBDEYjFKSkpMSkhJh4/zze9gK6y5HlyXOOMSVOxwZk2xK0SA6TuW3rdkXTxaDOvMhJMD0Ou5ZQtaIV5VXddvbNh0YOuOkIglCGzd0WDfxmJJrzHf5G+O6rEmQppgVVJOXwYATJgzw2AYmi6RqM+jzD6Hb9gzO3z64vE0zv1c7XAJUk2btySsZcKyvZ3d0blqE+Q+1A5LLpIXQZ6XD6VSiaICOQAg92EW5k2bheTzfwJQCffS4hJQFAWRSMSUlRKyDpn2YteoWwfyvHxcPXOR13m2wNCkyIoVKyxug9CXTCEvYoaOZa97BsB4wfV5r4HX3m1brv9LS0uDUqlEWtrr75OuFzn2doqicPPmTaxatQrjxo1Dt27dMHDgQKxevRoXLlxgvM3LFo1D7Vp+AABvL1fs+zEK707oi20/HdcrcPkK4OnvheDetQ2Y/h53PWLisSbYmvyCQhSVlAIA1q5da/B4Xc8bfR5lYybm2OHT7O8/ez9XO1yCVNNmdn4Kd3d3NGnSBE+ePNHqo6ioCPn5qvFULleNp0+ePMGsWbPw55+q8ZSuMlBcXMz0KXQdMu3FrlOnDvLz83Hp0iVe59kCe1gPrk9QcQlhU0KUufYBUBPQMTPmcQpeLu+2IVEvchJD5CqByMn8supQ+lEolAocSj+qZaOm+DRWlHJdn6G2+PZlyOvt6B5rq9aRthWk7qV98DQ7FzWDp0IsFuOnGyfg6uZm0f741oGmjxNLxJB6eSI/V6Z2Dt92uOom03Wg69atizMXzuHEieMY//Y4fLo4BpHzIplzc59lM4LJxc0V69avx+eLl2Dbzu0YMGQIOrVrjxtX/2aOZ9eVNlSvmb2fFqc07FrWfLBmLWpdfY1u3g1F8kL069cPcXFxnOdao+amPfXBVaPVHPbpq8vNbjM5OZlJvhYeHo7ExESteq1cNpaWlmLQoEHIycmB5FV0hkKhAACIRCJUr1YFnTvUx4jBnTFyWGd4e7/2uq3fnIK4L/dg/icjGcFb/81puJ/5DPX8q+LetQ1q17J+cwoi5nwDhULJuV8IXH3bGlJHumLRNSwa6X/dAgCcP3/e4v0JrXUvFovh6ekJmUymdg7fdrieNXQd6Lp16+LcuXM4fvw4xo0bh5iYGERGvh5Ps7Nfj6eurq5Yv349lixZgu3bt2Pw4MHo0KED/v779Xgq5LnJ3k+LUxp2LWs+WLMWNZ++NOtI01ijZrA99cFVX5rrXLGnC5wqu0Pi6QqwBXSZEor8YpS9KIQyv0Rnu7rs2bR7K5N8LWbGPCxZF48cWS58vX2QeeiqThv1bTfm3uhra9PurZidsBAKpcLkOtz2WJPabutIEyo246K+AgB0HdzP4iIaeO0dbtG+td7kWvRxdPi0phecLlvVon1rvf1xeVAnRc6EWCJGbGws7v7zL8a/PQ6lpaVYtGAhEuITmOPcPF6/kK5a+SUWLViI0tJSjH97HB7cuYPFi2IAAK7ubqhauyba9QhivOSGPLfs/fS1Nm79BsQSMboNFlaqyFBf1ijP5fwqOdShQ4d0HmON2XdrZFE1JeEPV5ktoaF9dLsBAQEYMmQI4xHSvK+a6wS51pGHhYWhevXqCAgIwIcffoj+/fujS5cuyMnJAaAK1RcBCOrUDH/sXYSynF14/M8G7N0RhbAJfdRENMDtNdYXkh335R4oFEpIJGKdIdt8MeSxJhAsDS2i6UgUS8N+FuhLrkUfN3fuXOY5wH4u8V32wfUMDw8Ph1isGk///fdfjBunGk8XLlyIhITX46lU+no8/fLLL7FwoWo8HTduHO7evYuYGNV46ubmpvZ847NOmr2fvtY33njDqNJ/hvqyl/Jc1vAYWmOtLt8+uLzb7HNFLhJkOuVC5qtAsRulLqIBwEkMiY87XBv5wbWhL0QuErV2A1t3VAsx17yvdNZten021zpyXR54zT74eqa57o2hCAKFUgGJWGJyCSxHzwBOhDTBKuTmFeDIedVM2vULGRYTWlylqK5fuKI3uRY7PJorVFqzbBVXXwC34B44MRTxm9eib9++aNHyTXy6OIbZR4tpiUgMN1dXeHh4YO3atYiNjWWO+XRxDBo1boyQAQMwcOgQvLfwY4wOD8PJA6la4rhF+9Y6Q9DpyQH6+vq/PRyVa1Q3uE5dE0Nrss1ZnktXW18d3G7wXGPWutkjfF+k+AhuYyYX6HYzMjL0rtHWDMukzwsMDMTGjRsxZcoUfP3113jy5AlSUlJw+vRpFBYWokPbRoiePRLXzn6FunWqoEyhwH+PsxHcpw2vpGCAerj29PdCMP+TkYj7co9W+DYtstd88T4RwASHhp1k7Pz58xYTWlylqDIyMvQm12I/i7ieS5plq7j6ArgFd2hoKNasWYO+ffvizTffZAQxAEZMi8ViODs7QyqVao2nMTExaNy4MQYMGIDBgwfjo48+QlhYGFJTU7XEMVtcs2GPLfT1DRs2DNWqVUPbtm0F3V9D45Q5J4RNaau81Aw2RyiyWOoMt6aVkVskg0KpRIG8QG9bd55kwq1pZYilzky76VfO6V2jPXtyBHy9fZg60Fz26LJRsw+hkx/sezR11ETMnhyBld+v0bpn9GdiZeRShxXA5oKEdhOswtyvtmDl1l/g7OKC0pISg2HSxsIVhn1wazK2JKjWkE2KnKkz/Hnz0lUoKSpG96H9EbkmljmvtLgEzq4uaNcjCNcvXEGL9q1x/cIVFBYUqIWBs/seHR7G9HngUAo6tXktWBPiE7BowULm99jYWMydOxfx8fFYsOB1Xe1Fi2MQtfB1XdAs+Utczc1UC0VnJ0XTF4KuGSatqw1TMWfot662igoLMbpZNwBASkoKKleubFI/lsISodWm2MIOvRZqD59roSgKly5dQkpKCo4ePYoXL16o7ffx8UGHNnUxZEAHjB/TA1WqqIdLCQmXZh9Lr1emw7X1hXeXZ6wd2j15SC+4ODsZPgGAxEmM8NEhqOJjPrtcnV3g460/wVZ5xanDaFAUxeRQMPX5oAtdS0YMPUuSk5Px1Vdfobi4GP3798eyZcuY80pKSuDi4oKgoCBkZGQgICAAGRkZkMvlamHg7L7DwsKYPlNSUtCuHWs8TUjAwoWGx9OYmBi1Otu5ubnIzMxUC0VnJ0XT9+zVfB7qasNUzBn6bUpotz1gjvBfY8OeaUQuErg1rYyf9u7GpMmTMGfOHETPj4LUjVtXxCfEI2bJYuzcvhPDBw9B0a0XoEoUgq/FmGsXcg77WDr6gL5Hpt4zR0VIaDcR0gSLo1Qq4dV1PIpKSjFhTjhSdvxssTW2xgo5WlgCr9cNs7dVrV0TABjxqVQo4enjDXcPD6YvXWuRr/19DQ2bNlbrT1NM+/j4IDc3l/k9OjoaH3zwAXyq+jHbShRlOP7khs5rZG8HwCmcaZFNJ1hTKpQWm9SwJEPrtgcALF++XHA4HV9MfYnhI4KFrMcz5UXKHIJc05bMzEz89ttvSEtLw927d1FQoD0zL5W6YuvGWRjUvx3c3FyMtl8TtlimxTQtwI1dv2yP656FYG0hXR4RiQBnJ36TA3zxcHc16wTCy7wCPM1RlW0KDw/Hnj17LLbG1tjnD/28AV6vG2Zvq1GjBgAw4lOpVMLb2xtSqZTpS9da5GvXrqFxY43xVENM6xpP/fxej6dlZWW4fv263rwP9HYAnMKZnTtixYoVFp3UsAaWFNKmCmE+gs5QH6ba4NrQF3eeZKJlm1YoLVUl+otdugzzIrVLd8UnxCN6oWoix9nZGVcv/4WG1f1RfCdHsC2WFrPs9mkxTdtl7D2zx3XPQiBCWgMipG3LrtTTeGf+l6hauwa+TTtga3M40eeRBlSebECVhbuooBCKsjLmOHYbbCG7JWEtqlWrhgvnLwAAigrkcPOQwk3qBolIjC/iE9RmyGliY2Mxc+ZMKEWAm/T1WvIieRG6du+GXmOGGJwk4BLOmuJb36SDNZOKGcOweh1AURR69OiBL7/80iJ9mCo++byEmsvjbA5b9KFQKDBw4EBkZ2dDIpFALBYzLxIA4CF1R4d2DdHqjbqY8E5PXLj8L1as2ivYu8xXwFpC9Dq6J9vaQvr9yf3g8ipfgSGePM/F+Qv/mtWOl7IC5BcUmbXNslIFHOWFSGhiK2uizyMNgMmdkJiYiMLCQpSVlTHHsdtgC9nExETVeHpBNZ7K5XJIpVK4ublBLBYjIUH/eAqo1kTTFBUVoWvXrhgyZIjBZyKfpIv6nrHWTCpmCpYU0qaKQT7CzJKCU+zpAtdGqomYz2OXYvGSxcy+2KXLMH/efEAEgALi4uMYEU3vp8V28e1sKAtKeCc043vtxhxryjmGcHRPNhHSGhAhbVvqDZyGh09fYP76Feg6qK+tzTEJTS8125PLJV7PHTiCP35PQVGBHEqlEmKxGD5V/eAkkkAiEsPPz09t5tzHxwcvXryAvLAQZUqFmpDOfZaNrl27Iq+40KAHWagQ1hX6ba/e6skdByD7yTOIRCKcO8c/67gQbJWV29R+zeHlLioqwooVK5CSkgIXFxcUFBQwWXBFIhH8a9dAl8BG+PPIFTx7IWPEpzEC1xoClo9dxCPNtx/VePry4Q/w9ibjqSGePstFfr75BP/d+1noN+wzSCQSHD9+HK6urmZr29poeqnZk4lc4vXIkSP4448/IJe/Hk/9/PyYyT2d4+mr49lCOjtbNZ7K5XKDk5hCn8m6Qr/t3Vttzx5pY/swtV/6/P17fkHL9q/X67M9zoBqwmb+/PmIi4tTm9ChRbS8SI4CeQGy7j5CY6cavLJkG2O7NQQsH7uIR7qcQYS07bj94DGavfUBXNxcsevGSd4JhPRhLm8p33YSIqJx8kAqug0ORsvO7XSut6bbY6+h9nB1x+XLl+Hk4oySomK4uLnCs5KXXo/0smXLEB4ejqKiIrXQ7vyXeWjVsiWadgxQ84Qbe11s+Hiw7YnzR09hyaQPVT9bofSLNTH1pcvQ+Vz7nz9/jt9++w2nTp3CP//8A5lMpnZOyzfqok+Plhj/dg90at+U2a4pPg2Vn+ISqtYQsI7ubeYDEdIVh1aBH+Hq3w/wySefYNy4cSa1Za4JQ77tLFiwAKmpqQgODkbbtm11rrem22OvoXZ3V42nzs7OKC4uhqurK7y8vPR6pNnjKTu0Oy8vDy1btkRAQICaJ9zY62LDt2ygvWHPa6SNxVRhSZ//97W/0axxU7V9mmJac0kB2xP9LPsZFEolcnNyUU/OvTRGU3waKj9lqvfaWBzd28wHi5e/CgoKgkgkQlpamnrHMhnatGkDV1dXpKamGtM0oZwx9fN1AIChU8aaRUQD5ssMrasdzWzcJw+kQqlQ4uSBVAycGIodV45gUuRM7EpMUsuQzc4STnutlU5iKMQUykpUYbD0/3Fx8WqDvo+PD/PzggUL8MUXXzAlsYrkRch5+gKPMh8i63GWVvZwPtdlqCyVZsZxruzl9kS1mtWZnwsLC21oCX/4ZuDWlcmVPn/BggV62+E6n913WFgYqlSpgrp162L8+PHo1q0bBgwYgP/973+4cOECKIpC9y5vYNigjqhdyw/rVk3DX+lfYfWKqejUvqlWlmx2GShD5afuZz5D3Jd71LZbo5SUPrsIBEdj8xpVqPKOHTtMbstcmaF1taP53EtNTYVSqURqaipCQ0Nx+PBhhIeHIykpSe2ZxlUxQCQSgaIoZlkJ/X98vOHxlC6JlZeXh+fPn+PRo0fIysrSyh7O57oMPcs1M45bo1SiqRQXF6OsrMzWZvCCb/ZtQHfGcbqNdxdG6G1r9uQItGvVBv516jDb5EVyPMt+hk8++UQtK7yaiI6NVYV7v8JD6gGJWIy6/v5q5bI0s2SzM3EbKj/FlZXbGqWkyksWd3NhlLKJj1cVCmcndygpKcFbb72FK1eu4Pvvv7dYAiCC4yDLl+PExb8BAGNmvmu2dg2VYNKFppjU1Y6mEO02OBhiiRgN32zOq3Yz3e6kyJn4Nm0/SqVOcPOQQiwWw81DqpVobFlsLJ4/f642K75s2TJ8/fXXAIDC/AJQFIWTJ09CLBEbvG6ucli0vesXxXOKaV0lvsyBJWpL1232OtlMSUmJ2dq1JHxfWHXVf6ZrOLNLtfA5X6FQYMOGDcjKykJCQgISEhLw/PlznD17Frdu3YKPtyeGDuyAbxNnIv/xdsgefY/jvy/Fvh+j8PDGJi2Bq0sQA+BVfkpTzLKFORf0/nFTVuk9Th+k7jOhPNGpQxM4Ozvj8ePHePr0qUltGVsqUFNM6mpH87kXHBwMsViM5s2b86rdTLcbHh6O/fv3QyKRQCpVjadSqZQza7e+8bS4uBgAcOLECV7XzVUOi7Z3xYoVnGJaV4kvc2Cp2tJ//fUXAEAisf+quELqWrOFJVu00m3sOfSr3ramjpqIEz8cVMvMXSAvgEKpRJlSgfnz56tN3ACqiZz581+tmX6F1E2Kqn5VIXWTQiR5vUPftfApP6VrgkDXxADfCQR9OHrdZ3Nj1DemR48eGDx4MA4fPoyjR4+CoiiEhYXh8OHDWLVqFd555x1z20lwQGLW/wgACOjaCVIvT7O1a6y3lK8nW1NgR66Jxb675/DyRY5W7WYuUatp36OCbLhJ3eBT1Q9ff/21moiOjo5GxMyZKFWUISIiAjGLFzP7Fi1YiOVLY0Gvvti2bRuvUlWannHaXjrbuD7xL3Rygg/mrC3NxbRp0yzSrrkR8sLKflliZ6ytUaMGmjdvrlVflYaiKNy8eRPr16/HrFmzEBISgs6dOyMnR5UpVKFQoLKfJ96b1Bep+2JQ/HwnHt/eiF92RuPdCX3h4eGm1SagLnZpQdylc3NOYcvH88xuT58wZ7f308+n9B5HIFQkPvi/AQCAJUuWmNSOsd5SvhODms+9ZcuW4ezZs8jOztaq3cz1bNS0Lzs7G25ubvDzU42nbBEdHR2NmTNnoqxMNZ4uZo2nCxcuVPMe7tq1i9d1a3rGaXvpbOP6xL/QyQk+mLO2NBv6PaNBrXpmbdcSCPWI0uJxybp4RrTSbQQ0awmJWILA1h11N6BQXwHrIfWASCRCWVkZ4uLi1DzRgMozHRcXB12ZC7f98hMjYmk7Alt35BS2fDzPXBMEuiYG+E4gEPhj9NTT8uXLIRaL8emnn2LOnDnYsWMHoqKiMGvWLHPaR3BQFAoFvtlzCAAw/fO5NrZGhaZY1CXwdAn10eFh8PTxRuGrMj9cxxzcmoyxrXtjbOvejAc2p6QAL4ry8e/t2/h88euXnkWLY/DBBx9AKQIgEcHFww3zF0aphagtXxaLu3fv4siRI2g7pBdTZouPh5d9vQMnhmL6Z/P0in+6bJc5PceadpiThm+q1is9fMhvPZelZvL5IuSFlf2ypOmRyc7OZrwdJSUlOHHiBBYtWoQRI0YgMDAQ48ePx6ZNm3Dq1CnI5XJ0aNsI8z9+C7Vq+AIA3N1csGnNTPTrHQBnnnWA2WKXFsSnz9zgFLZ8wqjZ7Rk6nt4/5q2ugsKzDXm6CQRHZnG0ymFhqWSLhtAUi7oEnq7nXlhYGLy9vSGXywGA85jk5GT06dMHffr0YZ7bBQUFyMvLw+3bt9UmEWJiVOMpoAoBd3NzQ1SU+ngaG/t6PA0KCmL6ELrkJjQ0FHPnztUr/umyXeYebywp0oUgJLza3Aj1iNLiEQAjwOk2nuU8h0KpQPoV3d8jqkwJlCmZ36VuUohFIqxYsULnkoLo6GjExcdxtvX52hWMiKXtSL9yjlPY8pk0YItnQ8fT+0f2G8p7MsKWf2tHwGgh3apVK0yYMAEnT57El19+iSlTpqjN9tEsX74cHTp0gJeXF6pXr44xY8bg3r17pthMcAD2/JmOwuISVK1dE3UaN7C1OVoc3JqMwoICePp4qwk8LpFKbwMAdw8P5OfKOL2rdG3m/FwZcwx97rrvvkHdhg2xbed2ODs7Y9HiGMyYNp0ph8Umcv48REdHw9nZGUlbvkfVatVwPechI9rZEwD6RLXmhIAhT/6WhLV49ugxk0zNXFhqvXWTgDcBqDJM88FSM/mWQPOljX7JzMvLQ0BAAFxdXSGTydC1a1d8/PHH+O2335CVlYUG9WrjvYl98eH0QahTuzJGDO6AZ89lqFe3Gj6dN0anEDUkOrnEri4BrCuMmsurTScY0xd2Te/f/u3HgsKz2WKdiGpCecPLyx3t2jSEUqnEb7/9ZlNbkpOTIZfL4e3trTM3g+Y2AJBKpZDJZJzPZLo2s0wmg0wmQ2JiItPWo0eP0KBBA2zfrhpPY2JiMH36dKYcFpt5816Pp99//z2qVauGzMxMRrSzxwV9olpzQsDQxCi9FIdOpmYu7GW9tZDwaltDi8eYGfO0BDgfobpp91b88NN25Oa9xLPsZ5AXybF+/QY1zRMbG4vs7Gy1bdELFyA+IV6tLWVeMWefuuzQNWnAFrfscw1NMtD7v1u6hvdkhObfmghrdUzK2j1nzhysXLkSXl5eePr0qdYDDAAGDBiAsWPHomPHjiguLkZkZCQePXqEv/76C05O/LwhpkKydluf+oOmIfPJCyzYtBKB/XuZrV1TskmzM1MD4MxSXVhQgPxcmVrZJ/o8T5/Xmfs0M3azjxOJRPCo5MUkJKP7ST7/J9pWboAHd+6gciU/pnyHm4dUrcY0zb+3b6Nuw4a49OIuZKWvE2qx7wG7ffp3+t4IvVdjW/dGfq4Mnj7e2HHliKB7awse3r6HGX1GAeCXudtRMqfSUBSFBw8e4ODBgzhz5gzu3LmDglfREADg7u6Oli1qI7hPACaM6YEWzf2ZfXSWaolEDIVCaTBbNTurNb3GmW8Wbb5Zt62ZOXv95hQs+GwbAGDZovGMqObbtyOWwiJZuyseh49dQd+hi1G5cmWkpAifJDLlmcjOTA2AM0u1XC6HTCZTqxBAn8f+jGpm7GYfJxKJ4OXlBQBqbbm7u6NBgwa4e/cu/Pxej6dSqVStxjTN7du3mePZCSrZ94AW1fQkJvveCL1Xffr0gUwmg7e3Nw4fPizo3tqCc+fOYcaMGWjs3wCXd58weLyjlzcSQothnVGnQV1mgiVx7VosY4toVh3pvIJ8xMfHqwnqBdHRmD9vPqRuUqaOtC743ldrZs5+d2EE9hz6FSP7DcV3S9cI7tsRPytWKX+1Zs0afPDBB6hevTqePHmC9evX81qrePfuXTRs2BAZGRlo3bq1MV0Lhh74e7RvASeJxOL9VfL0QJtmDSAWiQwfbGaq+VVCrw5vQmSDvit5uKOKbyXcffQEjYfNhJuLM3bdTjdrH6bUN2YLSwCcdZM9fbzh7uGhJj7p82TZOSguLGKEpqZQPbg1Was0luYxbhJntKhUGx5KJ0Y8a9aYpnlelI8bLx+hSFGqUxTrEtXfpu0XfK/sveQVF0PrtgcA/Pbbb6hWrZqNreGHrhcyOlz7999/x4ULF/Do0SMmKy0A+Pr6omPbuhgyoAPGje6OypV1P9xpIdilc3Ok/HkJgEpQ8qmhLER0rt+cgog53zBiXZ8It6Y41RTtQvt2xHJZREhXTLxqTUB+vhw7duxAkyZNBJ1rSqk99nMMAGfdZG9vb0ilUrVnHX1ebm4uioqKGKGp+VxMTk5WK42l2QcAODs7o3bt2nB2dmbEs2aNaZq8vDzmmarrGaxLVO/fv1/wvXK0iVuhQtpesGZ96m3btkHi5oxevXoxYzO7xBUApl70+vUbsHjJYgCqz+nxY8fQoWkAiu/k6O1ndsJCKJQKxsOs69qsKU5NrW/tiOWyLC6kf/rpJ4wdOxY9e/bEtm3b0KJFC0ilUty+fZspMaCLy5cvo23btsjMzEQdVjp5S0IP/ACsIjArQGlunUjEEigpJSiKQkC3Tli6fZ1Z27eU2OMSxZr9aHpsuTy4XPWYtySsRWlxCZxdXRiB7ePigTpSP/i5ekBZXMaIarGrE7KLC/BQno3cktfeRz51njUnCnTVuxZ6PwDYrcCmhXTjxo3x448/2tQWzRdLXfVR2S9kM2fOxOHDh/H333/j2bNnUCpV67DEYjHq1KqOLoGNMHpEFwwd2IH3emZNuEShPmEpRHSyPd9rvnhfsOfXUvC9BlvWtjY3REhXTKJifkDcqj3o27cvU1GFL5YSe1yiWLMfTY8tlweXqx5zYmIiSkpK4OLiwjxbPTw84OfnB09PT5SVlTGi2snJCfn5+cjOzlaL5uFT55nv81zo/QC0JwTsAXsT0myxBgBL1qk+2zEz5qmJN2uKNJGLBI+keTh58iSmTZuGJTGLMS9yHiOePaQeatm94xPiEbNkMb7ZuBFDBw2B2+MyUCUKne3T1yIRS7AycikTUm1rAcpXONuytrW5saiQ/vPPPzFo0CC0aNECx48fh7e3N5YsWYLFixcjNjYWUVFROs9VKBQICQmBq6srDhw4IKRbk6AH/v8OX4e3p5fF+3v09D88evLY4v1wcfH6FSapgrW5ee8fvMjNwd1H9/E85wVEAPbdP28T77ipcHlzNcUrl5BOiIjGyQOp6DY4GJFrYpl2aDTb25KwFr6VfODj64tBYWPQbeQATnv49G3IfmOu39PHG/K8fCgVSqPbsiTTe43Eozv3UbNmTfz66682tYV+OROLxfD09IRMJgMA5mXt2bNnSElJwZ49e5CZmak24ebq6opmjWugV/c3MXZ0D3Rq3xgbv0s1i5jjEoXm8riyPd+nz9xg/jenALWkqHVEz7MuiJCumLx4IUOVBqos0sePH+dcYmdruLy5muKVS0gvWLAAqampCA4OxrJly5h2aDTbS0xMhI+PD3x9fTF69GgMGMA9nvLp25D9xly/t7c38vPzoVQqjW7LUtibkGaLSm9PL+TIcgFAS1SaKvKEsi/tD3Qc2A3y/AK0erMVAOBZ9jMolEpIxGJU9auqdvzt27fRuEFDFN/JgVJeytWklo2BrTsi/co55n9zClBLilpH9DzrwmJC+uLFi+jVqxcqV66M06dPo2ZN1VpTmUyGBg0aQKFQ4M6dO2rhNDQUReH//u//cOzYMZw6dQpVq1bVOsZSWFtIV3RKSkvg17UhAKBVlw6I/dHxXlD5eL65jtHlkS4qkENRpkD3of0RuSZW7VgaIWuTDQlpPh52PtdPrxkXS8S8Sm9Zm/mj38e1MxcB8FsnbUno5DhKpRJeXl4oKytDaWkp3N3dUVpaqpYUzdPTE21a+WNgcFtMGtsLdWpXAWB8iLVQhIhTzfXGXMdbUpBasm1H9Dzrggjpiku77rNxKeMuPvjgA0yePNnW5mjBx/PNdYwuj7RcLodCoUD//v2ZetGaIlvI2mRDQpqPh53P9dNrxsViMebOnUs80npghzn7evsw2zU90obaoEWjOb27IhcJXOp4Q+zlCgA6PdKAKrlYyUMZ44netHurTu86jSUFqSXbdkTPsy6ECGneWbv//fdfDBo0CC4uLvj9998ZEQ2oHljz5s3Dy5cvsXz5cq1zKYpCeHg4Dh06hD///NOqIppgfVycXZC0VBVW/Nfp83j07z2r28C3RJQu2JmmdbXFlY26RfvWEEvEaNG+NXPMjitH4FutKiiKwvULV5hjR4eHwdX9tfegtLiEs3wWF5MiZ6Jq7ZqYFDmT85o1bRNay5k+n+5Hn4g29V6bwvKfNlq9Ty6eP3+OgoIC1K5dGyKRCHl5eSgsLERZWRlKSkrg5+uN4YM7ImndB8jN3Iq8/7bgRMoyRM8JZUQ0wF0WSle9ZlOY/l4II9YNtRv35R5k5+QjOydfZw1nXRm8jcmWrXnO/E9Gws/XE3n5hWbPum0oYziB4AhsXqsaB3bu3GmR9k0tHcjONK2rLa5s1AEBARCLxQgICGCOOXz4MKpUqQKKopCRkcEcGxYWpuaNLykp4SyfxUV4eDhTYpDrmjVtE1oBgj6f7kefiLZ1mUZ7YeqoiVgZuZTJtp156CoyD10VJNA0y0L5evsgX15gcrZpqkSB4js5KL6dDUVOIdyd3OAh9UCBvADyIjmoMiUUOYUovp2N4js5auHcK79fgxxZLnJkuToznuvK4G1MtmzNc8x5HzQRWpasvGBS1m4+UBSFmTNnYv/+/Th27BgaNLB+KSTikbYN9UMC8DznBVyl7ki+cdKqfZsa2mxsW7qO1eURZnul6URn9O9CbddnpyUTiZnzXhsDvU76q6++Qrdu3Szen1KpxK1bt3Dw4EGcP38e9+/fV/M2V/L2QkBLfwzs3w7jRndHXX/+E4eaodKW9Ezz9fTy8Uib2oehcyzhlS5P3miAeKQrMhRFwb3aOBQXF+OXX35BrVq1zNq+qaHNxral61hdHmG2V5pOdEb/LtR2fXZaMpGYOe+1EOzNI20ONEOl8+UFyJHlWsQb233KYLyU56GS1AsnvtW9dJWPR1oXxniTuc6xhFe6PHmjAQt5pI1l5syZ2LFjB7Zv3w53d3dkZWUhKysLJSW6078Tygent/wOACiWF2Lf5u1W7Xt0eBhTEspUNL3MxvSry8Pdon1riEQiuLq7YVLkTIwOD4Onj7dWfWs+6LtmvrWcjfEum/Nem8KRI5Yp2VVWVoYTJ05g0aJFGD58OIKCgjBhwgRs27YNt27dgm8lLwwbpPI25z/ehtyH3+PY70sx/5ORgkQ08NpDevrMDS3PNFf9Z8A4ry+g24vMZdOL+1vw4v4WwYKTbx+GzjGnx5s+L2LON8w9JhAcGZFIhI/CBwEAFi9ebPb22XXtTUXTy2xMv7o83AEBARCJRHBzc0N4eDjCwsLg7e2tVd+aD/qumW8tZ2O8y+a81xUd2kOafuUckzuIT81oY2okTx78DkrlxZg8+B2DNhnjXQf41bzmc445Pd70ebMTFjpMXXFzY3GPtK5EU0eOHEGvXr0s2TWDvXuky9tMDpv3Fn2Anb//DAD4+d90ODk729gi4bA9rnSZqRbtW+P6hSs6s2brW5esr561rbGmd9lcXvLQpl1QXFQMX19fpKamqu0zxnMgk8mQmpqKY8eO4caNG8jJyWESgzk5OaFh/aro0eVNeHi44uf9ZxFlAY+mKZmzNY8fN2UVfvr5FMa81RXbv/3YanZZo31jPdWG7pkjQjzSKspbpAFf8vIK4V17PADg1KlTcHV1tbFF3LA9rnSZqYCAAGRkZOjMmq1vXbK+eta2xpreZVO95Hw80pZ8V7WXtv37tUSOLBe+3j7IPHRVa79mTWVr2WWN9o31VGtmGy8POsauPNIURXH+s5aIdgTY6zjKG5uW/A8Ssap296yB4yzSh6XX6LI9rvRa45MHUrXWHGuuQ9a1LpndHu2BLiwo0LLfHNel2YahNq3pXRa6blsXHfqqwrlzcrTrM/JZy/bgwQNs2LAB7777Lnr06IE+ffpg+fLlOH36NIqKitChbSNEfTIS186sRsmLnbh5cS2+WROOvfvP4oGZPJpsz6pQITD/k5GQSMRQKJRY8Nk2LQ/tTz+fgkKhxE8/nzLJLkB9DbclENq+MR5v9nnlRUQTXmPpz6i94uXljv59VF5eU0oBWnqNLtvjSj+fU1NTtZ7Tms9uXc9ydnu0B1oul2vZb47r0mzDUJvW9C4LXbdtDJZ8VzVn22zPqililctDu+fQr1AoFdhzSHiVEM32LP3uL7R9Yzze7PPKi4gWisWFNMEwxn54HQGRSISdX3wLAHhw6w4yLZB4TIggM0acssOiaaHZbXCwluDUDAFni1JdicAGTgyFu4cH8nNljP30sVsS1vK6LnbbCRHRGN6gIxIiojnvjaF7xTcE3ByYS7RHxC3UuU/zRSY3Nxfnzp1DbGwsQkNDERQUhJEjR+Kbb77BX3/9BRcXFwT3DsD/VryHF/e+R0HWDzh7dAViF0/AGy381SJsjBVxXLBf/oUKgenvhWDNF++j3qswcs1zx7zVFRKJGGPe6mqSXYD2NY+bsgpOvqEYN2WV4La5EHpPjU0WRpKMlV/M+b10NGJjJgAAtm41PomQEEFmjDhlh0XTz+fg4GAtwakZAs5+lutKBBYaGgqpVAqZTMbYTx+bmJjI67rYbS9YsACdOnXCggULOO+NoXvFNwTcHFhDtFvyXdWcbbMFpFAxGTNjHpPgjOvckf2GQiKWYGS/oSbZBWhf87sLI1ApsB7eXWie+yv0nhqbLKyiJhmjsXhotz1g76HdFYFmQzrh0dP/4O4pxU9/mzeRhZAQYWNCl/m2r69tIYnA2DWc3T08BIWKv8h6AqVCCbFEjH13z/EON7dkMjJLU1JcglFNggAAmzZtQps2bZh9mZmZ2L9/P86cOYM7d+5ALpcz+8RiMerUqoGugQ0x+q2uGNCvDdzdTQuHNDaslH0eAKNDU83VDld7XG04+YZCoVBCIhGjLIf7ZdoSobamtOmIob98bCah3QQA8KkzCS9l+di+fTuaNm0q+HwhIcLGhC7zbV9f20ISgbFrOEulUkGh4k+fPoVSqYRYLMbZs2d5h5tbMhmZpbDHZGPGepPZ5wEwySNtjna42uNqo1JgPSiUCkjEErxMvy/4fGMx1WvvaEtT+dhssTrSjoo9C2lH/BAaw8OsR2g+rDMAYOqi2Rg+1TJh3jRcwpCu6QyoykfxFYy6hK1mf/raFiJU6WM112HzyQh+9cxFnDyQim6Dg5l61UKu0V7WatPwvW9D67UHKOD//u//8Pz5c1y8eBGPHj1CaWkpc0yVyr5o16YeOrVrjKGDOqJju8Y6czgYC581u+YUcvrasmT9ZRo+668tYYcpbVrjvpgbPjYTIe2YkyTmJmH1Xsz9dAtatmxptjBfLmFI13QGVOWj+ApGXcJWsz99bQsRqvSxmuuw+WQEv3TpElJTUxEcHMzUqxZyjfayVptG332zRyHNZ82uOd+h9bVlyfrLNIbWX1vKBlPatcZ9MTd8bLarNdIE/ZTn9dFs6tSojVHBwwAAmz5bieLCIgNnGBeGrS8seldiEvJzZXD38BDkdaVDkAFVYrAtCWu17DLUtpCQafrY6xeuqF0Dn1DxyDWx2Hf3nJaItqe10UIwFIpOXxdeTQdu3LgRe/bswYMHD1CzelW8E9oNu3+Yi+LnO/Hs7mak/LwIn386Dp3aN+EtooVkhuYTVmqONZy0TQs+26azLZNDXEWugMhD9b8Otn/7McpykvUmMTPGDkP33JRrc8TQX0e02RZU1PXRbGZ/oBpnr127plaWz5gwbH1h0UlJSZDJZJBKpYK8rnQIMqBKDJaYmKhll6G2hYRM08dmZGSoXQOfUPFly5bh7NmzWiLantZGC4FP2P69/x6Yvb4wGyGZofmEJpvjHZq2acm6eJ1tWWMJ5ndL1+Bl+n2dScyMtcHQPTfl2hxxaaq5bbaqR3r58uXYs2cPbty4AXd3d3Tp0gXx8fFo1qyZ3vN27dqFTz/9FPfu3UOTJk0QHx+PQYMG8e7X0TzSlvBS24vnu1JQPSgUCjRq2Rxf/bZN77HGeEn1eY/5eDf1HUPvKywoQH6uTM0uS4RG62uTz71hn08LUkvbbG4M2ciuww0ADepVw87vZ6NDW/N5m83twTSH14y2yc/XE16e7ubzwIn9AGd/QFIZELm83k6VAIoXQGkmoMw2vR8DWOz6yjHEI8393bK3pQXWoFOvuTh38TamT5+OqVOnAjDOS6rPe8zHK6zvGHqfXC6HTCZTs8sSodH62uRzb9jn04LU0jabGz4eaQAW9S6a24Npjndb2iZfbx94Sj1s/p5sbsr79VkCu/VIHzt2DDNnzkR6ejpSU1NRWlqK/v37o6CgQOc5p0+fxtixY/Hee+/h0qVLGDFiBEaMGIGrV7XT0jsK7NmhqaMmYvbkCKz8fg2TYdAS9djsxfO9LU4lRP69egOP7mivAWFjjJeUPmdS5EwtDzAfr7A+Dyh9/qTImVqZti2RpEtfm3zuDX0tWxLWorCgQKs2tbmyZlsSQ/dVdR9qML/v3joXHdvx9zbzwdzeQHMkuqJtWrZoPK+2DHrVRe7451FtPMlvhoISP3URDah+d6oJuHcC3DoAInf+bRsBfX2AdvI0Npbom+BYsD8D098LwfxPRiLuyz1MBnxL1Au3d8/3t2tnAgC2bNnClO4zxktKnxMeHq7lAebjFdbnAaXPDw8P18q0bYkkXfra5HNv6GtJTEyEXC7Xqk1tjazZpsLnvjpJJBb1LprbG2iORFe0TTEz5vFqi49X3ZSazMacpw/6+gDo1QGW6LsiYNM10s+ePUO1atVw7Ngx9OjRg/OYt99+GwUFBWqzhIGBgWjTpg3Wr1/PeU5xcTGKi4uZ32UyGfz9/e3GI605I8f+HYBF6rHZi0caYNWcc3LC3jumz0jy9aya6pFmY+qaYmt4g/V50K1lgzUokOXhnZa9AACl2T/BycnJtgbZIXq96uJKgFtHpJ+7irp160EiEaN61UoAgAJ5MfLyCgGoyut4SF1x+/a/aNyoHlB0DlC+1Os9NjX5mSHPn9BoAXv3JJqCpTzSusZTe/FIa34G2L8DsEi9cEf4HFVv9C6ePnuJ5ORk1K9fn/d5fD2rpnqk2Zi6ptga3mB9HnRr2WBJ7HGNtL3Cx6vOdcym3VuxZF08AFV2cK53cX3eY1OTnxnSAUKjBexJV5gbu/VIa/Ly5UsAgJ+fn85j0tLS0K9fP7VtISEhSEtL03nO8uXLUalSJeafv7+/eQw2E5ozcoGtO0IiliCwdUdm38h+QxkvNWD6TJE9pae/+NNRAICirAwbFn9hcnt8Pat8juPrWTZ1TbFQb7DQ9eJskTwpcianrfS1ArBoHW5L89NaVXk1sVjksCJas460qV5WzTZ0etVF7oBbR+z75SB69OiJxMS18PRwY3bnFxRBSVFQUhTyC4oQF/8F3mjZDvt+OQi4dQRE7nq9x6aU9QIMe++FRgvYuyfRHrH38VTzM9Clc3NIJGJ06dyc2Tfmra6MlxowPZLBEcqn/V9YMADgs88+E3QeX88qn+P4epZNXVMs1BssdL04WySHh4dz2kpfKwCL1uG2FOnp6bY2wWxo1pE21cuq2QYfrzrXMSu/X4McWS5yZLk6vcL6vMemlPUCDOsAodEC9hLpamts5pFWKpUYNmwYcnNzcfLkSZ3Hubi44Pvvv8fYsWOZbYmJiViyZAmePHnCeY69eaSNmQVivLavPNP0B9bcnmpb8eHy+fj25x8AAMk3T8HV3c3AGbrRleVa13H24IEVaotQD7iQ4+01YzdfJrTth5cvctC0cU3cvLjW1uYAEO6x4vKimbImm7en1q0Dbt99iTdatmMynC+P/Qzz580BoO6R3rhxPWIWq17KnZ2d8ffVi6CUcgT3H6LT42yKR7oirm01hYrgkebz9+P67FeuNwnZOfnw8/XEi/tbmGPM7am2J/LzC+FVazwA4Pjx45BK+f2tdGW51nWcPXhghdoi1AMu5Hh7zdhtiL59++Lly5d4760JWB0VZ2tz1BDq+eSK8jRlTba51nUb8kgb8jib4pEuz7mXLIFDeKRnzpyJq1ev4scffzR7266urvD29lb7Z0sMzdqwPdKA6sOZLy+ASCSCQqlgPqgSsYT5nT7Ov19L+PdrqTbb5gjrHP4XFQcXZ2cAwPTepq091ZXlWtdxthbRmrbw8Ta3aN8aYokYLdq35tW+psf84NZkjG3dG2Nb99bqx14zdvPl5YscAMDEd3ra2JLXCPV8sr1qfL2sXF41eluXzs0NtyH2Q0GxF7wqVUHU/HnM5qjoRYiL/wIQOcHDwxM1alRBUtImRkQDwGdLPkXjxo3g7VsXNWs3QtyXezi9dOxtQr14lvAem9uTWBHWaNvTeMrnM8H2SAOqv1FurnoelvmfjIREIoZCoWTaWr85BZXrTULlepM4v1OO9jf29HTHoOB2ACDoPUtXlmtdx9laRGvawsfbHBAQALFYjICAAF7ta3rMk5OT0adPH/Tp00erH3vN2G0I2qe2YNocG1uijVDPJ9uzytfLyvXeTG8LbN1RUBvvLozgfAefOmoiMg9dReahq5zCk32dXN5j9jahUaaW8B6bO9LVEbQLFzYR0hEREdi/fz+OHDmCOnXq6D22Ro0aWp7nJ0+eMOUTrI2uP7S+D4DmF1nz2PQr56BQKpB+5RyA1+EfAODr7cPM9ozsN1RNcGuGifBJ4W9PnP/xCADg+X9PkJ5y1OT2aEHYon1ro0KVjSm3Zeh8Pm3qC/Omz794PA1KhRLXL1zh1a/mpAFdnis/V6bVjz1NMJjChDH2I6SFhhzrEpz6XuK5RAW97fSZG4YFo7M/8vIKoVAoMW36DERFRTG7oqIXIS7uCwASxMV9gajoRcw+tsfa08MNM6a/b3QiNn3X5wgln0iouGno+vvr2s71mdA89vSZG1AolDh95gYA1d9I+UokhPRtC0D1fRvzVlc1wR335R5k5+QjOyefCQM3VGLO3vnsU1Uk3w8//CD4XFoQBgQEGBWqbEy5LUPn8mlTX5g3fX5aWhqUSiUyMjJ49a05aUCX55LJZFr92NMEQ3lBaMixLsGp7z2dS2jS29KvnOMlGOl37+TUfUa9g5uaiE2IDrFHHDVU3KpCmqIoRERE4Oeff8bhw4fRoEEDg+cEBQXhzz//VNuWmpqKoKAgS5mpF11/aH0fAHZm7ncXRqhl5d60eyte5KrKybzIzcam3VtR1bcKANX98pR6AAD8+7VEcuo+KJQKHEo/ysyS+Xr7MGKbtgGA3X9hAKChf320avIGAGD5tEij26G9rVsS1mJ0eJhBz7Qu+NYsToiI5hTHXOdrbuMSvfo8wvT5AAweo0+It2jfGp4+3lqZux2dQz/9wvxcv351G1qijmb2YGPRJ9Q0PW+AQPEpqcz8KBIBs2Z9iCVLYphtUdHR8PXzQ1R0NLONLaIBwEPqikkTRglKHsYWPfquzxHWoTqC2LdndP39dW2nPxOAKoR73JRValm5129OwbMXMogAPHshw/rNKahapRJz/ukzNxjP887dJ6FQKJHy5yUmisPP1xN+vp7Md/d+5jMAcNi/cfs2jeDnUwkymQx///03r3Nob2tiYiLCwsIMeqZ1YWjdMi1YFyxYwFlHmutcze1colefR5g+H4DBY/QJ8YCAACY6w9E8z46IZnUbY9H3nq4ZGQoYLz7dXd2MOk+oh1dTOBvSIfaSJ0kXjiD2ubCqkJ45cyZ++OEHbN++HV5eXsjKykJWVhYKCwuZYyZNmqTmGZk1axZ+//13rFy5Ejdu3MDixYtx/vx5RETY5kbr+kMb8jrTH/A9h36FQqmARCxhHgzyItX1y4sKsfL7Nci4+bq0V2DrjoznmaIoSMQSAGBmydhhIkJT+NsDx78/AEC1Zn5FxHyj2tD0thobqmzoPFqwnjyQyilcuTzimm1uSVjLlKSiGTgxlKn1rCvsmqucl75+NW2+fuEKdlw5gh1Xjji855lNyo97AQCV7CB7sCbm8FbqE2qanjdAt/jU8vCJXAGRC7y83CGRiOHtJUX1qpWw6NMFWB4by5yXm5vL/Lw8Nhbz56u+owXyYjx59hIF8mJVaSyRK6/r0bwnji5EHUHs2zO6/v6a23VNwPz08ykoFEpIJGJG/MrlxaAAyOXFiPtyDy5ducO026Vzc8bzrKQoSCSqVyA6iuPF/S14cX8LMxEmpMScvfLZwjEAgKVLl/I6XtPbamyosqHzaMGampqqJVzZ57LFsmabiYmJTEkqmtDQUKbWs66wa65yXprHcHniaZszMjJw+PBhHD58mHierYQ5vJX6hJpmZCigW3zq8vzGzJgH/xq1ETtrkUmCmC+a98RRhSiNI4h9LqyabExXbdfvvvuOeTD26tUL9evXV3uo7tq1CwsXLsS9e/fQpEkTrFixAoMGDeLdr0wmQ6VKlayabEwzOQG9KD+wdUekXznHhGtv2r0VH6+IBkVREAHweZXynvYs+3r7IGbGPLUEBQC0EhYYs+jfXhIFbPgpCbO/WAgA+C7tAKrUFha2f3BrMiNMJ0XOtHg5KUNJzfQl7wpt1hXFhUVwdXdD8s1TvM7hi2YbCRHROPHrH3Bxc0Xn4J5aNttT8jVjeatRZ5SVlqFvr1Y49MsSi/YlNFGVpRNbCUnkpZWASeQBSLtrNypyBiCBr5+fmoj28fFBTnY2AAVAleLJs5eMgKletRIgPwFQBdrt6bHZlHtSnpOGGYulko1p96MaT62ZbEzz80v//bt0bo7TZ24wn4P1m1Mw85ONUFIURCLA18cTnp7uePDKs+zn64lli8ZjwWfbAADLFqmScbF/p9sxd6k2W0FRFJx8R0OpVOLw4cMGPxvJycmMMA0PD7d4OSlDSc30Je/q1q0bioqK4Obmppa01hwJvzTbWLBgAf744w+4urqiZ8+eWjbbU/I1Y+jTpw9kMhnu/H4Z1fyqWLw/oe+fln5fFZLIy1zJx0xtz1z3xF60gD1ht8nGKIri/MeesTx69KhWSM3o0aNx8+ZNFBcX4+rVq4JEtK1gzwyxP6TfLV2jFaLi7uoGkUgEdzd35Mhy8fDJf0w7ObJcnLp0BpmHriJmxjxm5slT6qGWQp/PbJ2QMBBrMm1MGBPCPmvweMHnD5wYanFvK1t0Rq6J1bumWF9iMGdXF7X/acyR8EuzjZMHUkFRFEpLSjjD3YWW4LJHykrLAADLPh1n8b6EepiFeis1vW6Gkhyx2zdkm7bnr0yHFRTi4uLURDSg8kzHxcUBUM27ujg7qf2vuz3dNpsCWZtcsWB/ftmCdfu3H2stoXBzd4FYJEL9+nXh41sNpaUSpp3snHwcP/U3XtzfgmWLxjOfHy9Pd2ZtNMDv8yVkmYItEYlEGD5YFa66bds2g8eHhoZa3NvKFp3Lli3Tu6ZYX2IwFxcXtf9pzJHwS7ON1FTVeFpSUsIZ7i60BFdFR+j7p6lhz4a8vuz2Ddlmbs8vV1g5H8zlwbUXLeCo2LSOdHlC80vK/oDTCQiiV3+GFsM6I3LlIub3JeviIS8qBEVRkBcVwtnJGe6u6qWgdv2xF5t2b0X06s+QmfUIH8VHMVkEA1t3hH+/lniRmw2pmzte5GZrZfGmsecwkBPf/wYAkGXn4ujPB/Uea2pSMGPgsw6ZtodODHbxuHatc7qm86TImWrbzZHwS7ONboODIZaI0W1wMKdQd/Rs3U8fZjE/d+7YzOL9WToMmX4Rj5jzDSMWNF/MhSRhYqMlYKligCrRtiEuTm1NtI+PD/NzVHT0KzENlLyawCgpLVO1QxXDmjh6SDhBP5qfc/bnl07+NXtBEuq/OQ0fzt3M/L7399tISPgSly5fxsmTaTh58gQuXLyAjIwMrF27FkFBQdiRfALrN6dg9oIk3M98hhkfb2Cy3Hfp3ByV603CsxcySKWuePZCppXFm8aRlinELVG9aG/btg2aQYimJAUzFj7rkGl76MRgaWna4yld0zk8PFxtuzkSfmm2ERwcDLFYjODgYE6h7qjZum2Fpd8/6ffd2QkLGWeWpljUJa4N2WbuEGSusHJrYk9awBGxWR1pa2KN0G59oRnVejRh1kFrIgLt43nN6P4jkJy6T23Ak7q5q7Xh+yoEPF9ewGT4pstjAeC0w97DN4ZGjMWRsycAAD//mw6nV+WxNLFF3WN9YdCa9oxt3Rv5uTJ4+nhjx5UjVrGvIrJlxVrsWvMtRCIRlC9328wOXWGmxrQTMecbKBRK5oVcM1SUd31oHn15+HXGkCFDUVJaBk8PN3z99dca2blVa6I1xfXy2M/wwQcfIL+gCJ4ebvBwyQaKM9Tuhb2Ft1YEylNot77PuUeNsZDLi9XGTn9/fyxfvhy9e/fWasvdzQWFRa8njY4cOYLPPluMW7duM9v8fD3h5emOvPxCZOfkAwBTHgsApx2O9lmv3Wwq/nucjdWrV6Nr167MdlvUPdYXBq1pDx1y7O3tjcOHD1vFvoqItUO7daFrGaQx7cxOWAiFUsGIRM33X3PWhzbFZl3v5vb+zl6esdvQ7vLKuwsj8PDJf5C6uSOwdUetGS5XF1UiHmcnZ2iuEtcU0f41amPPoV+1Zo3ZIlrq5g4AauuopW7uEIvFEL3az2WHvS/k/3XNDmYd/eJJH+g8zlKeVH2ebn0eY017dHmdCeaF/jvVrOFrUzvYiY9MCe2c/l4I1nzxPiOiucKgDXm9+Na8jftyD9at/waFRSVQKJRYHpegXeJq/hwACsyfPwfLY1/XkI6KXoSvv/4a1atWgofUFSjN1LoX9hbeqg9HrRNcXhk3ZRUePHwOqdQVXTo31/rbuLmqJlidnJ0gEgFt2rRBSkoK6tevr9WWs7NETUQDQP369bF37y9o06YNAEAqVY3P91nrqKVSV9V4KoJOOxwt0VzckgkAgA0b1CcELOVJ1efp1ucx1rRHl9eZUD5hJ+Y1Jdx46qiJWBm5lBHRXO+/hjyxfJOAmWqzrndzRwy5dtRa0KZAhLQZoIVvcUmJKpO2xge/X2AvJtu2Iff/w6xHjFdZE4lYgnYtAuDq4orikmL4evugQe16kOXnAQBKy0pBAajs48dphyOwdmECACDj1DnkPMvmPMZcdY81hbOxa4ZtWYdZ8xpsEfZuK/Jlqs/9l8sm2dQOWtyOeauryaGdhl7ODe3nK2TnfzIS/z28jbv/XsODB/exYkUCs48pcUWVAVQpQJVh/jx1Mb0o5nPcvv0vbt28gvotRjMCw57DW3XhiOK/PPPTz6dU42lxKU6fuaH1twnp25bJtl2njj+2b9+O9PR09O7dG2vWqI93paXqY+maNWvQu3dvpKenY/v27Rg8sDvcXJ1RVFwKP19PNKxfAy9l8lfnloGigKqVvTntcDRGDg2ESCTC33//jby8PGa7ueoeawpnY9cMs+2xdgIvzWuwRdh7RYcWtyP7DTU53NiQ88jQfr5C1lw2a4pQRwy5dkTxbypESJvIpt1b4eriApFIhJH9hnImDaDXP5SWlRpsT5fQdnZyhkKpwMXrGciR5UJeVAhPqQcybl6FQqlAYXGRWk1p+gvI5Zm2ZyYNfQdVfPwAAO91GWzRvjSFs1BPty7RKkSQmyp8Nfuif1+/KL5ci2l5QQHwKmpjyMBONrWFFrfbv/3YYh4qvl5TvkKWLu8TGTkXzZrWx66dP8DZ2VmrTrRa26/EtLOzM3bt/AGNG9VD2LvT1QSGo3npAMcU/+WV9ZtT4OrqDJFIhDFvdeWslU6XfSstLcPy5cuRnZ2NadOmobS0FMuXL9cS0zRr1qzB8uXLUVpaimnTpiE7Oxtjx09Ddk4+5PJieHm649KVO1AolCgqLFGrKU1/Rrg8046Ch4cbhg3sAAD48ssvzd6+pnAW6unmEq1CxbipwlezP/r3FStWEDFtJWhx+93SNRaLoOTrNeUrZOnys6aEogPaItTeo0i5cETxbypESJsIXQe6TvVa+G7pGrWkAZt2b2USgXEV/qJDtA0hdXPXEuG0YA5o1hIAUKd6LXhKPdTKY+XLC3Ao/ahVZofMGc4xJ0wV1l1aXIIdqzca3Y4hkaopnIV6lnUJZiGC3NTM2Zp9jQ4Pg1gihlKhtFo2blt4we9cu8n87OHhpudI+8LYMGK+XlMhQjbuyz1IO3MZE8aPx/BhA/H31Ys6RTTN/Hlz8PfVixg+bCBQdA6T3unMS4Tac/i0I4r/8gpdB7punSrY/u3HarXS129OYRKBiURAUFAQevfujQYNGmDOnNef2+XLl2Pt2rWQSqXw8vKCVCrF2rVrsXz5cuaYOXPmoEGDBujduzcCAwMZwdy2dUMAQJ06VeDl6a5WHisvvxApf16yimfanN8Xdltfxb8HAEatNTYkUjWFs1BPN5doFirGTc2crdlfWFgYxGIxlEql1bJxEy84f8xVf1kXQoSspetcs7Hn8GlHFP+mQoS0iWh+8Onfq/pWwUfxUYz3mAtd2w0dN7r/CKYU1t1H9wEA/z3NYjIULlkXjxxZLpOEzBqeaXOGc6z9cRPz8/aVG1Baop1dmA+GRKqpIdm6BLOQdk1d763Z18CJoZj+2TyrZuO2RRmtn9d9D+D1eklHwZiyOgA/rynfl2/6ODpT8dCQpkDhKTRuUAkAUCAvxpNnL1Eg587E3biBN1B4Cuu/+Yl3siX2dRsjEuxZiBPMh+bnnP69apVKmPHxBsZ7DAATJkxgzouIiEBUVBTze2xsLBITEyGVSpGYmIjY2FhmX1RUFCIiXr+oLomZy5TCunNPVQng0X8vmAz6Cz7bhuycfCYJmTU80+ZcbsBuq369aqhaxQ8FBQW4fPmyoHYMiVRTQ8S5RLPQNk1d763ZX2hoKObOnWvVbNykjBZ/jCn7CvATrHzFKn0cXUlHs00h7fBNLMa+bmNEtT0LcUeECGkToWdfAFUGwC2//Ij/nmbh4vUMteOcnMz3wp+cuo8phQWAWZvBztpNJyADVA8N2jP98Ypoi3x5zBnOQbdFJx77cIBxNYItXd7JVCHOlQncFO8ufS4AJpu50LaM6d8WZbQunTgLAOjZ7U2r9WkO+AhirpdoPl5Tvi/f9HGnz9x43SZVCBSdBwrPYvfuPcjKeoL8gqLXJ1ElQNljoPAsUHQBoAoFveyzr9sYkUDWMVcM6M85oMrcvXnLn3j43wtcuHRb7TgnJyd069ZNbVtERASiWdnlFyxYgCpVqmDBggXMtujoaDURDQDVazZkSmEBYPIdsLN20wnIANVnmfZMz/xko0XEtDmXG2i2Ff/ZWABQm1zgg6XLO5kqxLnWU5vi3aXPBcBkMxfaljH9kzJa/OHz3skltvl4Tfk6h+jj/r+9O49vqsweP/65SXdKVwoIlkX9zcioQEFWEamCDLKILI46AxTEYeu4sFi2AUHZLODWssygssm4FEEdvrggICqLgEBF0ZG9lK0KbeneJvn9EXJJuqdNcpP0vF+vvmjSm5vTkPTec8/znGdv6v5aNQyzpxhl/XvXpIhVF+cxO5Mk0tVU1RUcyxvz+2NHym0W5uvj47BYLGtOK4pCz8491PkklmS6Z+cepG07SmRYBFezM20+LCaTySkfHkcO57Dsa9fqLQCcO36KH787ZPd+tGwCVh3lVXJrU92taL60PfuqyWNc/TobjUZ1lMI/E4a65Dlry1JRBapMiGt6El3djt6WSnS52xmvkHf1Ox7u34tPP1oCeV9D3g7I225e4sp4owGgPXFaXwioyXxTmcfsXaoaYWC5cHLg0HEMBmOZ3iE339yEBg3KLtEzZcoU5s2bp97OzMxUv583bx5Tpkwp85ioqCiCg0PRKQq9H4hR+x1YkuneD8Tw+5m1REWGcOVqjs3FHKPJ5JSLO46cblB6XyP/9gB6vZ6TJ0/aNB2riqOakjlLeZXc2lR3K5ovbc++avIYd3+d3YHlfByo8ryzpkWe6nb0rqgSbe/z2xOn9fl2Tfoh1cV5zM4kiXQ1VXUFZ9KIeLUzN5Sd/+zv52/z84ooikJ4SFi1YjKZTHy47RNWbVzHyJnxfPD5ZptF3SeNiCc8JIycvFx6du5h04zME8S0ak2Lps0AmPHYWI2jcbzyKrn2VHdLV4/Lmy9tb6W4NtVlV82VLiq4MeS4093/z6nP5SiWxCB+8r+rTBxrehJtSVItw6crisGmEl3J8494vCuYcsFU/hDv2sR5+seVdnVClnnM3qWqEQZTJw5SO3PDjSWqLMLDQst9nF6vJyEhgbCwMJv7w8LCSEhIQK8v/xgcHByM0WTi/U3fsuLNz3hi1Cv8J+VrdX62JaaI8GCu5eTT+4EYm2ZknmbQ9STEm4YPl1fJtae6W7p6XN58aXsrxbWpLstc6YpZzscnJc6sMnGsaZHHkqRahk9XFENFlWh7n782cR77eJ9dK/XUxXnMzqSYSi9Y7IWys7MJDQ3l/PZjhATXr/bjrOcsAFXOX1i1cR3TX5tLfmEBMbe35lT6GQqLbpyEFpeU2DQN8/XxLdNE7NWEBaz9+F11aHhQQKDNHGlFURjS62G27PqswjnWQx8cyNsvmT9MjlpwXit5+Xk0vO8PAPSNe5SxcxM0i6W8odjOeEx1jerSj4z0C0Q1vUkdyq0lV8Wz9uVkPkh6C0VRMGZtdNrz1NaKNz9T5xADxE/+NwaDkebRUerwVUdrcccYzqRllPsc1vG4Q0LqbvF4uuzsPEJv/htZWVmEhIQ48XnMx9Osc+sJCQmq9uNKfx6q+r9f8eZnTJqxmoL8Itq1vZWTpy+Sk1NAcYmBBx/oxNtrN1L67MUyJ9p6OLfFvHnzmDBhArm5uTb3Bwb40a59O06fNq+HrmC7esbjQ+5lw1vPAZV/vjzJmbOXaXHnWPz8/Ni9e7cmMdRkaStnLofVr18/Ll68SOPGjdWh3FpydTz3338/2dnZnPz0MA0jyo720Frp8/FJiTMxGA1OPbet7PzZnjnNruBu8Xi67JxrNLm/VbWOp1KRroR1Fdr6Co71MO/SQ77zCvIxmUwc+jmVtG1HubzrV/z9/MkryLdJmoMCAgkOqlem+jz9tbk286v7du+tzhUGCKtvvhJfWEkDLktFGjx/CEdQYBAJo54BYMvq98n6/apmsdRmqLQzlqNq1b41Or2OVu1bV7iNKztqu2qu9LYPPgbglpaNnPo8ULsmV9ZVt7FP9iZp8VO1bhhW+uelb1c2BLqiqq5Wjbykyly3lP48WP7vrd9/pd+LeXmFGE0mvj98gt/PrOWmxuGYTCY+27aXjIzf1H0rCugUhcWLF9sk0daV6RkzZrB06RKbmHSKQnZ2Jmlp6ep9pSsLloo0eM8Ug+bNGtIsOoqioiJ27typSQy1GSrtjOWo2rRpg06no02bNhVu48oqsTfOla5Nk6vS5+NLprxU64ZhpX9uzzrOFVV1tWrkJVVm7UhFuhIVXeGxXKUKDwkjO+eaelUMUBuA+fr40rhBQ6LCG9gkxjqdDpPxxpyv0hXn0qIbNyUnL5er2ZkoioLJZLL5N+b21jb71+v0LJnyktd9mCK73UphUSENb27Cm7s/qdW+alol3rouhbWJyQAMnzKhWo/dui6FFbMWYTQYHV6prU4F2N2q1o7Qv1l7AOZM+wuzpv3Fqc9VmwpUTSquVT1f6Z87okLmLVW2us6TKtLWnwfL+y8iPJis7Dx11AagNgDz9fWhSeNwTMDZ6/clJyczcOBAdT+WdaIt5s2bR0JCAosWLbJtODZtGhOsGo5t3ryZCRMmoFMU2rW9lYOHjqvHZ71eR9Lip7zyYs97H37DY3FL+cMf/sCGDRtqvJ+aVolTUlJYtmwZAOPHj6/WY1NSUnj55ZcxGo0Or9RWpwLsblVrR3JFRbo2IyRrUnGt6vlK/9wRIzg9fRSoMJOKtINUdIXHcpUKwGA0oNfpmTQiXp2THB4SRnBQPbX5mDWj0bZxSmVJtKIonLt0npZNmxMeEoaP3semOq1TFI78ctRm+0E9+3tdEg3w5arNAFw+d57/HTpa+cZVqGlDrz7DhhBYrx45mdnVfqwzl6OqTgVYi47arjL5mYed/hy1qUDVpOJqPQ+zvApdRcsD1WYdZ1dW2WQZq7qros+D5f0HYDAY0et1TJ04SP0sRIQHUz84gDNpGWoSDbB+/Xr1+9JJ9PTp0xk/fjx5eXmMHz/eppv3/AULSE5KKrMfRVE4lHpSPT7rFIVHH7nHK5NogAF9OqDX6fjf//7HlStXqn5ABWra0GvIkCEEBQWRnZ1d7cc6czmq6lSAvbFK7Eq1GSFZk4qrdZ+g8kaQVrR8bW3WcXblKFBZxso9SEW6Fiq7QrZq4zp1PWd7hYeE2TxOAXRWS1tVprKrYJ4+h+L2/h05d+k8Or2ezSf32VxUsEdt5i07c86zqNr2lC28MnEWAKZs71wKybpCDDikIm4ZVlvTqrOj5jNL9dt53L0iXZXK3mMr3vxMXc/Z2vr162nRogWxsbEUF5unTpVeJ1qnKBhNJptk29fXlx07dnDq1CmGDav4WFjZ+9Qb5vgPGfYyGz/aS58+fXjxxRdrtI/azFt25pxnUX0mk4mOHTtiMpncdo50TVlXiAGHVMQtw8xrWnV21Lm4VL+dRyrSLlLZHIk5yxeReS0LMF/pDgoIJCggEJ2u6pe8sKjQpsO3CXPlW8G2G7gljYxu3JSggEAURaFz6w4V7re6a8e561WuHzfvAcBoMLB85sIa76c2yzW5+5Ja3m77RvOw/vr1A6vY0nNZV4hrUi22VH1nzH1HnZNakyWnrFnPb31i1Cv4hA/hiVGv2PurVfj7SKVaVDaHf8bcd7iaaW4SplMUgoL8CQryZ8aMGURERLBy5Up8fX3LJNEApus15vj4eKZNm4avry8rV64kIiKCF1+co25nuS7bLDqKoCB/dIpC1063Vxhvddc2d+f39muLRgHw+eefqxci7FWb5ZpkqSf3kJWVhaWmFhkarnE0jmVdIa5JtdhyPjxn+SL1/LkmS05Zsz4XHzkzntDOzRk50/4Kdnm/j7uev3szSaQdxPrNu2RNElezM9U/TL56H/IK8s1rP2NbRfX18S2zL38/fwb17G+TgAOEhYQRGRahbmcZSnD+8kUiwyIwmUw2jcZKq+4fEXddrF2v1zNzzGTAXBnOy8mt4hHCXq5sTlYTlvXE29zRXONInHOCXLrKVZPh4ZYTfEBNWmuy5JQ16wT4/U3fYjAYeX/Tt3btAypOlqqblDibOyc9dYn1/8PCpR9y5WqOejw1YSI/v5C8vELOnUvjiSeeoHPnzuzYsaNMEg2goBAY4AfAP/4Rz86dO+jcuTPjxo6mMD9L3c4yNi/9/O9ERYZgNJlsGo2VVt2LXO7y3i5P0yYNuP3/tcRgMPDdd99pHY7X8bQlrEKDQypcIs7ZnJEAlq781mR4uOV8GFDPn2uy5JQ163PxD7d9gsFo4MNt9vf+Ke/3cafz97qS1Esi7SDWb95JI+LVYceKolBk1a17UM/+hIeEqT/39fFRf2ZJsQuLCtmy6zP1xMHfz5/wkDBmj0uwmYfdrlUb9Do9g3r2rzBJtn4jV/ePiDt3+k4Y9Yx68WFs7GCNo/E+FXUZd5cEu6S4BIDF80ZoGgc45wTZEfu0nODPm/XXMkmrvRVuS0IDqPt69JF70Ot1PPrIPTWOsaKYte6G7M5JT11i/f8wdeIgdFbTeEwm85flPXj2zHH6/PnPnD59mvJm+5gwUVB4Y5WL9HNp/OXRQTz2yF0287DvjrlN3Wd1Rk5U9yKXu7y3KzJr6kAAlixZUvmGwm4VdRn3tATbFZyRADpin5bz4dnjEsqcP9t7rmw5HwfUfQ3q2V89j3cEdzp/d6ek3plkjrSDlL7yZbnduXUHtuz6jPzCAm5u1IRzF9NtOnb7+/mXO4+6dIduywfD0V0LPdFPJ36m4+M9AZi95jXuju3m0uf35nnSFXUZd4fu3z8f+oEpD8cB7jE/urZzJMt7fG326Yw5m5HNh3Plag4R4cH8fmatQ/bpzjx53qunz5G2Vvr/wXI7qkEo3x8+QUCgHw0iQ9TmY76+PtQPDuDuDh0ZMGAQ3bp1IyoqSt1fRkYG33zzDe+88w579uxRE1tHd9X3VL4Rj1JSUsLnn39ORERE1Q9wIG+eJ11Rl3F36/6dmZlJz549CQ0OIX37T5rEUNt5w+U9vjb7dEZPoeied3I1O5PwkDDSttWuaa4n8OS+TDJH2kXKq/YCNlec7onpRGRYBK88P5/zly/adOzOLyygsKgQRVGIbtxUrUgHBQQSc3tr9Do9Mbe3tkmi7b26405XpxzlT7feTsztdwEwJ+5ZSkpKXPr8Ne367SjOrA5X1GXcHbp/p35rHnro46PN0LPSarsOcunqZ22TOKmm1p6sba2d8qq9gM2IiCeHP0D0zQ1YMi/OpoN38fWRKt988y1P/+MfDOjfi3btYrj33nu5557OjB45hGeeeZriggybJNrez4u7V5drauyTDwLwyiv29z2orZp2/XYUZ1aHK+oyLt2/y6rtOsilz49rm8TVlWqqM9WVta0lka6F8j64kxJn2txnvU3poRuB/gHkFeRjMpk4d+k8DRs14pZbbiH65mgyrv6GwWjg+2NH1AZiOXm5hIeEMWlEPKs2riO6551E97yz3Lb+Ft76Rt7x1vX5JCYTS5/9p0ufW+uksqJE3lEJdnkN1dyhydqmf5uXqYmMcM6oElcrfVJe20TYGSf582b9VR0mXh0yx1jUVHkXluIn/9vmPutt7o65TX2spYlnXl4hRpOJc+d+49Kly5w8eZLsrKtk/JaFwWDkwKHjagOxazn5RIQHM3XiIFa8+RmRzYcT2Xx4ucvOWXjrhZYXZzwOwBdffOHy59Y6qawokXdUgl1eQzVpsuZ4pYtGtU2EnVGEmj0uQR0mXh11ZY6xp5Oh3bVQ+oqXZRi1XqdnyZSXbIZ4W7YJ7dxcXXt6yZSX2HpwB0P/8iidOnUkIjISMA/rvvL7Fb7a9RXr169nz549RDduStrFdHWN6py8XHVIeG3b+nuq5HdXkbD0BQDe/fEr6tUP1jYgF6loaLk7DL92pgHN78ZkMvHOqmd44tH7tA7H4bQeVuyI5/fWoa+ewNOHdpd+/1neS3q9jqTFT9kM8bZs4xM+RF17OmnxU8yY+w4ABYXF5OUVAhAU5E+Av6+6dJaCuTP3mbSM62tUB3ItJ1/9eW2XnfNUt7UZz4lTF1mwYAG9evXSOhyXqWhoubsNv3Ymdxja7WhaDyt2xPN749RMTyFDuzViuYJlSaLBtiK8auM6/P38UBSFpx4fyYQpT5OyMYV+/frSpElTdT8mk4nwiHAGDhxISkoK69ev564/3WmTMGdeyyIoIFCtUHvjEO6qTHhsNMFB9QCI69hH42hcp6LqsNaVcmcqyMtXm+/17X23xtE4h9bVrvIq4vZWmL116KtwPct7yZJEg+1nZMWbn+Hv74tOUXj0kXsY+2Rv5s36K/WDA3n4oY74+pobeRYXG2zWnzYBUQ1CbRLmzMxcgoL81Qp1XXwfv/7ykwAsXbpU40hcq6LqsNaVclE7Wo/GLK8ibm+FuS6e13siqUjXQmVXi8q7GmVpNNC9273s+PxLjp86SWhEKAajscy+9TodOp2O4pISTp06RUREBC2Mkfxr3VtMSpyJwWiocQMyb3L59wxu6RMDwPNJ87l3gHcNuRNmv/5wjIl9/waAMWuj2vVeOE55FWl3qDBrXan3FJ5eka7svVbee6C8RnjWVWyT0YTRZEKnKETf3ICoBqEcOHQcMI/6MmZtVIePGwzGGjcg8xYmk4ngxn8lL7+ArVu32jRrE97NGyvSWisvB3CHCrPWlXpPIRVpF6nsapHlatSkxJk2V5+io6NZ8a+VvL95I3e2vYsVK26cMFiSgwD/AKIioogMiyQpKYnY2Fj27t2L/y3hPPV4HEumvFSrBmTepGFkFPfdbV6G5+X46dSB60J10quTZgPg7+8rSbSTlFcRd4fKnDRRqxsqe69Z3gPxk/9dZnREZmauet/UiYPQ63UYDEYCAv3Q63X8ZXA3Tv+4kv1fvUxQkD8AgYHmdaXHPtmbpMVP1aoBmbdQFIUBfc2jfepaVVoIRyuvIu4OFea6njM4gyTStVDZ0JFJI+LR6/QYjAb1DTt7XAILFy7kypUrDB8xnOLiYl6Y8wLLly0DwEevp3GDRoTVDwVgUeIiFixYQHFxMWPGjOH4qZP43Rxi87zu8MHU2pZl76nJ1XPXq5bCcdxhDemLp88BN7rLitqp7pBtrYebg3sk88L5KnuvWSfIliR33qy/otfrMJpMaoI99snexLS+BYA//TGakqspbHjrOXU/S+bF0Tw6iiXz4sp93rr+Xnt14UgAtm/fTmFhocbReCdZQ9r7VHfIttbDzcE9knlvI4m0k4wePMymcrxq4zp2/riHP/f+My1btmTy5Mnqti/Nm0dycjJ+fv4oegVFp/DSgpeYPnOGus2c2S9w2223oavvj66en83zaP3BdAcfv/EfAE4c/Znzp9I0jsa7aL3cl8lkoqjAfFLXz8750e7YRdpVMZXuRGxtxtx3OJOWoTZncmfukMwLbZWuHFuGej/6yD3oFAWDwai+lw+lnrT5F2585oBK30t1/b3WqGE4re9sjsFgYPfu3VqH45W0Xu6rNtyxi7SrYiq9Uo61OcsXkXYxnTnLFzk1BkeQnMHxJJGupco+xNZrS09KnEmvPr0pLi5CURTi4+OZNm2auu38+fNJXpaMSYGFiYuY/cILN3720jwSptxol+8TGei8X8hDxXbsRmRoBADxvYZqHI130bqJWUnxjXXCe9x7h12PrWioppYJtquGjy5c+iFXruZw5WpOnRyqKjxPZZ9L67WlLcti7d73M2Fh9Wy2e/SRe1AUBX9/X3U/dXnItr1mJZiPn1qsKV0XeHITs4qGBWuZYLtqqPKSNUlczc7kanamDIsWNiSRrqXqfIiXrEnCYDTQrVs3DEYjPno9ep2OyZMmM2PGjarzjBkziIyMZPr06ep9c154wSaJBtDV93f8L+IF/rdlPwDFRcWsS1ymcTTur7pDtrVeQ/q911YB5jl8Pj4+dj22oqGatT2xruyEv6ok3VXDR6dOHEREeLDaidiavetDC+EK1flcLlz6IQaDEQVIO/cbt7RoTPPoKHo/EEOLO8bQ/Z4/0ezmBuTlFar78eQh2yaTiaKiYpd99e/TAV9fX86fP8/ly5e1/vU9RnWHbHvyGtIVDQuubTJbWSJeVZLuqqHKk0bEEx4Spq6UY83e9aGFd5Gu3bVUnQ54qzauY+3/vccnn25R72vcoBEZVzLwDwhg2bJlNgm1xfz585k6JQGTsex/Uf6Pl6GkbLfvum7y4lmseP8tADb+uhs/f7noUBFPWXd6ZOe+/Hb+Ii2bN+TkDyscss/adoKurMOwO3S6tpc3dsb2xt+pMq7u2h3VIBSdzrGN//LyC8nNLaBevQCCAv3Jys6lqKjEZhuTyUTpsxadTsFodZxUFDCZzP/Wpjlhec9V1+zevRs/P7+qN6zjPHnd6dp27a5tJ+jKulm7Q6dre3ljZ2xv/J0qI127Xag68w1GDx7G1+u3EuAfAKD+Wy+oHr6+vkydOpWwsDCbx4SFhTF16lSo4BxA0Uvn4vIkTpqDXqcHIL7XXzSOxr1pPWS7un47fxGARx/p6rB91nYuZGUVLldWv6ozRL0623jj0Fdv/J3cScZvWVy6nOnQr2vX8jEaTVy7ls+ly5kUFBRjNJpsvspLbI2lLjZbtjGZKPN4e77cJYnW+/q4/Muia1fH/d31Zp48ZLu2ajvvtrKqsiubY1VniHp1tvHGztje+Ds5issr0rt27SIxMZGDBw9y4cIFNm3axMCBAyvcfufOncTGxpa5/8KFCzRu3Lhaz+nMinR1KT46Au5oWPZ+ncLCxEU2w7ktLBXp3LxccvNyqRdUj6AA87qdUpGu2L4fDvDAkwMBeHnTW7Rq30bbgESt9G/WHoDcixsICgrQOBr3Up3qd3W28cbqrTf+TpVxdUV6zIsJ+AU4d8RPRKMo7u5Rs0SuohE3W9el8MGy1QwdH6fZdBVP8N2Xu3hxpLnjeWBgIF9//bXGEQlnkXWkzapT/a7ONt5YvfXG36ky9lSk7Ztw6AC5ubm0adOGUaNGMWhQ9Ss2v/zyi80v07Bh2aTUnZlKjObE18d2EMDCRQttunOHhYWRmZkJYE6ujSZGPTkKg9FIbl4uQQFBN/YlytXprru5rVlLjp89RcLg0Ww+uQ+dTgZfeKKdm7aq30sSXZZl7dvKqt/V2Wbsk71rnWy6W+LqiN9JVOz+QQ8RVD9Y6zAqNHR8HGsTk8nPzWXruhQ1abZehUAS6Yp1fKA7vR5/mC/+8xH5+fmMGzeO5cuXax2WEE4zaUS8mizWZpvRg4fVOtl0t8TVEb+Tt9J0jrSiKNWuSF+9erXM8OeKFBYW2qyBmJ2dTXR0tKYVaQC/5qHowwLJK8gjNy+XFStW8sKcF9Sfz58/n+eff55FixbZzJl+YfYLjB07hnpB5u6kGzduJPOXi/KmrkRRcRER95jXE+0b9yhj50oTCE80N+4Z9m//huDgAK6d36B1OKISnjg33Js4qyJd0fH0vR+/cotEunSF2XK7VfvWfLPlC4wGo01V2np7QKrTVXj0jvvIv5YDwIQJExg5cqTGEQlHk4q0+/HEueHexCvnSLdt25abbrqJXr168e2331a67YIFCwgNDVW/oqOjXRRl5Up+zwcgNy+X115/3SaJnj59OuPHjScvL48J4ycwx2r5qxfmvMBbb75FUECQOQH/10qZp1AFP18/EkY9C8CW1e+r6xALz3JwxzcAtL2rhbaBXOeO61K7C0/ujCwq5q7HU4vS69xbbluSaJ1ex9DxceoqBYC6CkHpx4qy3v/xK7VhW3JyMgUFBRpHJLyFO65L7S5cOTdc1I7bV6R/+eUXdu7cyd13301hYSGrVq1i3bp17Nu3j3bt2pX7GHeqSFsPzwBo1vGPREdH0/WerhQXFwM3Ks6+vn4UFxep/772+ussWLAAAF9fX44e/gFjYTEPPvTnCod7uNtwEK1FdruVwqJC6oeHsuHIdq3DEXayzI/OTFtLaKj21S+pugp3VRcq0uVVk1u1b82xg6nlVqSPHUxV/83PzSUnM7vC6nR5FWmZT21mMBgY2LKjevvAgQMaRiMcTauKtFRdhbuypyLt9ol0ee677z6aNWvGunXVu4rliGZjNU1QLX8owkPCzP8xTZvw5ZdfcvTIUf7yxF+YM7vsOtEZVzIwGI0oisIbb7zB4sWLeW/Dezzctx8F//sdU5GhyueTP0xmp86d5q5B3QCY+04yMfd21jgiUV2/pv7ExH7mz5op2z06L7vbPGAhLFzdbKw2iXRNE1RLA7HgsBDyruWUGbZd2WP8AwMoLiqiW99eTEmab9fzufsSga5w+pfj/OP6Shg6nY7vvvtO44iEo6xdu5bXX3/d5Ym0FH6Eu/LKod3WOnbsyPHjx136nDVt/W4ZngFgMBo4n36e/Vu/4b7u97Lrq68Y9eQoLv52id8zfyevII+MKxk2jbHi4+PZsWMHre5sxbv/Xl9pEm39fDIcxKzlzS2IadUagNl/nUAdWDbda/z03REAfHzc589UbZfNEkKUHY5dXZYl+4Byh22P6tKX/s3aM6pLX0Z16cfWdSm0at8anV6nPmbXx58x5I/3sHVdSrWfz92XCHSFFn+8jafmTAbAaDQyZEjdrdB7m48//hiAXl3KrpDjTLVdNksId+A+Z6h2OHz4MDfddJNLn7OmCarlD8XscQmEh4QRElyfjPOXeOihhzhx8iQGo7n7dnFJCbl5uRiMRopLSgDUpO/UqVM8+OCDTF3wT0bOjCe0c3NGziw/jtGDh6ldBWXeidmu1VsAMAFJCS9pG4yotveTVgEQFlpP40i0I3OyhTeqaYLaZ9gQ3trzX4ZPmUBwWIhaEbck5hnp5jXnM9IvkpF+gRWzFvH9rj0YDUYK82/M7S3ML+CDZatJjJ/Owy07kBhfdvlJy/MNHR/HB8tWVyvx9nYDRj5Osz/eCsDp06dZuHChxhEJR/pbv6Fah+BUMidbOIPLE+mcnBwOHz7M4cOHAXOSePjwYc6ePQvAtGnTGD58uLr9q6++ykcffcTx48c5evQozz77LNu3b2fChAkujbuqK2elP6DlfWAzr2VxNTuTOcsX0SyyKcOGDWPw4MFs2rSJzKuZaldugIyMDDZt2sTgwYMZNmwYFy9cZNKIeD7c9gkGo4EPt31S4fPLwum2FEXhn383X0n//N3N5GRlaxyRqI5rmeb/p1cX1N0usQuXfsiZtAwWLnWPoe1COIIlIa5oWLelwmxJXkvfBsjNukZOZjZrE5Np1b612hALIDgsBJ1eh9FgpLiwqMz+fXx9GDo+Tm1I9s2WLyp8bmlIZiv5i/fxCzQvRZiSksL+/fs1jkiI6pFzY+EMLk+kDxw4QExMDDExMQBMnDiRmJgYZs2aBcCFCxfUpBqgqKiISZMmcdddd3Hfffdx5MgRtm3bxgMPPODq0G2UTpQtH9BJiTPLTWaXrEmyGVa8N3U/JpOJvXv3Eh8fT7eu99D9nnsZN/LvxMTE0LZtW+Lj49m7dy8Avj4+LFmTRJs/3olep6fNH+8kuqf5y/r55ixfxO+ZV1AUhc6tO7j+hXFTCaOfJcDffPAf26NudRUu7yTU3V3LzMJ0fbRG3z/frXE02pFO2KIuqCh5XTFrkTqfunRnbuvj6bGDqWWm7QTVD8Y/MKDcFRv0Pj58sGw1t9xxOzq9jlvuuJ3HW8fyeOtY1iYmq8+1dV0K2VeuoigKrdq3dt4L4GE2/nJj5ZRx48aRmZmpXTAulpKSQr9+/UhJ8ZzjqTCTqY/CGVyeSPfo0QOTyVTma/Xq1QCsXr2anTt3qts///zzHD9+nPz8fH7//Xd27NhBbKxr53GUxzpxbTWgE51bd0BRFAxGA3OWLyrzgZ00Ip7wkDDCQ8KYPS7BvP31ffn5+JJ5LYtDR4/wyRf/x+XLlwFQrJ4vv7CAtIvpHPnlKIN69ufIL0e5mp3J1exMJiXOpHPrDupc7LyCfHOSnrpfhrJY+WbtVgCyfr/KoW/qTiM2T6yoXL38O2D+DISFad+tWytjn+zN1ImDWLj0QxneLbyW5W/U2sRkRnXpp1aYjQYjaxOTywwFHzo+juCwEILDQhg+ZYJNkuvj66tWq8NDw2jZsiWNGjWyeb6igkIy0i9w8sef6da3Fyd//JmczGxyMrPJzbpGcFiIOqS7ML8Ak8nEsYOpHnlR0lnWHbpRxe/ZsycGQ+X9W7zF6tWruXjxonrOKjyHTH0UzuCRc6TdgXUTsbSL6exN3U9Y/VD159ZDwVdtXMec5YsoLCqksKiQ6a/NJeXzzViunxeVFKtX0y3/KkBYSJi6P0VRUDA3LPvg880YjAaU6/cbjAb2pu63mYsdFBBITl4uc5YvkqEs193e8v/R8U7zkmmz/+raqQFa8sSGOS//wzxn0dfPR+NItCfDu7Ujc9Rdw7qJWEb6BY4dTKVe6I0VNqyHgm9dl8LaxGSKC4soLizizZdeYdfHN/5/Otx9N0lJSRw5coSDBw/y9ddf8/333/PD0R9ITk6mS5cu6jBwS/Mxo8E8+kVRFEwmE4H16qnzo4PDQvAPDCA/N9emWl3XhUVGsGDjv9Xb3bt31zAa14mLi6Nx48bExcVpHYqoARnerR1vLexJIl1D1k3ELJVny/ezx9kuZ7VkTRJXszPJK8hXv6rqHW0CrmZnqreNRiMois02YSFhDOn1MHqdnqjwBrQa0AmAtG1HiQyLUB8vQ1lu+Pxf5mTEZDIx47GxGkfjGlXNR3RHl86kAfD3uF4aR6K9ioZ3V5bkSQLoGHIRwzWsm4hZLvpZvh8+xfai5wfLVpOTmU1hfoH6BRAdHc369etJSUlh4MCBNGjQwOZxEeERDBw4kJSUFNauXUuzZs1sfh4cFsK9/R9Ep9cRGhnOqC79APhP6g5CIsLJud6zwdMuSjrTnR3a8deJ5uNoYWEhf/vb3zSOyPmGDBnCf//7X+la7qEqGt5dWZLnrQmgq3nrRQxN15F2FUesI20v6/XxAOYsX0ROXi4lJcX4+PhSXFKsbhsUEIi/n79N4mxNURR89D74+thW5/z9/AFzwq3X6TEYDer60bI+X8Xe+nA9Ty+cCsDag58THhWpcUTaqumars5iMpkY0Nw8L/qjd6cy4KGOGkfknlrcMYYzaRk0j47i9I8rq/0zUX2etG64J60jbS/rv1EAaxOTKcjNo6S4BB9fH+684042bNjAlStXaHnLLShQ6VKHp06dIjIykhEj4zhyxLzMnq+/HwA5mdlqozLL+tHu9jfS3Yzq2o+McxcAeOKJJ5g4caLGEWknJSWF1atXExcX5zHJ9pAhQzh9+jSbX1tPzy49tA5HE60GdCLtYrp6Dl3dn4nq86S8xOvXkfYE1ldeRg8eRtq2ozRu0BATUGIosdnWunpsodfp1e9vbtQEo9FIXkE++YUF+Pv5q4l3YVHhjeZjVlfZZH2+io0a9DfqB5lPAMd0H6htMG7A3eZQW58A9+nVTsNI3FtljcikSZljyLrh7sH6b1SfYUP4T+oOwhtGAdDkpiZs2LCBvXv3EhsbS3JSUqVJdHJyMrGxsezZs4fVb71Ny1tvwdffj5zMbIoLi9TmY9aVZ08c1eNKb+3+L3pf84X+DRs2qH1e6iKZQ+2ZKmtEJk3KHMNb8xJJpB3IeviHpblYTl6uOhxk0oh4dQ6WtZy8XJvb4SFhDOrZX72ddjEdg9HcyMNkMtkM2fb388dgNJBx9bcyb1AZjlKxX7ccACA/N4+UZWs0jkZb7jaH2rKmq6KAr6/Mka5IZUmeJIDC01k39rLMVc7PzVUbfQ0dH4eiKMyfP58rV64wZswYiouLmT9/PklJN4YOKoqCX4B59FZSUhLz58+nuLiYMWPGcOXKFWZONf+9iWp6E77+fhgNRrJ+v1omcZZGY5X78Nc96vcPPfQQxcXFlWztvWQOtWeqLMnz1gRQOIYk0rVQ0RJYlvH/2TnXuJqdqXYInLN8kU0SrdOZX/7S1ejMa1ls2VXx3EYFc/J96bfLXM3OJCggkEkj4quMp6K466LgesEM6fUwAGsWvl5pBcPbuVu15dfUYwB07XS7xpEIIVylqvWb867lkJOZrS5L9eZLr9C5c2diY2Np2bIlkydPVve1YMECkpOTCQoKon79+vjqfUhOTmbBggXqNpMnT6Zly5bExsbS9s7WXL38GzmZ2fgHBjB0fFy115OWBNtMp9Px7k+71NtdunTRMBrtyBxqIeoWSaRroXSiaj38Y8maJHNnbUUhJy+X6a/NLZMwG6+vk1uayWQiryC/wuc1AcUlxRRdn2edX1jAkjVJaofuZxdNo/uIvuTk5RIeEkbn1h1oNaATI2fG02pAJ+nkfd2bc19Xu7c+95BzmqTISZb9Lp09B0D7trdoHIkQwlVKJ6rWI2U+WLYao8GIoijk5+by5kuvUJhfYNPcKj4+nmnTpqm358+fz7JlywgMDCQ5OZn58+erP5s2bRrx8TeGaT722GOUXK+gFhUU8sGy1WqH7mUzFvBcv2Hk5+YSHBZCq/atGdWlH4nx0xnVpZ908rZSL7ger2xZr96+//77Hbp/WcNZCOFuJJGuBUvibElUAXX4h+Vngf4BXM3OJL9UYqyAunyWNb1OryZ3pZW+38/H1/yNyUTaxXQKiwrVn31/7AhXszPJzrnGtr07SbuYzofbPiHtYjognbwB9Ho9m18zH/RP/Pgzv6b+5PDncLf5x55kwQve3wFWCGFmSZwtiSqgjpSx/MwvwF/t2A3QrVs39fE6nY74+HimT5+u3jdjxgwaNGjAjBkz1PumT59uk0Rb9uPjaz6emkwmMtIvUFxYpP78eOpP5GRmk3cth+937SEj/QLfbPmCjHRzgy13mhqjtdvuakXbezsD5sZ0CxcudNi+Zf6xEMLdSCJdC5Z5E3tT95N2MZ05yxepQ6YtybSlsqzobrzUiqJgArVibM1oNJQ7zHjogwN55fn5ajKtKAovT5pLdOOmmDAn4JYu3mCeZ23p5F26IdnscQky3+O6BzrfR7PrFzQSBo1y+P7dbf6xu/vKaj3YoKAADSMRQriSZYrJsYOpZKRfYG1isjqax5JMWxJonU5Ho0aNbJa4MplMKIrClClTmDdvnnp/Zmam+v28efOYMmUKfgH+Nh3Ho6KimJT4grqWtU6vU7t4g3lpLEsn79INyYZPmeBWU2PcwYvvJKvrgKekpPDVV185ZL8y/1gI4W4kkXaAzq07oNfpKSwqVBPq6J538tzLN66MWw/jtiTKhUWFNlVkwGZ96aCAQPX7bXt3AhDoH6Duw9JGPrpxU5ZMeclm/ersnGssmfJSlQ3JBOxa+38AFBcVs+Zlxw53d7f5x+5uz5YvAagX5F/FlkIIb9SqfWt0eh3FhUVqQv1461iWz7xR2TQajdSrV8/mcabrX3q9noSEBMLCwmx+HhYWRkJCAnq9/ka12WqU1/b3P1EvfI6dm2CzfnXetRzGzk2osiGZuOE/qTvU7ydNmsSlS5dqvU+ZfyyEcDeSSDvA3tT9GIwG/P381eHaV7Mz1YTZOiG2UIC8gvwK50LrdTqbnxUWFfLcy9NvVLgVhajwBkxKnEnn1h0YPXgYowcPY+iDA1EUBX8/89X0Yx/vY/a4BBnKXYkGYZGMfXQkAClJb1OQX/H8dOFcuz/bDsCdraI1jkQIoYVjB1MxGoz4+vupFeKczGz1eOofaL6YnJtru9oFJhOYTBgMBhYtWmRTiQZzZXrRokUYDAZMmJNjrJt/+vmyYtYiWrVvTZ9hQ+gzbAjdB/RGURR8rx9P39rzX4ZPmSCjjKpBURRS/vetertv3751uqmnEMI7SSLtAJaqsGXIdM/OPWx+nleQX2YId1WHE0OpRmT5hQU2ByGTycT3x45gMBpI+XwzoZ2bM3JmPPfEdEKnmJPwSYkz1WHmUomu3OLJL+LrY15qacIDj2ocTd1lMprf459/NFvjSIQQWrBUhS1Dptt1t+3+XJhfQHBYCJcuXeK3334r8/jExESbOdHWlekZM2aQmJhok0ADZGRksG/n1xgNRnZ9/BmPt45l67oU7uzUDkWnUJhfwIpZi9Rh5lKJrh7/gACStr2v3q6rnbyFEN5LEmkHKJ2o7k3dX2ab0h277WVJonU62/8yy3xrg9HAB59vZs7yReqa0wajoc535rbH1uXmTqCXz53nf05oPCYqd+rYr+r3ISH1KtlSCOGtSieqxw6mltkmJzMbgG+++cbmfss60Rbz5s3jt99+s5kzXXqd6fL2k5OZzYpZi1ibmIzRYL6obTQYpWlkDTT/w63ETX8GgJKSEh566CGNIxJCCMeRRNpBrNdmnjQinvCQsBtdtUspXZ22R+klsyxzpi0sjcXatWpTZji3JUbLMlh1eR3p8nRucze3t/wDAM8PGK5xNHXPr4eOAqDXy58lIeoy62UDh46PIzgsRO2qbW39+vVqA86kpCSbdaKnT5/O+PHjycvLY/z48TbdvBcsWGCTTK9fb169wcfXR73PurHYba3/VGY4tyVGyzJYssRhxQaPHc6fOsUAcPnyZZYuXapxREII4RhyxlpN1olyeazXlB49eBhp247SqEFDAHxLJdTZOddqlUxbyy8sILpxU4Y+OLDKxmKWGC3LYEm1uqw9681dow1GEwv+PkXjaOqWlS8kAhAZEVzFlkIIT2adKJfHetnAPsOG8J/UHYQ3NHfotk529+zZw65vvubMmTMsXrxYvX/atGlMmDCBvLw8rl27Rl5eHhMmTLBZZ3rx4sWcOnWK7du3s3fvXgAMJQaimt5E9wG9q2wsZonRsgyWVKsrt+iDVeivT5/asGEDu3fv1jgiIYSoPUmkq8k6US6PZZ60dQXYUpkuMZQA5mHYiqJgMBrKHepduoLt6+NLdOOm5TYrs4i5vTXHPt7HPTHmdax7du5RYWMxS4yDevaX5mMV8PX1ZdbY5wHY/el2cq/laBxR3VFSVAzAto9f0DQOIYRzWSfK5Slv2UBLZdpQYrDZduaMmYSFhbFy5Up8fX2ZNm1amXWiAfQ+Pjz99NNMmzYNX19fVq5cSUREhE2l+ta7WvHWnv9yZ6d2ALTr3qXCxmKWGLv17SXNx6pp88l96vdPP/00xcXFGkYjhBC1p5jqQBvF7OxsQkNDOb/9GCHB9Wu0j1Ub16nLTZVu2tV9RF++P3aEdq3asGvNFpuftRrQibSL6eh1egb17M/m7VsoLqn64OHr40vipLl8e2gfKZ9vrrA5mSUhnpQ4E4PRQHTjphz7eF8FW4vqatDtNgqKCggJD+OdI19qHY7Xy82+xmN39gDg0om3aBgVpmk8Qnia7Ow8Qm/+G1lZWYSEhDjxeczH0/d+/MpmLWZ7bF2XwgfLVjN0fFyZpl3P9RvG8dSfuK31n3jlv7YjwEZ16UdG+gV1Hefj13tZtG3blg0bNnDlyhVatmxZ5vl0Oh1hURHkZF2jqKCQU6dOERERwRNPPMHhw4fV7SwJ8YpZizAajEQ1vYm39vy3Rr+jKF9JSQmP3NJJvX3gwAENoxEAHTt2xGg08knSf4jteK/W4QihueycazS5v1W1jqdSka6myjpff3/siM2/1qzXed6bur/CJLpdqzY2FWnLdh+Uk0QrVmtfdm7dQW0wpiiKVJkd5OdPzEP9sq9m8vm7m7UNpg7Ivpqlfi9JtOdY8eZntLhjDCve/EzrUIQHqazztSU5Pl5Ow0frdZ6zfr+q3n/48GF69+7N6dOnAdQhxBZGo5GCvAKKCgoBOHXqFL179+bw4cM2x9NW7VurDcYURZEqsxP4+Pgw+fUbzd+6du2qYTQCbvTe6dq2o8aR1G1VTSEV7kkSaQdo16qNzb/WrBPwSSPiCQoIRFEUohs3RVEUggICeTVhAbvWbCmz5FVFw8jD6oei1+kB2w7hYfVDZYkrB2kQ3oD7r1+ZTUp4SeNovN/C68PpfX30Gkci7LFw6YecSctg4dIPHbpfSdDrrtta/8nmX2vWCfjQ8XH4BwagKApRTRtz7tw5nnxqNBu++Ij8QIWMjAybx6anpbFp0yYGDx7MsGHDSEtLA6BeaH101xscWncIrxdaX5a4cpL7Bv6Z+x7pC0BRURHPPPOMxhEJH18f/P38tQ6jTqtqCmlNSYLuXJJIO8CuNVvI+e5cmWHdpY0ePIzLu37l2r40jn28j2v70ujbvTeTEmfSfURf/P38bLbv3LqDTaOydq3aEBQQSOa1LJo0bIxep6dz6w7MHpegrmMtHCfllTWAeemxmY+P1Tga73b+9FkARo/oqXEkwh5TJw6ieXQUUycOcuh+nZWgV5ck8tp55b/r+OTswTLDukvrM2wIKb98y8dnDvDWni18fOYAnXrdR8KT8fTq1oNOXToTExPDvffeS0xMDOOnTeLpp59m79696HQ6ug/ojX9gALlZ14hs3BCdXker9q0ZPmWCuo61cJ7Jr82lfmQ4AN9++y0pKdL1XNRt5fVacgRnJejVUReSeEmknayiN5Hl/pQvPsJgNPD9sSPkFeTbbLM3db9No7Jda7ZQWFSEyWQi7WI6BqOBvan71Wr3kjVJXv1mdTU/Xz/eftH8h+fIt/tJP3Fa24C8kMlkwmQyUZBrfu/3f6iDxhEJe4x9sjenf1zJ2Cd7O3S/zkrQq0vrRF6Ur6Ju35b7v/7kc4wGI8dTf6Iwv4DLly9z8uRJLl++zLGDqVhawphMJqYkzaf4+vE0I/0iRoORYwdT1Wr3B8tWy5JWTrbh0DZ1aP3ChQs5d+6cxhEJoZ3KppDWhrMS9OrQMol3FUmkr7NOeKtKfq3vL31f6dsVvYks9wf6B6DX6dXKc1BAIOEhYQQFBPJ75hX1wO+rN8/5GtSzf7nrRNeFN6sWhvYeSIPwSACe7fs3jaPxTtZzFPv0aqdhJMJdOCtBry6tE3lPZ53wVpX8Wt9f+r7Styvq9m253y/AH51epy6R5R8YQHBYCP6BAWRfuaoeTy1zqLv17VXuOtFVdRUXjvPR6RvT0wYOHEhJSYmG0QjhfZyVoFeHlkm8q0jX7uss3bWjGzcFUL+37oBtvY3lfuuu3EumvKQmtJZtKur2PXJmPB9u+4RBPfvz9ktJ6nadW3dgb+p+cvJybZbICgoI5PKuXyv8HSvrKi5qJ/3Sef7Y39yEY/QLk3l41OMaR+RdEp+ewa7NnwJgypYKoBA14U5duy3dtaOa3gSgfm/dAdt6G8v91l25x85NUBNayzYVdftOjJ/ON1u+oFvfXkxJmq9u16p9a44dTCU/N5eczGx1e//AAFJ++bbC37GyruLC8TLSLzKqi3nOtKIo7N+/v4pHCEe6++678fH1IfPb01qHIoRbkK7dNWB91aSiKygVrRWt1+kxGA1qIlvV1ZdVG9fx4bZP1KHZqzauY87yReTk5bJt707SLqZzNTsTxeoxVTWB0PKKk7dr2qgJQx98GIBVc5ZQB649udSJ1GMAdO7wB40jEUI4gvU60OWtCV16G+v7dHodRoNRTWSrWqN567oUvtnyhTo0OzF+OitmLVKT6Iz0CzZJNICvv18FezOrrKu4cLyopo2Z9Lq5qafJZKJnT+mVIYTwDD5Vb1I3jB48zCYJLS8hLb2N9XaWJLo0S4V6UuJMdfsla5IwGA3odXp1brOl+hweEoaiKOa5o9dvBwfV8+phEZ7gzblv8MHnH4HJxOxh8cxdn6x1SF4j/eQZAO66o5nGkQghHKHPsCE2SWh5CWnpbay3syTRpVkq1CtmLVK3/2DZaowGIzq9zmYNaEuFOiP9gvr44LAQAuvVk2Wt3FCPgX347suv+fqjz8jMzGTixIksXbpU67CEEKJSUpF2AOtqcOm5yqUr1pb7LGtLWxqFhYeEER4SxuxxCfjob1zfmD0uQd13Xeh+5650Oh2rXzInz4d27eXc9eRPOM7rLz+pdQhuQ7pGi7rKuhpceq5y6Yq15T7L2tJ9hg1R5z1369vLZjkrH18fhk+ZoO67ornbQjvPvzGfgHqBAOzatYszZ+Q4K2pPzp2FM8kcaQcrPdfZuhlYdecv1+8UjclkQlEUru1LU+8vb462cK1mve7iStZVAoPr8f5Pu7QOx+N9uHItb897DZD50dZa3DGGM2kZNI+O4vSPK7UOR3gAd5oj7Sil5zpbNwOrzvzlretSWDZjAQA6vY6PTt2Ye1veHG2hPZPJxIDmd6u3d+/ejZ9f5UPxRe14+xxpOXcW9pI50hqyVKf3pu5XK9PVmb9sfcVsSK+H0ev0DOn1sM02daH7nbvbvc7cFCs/J5dP3npX42g83+mfjwPQqGGYtoG4GekaLcSN6rRlrvMHy1ZXa/6ypdoM0H1Ab7VCba0686+F6ymKQsr/bjSC69q1q/QlEbUi587CmSSRdpLSH9yqhpZYDwl/+6Uklkx5SW1EZiENxbR3c+OmDOrZH4B/vZBIYX6BxhF5tp2btgLQpHG4xpG4F62XfxLCnZROeqsalm09JHxK0nzGzk3g2MFUm+2loZj78g8IYNn2jerte++9V8NohKeTc2fhTJJIO0npD25V6zyXTrxlXWj3tXb+cvQ6PQBThzylcTSezWQ0ArDr0xc1jkQI4a5KJ71VrfNcOvGWdaE9T/RtLej1+CMAFBQU8Nhjj2kckRBClCWJtItUNbSkdOLduXUH9Do9nVt3cGWYoprWLzTPWz3+w0+cP31W42g809n/nVC/Dw4O0jASIYQnqWpYdunEu1X71uj0Olq1b+3CKEVtPb1oJk1aNgfg+PHjrFmzRuOIhBDClssT6V27dtG/f3+aNGmCoihs3ry5ysfs3LmTdu3a4e/vz2233cbq1audHqej2Tu0ZG/qfnWdaeF++vf4Mzc3agLA+PuHahyNZzpx9GcA9Hq5nieEqD57h2UfO5iqrjMtPMvKrz7Ex9cXgDfeeIOjR49qHJEQQtzg8jPY3Nxc2rRpQ3Jy9dbhPXXqFH379iU2NpbDhw/z7LPPMnr0aD77zLuXhZHmCO7v0AdfAWAoKWHVHFnv0l5JU+cDEB7m3M6/Qoi6TRqLebb3jt1YISMuLo6srCwNoxFCiBs0Xf5KURQ2bdrEwIEDK9wmISGBLVu22FyFfOyxx8jMzOTTTz+t1vO4cvkrUbfEz3ue1R9tACDll2/xDwzQOCLP8XDLDhgNRg589TLtY27TOhwhPJo3Ln8lhEXG+YuM6twXMJ877t8vo/UcxduXvxLCXl61/NWePXvo2bOnzX29e/dmz549FT6msLCQ7Oxsmy8hnCFpxsv4+piHnY29f7DG0XiOvNxcjAZzozFZ+koI9yXHU+EOopo0ZsabiwHzWtM9evTQNiAhhMADEumLFy/SqFEjm/saNWpEdnY2+fn55T5mwYIFhIaGql/R0dGuCFXUUfvf/RKA39Ivsu+LrzSOxjPkX8tTv7+5aQMNIxFCVEaOp8JddO4VS/cBDwKQk5PDyJEjNY5ICFHXuX0iXRPTpk0jKytL/UpLS9M6JOHFbmt2C3fcejsA85+arHE0nmHe6IkA+Ph45Z8gIbyGHE+FO5mStIAGNzUE4IcffuDLL7/UOCIhRF3m9mexjRs35tKlSzb3Xbp0iZCQEAIDA8t9jL+/PyEhITZf7mrVxnW0GtCJVRvXaR2KqIVv1m0FwGg0kviP6RpH4/7OnTwNwMi/3a9tIEKISnnS8XTruhRGdenH1nUpWocinOjtfVvV7xMSEsqcIwohhKu4fSLdpUuXMlccv/jiC7p06aJRRI61ZE0SaRfTWbImSetQRC34+viyePJcAHZ99Bm/nZcDe2Xyc8xDu/v1bq9xJEIIb/HBstVkpF/gg2WrtQ5FONnHZw6o3/ft2xeDwaBhNJ7rwIEDVW8khKiQyxPpnJwcDh8+zOHDhwHz8laHDx/m7NmzgHkY2fDhw9Xtx44dy8mTJ3n++ef5+eefWbZsGe+//z7PPfecq0N3ClnmynuMfXQUwUH1AHjmoSc0jsYzDOjbSesQhBBeQpa5qjsUReHdH2/0JOnatauG0Xiud999F4Bbm7bUOBIhPJPLE+kDBw4QExNDTEwMABMnTiQmJoZZs2YBcOHCBTWpBmjZsiVbtmzhiy++oE2bNixZsoRVq1bRu3dvV4fuFKMHD+PYx/sYPXiY1qEIB/h6zf8BkH0lk682V295trpm6XOztA5BCOGF+gwbwlt7/kufYUO0DkW4QL36wQweMwIAg8FQZoUXUX0Px/bROgQhPJKPq5+wR48eVLZ09erVq8t9zKFDh5wYlRCO8f+a30psh27s2P8Ni5+ewT19H8DH11frsNzKsQNHAGjf9haNIxFCCOHJ4mY8Tep3B/j10I9kZmby6quv8uyzz2odlhCijnD7OdJCeJpPkt9FURQA5sQ9o3E07ufimXMA3N/9Lo0jEUII4emWfrQWvwB/ANavX8/333+vcURCiLpCEmkhnCB5ZiIAh7/eR+ZvVzSOxj29/NIIrUMQQgjhBVJ++Vb9/u9//zvFxcUaRiOEqCskkRbCCYb3f4zIsAgARnXpp3E07uOTt9/VOgQhhBBeRlEU3v/5G/W2t6zsIoRwb5JIC+EkqRvNB/XiwkLee+NNjaNxD2f+dwKAhg3cdy1aIYQQnicwKJBFm95Sb0snbyGEs7m82ZgWLM3NruXmaByJqEsUReGhe3vyf19v46NV79A/7i9ah6S50Ehzlb5BZAjZ2XkaRyOE98i+Zv48VdbM0xEs+8/LyXXq8whREy3+cCsDRj/Ox6v+Q1FRETk5ct5XmYYNGwIQGR5Jds41jaMRwj1Y8sXqHE8Vk7OPum7g3LlzREdHax2GEEII4VRpaWncfPPNTtu/HE+FEELUBdU5ntaJRNpoNHL+/Hnq16+vdlN2pOzsbKKjo0lLSyMkRIasuoq87tqQ110b8rprw1Ned5PJxLVr12jSpAk6nfNmbcnx1DvJ664Ned21Ia+7NjzldbfneFonhnbrdDqnXqG3CAkJces3hreS110b8rprQ153bXjC6x4aGur055DjqXeT110b8rprQ153bXjC617d46k0GxNCCCGEEEIIIewgibQQQgghhBBCCGEHSaQdwN/fn9mzZ+Pv7691KHWKvO7akNddG/K6a0Ned9eS11sb8rprQ153bcjrrg1vfN3rRLMxIYQQQgghhBDCUaQiLYQQQgghhBBC2EESaSGEEEIIIYQQwg6SSAshhBBCCCGEEHaQRFoIIYQQQgghhLCDJNIONmDAAJo1a0ZAQAA33XQTw4YN4/z581qH5dVOnz7Nk08+ScuWLQkMDOTWW29l9uzZFBUVaR2a15s3bx5du3YlKCiIsLAwrcPxasnJybRo0YKAgAA6derEd999p3VIXm3Xrl3079+fJk2aoCgKmzdv1jqkOkeOp64nx1PtyPHUdeR46lrefDyVRNrBYmNjef/99/nll1/YuHEjJ06cYMiQIVqH5dV+/vlnjEYjK1eu5Mcff+SVV15hxYoVTJ8+XevQvF5RURFDhw5l3LhxWofi1d577z0mTpzI7Nmz+f7772nTpg29e/fm8uXLWofmtXJzc2nTpg3Jyclah1JnyfHU9eR4qh05nrqGHE9dz5uPp7L8lZN9/PHHDBw4kMLCQnx9fbUOp85ITExk+fLlnDx5UutQ6oTVq1fz7LPPkpmZqXUoXqlTp0506NCBpKQkAIxGI9HR0fzjH/9g6tSpGkfn/RRFYdOmTQwcOFDrUOo0OZ5qQ46nriXHU+eS46m2vO14KhVpJ7py5QrvvPMOXbt2lYO+i2VlZREREaF1GELUWlFREQcPHqRnz57qfTqdjp49e7Jnzx4NIxPCdeR4qh05ngpvIcdT4WiSSDtBQkIC9erVIzIykrNnz/LRRx9pHVKdcvz4cd544w3GjBmjdShC1Npvv/2GwWCgUaNGNvc3atSIixcvahSVEK4hx1NtyfFUeBM5ngpHk0S6GqZOnYqiKJV+/fzzz+r2U6ZM4dChQ3z++efo9XqGDx+OjKC3n72vO0B6ejp//vOfGTp0KE899ZRGkXu2mrzuQghRHXI81YYcT7Uhx1MhvJuP1gF4gkmTJhEXF1fpNrfccov6fYMGDWjQoAF/+MMfaNWqFdHR0ezdu5cuXbo4OVLvYu/rfv78eWJjY+natSv/+te/nByd97L3dRfO1aBBA/R6PZcuXbK5/9KlSzRu3FijqISoGTmeakOOp9qQ46l7keOpcDRJpKshKiqKqKioGj3WaDQCUFhY6MiQ6gR7Xvf09HRiY2Np3749b7/9NjqdDLaoqdq834Xj+fn50b59e7788ku1OYfRaOTLL78kPj5e2+CEsJMcT7Uhx1NtyPHUvcjxVDiaJNIOtG/fPvbv30+3bt0IDw/nxIkT/POf/+TWW2+Vq+dOlJ6eTo8ePWjevDmLFy8mIyND/ZlcYXSus2fPcuXKFc6ePYvBYODw4cMA3HbbbQQHB2sbnBeZOHEiI0aM4O6776Zjx468+uqr5ObmMnLkSK1D81o5OTkcP35cvX3q1CkOHz5MREQEzZo10zCyukGOp9qQ46l25HjqGnI8dT2vPp6ahMOkpqaaYmNjTRERESZ/f39TixYtTGPHjjWdO3dO69C82ttvv20Cyv0SzjVixIhyX/cdO3ZoHZrXeeONN0zNmjUz+fn5mTp27Gjau3ev1iF5tR07dpT73h4xYoTWodUJcjzVhhxPtSPHU9eR46lrefPxVNaRFkIIIYQQQggh7CATX4QQQgghhBBCCDtIIi2EEEIIIYQQQthBEmkhhBBCCCGEEMIOkkgLIYQQQgghhBB2kERaCCGEEEIIIYSwgyTSQgghhBBCCCGEHSSRFkIIIYQQQggh7CCJtBBCCCGEEEIIYQdJpIUQQgghhBBCCDtIIi2EEEIIIYQQQthBEmkhRI106dIFRVHYs2ePzf3Z2dm0bdsWf39/vvjiC42iE0IIITyDHE+F8EySSAshamTRokUAzJw5U72vqKiIRx55hNTUVNasWUOvXr20Ck8IIYTwCHI8FcIzSSIthKiR7t2707dvX7Zv387OnTsxmUzExcWxfft2XnnlFR577DGtQxRCCCHcnhxPhfBMislkMmkdhBDCM/3www+0bduWrl270rFjR5YuXcq0adOYP3++1qEJIYQQHkOOp0J4HkmkhRC1MmLECNauXQvAqFGjePPNN8ts8+GHH7J8+XIOHjzI1atXOXXqFC1atHBxpEIIIYT7kuOpEJ5FhnYLIWolKioKgPr165OcnFzuNrm5uXTv3p25c+e6MjQhhBDCY8jxVAjP4qN1AEIIz5WUlMSSJUto1KgRly5dYs2aNYwZM6bMdsOGDQPg6NGjrg5RCCGEcHtyPBXC80hFWghRI++//z7PPPMMsbGxHDp0iNDQUObMmUNeXp7WoQkhhBAeQ46nQngmSaSFEHb78ssvGTZsGHfddRebN2/mpptu4rnnnuPChQu89tprWocnhBBCeAQ5ngrhuaTZmBDCLt9//z09evQgMjKS3bt3c9NNNwGQnZ1Ny5YtMRgMnDx5koiIiDKPPXr0KHfddZc0RxFCCFHnyfFUCM8mFWkhRLWdOHGChx56CD8/Pz799FP1oA8QEhJCQkICWVlZLFiwQMMohRBCCPcmx1MhPJ9UpIUQLiNX0IUQQojak+OpENqTrt1CCKe7cuUKZ8+e5cSJEwD89NNPZGZm0qxZs3KHrAkhhBCiLDmeCuE+pCIthHC61atXM3LkyDL3v/3228TFxbk+ICGEEMIDyfFUCPchibQQQgghhBBCCGEHaTYmhBBCCCGEEELYQRJpIYQQQgghhBDCDpJICyGEEEIIIYQQdpBEWgghhBBCCCGEsIMk0kIIIYQQQgghhB0kkRZCCCGEEEIIIewgibQQQgghhBBCCGEHSaSFEEIIIYQQQgg7SCIthBBCCCGEEELYQRJpIYQQQgghhBDCDpJICyGEEEIIIYQQdvj/gi+0OL2Kj48AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates and saves Figure 9–5\n",
    "\n",
    "def plot_clusterer_comparison(clusterer1, clusterer2, X, title1=None,\n",
    "                              title2=None):\n",
    "    clusterer1.fit(X)\n",
    "    clusterer2.fit(X)\n",
    "\n",
    "    plt.figure(figsize=(10, 3.2))\n",
    "\n",
    "    plt.subplot(121)\n",
    "    plot_decision_boundaries(clusterer1, X)\n",
    "    if title1:\n",
    "        plt.title(title1)\n",
    "\n",
    "    plt.subplot(122)\n",
    "    plot_decision_boundaries(clusterer2, X, show_ylabels=False)\n",
    "    if title2:\n",
    "        plt.title(title2)\n",
    "\n",
    "kmeans_rnd_init1 = KMeans(n_clusters=5, init=\"random\", n_init=1, random_state=2)\n",
    "kmeans_rnd_init2 = KMeans(n_clusters=5, init=\"random\", n_init=1, random_state=9)\n",
    "\n",
    "plot_clusterer_comparison(kmeans_rnd_init1, kmeans_rnd_init2, X,\n",
    "                          \"Solution 1\",\n",
    "                          \"Solution 2 (with a different random init)\")\n",
    "\n",
    "save_fig(\"kmeans_variability_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 166
    },
    "id": "QwvNt9AB3EdW",
    "outputId": "6a7aff3c-32e7-4108-df99-1c2a9677be89"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       "#sk-container-id-1.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       "#sk-container-id-1.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(init=array([[-3,  3],\n",
       "       [-3,  2],\n",
       "       [-3,  1],\n",
       "       [-1,  2],\n",
       "       [ 0,  2]]),\n",
       "       n_clusters=5, n_init=1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KMeans</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_clusters',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=n_clusters,-int%2C%20default%3D8\">\n",
       "            n_clusters\n",
       "            <span class=\"param-doc-description\">n_clusters: int, default=8<br><br>The number of clusters to form as well as the number of<br>centroids to generate.<br><br>For an example of how to choose an optimal value for `n_clusters` refer to<br>:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_silhouette_analysis.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('init',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=init,-%7B%27k-means%2B%2B%27%2C%20%27random%27%7D%2C%20callable%20or%20array-like%20of%20shape%20%20%20%20%20%20%20%20%20%20%20%20%20%28n_clusters%2C%20n_features%29%2C%20default%3D%27k-means%2B%2B%27\">\n",
       "            init\n",
       "            <span class=\"param-doc-description\">init: {'k-means++', 'random'}, callable or array-like of shape             (n_clusters, n_features), default='k-means++'<br><br>Method for initialization:<br><br>* 'k-means++' : selects initial cluster centroids using sampling             based on an empirical probability distribution of the points'             contribution to the overall inertia. This technique speeds up             convergence. The algorithm implemented is \"greedy k-means++\". It             differs from the vanilla k-means++ by making several trials at             each sampling step and choosing the best centroid among them.<br><br>* 'random': choose `n_clusters` observations (rows) at random from         data for the initial centroids.<br><br>* If an array is passed, it should be of shape (n_clusters, n_features)        and gives the initial centers.<br><br>* If a callable is passed, it should take arguments X, n_clusters and a        random state and return an initialization.<br><br>For an example of how to use the different `init` strategies, see<br>:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_digits.py`.<br><br>For an evaluation of the impact of initialization, see the example<br>:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_stability_low_dim_dense.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">array([[-3,  ...    [ 0,  2]])</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_init',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=n_init,-%27auto%27%20or%20int%2C%20default%3D%27auto%27\">\n",
       "            n_init\n",
       "            <span class=\"param-doc-description\">n_init: 'auto' or int, default='auto'<br><br>Number of times the k-means algorithm is run with different centroid<br>seeds. The final results is the best output of `n_init` consecutive runs<br>in terms of inertia. Several runs are recommended for sparse<br>high-dimensional problems (see :ref:`kmeans_sparse_high_dim`).<br><br>When `n_init='auto'`, the number of runs depends on the value of init:<br>10 if using `init='random'` or `init` is a callable;<br>1 if using `init='k-means++'` or `init` is an array-like.<br><br>.. versionadded:: 1.2<br>   Added 'auto' option for `n_init`.<br><br>.. versionchanged:: 1.4<br>   Default value for `n_init` changed to `'auto'`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=max_iter,-int%2C%20default%3D300\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\">max_iter: int, default=300<br><br>Maximum number of iterations of the k-means algorithm for a<br>single run.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">300</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Relative tolerance with regards to Frobenius norm of the difference<br>in the cluster centers of two consecutive iterations to declare<br>convergence.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Verbosity mode.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Determines random number generation for centroid initialization. Use<br>an int to make the randomness deterministic.<br>See :term:`Glossary <random_state>`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy_x',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=copy_x,-bool%2C%20default%3DTrue\">\n",
       "            copy_x\n",
       "            <span class=\"param-doc-description\">copy_x: bool, default=True<br><br>When pre-computing distances it is more numerically accurate to center<br>the data first. If copy_x is True (default), then the original data is<br>not modified. If False, the original data is modified, and put back<br>before the function returns, but small numerical differences may be<br>introduced by subtracting and then adding the data mean. Note that if<br>the original data is not C-contiguous, a copy will be made even if<br>copy_x is False. If the original data is sparse, but not in CSR format,<br>a copy will be made even if copy_x is False.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('algorithm',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=algorithm,-%7B%22lloyd%22%2C%20%22elkan%22%7D%2C%20default%3D%22lloyd%22\">\n",
       "            algorithm\n",
       "            <span class=\"param-doc-description\">algorithm: {\"lloyd\", \"elkan\"}, default=\"lloyd\"<br><br>K-means algorithm to use. The classical EM-style algorithm is `\"lloyd\"`.<br>The `\"elkan\"` variation can be more efficient on some datasets with<br>well-defined clusters, by using the triangle inequality. However it's<br>more memory intensive due to the allocation of an extra array of shape<br>`(n_samples, n_clusters)`.<br><br>.. versionchanged:: 0.18<br>    Added Elkan algorithm<br><br>.. versionchanged:: 1.1<br>    Renamed \"full\" to \"lloyd\", and deprecated \"auto\" and \"full\".<br>    Changed \"auto\" to use \"lloyd\" instead of \"elkan\".</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;lloyd&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling\n",
       "        .textContent.trim().split(' ')[0];\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "\n",
       "\n",
       "/**\n",
       " * Adapted from Skrub\n",
       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-1');</script></body>"
      ],
      "text/plain": [
       "KMeans(init=array([[-3,  3],\n",
       "       [-3,  2],\n",
       "       [-3,  1],\n",
       "       [-1,  2],\n",
       "       [ 0,  2]]),\n",
       "       n_clusters=5, n_init=1, random_state=42)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good_init = np.array([[-3, 3], [-3, 2], [-3, 1], [-1, 2], [0, 2]])\n",
    "kmeans = KMeans(n_clusters=5, init=good_init, n_init=1, random_state=42)\n",
    "kmeans.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 393
    },
    "id": "g0JtVi_c3EdW",
    "outputId": "25078278-1486-4516-eabe-780833eeff18"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code\n",
    "plt.figure(figsize=(8, 4))\n",
    "plot_decision_boundaries(kmeans, X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6hyd5C_E3EdW"
   },
   "source": [
    "### Inertia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6R33no0D3EdW"
   },
   "source": [
    "To select the best model, we will need a way to evaluate a K-Mean model's performance. Unfortunately, clustering is an unsupervised task, so we do not have the targets. But at least we can measure the distance between each instance and its centroid. This is the idea behind the _inertia_ metric:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ivfJ2Gh63EdW",
    "outputId": "f30dfbc3-67db-46fc-c7f1-6a9329dc8e3e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "211.5985372581684"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5b5SW_BB3EdW",
    "outputId": "81071aff-d0cd-437b-ebe3-1604d409ce1f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "219.58201503602288"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_rnd_init1.inertia_  # extra code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "U76MrCJM3EdW",
    "outputId": "ef1de6af-6a30-40f7-8ac9-8669d60527ce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "211.59853725816836"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_rnd_init2.inertia_  # extra code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MflBbR0p3EdX"
   },
   "source": [
    "As you can easily verify, inertia is the sum of the squared distances between each training instance and its closest centroid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VYwRXVu_3EdX",
    "outputId": "67ea3da9-b33d-479b-811c-2ae74bbc9bb3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(211.59853725816862)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code\n",
    "X_dist = kmeans.transform(X)\n",
    "(X_dist[np.arange(len(X_dist)), kmeans.labels_] ** 2).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EQ6MxPxq3EdX"
   },
   "source": [
    "The `score()` method returns the negative inertia. Why negative? Well, it is because a predictor's `score()` method must always respect the \"_greater is better_\" rule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lUOs0HAn3EdX",
    "outputId": "f1cd883e-ce5d-49c3-958d-0076c992c87c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-211.59853725816836"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.score(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x1fv5IDw3EdX"
   },
   "source": [
    "### Multiple Initializations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ygquHyIO3EdX"
   },
   "source": [
    "So one approach to solve the variability issue is to simply run the K-Means algorithm multiple times with different random initializations, and select the solution that minimizes the inertia."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FdmrokIf3EdX"
   },
   "source": [
    "When you set the `n_init` hyperparameter, Scikit-Learn runs the original algorithm `n_init` times, and selects the solution that minimizes the inertia. By default, Scikit-Learn sets `n_init=10`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "id": "vUei3G1H3EdX",
    "outputId": "d5043fdb-514c-484c-dd3d-444b5749ad8d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       "#sk-container-id-2.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       "#sk-container-id-2.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(init=&#x27;random&#x27;, n_clusters=5, n_init=10, random_state=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KMeans</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_clusters',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=n_clusters,-int%2C%20default%3D8\">\n",
       "            n_clusters\n",
       "            <span class=\"param-doc-description\">n_clusters: int, default=8<br><br>The number of clusters to form as well as the number of<br>centroids to generate.<br><br>For an example of how to choose an optimal value for `n_clusters` refer to<br>:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_silhouette_analysis.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('init',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=init,-%7B%27k-means%2B%2B%27%2C%20%27random%27%7D%2C%20callable%20or%20array-like%20of%20shape%20%20%20%20%20%20%20%20%20%20%20%20%20%28n_clusters%2C%20n_features%29%2C%20default%3D%27k-means%2B%2B%27\">\n",
       "            init\n",
       "            <span class=\"param-doc-description\">init: {'k-means++', 'random'}, callable or array-like of shape             (n_clusters, n_features), default='k-means++'<br><br>Method for initialization:<br><br>* 'k-means++' : selects initial cluster centroids using sampling             based on an empirical probability distribution of the points'             contribution to the overall inertia. This technique speeds up             convergence. The algorithm implemented is \"greedy k-means++\". It             differs from the vanilla k-means++ by making several trials at             each sampling step and choosing the best centroid among them.<br><br>* 'random': choose `n_clusters` observations (rows) at random from         data for the initial centroids.<br><br>* If an array is passed, it should be of shape (n_clusters, n_features)        and gives the initial centers.<br><br>* If a callable is passed, it should take arguments X, n_clusters and a        random state and return an initialization.<br><br>For an example of how to use the different `init` strategies, see<br>:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_digits.py`.<br><br>For an evaluation of the impact of initialization, see the example<br>:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_stability_low_dim_dense.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;random&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_init',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=n_init,-%27auto%27%20or%20int%2C%20default%3D%27auto%27\">\n",
       "            n_init\n",
       "            <span class=\"param-doc-description\">n_init: 'auto' or int, default='auto'<br><br>Number of times the k-means algorithm is run with different centroid<br>seeds. The final results is the best output of `n_init` consecutive runs<br>in terms of inertia. Several runs are recommended for sparse<br>high-dimensional problems (see :ref:`kmeans_sparse_high_dim`).<br><br>When `n_init='auto'`, the number of runs depends on the value of init:<br>10 if using `init='random'` or `init` is a callable;<br>1 if using `init='k-means++'` or `init` is an array-like.<br><br>.. versionadded:: 1.2<br>   Added 'auto' option for `n_init`.<br><br>.. versionchanged:: 1.4<br>   Default value for `n_init` changed to `'auto'`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">10</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=max_iter,-int%2C%20default%3D300\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\">max_iter: int, default=300<br><br>Maximum number of iterations of the k-means algorithm for a<br>single run.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">300</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Relative tolerance with regards to Frobenius norm of the difference<br>in the cluster centers of two consecutive iterations to declare<br>convergence.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Verbosity mode.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Determines random number generation for centroid initialization. Use<br>an int to make the randomness deterministic.<br>See :term:`Glossary <random_state>`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy_x',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=copy_x,-bool%2C%20default%3DTrue\">\n",
       "            copy_x\n",
       "            <span class=\"param-doc-description\">copy_x: bool, default=True<br><br>When pre-computing distances it is more numerically accurate to center<br>the data first. If copy_x is True (default), then the original data is<br>not modified. If False, the original data is modified, and put back<br>before the function returns, but small numerical differences may be<br>introduced by subtracting and then adding the data mean. Note that if<br>the original data is not C-contiguous, a copy will be made even if<br>copy_x is False. If the original data is sparse, but not in CSR format,<br>a copy will be made even if copy_x is False.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('algorithm',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=algorithm,-%7B%22lloyd%22%2C%20%22elkan%22%7D%2C%20default%3D%22lloyd%22\">\n",
       "            algorithm\n",
       "            <span class=\"param-doc-description\">algorithm: {\"lloyd\", \"elkan\"}, default=\"lloyd\"<br><br>K-means algorithm to use. The classical EM-style algorithm is `\"lloyd\"`.<br>The `\"elkan\"` variation can be more efficient on some datasets with<br>well-defined clusters, by using the triangle inequality. However it's<br>more memory intensive due to the allocation of an extra array of shape<br>`(n_samples, n_clusters)`.<br><br>.. versionchanged:: 0.18<br>    Added Elkan algorithm<br><br>.. versionchanged:: 1.1<br>    Renamed \"full\" to \"lloyd\", and deprecated \"auto\" and \"full\".<br>    Changed \"auto\" to use \"lloyd\" instead of \"elkan\".</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;lloyd&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling\n",
       "        .textContent.trim().split(' ')[0];\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "\n",
       "\n",
       "/**\n",
       " * Adapted from Skrub\n",
       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-2');</script></body>"
      ],
      "text/plain": [
       "KMeans(init='random', n_clusters=5, n_init=10, random_state=2)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code\n",
    "kmeans_rnd_10_inits = KMeans(n_clusters=5, init=\"random\", n_init=10,\n",
    "                             random_state=2)\n",
    "kmeans_rnd_10_inits.fit(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "70jbsmpH3EdY"
   },
   "source": [
    "As you can see, we end up with the initial model, which is certainly the optimal K-Means solution (at least in terms of inertia, and assuming $k=5$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 393
    },
    "id": "PBQDEZ933EdY",
    "outputId": "308f1a50-a813-47ba-95c6-8fe26433b350"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAq8AAAF4CAYAAAB6shCXAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAkNtJREFUeJztnXtcU/Ubxz/buA6YgKKiYWr9SrqIl0ylm5pEpZUlVmYqZaUC5hURME1TkdDsAmoXDbW0Es3KUjMvWYnm3SytzBuZKMplcodtvz/wHLexjbPt7Fy25/168Wqc8z3f73POZnz2fJ+LwmAwGEAQBEEQBEEQMkAptgEEQRAEQRAEwRUSrwRBEARBEIRsIPFKEARBEARByAYSrwRBEARBEIRsIPFKEARBEARByAYSrwRBEARBEIRsIPFKEARBEARByAYSrwRBEARBEIRs8BLbACHQ6/X477//EBQUBIVCIbY5BEEQBEEQhBkGgwFXr15FmzZtoFRa9696hHj977//EBERIbYZBEEQBEEQRBMUFBTghhtusHreI8RrUFAQAKDg+AfQBKlFtoYg+Kfochlu7pIIAJj1STY6db1TZIsIT+W5Lv2hq6uDUqnE9u3bxTaHIAgZUVFRgQEDBrC6zRoeIV6ZUAFNkBoaDYlXwv24ved4AIBCqUS3+3uLbA3h2RgAALGxsQgMDBTZFoIg5EhTIZ6UsEUQMqeurh7/nr8CAHjptYkiW0MQBEEQroXEK0HInBlzP2Nf93/2CREtIQiCIAjXQ+KVIGTOm29/CQBo3a4t1AEBIltDEARBEK6FxCtByJiCf4ug1zfEGM5f95HI1hAEQRCE6yHxShAy5p6H0gEASqUSzVu1FNkagiAIgnA9JF4JQqZotRUo+PcyAOC5yWNEtoYgCIIghIHEK0HIlJwPN7GvB48dKaIlBEEQBCEcJF4JQqZMf2MNAKDlDW3g5eURJZsJgiAIgsQrQciRH38+xiZqvfPdpyJbQxAE0UBeXh4GDhyIvLw8sU0h3BgSrwQhQ15KWgwA8PLxRmCwRmRrCIIgGsjNzUVhYSFyc3PFNoVwY0i8EoTMqK6uxclThQCAR4fHiWwNQRDEdeLj49G6dWvEx8eLbQrhxlCgHEHIjLRZn7CvX545RURLCIIgTImLi0NcHH2pJlwLeV4JQma8u/Q7AMBNnW8T2RKCIAiCEB4SrwQhI77dvB86nR4AMD5rhsjWEARByBtKMJMnJF4JQkYkTv4QAODt64MOkf8T2RqCIAh5Qwlm8oTEK0HIiLMFRQCA4cmJIltCEAQhfyjBTJ6QePUAli7bgva3j8bSZVvENoVwgiefm3/99SvPi2gJQRCEbeSyHR8XF4eNGzdSkpnMIPHqAcx/az3OFhRh/lvrxTaFcIKvv9sHAIjsESWyJQRBELah7XjClZB49QCmTXoKN0aEYdqkp8Q2hXCQQ0dOsx21EuamiWwNQVhm06o86OrqxTaDkAC0HU+4Eqrz6gGMGRWLMaNixTaDcIIHH58JAPDy9kL7TjeLbA1BWGbt4lyxTXBr8vLykJubi/j4eMlvc1O9V8KVkOeVICROVVUNSkrKAQDJ2RkiW0MQ1hmSEC+2CW4NbcUTRAMkXglC4ryUtJh93ePBe0W0hCBs88jwOKi8aUPPVdBWPEE0QOKVICSMwWDAmryfAAD/63I7vH18RLaIIAixoMx4QkykVEGCxCtBSJjjfxbA0JCnhZm574prDEEQBOGxSClshcQrQUiYex9KBwB4e6vQLDSYPb5pVR5e7D0Qm1aJ/w2YICxx7tw5yXhpCIJwHimFrZB49XCogYF0uVRUipLSCgDAuAWzTM6tXZyLovMXKLubkCz//POPZLw0BEE4j5TCVki8ygy+xSY1MJAuM+asYV/3GfSwybkhCfEIaxtO2d2EZLnpppsk46UhCMK9IPEqM/gWm9TAQLq8//FWAECH22+BQqEwOffI8Dgsz9+IR4aL/w2YICzRrl07yXhpCIJwL0i8ShhLXla+xeaYUbE48/v71MRAYnz97V729aKNn4hoCUEQBEFICxKvEsaSl1UosUmxsOLyyvilAAA/tT9UKpXI1hAEQRCEdBBUvC5ZsgSdO3eGRqOBRqNB7969sWnTJpvXrF27Fp06dYKfnx/uvPNOfPfddwJZKz5ibulTLKx4FF0uw8VLZQCAZye8JLI1BMGdkktF0NXVAwCaNWsmsjUEQbgrgorXG264AfPnz8eBAwewf/9+9OvXD0888QR+//13i+N3796NoUOHYtSoUTh06BAGDRqEQYMG4dixY0KaLRpibulTLKx4jE9Zxr4ePCZePEMIwk72bfuFfT1hwgTxDCEIwq0RVLw+9thjePTRR/G///0Pt9xyC+bOnYvAwEDs2bPH4vh33nkHDz/8MJKTkxEZGYk33ngD3bp1Q3Z2tpBmC4pUtuspFlY8PstrEABdH+gtsiUE4RgKhQJeXtQmVs5IqZsSQZgjWsyrTqfDZ599hoqKCvTubfmPdH5+Pvr3729yLDY2Fvn5+TbnrqmpgVarNfmRC7Rd79l88PEWGK611BqfNUNka+QPNXMgCMeQUjclgjBHcPH622+/ITAwEL6+vhgzZgy+/PJL3HbbbRbHFhYWolWrVibHWrVqhcLCQptrZGRkoFmzZuxPREQEb/a7Gmvb9VLxyBKuJW3WagCAf1AAmrduKbI18oeaORBSRA5eTeNuSnKwl/AsBBevt956Kw4fPoy9e/di7NixGDlyJP744w9e10hNTUVZWRn7U1BQwOv8XHBUbFrbrk+f/SnOFhQhffanFufnup4zIpgEtOu5UnwVAPDKzMkiW+IeUDMHQorIwatp3E1JDvbagsS3+yG4ePXx8cHNN9+M7t27IyMjA1FRUXjnnXcsjm3dujUuXrxocuzixYto3bq1zTV8fX3ZigbMj9C4evufmT9pyodYumwL5/WcsYtCGlzLA49MZ1/3f/oJES1xH6iZAyFFxOgR74yAk1JPe0eQu/gmGiN6nVe9Xo+amhqL53r37o1t27aZHNu6davVGFkp4Wy2PuPlfO7FRWh/+2jEPtgVN0aEYe6MYez8KpUSOp0e899az64X3bOTTe+oM3ZRBQLXYTAY8NPuhh2I6EcfFNkaaUNxrIQzSMELJ0aPeGcEnJR62juC3MU30RhBxWtqaip27dqFM2fO4LfffkNqaip27tyJYcMaBNmIESOQmprKjh8/fjw2b96MhQsX4sSJE3j99dexf/9+JCUlCWm2Q9iTrW9pO57xcn7x5S84W1CE3XtPmMw3ZlQsshe8zIrJMaNiMW3SU+z4+W+ttzgvM445z9Uee++JsI+vN+3DtTwtvJg+XlxjJA7FsRLO4I5eOC6C3JMFnNzFN9EYQcXrpUuXMGLECNx666148MEHsW/fPmzZsgUxMTEAgHPnzuHChQvs+OjoaKxevRoffPABoqKikJeXhw0bNuCOO+4Q0myXY2k7nvFyPv3kPY28nYy4BGAiJtNnfwqdTg+lQsEKVPN5ly7bgqQpH9rc/qfwAOEZ9uIiAICfWo1WEW1dto47eC0pjpVwBncUcVwEOQk4wp1QGJi6PG6MVqtFs2bNUPbvJ9Bo1GKb0wgmZpXxoDZF+9tH42xBEW6MCMOZ399njze/cQSKS8qhVvsirLkG0T07Ycu2QwCA2Ae7YvfeE7haXoXiknIoFQoEBwdg7oxhjda01x7COa5erYSm7fMAgMz1y3DbXV1cttaLvQei6PwFhLUNx/L8jS5bh/BMvl+zAe+lvAGFQoF9+/aJbY7HkJeXh9zcXMTHx5M4JWRNeXk5+vTpg7KyMpv5SqLHvBL2hxhcLa9CaEhgo9jTuTOG4caIMPj5erOhBkGB/iguKWfDCUpLKxAaEojg4AAUl5Rb9K5SeICwPP5MBvu6U7fOFsfw5TElryVBuB/kVSU8DRKvMmP+W+tRXFKOoED/RuKSEZ2MiI3u2YkVuk8/eQ9UKiX0BgOCAv0R+2BXqFRKRPfsJNKdEEBDs46dPze0R+4Zcz+USsv/JPmK86Tse4IgCELukHgVGS61U43HWMr4Nz7PbPlH9+yEL778BcUl5QCA3XtPmMTPbtl2CDqdng0rcMQuwjZcnuHuPSfY1xPenm11HHlMCcJzkEJFBIKQMhTzKjLW4lftGWN8HgDOFhSxZbSUCgUMaCjFxFy/dNkWjJ3YMI9a7YuKwjUO2UXYhsszVLd6FlVVtfD190fenz8LbCFB8AvFvPLDwIEDUVhYiNatW2PjRopNJzwHinmVIJY8ccaeVGueuuienWxu8RvPwbzu2rkjVCol/Px9wHw/Ya43jnP18/W2aB8XuwjbNFUX9/TZQlRV1QIA5qxeLKRphIO4Q7UGQvq4Y0UEguATEq8C0lQJKqYF7OT0XBOxuHvvCeh0euzee8JkvLWSWQBw6kwhdDo9/Hy9oVIp2XmABhGrUCigVvuyTQ+M7Uuf/SnSZ3+Koita9jWVzrKfphLfXk1exr6+tdudQpklSeQiCqnGLCEEziRgUcgB4QmQeBUQS544S4K2qqqWbf363IuLTKoLLF22Bc1vHIHmN46wKCqZY9U1dbgxIgyxD3ZFM43apDrB7r0nYDAYENZcwwor4yoGAFBcUo7KyhoUl5SzFQqa6t5F2MfGzQcAAHdG94BCoRDZGnGRiyik2GPPRg7C0B2bMBCEOSReBcSSJ85Y0DJVAp4dfC8bs7om7ydWQALXqw0Ul5Tjanm11XACP19vtuMWU50AaIjDjO7ZySQkoPmNI5A46QN23NwZwxAaEgi12hdKhYKtULB77wnywPLEitXb2dfzPlsqoiXSQChR6KyHl6o1uB/2CFI5CEMKOSA8ARKvImMsaJnXq5dPRPaCl9ntfgDQGwxIn/0pLhSWsMfq6+vZigGMCK2uqUNoSCDmzhiG+W+th06nh0qlNOm49cWXv7ANCBgxrDcYoFAAV8urAABXzq7EwrnxCA4OYL2u1urLUkys/Yyb0hAyoAkNEdkSaSCUKJSLh5fgH2si1R5BKgdhyEfNV3sEvRy80YT7QeJVYJ57cRG8QuLw3LV2oAzmAnDMqFg8/eQ9UCoU8Pb2glrti9LSCtTW1bPX+Pv7sq8ZEVpZWdNQA/alxzFv1iu4q9vtyF7wMsaMisW0SU+xHt2Eie9D1Wwwwlo0Y7esFVCguKQcSVM+ZEtuMd7Y3XtPWK0vS+1k7ePkPxfYLwkvvz5ZZGs8C2MPr1zibAl+sCZS7RGkntIMwB5BLwdvNAMJbfeBxKvAfPHlL9Dp9Pjiy19MjlsSgLv3noDeYECb1iEIa66B3qiqmUIBLJwbjxsjwjB3xjBMm/QUYmMfxPvvL8W3m7ajAtF4bmQ69u3bhyHDpmP5F5exYfNJtlmBAQ3e3IOH/0FIcADUal/4+ftAqVBAp9Oz7WHNqxhYypxvKqueMIUpUwYAfQY9IqIlzuEK8edqQWns4SUvrGdhTaTKXZDaK8iMx1u71h5BLwdvNIOchDZhG6rzKjDPvbgIX3z5C55+8h6sXj6RPc54OpntfOZY+uxPAQCxD3bFmryf2PFD4+67fr3CH/C9HVC1wMWiMjZUoFVYMwBgjwHAL7/8hBrtYbw4dgHq6urh7e2Furp6NrZVoQBCghvCDqg9rGtQaBpEfp+nHsXkt98Q2RrHebH3QBSdv4CwtuFYns9PLUpXzGmNTavysHZxLoYkxFMMK09QnVfhsbcmrPF4AB5VTzYvLw+5ubmIj4+X7ZcVd4fqvEoAS7Ggq5dPRH1JnolwtQYjHotLyvHZup9xV9eboVIpERISiDV5P0EVHIc+AxegpDYKR/8owsWiMvh4e7He0/8KS1B0RQu9Xs/O2abNDRjw5KuIefA+AICuXofQkED4+fsAAAwGWAwNYO6HqXRA8a2OMSvjM/Z14rw0ES1xHlckWQmZzU/JV4Q7YK/n03i8nLymfCB3LztxHfK8uhBrHZYseVmNxzLJVdMmPYX02Z+yLV6ZeRjPXUREBLZs2YJff92Ll19+BVOmTEFSUpJVe7Kzs7FgwQJ88P77iL4nGg/FPISz586xazJeXqb2qzUbjW0h7CMwfCgqKmoQ0rI5Vu7/XmxzCIJXyPNKEIQzkOfVhXDNrrfUGWvpsi1ImvJho/hWJm40umcn9nzCpA9wtbwaCgXg7e2FoitaNL9xBNpdawObkZGB4uJivPzyK6irq0NGRgays7Mt2pKdnY2MjAzU1dXhldGjUVR0GVkL3mSF65hRsbhydiWunF2JMaNi2XqxiZM+YO9z2qSnEBoSaLHigCPPx9O4erUKFRU1AICxc+TtdXUXKGmLkAuUbEQQ1yHx6gBcs+stdcYyL1/FwJTJYq4BAIPBgLq6ehgMQF1dPds0QAFg/WeZ6Nu3Lzp06ICpU5PZeTIyMpCTkwO1Wo2goCCo1Wrk5OQgIyODHTNlyhTc1LEjHn0kFmeOr7UZ26o3GNj7NBe4DOZilaoPWCY6JpV93fvhPuIZIhJSFIqUtEXIBWvJRs6IWkvXyl0ky91+ghskXh2Aa3a9pXHMMaZ8lbVrhsbd16jrkkIBqNW+mDbpKTz5ZBz8/RriVCdPmoi01OvCaN68eVi8eDHUajUWL16MefPmsedSU1ORlJQEvcGA2tp6rMz7x8RDygjR2Ae7NvKyWvOomotVqj7QGL1ej2N/nAMAPPHSsCZGuydSFIrUMYuQC9biU53JoLd0rStEMlf4WIMqCngGFPMqMcwrDHy+/heThCsm1vRKdQ/U1F3/7uHtrcLbb79rIlSDg4NRWlrK/p6enoaEhEST9YqKihAd3RN+vt7sseKScoSGBCIo0N9qXG5TMbyEKe8u+RbjUxoaE6w6+D2CWzQX2SLhcefsfvN7c/Ze5fqsKOZVeJzJoLd0rbX57K1q4Ah8rEEVBeQN15hXEq8CYI+4a37jCJMELQA4W1AEpUKB4OCAhhJWLz2OixXd2PACBsbTmp6e3mjeuXPnIiEhAZWVlY3Ode/WDYUXLwIAK1qvllehuKTcRKhaK/Plidgr2H2bP43aunoEtwjBqoM/CGAhISTmJb6cLfklZMkwPiHx6r4IIQpJeBKUsCUCXLfVmxoPAAqFAtMmPcUmfT0z+F6jWFMvBAb4NbpGpVIhJSUFwcHBJseDg4ORkpIClUpltgagVCjQ8+7boVQooFb7Yu6MYTjz+/uYO2NYo61/SzG8noo9cb0lJVfZzmgZectcbZqoWItr5RrvKsW4WC6Yhx84G45A4QyE1LBWZorPcAIqZUVwhcQrj1gTNNZiQC2NZ0Tj4rdewZhRsVYEY4MQUprFxAIGZGZmmoQKAEBpaSkyMzOh0+lMRxuAoCB//PtvIfQGAyora7Drlz8AXE8gM/YqUizrdex5FvfFTmdf39DxRlea1SSuFofW4lqbindl7FqZlSO5uFgumNeMdbaGLNWgJeQCxZgSYkDilUesCRpGCAJgPa1Ll23B1fIqNiGKaQAwOT2X7Xtvac6ly7ag/W3xOH2mwKRdrFKhwJuZb5qEDBh7YNPT05GVlQUAbKIXAJz85yyOHvuL/X1N3k947sVFFj3ClgStp8L1WdTU1OL3EwUAgIEjnxbCNJu4OmnKmsewKU8iYxcAl3schfTuytWTTLgnrki68rRGB4Q0oJhXATFOeAJgkvxk3AAAsNwEgKkRq9PpsXjxYjzxxBMNW/9KJd5fugQzX5/Njp07dy5SUlKQmZlpImhTU1Mxfvyr0Ov1MBiAL7/8slFjA6ZVLDUicJ5PPtuJ4a+8CwBY99du+Pj5imqPFBOBNq3Kw8qsHADAiOREl9vFxJMqVUqMmZ3i0vWMY1eHJMRL7tnzzeCbeqK2rh5KpRK//vorb/NyjYWUc8ykELYLkXQFyPt9IMSFYl4liLEXlSn4f7W8CkuXbWF/V6t9LTYBMBauKpUShf/+BpVKCU2QGh8v/9BEuKalpbHJWQkJCUhLu14QPyMjA4tzcsB8Zfnkk0/Yc0wUgspL1djbS00HHOKFhAZRFhTSTHThCnDbjhbaW7h2cS7KS7XwDwgQRNQNSYiHUqWEXqd3eXiCscdZiqXC+IaJ7Z42bRqv83LdmpbzFraztnPxqgrlJZXz+0DIAxKvAmK81TxmVCyCAv1RXFKO+W+tZxsALJwbj6BAf5PrzIVr9oKXMWtaDFqF1uGff05hxsw32LGpqalITExEZWUlrl69isrKSiQmJiLVqA5sxvxMnD59Gjt27MCBA/sBNNSP9fdvEFfe3iqTLXFqOuAYx/44i/r6hjjjd7771OXr8SU6hRZZQicnPTI8DmNmp3Be057naj7W+MuCJyVh3X///bzOx1V0yXkL21nbuQhGPhKipCSSCc+FwgYchGupJFvjzGu6btl2CKWlFeyW/bRJT2H+W+vZslWMcGXnUfjjr/864JdfdmP06NGYnp6KV14Zw8bCenurUFd3PUkrOzsbCxYswPvvv49evXph8buv4crlS/h83c/w82+Ig62srIG3txf0ej1bEsvYzrkzhlHMK0d69UvB3v1/Q6FU4Osz+5sc7+yWPl/llaQYWiAm9jxXuZa44ovH2nUHAGzevBktWrQQ2RrPQqitekdCDyiMgOAKhQ24GK7eSFvjjL2vX3z5C4pLylnhGdaiGZKmfMjGwTJduYCGWrA+zZ+GSvMonn/+efTq1Qs7duzASy+PNkni0tWb1oFNnjIJu3b9iHvuicZzzz2Hb77dhd17T7CVBurqdLgxIgy6eh10Oj3W5P2Epcu2sGK1uKScFbEMFFJgmfp6Hfbu/xsAcO/AGE7XOOvx5MuzR5nuptjzXD3Ju0pIC3u9qo4mbzniVXUkjEBObV7lZKu7QOLVQZxpEWvpfNfOHU2OHzp6ig0TYGqvjhkVi/lvrUdxSTnq6uqhNxiwb99+xMbG4syZM43m1ps51Wtq6nDhv/N4b1E6Sq6cR1iLZjj372X2vK5ehzO/v49nBt/LHjMXqwxMdYTESR9QSIEF3lv6Lft6anYGp2ucFT5yEp1yysK357naM1ZOz4BwHKkKG0fjUh0JPRBK8IqFnGx1F0i8OgjXUklNjWPOF10uY4+p1b54+sl7WG+r8bXRPTvBvLxrQUEBnn/+eQwePBhffvklioqKTM4XFRVhw4YNGBn/PJ4aPARLPvwS0yY9hQOHTsI4aqRbl5sAAKuXT0RoSKDJHEz92bkzhgEAK6L1BgNUKiXVfjUjdVZDIlzLiLacr5GL+ORDdNnrZXZHoecJCVyEdIWNkHGpQglesZCTre4CiVeJwHhglywajYrCNVi9fKJF0bt77wlYilJWq32xZ88ezHhtGp54LAYvjHgKD9x/P+6+uwf69b0XP+/4DPv3/QqFQgGdTt/gKTUTwcYCOvbBrlCplIh9sCuAxiKcqY4QGhLYSGB7Orv3HkdNTUPW9fgFM0S2hn/4EF32epndUehRiIFnwJewsceDy2WssaCUondYTt225GSru0DiVSKYi0NrsaTRPTuxmtPH28tEfzKe0TO/v48nB3ZBXa0W06c8hitnV2L33hMoLimHv78PVColont2wrOD74NSoYC3t1ej8lzWWsEydgHAlbMrjVrW2saTYmMTJn0AAFCqlOjc+y6RreEfPkSXvV5mdxR6cvG0i4kURZW98CVs7PHg2uvtFds77A7vMyEsJF4lirVEr917T4BxvNbW1UNxLYagqqrWpKKB+fXRPTtBpWp4uxlRunr5ROjK1qH2yheNRGh0z05QKhQouqI1EZyOls3ypHJbR347CwB4fNRzIlviGsQQXST0PBOxRZWU4OrBzcvLQ2VlJTQaDWdvr9jb3mK9zySa5QuJVwEw9jpy9UBaSvRiWsp6X/O4qtW+eGbwvVAqFDAYDCbJVebXM55UP19vTolmxlUIbM3LFUevkxtjJ13vSPZC2nhB1nTHeFCCABqLKk8WG1w9uIsXL4ZWq2WvsWduAKI8X7HEM305ki9U51UAbLWFdWQeBoVCgcVvvYLJ6bmorKyBWu2LisI1AK7Xl43u2Qlbth1CdU0dAMDP15tTrdaly7YgcdIH0BsMCA0JxJWzK+2y1VPxbf40auvq0fbm9li6fZ0ga7pLbVGqLyt/XF3nVaj2pnKmX79+0Gq10Gg02L59u8m5puqtetrzpfqz0oPqvEoI87awzGtbXlhL56J7djIZYzAYMP+t9fDz9WaPtb99NHo8MBVjJ76PswVFbP3YysoaVFfVorikHElTPrTq+TWOac156xWTCgOEbU6fvci2x0yck9bEaP5wl3hQd0zKIvhF7O1tOZCQkIDWrVsjISGh0bmmPI1cni/f3m8xvemUaCVfyPMqIsYeWXMvrKVzxp5XBQB/ta/JNVVVtTB/O4fG3YfP1/0MvcEAtdoXNTV10On0CA0JRFCgPxsny3hqmW5ejniGPZ32t7+CswWXoVAp8fXpfWKbwxtCeUTl5HmVk62O4sg9Uoct60jBy8eHDXx7Z13t7ZXCc7cHudnLN+R5lQG24kAtnTMuT7V40WiENdegsrIGlZU1qKmpg+FazdW7ut4MlUqJoXH3YfXyiawHdeHceGQveNkkfIFJoGISqgB4RGwqV7jGKNfX1+NsQUPDhxfTJwhgmXCszMpB0fkLWJmV49J15JSU5QleYne6RynEykohvtJZT6MjyWBNER8fD41Gg9LSUvTr14/390gKz90e5GavWJB4FRFbDQwsnRszKtakPJWxmH36yXsQGhIIX19vnDpTiOwFL2P18onsddMmPcUK1TO/v882HWBEKiOWjbt5EdyrJEx97XpM8ENDB7nYKoIPmES3rKQ0uxPemFCNyO6d3TZZzl3CUQBpCAJ3CHnIzc2FVquFWq3mzSsYFxcHtVqN6upqaLVaXt8jV4htV8Pn50QKX9pcBYUNyAxme9+4LBaDcViBcVgAACRN+RA6nZ7CAezE1vM2xiskDjqdHm06tMP7P34JwH22luV8H7ZsZxLdlCol9Dq9Qwlv7pIsxxdSDRtwh61YZ+6Br/t31XPMy8vD4sWLATTE7PI1t6cloJkjx/unsAGRcVVRflueQMYTq1b7oqS0gh03/6310On0Ftu4WrPTk5oK2IJLG+DLl8ug0+kBALM/yWaPu8u2q9S28+0pDWbrPWA8i/cOiHHYw+hO3kl3pqntcjl4qJzxHvPleXZVglNcXBy2b9+O7du38zq3O3i7ncGd75/Eq4vgsyi/sZC0FSfLhBWENdew8a/GFQ4stXG1Zicf9nuKAO5yz2QAgFKhQKuItuxxR4QN3zVb3bEGrD1fCmy9B4woT86ex0mcW3qW9gp7d3w/3AEphBU0hTNCxJUiRsrCn6vYlvI9OIM7V1Mg8eoi+CjKz4i/9NmfskKyKU8g08ggNCSQFavWrmHGqtW+uFpeZSIy+bDfE7pqabUVOH+hGADwwmsTTM454rHk21vrLt5fYywJUmuikE+vMR+Ja1zeDxK4juGMAJG6h8p8u97eezUWMXl5eejXrx/n5Kim1pKD8G8Kd7gHT4PEq4vgst3cFI5UAJj/1noUl5QjKNAfAGx6PpmxVdfqvxp30uLDfk/oqvXe+5vY1wNHPuP0fM5sQ1sSPc5ua4stpLh6O8UW6ZbsdPT9EPte5IK5qHJGgIjloeIqQo3vLS8vD2+++aZTIQRarZZzcpSjtWHl5M2U+pcXojEkXiWMIxUAjAVjU55PZqy/vw/fpgPgRwBLnRlzGzqatWgbDi9v7yZGN40znkJLosdZz6PYQorr+nyLfkuMSE5EWNtwjEhO5GSn+TGuiW8UR8sNc1ElRwHCVXAb31tubi70ej0UCgUqKyvtFodMaSquGfhNPVdrwl9O3kx33l53V0i8ShhnxR8jTqN7drLogWXmXzg3njppOUD+ryeg1zcU61iyba3I1rhG9PAxpzPeW67r8y367V3Dkp3mx/hYh7iOuahiBAgA1uMnde8fV8FtLK6Ya4KCghwuLaVWqzln9XMRdpaesxy/TBDygUpluRm2OnNRmSx++V+XBJw8VQgvH298eXKP2OZIFrHLSTXl8aQOYvwhhVJZxuWBAMiuVBBXHC1bxTwfpVKJqVOn8uJtlGNJJkKaUKksD8VaZy53jz0VmoqKapw8VQiAn1hXdyaye2coVUpEdu8syvpNeTyF8nSSR1UYjD1+Uvb+OesVtuURtTV3fHw8lEol9Ho9b1v6Un7OhHtC4tXNsNaZy91jT4VmzptfsK9fnD5BPENkwPEDR6HX6XH8wFGXrmMtPEFI8bxpVR6Gdu6LoZ37il4tQOxkO7EwFnXmWfZSCiFwZUyorbnj4uIwdepUXsWmvTGj6enpuPvuu5Gens7L+mIitc+Vp0DiVYZ4Sv1UKZP17tcAgI533AKFQiGyNdJGqHq31jysQolnxobyUi3KS7WiVwsQO9lOarhSLDoiYFzprXQ0yUootm7dCr1ej61btzo1jxSEo5wS09wJEq8ypKkqAiRuXcuGb/awHbWS38sQ2Rrpw2e9W1ui1ppIFjJ7f0hCPAKDNQgM1oheLUCKVQvEFBt8i0Xje3FEwNjyCrsypMB4/vT0dFHej5iYGCiVSsTExDg1jxSEI4VMiAMlbMmQpcu2YP5b6zFt0lMWQwEoQcu1MM/X29cH6//OF9scWWApWclWApO1c2Inf4mFXJK9mkrYkkNiD9dEKON7YUpY2Zs8ZWmujRs3uvw5GSdt6fV6Sb8ftmDeq6ioKBw5csSkiYMz7wchHpSw5cY0FcPqTIIWeW2bhmkc8cyro0S2RHgcjaPkUgfVGGveWnu6a7niPsTCXUIA5OClcqT2qrNtSM2fi7PPqSnPLTN/TEyM5N8PWzDP/ciRIybvmRQ8soA0whrcFUHFa0ZGBnr06IGgoCC0bNkSgwYNwp9//mnzmtzcXCgUCpMfPz8/gSyWJ2NGxbJNCoxFKBdh6gktXZ1hyPA32dfPjHtJREu4w6dQc1REcamDyoVHhsdhSEI81i7OZe/HEZuYa5bOyJSFgJViCIAjiB1ryQVHaq9yxZqoMp/LUs1ae2hKvDHzz507V/LvBxeYxgtM0wbmPYyKihJVPEpFRLsjgorXH3/8EYmJidizZw+2bt2Kuro6PPTQQ6ioqLB5nUajwYULF9ifs2fPCmSxfLEkQrkIUyqrZZv13+wFANx2d1eRLeHGplV5WDojkzevnaMiypLodLR0lLlYdcSmIQnxUKqU0Ov0dj0XsTy2VGZLOFwpsO31qDoqfuTg4eaTuLg4qNXqRk0b8vPzRRWPnvY+CImg4nXz5s2Ij4/H7bffjqioKOTm5uLcuXM4cOCAzesUCgVat27N/rRq1Uogi+WLo/VeqayWdfJ//ZPtqPXKrCkiW8ONtYtzodfpoVQpnfba2Rt3aS70+Nr6Nher9go75j7uHRBjU/RaEqrusn3vCvR6vcvmdpftV3uFsSPix1q8p6VnyPWYHDBvoVtY2FCHW0zxKIedBrkiasxrWVkZACA0NNTmuPLyctx4442IiIjAE088gd9//10I82QN1Xu1n6bCKh5/Zh4AwNvHGzfd3klI0xyGEXpjZqfYFHdcPIr2Cjc+PKSWcMQLaXx/jF0Hd9lOtrN0v1Levhc7jvf1kePY1wEBAbzObY8HUq7iyxJ8hiZYOs71mByw1EI3ISGBxKObIpp41ev1mDBhAu655x7ccccdVsfdeuutWL58Ob766it88skn0Ov1iI6Oxr///mv1mpqaGmi1WpMfgmgKW2EVtbV1uHzlKgBg8rtzhTbNYbgKPUtCzVwMMWWgqioq2GO2ivI76yHlE+P7Y5oW1NXU2hTjloQqH/fgKpEptlf4v1MN4VzNmzeHv78/r3Pb44EUU3zZEs5CiWprz8rSceNjjH1RUVGy3+omj6f7I1qprLFjx2LTpk34+eefccMNN3C+rq6uDpGRkRg6dCjeeOMNi2Nef/11zJo1q9FxdymVRbgGWyXIhozIQt6GBk/dl//sgZe3txgmugxLIQGWylKZH2N+B8AKPXvLXwl9f4zICwzWwD8gwCl7HLkn5pkpVcomPeKussUV78VL9wzExYILaNOmDb7++mte5nQEMcsk2SpxJfUyYf369YNWq4VGo8H27dvFNocQCbHLjEm6VFZSUhI2btyIHTt22CVcAcDb2xtdu3bFyZMnrY5JTU1FWVkZ+1NQUOCsyZKDSlrxj7WwCoPBgHVfNQjXTndFscJVqG1aIdax5FHkUiHAuCh/ZPfONpPDxPQMMvcHAFUVFQgM1mBEcqLTXlRH7snRZLGmsMcrLLaX1pWI6XWz5SHm6j12p7AHQn7IJWxEUPFqMBiQlJSEL7/8Etu3b0eHDh3snkOn0+G3335DeHi41TG+vr7QaDQmP+4GlbQSjr9PngezP5H+QRZ7XCgBIJbQsCSGzI89MjwOI5IT4R8QgIO78m0mhzHb9ZHdO/NuK1eBz7Rv9Q8I4MXj6Gi5rzGzU0SNnZVy7K6ziCn+bAlnrqKaL/Fg73NISEhg40SlBgl64ZBLhQRBxWtiYiI++eQTrF69GkFBQSgsLERhYSGqqqrYMSNGjEBqair7++zZs/H999/j1KlTOHjwIJ5//nmcPXsWL70kjxqbroJKWgnH3f2mAQC8vVQIbtGcPS6UAJC60GDENQCbyWHHDxyFXqfH8QNHm5zTXm8zV4HvyLO0ZYujMbBil74Se31XIqTnyBWiii/xYG+Sm5Q7UsnFG+gOyCVeWFDxumTJEpSVlaFPnz4IDw9nfz7//HN2zLlz53DhwgX295KSErz88suIjIzEo48+Cq1Wi927d+O2224T0nTJQZUDhOHKFS3KyioBAMmLM03OCSUApCw0Nq3K47wNby4cbYlCe73NXEWpI8/SnbfY3REhPUdcRJW9AtfZBgUMckly44JcvIGEcIiWsCUkWq0WzZo1o4Qtwm7GTlyKpcu+BwB8dWYflErHv+9JsT+9szZZSuriytDOfVFeqkVgsAZrju6wyy4hn6UU3zcpYithi4tnT+reP0sY2wzAov2OJmoJmeAlx2dPuCeSTtgiCLnACNf2kf9zSrgCrvPgOZPQ5axN9m7Dc7W1KQ/pyqwcFJ2/gJVZOfaabDdS9nzLBS6ePal7/yxhvMVqzX5HvYZCehtdvVUsZDMEKcTHSsEGd4fEK0FYYcsPh9jX725e4/R8XISeI0LUGQHqbDytcRY/14QpxtYRyYkIaxuOEcmJohfYlzNyeHbmQszSH3e5bw1bs99RYSiX2EMuWBL2ixcvRmFhIRYvXuzytYRGCja4OyReCcIKI0a/CwDwVftBoVA4PR8XD56jpZccFaB8eRUdSZgyXtvW9ZbEmbHw9XTkEJNrLsQs/XGXu1izZL8jHjh7rpGLh0/ILyZ8ruXo85X7FzE5QOKVICxwobAYl4oa2hc/P1m40jGOll4Se1vb2YQpW9dbEmdSuGepIPVqFJYQ6487FzHCpyB0xANnj0eSmf/NN9+UtIC1JOxdUZrLmdhdS++7ox5UuX8RkwOUsEUQFhg5+h2sXPMjAOCbcwdEtoYbckksstdOsTtHEdyQSoctWzBJUEqlElOnTrUoLvhMlHJETNnT6SovLw9vvvkm9Hq9ZDt3CYkz752laymRTXgoYYsgnGDVZ7sAAFH39rDrOjHjD7luH/Nho5BJYo8Mj2PbupqvZ26HEFvocogxdWec8YzGx8dDqVRCr9db9abx6RV2xANnj0cyLi4OU6dO5d2L7cpwBFfO7cx7Z+la8qBKFxKvBGHE0mVb0PzGEWA2JJKzM+y6vinx5ErhwzUhzFYLV64InSRmbT3z49bm5uu58/X8CMdxJhmGi9gTW7DYW+eVr7qwxriifq09czuKM++d2O+7MXKJZRYTEq8EYcT8t9ajuKQcAKAOCkCz0BC7rm9KmHERfY4KLa4JYbZauHLF0n3yVQaLy3rMWpHdOyOsbTgiu3fGi70HAoDF6gd8eWT5en6E4zjrGeUiUsQUD8zaTOwrV5HHpyjk8owdXc/S3CTWTKFqBU1D4pUgjEge/wT7euS0cbzPz8Xr6Mqtb2Z9ay1cbbFpVR6Gdu6LoZ37AkAjAepKu80FL7PW8QNHsTx/I44fOGqyNlePrL048/wIfmhKfPIhhJwVD1xtsJUkBMAukS50uIOj61mam8SaKVStoGkoYYsgjOg74DXs/Ol3AI4lajnTcYpBqklHzL0BsHh/5nbzcR/W5rC0FtOwgCmfJcVn6M5IJWHLnqQdawk5zibqcLWBGafRaKBWq2126nJn0tPTsXXrVsTExGDu3Llim0OJWiJCCVsEYScGgwE//twgXO959EGH5uDDw2fuZXQ2XpPr9U2NG5IQj8BgDQKDNRbvz5p31BWxteZrPTI8Dv4BASgv1WJlVo5LhCvzfLKS0jwiYUuuiWn2eK2sefycjX/kagMzDgBrhz1r2/Lwyinp6siRI9Dr9Thy5IhD6/Ftj7XPhb3rCBEO4akhFyReCeIan6/7Gcw+xMjUVx2awxX1R50RgeYJRrYESVPrPDI8DmuO7sCaozs43R8jdqsqKuwSQL5KL6hVPvhl/WZUVVRYFcsM5vGvAFwSvsA8n5+++Z7X1rRSFYlyaH5gCXvEn6u2Z7nawIxjKgzYa4et7XZXbsXzPXdUVBSUSiWioqIcWo9ve6x9LuxdR4hwCE8NuSDxShDXeHncEgCAOjAA4TfeILI113HGm2ueYGRLkDizjiUBZu4NtSXQQnwCcEdIBO5vFYn7WndCdKtbMP6ZF/HLjz/hzXnz0aFtO6trr8zKQdH5Czi4Kx/L8zey3bf4Tqhino+Pny+v80pVJLqi+YHUvERSyTB31A5b4tuVcZPW5ubaBKJfv37o168fO64pz2tT98L3vVp7P+xdR4jYVU+Nj6WYV4IAUF1dA/+WQwEAGWs/wh09u4psUdNwiSl1RRyqJazF+jLrVVVUoLxU2+i8n8obkc3aorlfYKM5qyurUXm1ofKDUqmELsgHx8vOo1pXZzJuaOe+KC/VIjBYgzVHd9i0k+v92xrH9zOUaoyzvXCJeeWzAQAhPbi8v8wYAOw4vmJMpRKrKhU75AjFvBKEHfR/fBb7+va7u4hnCAcYLyfjcbTlsbMUG2otrMGZ7WtrXjpmPUveUI23P3qG/Q+l/xZanNNP7Qd1UCCUSiX8AtQo/bcQPcP+B423v8k4Zm4mUcvWvXD1ctoax3doiD3zCRli4Iq1PNVLJBWseUa9vLzg4+MDLy8vp+bk8v7Gx8dDo9FAo9Gw4/jygEtlC10qdrgzJF4Jj0en0+GXPScAAL7+ftj8yTpR7WlKNDDCCgCv27rO1KBtSoCZn/dTeaNr8w7YvHEjut7ZBVmZWY2uKS+7isqr5fDy8cZ7772Hrnd2aRjfvAP8VN5W57bVSIDrVritcXLooibVtaSyTe+pGIuqgIAAREREIDIyEpGRkbj11lvZ1xEREQgICLB7Ti7vb1xcHLZv347t27fz/jkQ6stRU+ER9CXN9ZB4JTyeX/JPsK9rqqpFjz1sSjQwwmpEcqJdHkBLosv4mLFgs8dz6YiYi2zWFudOncKwZ55DXV0dZqRPbyRga6trAABvLViIGenTUVdXh2HPPIdzp04hsllbq3PbaiRgq9Us13Fixqi6Ig5VCmt5MkLGAcfHx6N79+7YtGkTOnbsiODg4EbeVi8vLwQHB6Njx45o3749vL29rcx2fU6pCDVXfDmyVYvXmmeVvqS5Ho+KeZ2V9gz8/Hycmqt/n864PdJ68ogr8PZWQamk7xmuwr/lM6iuroPK2wuhLcNEjz10RQxkVlIadn29BYBpjVYmVjUwWAP/gAB2TeMYVkbEMSLG3DZL8a7mdVeN7yPEJwDdW3RosCszCzPSp7PnZs+dg2nTUqCAAtVV1ciYNw/z5s1jz6elpeG12TMBAPsvn0ZpbYXdz49rLd6m4ngju3fG8QNHRf+8SAWp1Hl1Ba6MYRQqDjgvLw/79+/Hp59+isuXL+Pmm29u8pqTJ0+iQ4cOOH36NKqqqlxmG2Of2HGilmyw9P7wbasU7l0qcI159SjxKld8fbzQqmUwoFAItmZocADu7R0JlUo40RzcLADxw/rCx8f2N30+uVRUiq73TgEALPruE9x8R6RgawvJEx16QK/TAwAS5qaabLFbSqgyFoCMp9Ga2LMkFm01NLgjJAKt/a//ezQXsPPmzcO0adMwf/58pKWlscfT0tKQPC0Ffmo/AEBhZRmOlRbY/Sws2cv1mDF8NKRwJ8QWr1ITmFztEUq4jB49GqtWrcLevXvxyiuvYObMmUhOTrY6PisrC7NmzcLq1asxYMAA/P3336irq7M63lmkkMwnhFDluq6nQuLVCEa85r2ViwB/x6sNVFZX4fPNX6K8qpJH62xj0Otx8J/DqCx37bdekzUBVJRXwP0/GaZ8fXY/FAJ+QRCSrKQ0/PztVtw7IAbJ2fManec7u96W5/X+VpHwUalM7TMTsMHBwSgtLWV/nz13DpJTTP/Q1urqseviCfCBI0LUXaoE8IXY4tWVAsARASM1QaJSqVBVVYUHHniAFaFz5syxKGCzsrIwfXrDv0dvb28cPnwYrVq1wpkzZ2yuYes5NfUMpeB9FMsGKdy7VCDxagQjXv/bfhyawCCxzZEFVyvKcbn0iqBr7jt2CD/t3y3omkf//gMH/jgMAFh9dDuCguXroXcFTYlewL6SXaOSkzAzaUqj8yqFEgsys0w8rQzz5s3DlJRk6Az6Rud2FZ5Arb7eaSHpSiHqKSJXbPEqNQEgJXsCAgLQsWNHAKbCFGgQsCkpKVAoFDAYDMjMzGx0nhG4p06dQkVF41AdBluCXWpfLlyJ1OyREyRejSDxStiixX03o7qmGk+OHo4X0yeIbY6kYMINlColvjq9z+IYLl5LZkyP6F7YtevHRue9FCqoFEqEhoaaeFyDg4NRXFwMnUGPeoOu0XW7L/6FSl2t6Fv4tgSq2LYJhdjiVS6IIWwiIiIQHBzM/m4uYK2F6ph7ZktLS1FQYD1Ux17PK1/PQmpebqnZIyeozitBcGTc0JcBAN98/JnIlvCDo6WcLF1374AYKFVK3Dsgxup1lrLSzedixjz49GMW5zDAgPnz55sIV6Dhj+X8+fNhgAHVldUoLSpGdWU1e77+mjc2sntnKFVKRHbvbNc984WrOpcR7ocra4Baq1wQGGjaBCQ5ORlz5sxhf09LS0NoaKhN4crMY6s6gq0se0vnuDwLLtUYpFTxAJCePe4IeV4Jj6eiqhKtHrgFADBtyZu4Z8CDLllHqO1jRz19zHVKlRJjZqc4tf1uqaMWc+7QwQNo3ry5yXXz52Rg1uuvs79binl9+cVR0Ov1UCqVCA4LNYl5dca7yUe8r6eEBtiCPK/cEDqxzMvLC5GRjRNRlUol3nzzTaSnpzc6N2/ePCQnJ0Ovbxyq0717dxw7dowXryKXZ0FeTM+CPK8EwZEAfzW6dLoTALDzy+9cto5Q9UGb8vRZ88wOSYiHUqWEXqd32EZbDRSYc99v2mJyTVZmlolwnTdvHq5cuYK5c+eyx2akT8eS95ey3bYAoLimAptW5WFo577QFpcgMFjjkHfT1vvC9T3jWj/Wnbn47wWxTRAUR+uzcqkBysydnp5u1xqWPH7WyiwqFApMmzbNJJwAaPjiOG3aNKvJqyNGjGjSq8j12XB5FuTFJCxB4pUgAAyOadjO3vP9ToveBj7ge/vY0W5X1gTZI8PjMGZ2ilOdpWw1UGDOXcH1bX/zKgPp6emYMHEiKquqkJCQYOIVmv36LHy4fBlbKitvfUMnrfJSLWqqquEfEOCQ19PW+2LPeyZm8wKxKSsuaShTAuDll18W1xiB4Hv731jwMXNv3brVrjUsiUHm/2fV1dUoLi5GdXXDvz8mOctqqI6VTdmHHnqoScFp77NxNBSBy/WOrktIGxKvBAFg4vAEKBUN/xx2fbWlidGOYU8Pey44KpZsCTJnO0vZukfmXKd+d+NKdTn+OXkSb7w+iz2flpaGhIQElJWWol6vQ3V1NaakTMXsuddj8954fRb+OXkSl6vL8Wb6G9Dr9FAoFA57Xc1tNhfo9nhUxY5tFbNtra7+ejLdY49Zjmt2N/jwCFoSrMw2euvWrRETEwONRoPKykqHBVZ9fT3q6+tRWVkJvV6PysqGUo+ZmZkmMa7GHti0tDRkZmZanasp7H02zn4RsOd6a8+ckBckXgniGg/f2w8A8MHrC0S2hBu2xJItIeOoZ5ZPcXa87DzadeyITz9fDW9v74Y6rtNS2LAAP7UfgsNC4af2Q3JKMma8PhPe3t7IXbkC7Tp2xNKPP0RVRQUCgzUYO2ca1hzdwcuXAkv3bk/oAJ9fTuzFkz2/YsBHC1BLgpWJ/9y4cSPmzp0LtVoNrVbrlMAqLy+HWq2GUqmEt7c3Zs2a1ajaQHFxsUk3u+nTpyMrK6vRPJYw92Ay9gPg5Nm0R+xa8pbac721Z07IC0rYIohrnDx3Cl3i7gcAvLflM7SP/J/IFjkOHwlMjrQ/tSdxSePtj67NO+DcqVO4qYlWlaVFxfjnn38Q1rIlCoPqEXfXg3Z3/XLUfrkkY4lpZ/Glyxh5VywAYP/+/YKuLWe4JCzxkeBlXOd11qxZJiKVjzqv1pKqXJFs5eycVINV2lCdVyNIvBJc6RAbhaKSK3h+ylg88+pLYpvjMHwIGXsF8KZVDTGoep2e9dA2ZYOfyhuRzdqiuV+gxfMM1ZXV+GHrVhz+7yR6PREj2dattjqLuTN8iFcSFfxg/BwBsK+nTJmCixcvokuXLrx32LL23rniPZXS50RKtrgLLq020Lt3bygUCuTn55sc12q16NKlC3x9fbF161ZHpiYIUXmw1wMAgM/f/kBkS5rGUmgAcwyAxS1sW+EE1mqzcg0TWLs4l21owAjXpraxq3V1OFR8Bvsvn0ZhZRlqdabxdLW6ehRWluFY5QX49eiAXk801Jttaove2HYhY0HXLs5FeakW5aVa2r63E4o/5Afj52j8+vz58+jQoQNWr24I1bEmXAFg3LhxSEtLg7e3N1avXo0OHTrg/PnzVte0FkLBR2gFYBoqwNecfGDpM0tJYMLgkHhlArmNtxZqa2vx5JNP4ujRo1ixYgViYqwXNScIqfLRrHcBAHX1Ovy+75DI1tiGa3ymsXjjWhbKHs8tM39k984IaxvO1ojlIn6Za9csW4FjpQXYdfEEXs9egPvvfwCvZy/ArosncKy0AKW11ltSWsJY3Db1TPhkSEI8AoM1TiWQeSoUf8gPxs/R+HVdXR1Onz6NAQMG4PDhw1aFKwBUVlYiMTERP/74IwYMGIDTp0+z3lpHcUbUSfWLjaXPrFRtdTccEq/3338/BgwYgO3bt2Pnzp0wGAyIj4/H9u3bsWjRIjz77LN820kQgnFr+4b4y6XpjbNtnYFvwWRJHFo6ZizeuJaFsif5hxl7/MBRE28olwQmS+ssz8rGvt17sDwru8m1udDUM2Hg4/15ZHgc1hzdwVsCmSchJY+avUjJ22b8HM2faVVVFf7++2+0atXK5hxMcpefnx/+/vtvVFVVOX2Pzog6OX2xcdZWKX2WpIzDMa+//fYbunTpgujoaNx999146623kJqaahIILhUo5pWwh40/bsGzyaMAAJ8d24kADT+fGTHjMO2Jf7U3bpPvBC8hEo8srSHW++MOeHrCltS7QFmKzQwICEBoaCgCAwPh5eXFjq2vr0d5eTmKi4tNkrNckSgl95hRKSakyR2Xd9i688478fzzz+Pnn3/GW2+9hRdffNGicM3IyMBdd92FoKAgtGrVCk8//bTVoG+CkAIDH4iFr7cPAOCvI7/zNq9YdUCteUCteRqZuE3zov9NNUU4fuCoRW/m0M59MbRzX85lu+wtOeWIx9TSGkK9P2LWYyWaxprny5ZHTOqeQUtez4qKChQUFOD48eM4fvw4/vzzT/Z1QUFBo6oCztyjNZEq9y12R5+JnD9LUsGpOq9hYWEAgKCgIOTk5Fgc8+OPP2LcuHHYu3cvNm/ejOLiYjzyyCOcCh0ThFjccXNDL/CsxLQmRnJH7Dqg5thbz3VlVg6Kzl9gvbLmWNueZxKYVmblNCnaHBF2fIQAuMrba8kOqscqbYwFFdeC9lIPebAliPLy8vDwww9j9erVNv8uO3OP1p6dGEKNz215R5+JnD9LUsFh8ZqdnY2FCxeiVatWuHr1KlasWGFx3ObNmzFy5Ejcdttt6Nq1Kz788EOcOHECf/zxh8NGE4Sr+WLhxwCAq6Vl+O90gcjWuAZrItURkW1N/BknMAGwKtoYkbdsziKbApnrfTQltM1xlaA0n3fTqjy2uQIldEmT+Ph4tqvV4sWLXVrQXqj4RluCSAjvp/GzE7tygBS8veRddR6HxOsXX3yB8ePHo2/fvjh06BCaNWuGWbNmsW3nbFFWVgYACA0NdWRpghCEVi1aIjioGQDgEzuElJywV6SOSE5EWNtwjEhObHTOmvgzTmBirrck2pjra6trAAAVZVc5e0358GhbE/LObvGbz2stJIOQDnFxcWxXKwCNul7xWfbJU4SU8bMT+56lIBzJu+o8dovXbdu2Yfjw4bjzzjuxYcMGhIeHY+LEibhw4QLeeecdm9fqdDpMmTIFjz76KG644QaHjSYIIRg3bDQA4OeNVLMYsC0SI7t3hlKlRGT3zg7NzYi8Fm0asqANBoNTXlBbQtsS1u7NWY+s+bz2xtVSfKw4MAInISGBd5Ehtfakzgqp9PR03H333UhPT+c0Xux7JuHoHtglXg8ePIgnn3wSbdq0waZNm9hMsIkTJyI0NBSZmZkoLi62eK3BYMCYMWNw7tw52QZnE55FyouvNrRMBFB04aIoNggtXhxd7/iBo9Dr9Dh+4KjVMbaEICPyrhReYo8xAq+ppC9LNOWN5XqffCdx2eslpvhYcXClwDEWb8briFkiyZm1t27dCr1ez7kxUVPPls/nQGWn3BfO4vWff/7Bo48+Ch8fH2zevBnh4eHsOY1Gg5SUFJSVlSEjI6PRtQaDAQkJCfjhhx+wbds2NtGLIKROq+YNn9XZI18VZX2hxYuj63EReVzG3DsgBkqVEvc/HssKPFd0reJ6n2In2YlVoYJwHYx4A2AirMTcTndm7ZiYGCiVSt4aE/H5HKzNRaJW/nAWrzfddBMKCwtx+fJl3HrrrY3OT506FQaDAVlZWSbHDQYDEhMT8e2332L79u2IiIhw3mqCEIj3ZywCAJw5cRLlZVrB1xdavDi6HheRx8UbevzAUYyZnYLk7Otl91zRtUouolBs8Uy4DnNhJeZ2ujNrz507F7/++ivmzp0rui1c5xL6i0JeXh769euHfv36kWDmCYebFHAlISEBa9aswTfffIObb76ZPR4aGgofHx9XLs1CTQoIRzEYDAi9pwPq6usRn/YqBo8ZKbZJboVxlQLGGyr3JgFCNFmQGp7epMARnC3QL/cC/2LC5dnx+XyZxgMAPLb5AFdc3qSAK0uWLEFpaSnuu+8+hIeHsz+7d+929dIE4TQKhQKD+g0AAHyyYInI1rgW8zhQ499txYg6E5fLtXWtnJBynKpUEsBo29b5uFqmjNfixYubHCvk85bKe2vLDi7Pnk/vLFN+TaPRUHksnnC5eDUYDBZ/+vTp4+qlPYaP1q1C5OM98dG6VWKb4pZ8NOtdAEB9bR1+//WQS9aQgqgwF13Gv9sSZM40CTAWrO6yRS5lES4VYS12uSRAXJEl9Nr2CF2uWLsHKby3fNjBZ/hCXFwctm/fju3bt5OXnCdcLl4J17NwRTYKCs9j4YpssU1xS1QqFW6O6AAAWP/+SpesIQVRYS66jH+3JcisddZyNCGqKeHraqFvbX6u60pZhEtFWItdLgkQVmSZCz0+1k5ISGDLeYkhxKXUNcsSztpBJbWkjctjXqWAu8e8frRuFRauyMbkkUl4afBwsc1xS+Z/9DbmfLAACoUC60/mw8vbm9f5HY2TlGp8pSW7uNr6Yu+BjWJf+YiNdWZ9W8cJeca8ChkzysQ8MvGOfK9tPr85rrhXirklXAHXmFcSrwTBgfr6eoREd4ABBsz7/H3c2fsusU0CIA1B5awo5DKf8bWMgLVXsJuvn5WUhp+/3Yp7B8SYVDewdj9N3adUv0gIgRzFq5C4WuhJXUimp6dj69atiImJ4a0qgauQ+rN0dySTsEUQ7oCXlxe6dLoDAJCZmCqyNdfhcxvY0e14riECXG21tO3OJTa2KfvN1//5263Q6/T4+VvT4urW5m8qHEAKoR+E87hiC97VW9Dm80slaYrB3kYGYtrPdziJ1N4Ld4HEK0FwZPWbHwEAyi4X478z/4psDf+ePmPxxUXIMmMiu3d2WJRyxfhaa7Y1JR7N12caItw7gJ/i6lKJJ+UbKSQTComj4kUokcJlHakkTTHY28hATPv5jtmV2nshJK78N0HiVcZQlQFhiWjdlg072b3pB0HWtCUc+Pb0GYsvLnMzY44fOCpogpI12+wVj8nZ8/DV6X0mIQPm2CPcpJyo5Qxy9Cg780fTUfHiSpFifD9c1pFK0hSDvY0MxLSfby+51N4LIXHlvwkSrzKGqgwIz33degEAPrNQ89UVHipbwoFvT5+x+GLmjuze2eo9ieVptLauuXjk4/2Qo3DjG3vfZylskTrzR9NR8eJKkWJ8P1zWkXumPJ/2i71tL/f3whlc+W9CUPGakZGBHj16ICgoCC1btsSgQYPw559/Nnnd2rVr0alTJ/j5+eHOO+/Ed999J4C10mfyyCREtG6LySOTGp0Tyyvr7t7gFXMbRGtNXT0unS80OceX0DEWXbaEgyOePnvLPR0/cNTqPYnlaeS6rr3vh6Vn466hAPZg7/sshS1SMbxdrhQpxvfjyWLIEYyFv9hC1tNw5WdV0GoDDz/8MJ599ln06NED9fX1SEtLw7Fjx/DHH38gICDA4jW7d+/G/fffj4yMDAwcOBCrV69GZmYmDh48iDvuuIPTup5WbeCjdaswOWs6dHodIlq3xfGv9wq2duTjPVFQeF7wdYXkpke64uKVItzc+TYs2nhdpPMVg+rKCgL2zi3nDHp7bfekZ8M3xtUGjEUWQYiNcfUARshyadFKVQfEQZLVBjZv3oz4+HjcfvvtiIqKQm5uLs6dO4cDBw5Yveadd97Bww8/jOTkZERGRuKNN95At27dkJ3tmVvl1jybxscXrsiGTq+DSqmy6JV1Jba8we7COykZAICTR/9AufYqe5wvT6QrvX32zs3cEwDZJe3Y+37Y+2wopMAy5BWUJo54Hfn0VIrl9TT2/tnjkffkRCs5IGrMa1lZGQAgNDTU6pj8/Hz079/f5FhsbCzy8/NdaptUsRbnanycEZALk+cI3rTgpcHDcfzrvW7dLGHAA7HwUnkBAM79ecrp+cy3q7mKLkdiOh0V2K4SalLKZOdS0cAYCikg5IQjYqypa+wRpFzXtzQnX8LXnm1sPkJPKEzBdYgmXvV6PSZMmIB77rnH5vZ/YWEhWrVqZXKsVatWKCwstHIFUFNTA61Wa/LjLljzbBof9wQBKSYKhQIdb7gRADD3lclOz+eoMBTS8+cqoSYV76W5WOVil7tWFyDcE0fEmK1r8vLy8Oabb3IWxOZzWRN2lkSuGF5QPuI1yXvrOkQTr4mJiTh27Bg+++wz3ufOyMhAs2bN2J+IiAje1xALa8LUlmA1DzVw96QqIfg253MAgPZKiUnogCM4KgyF9Py5SqjxcQ+uqCpgr11S8iCLwXcrvxDbBMkihvfN0pqOiDFb1+Tm5kKv10OpVHISxOZzWRN2lgSzXMtNydVuOSBKe9ikpCR89dVX2LVrFzp06GBzbLt27TBp0iRMmDCBPTZz5kxs2LABR44csXhNTU0Nampq2N+1Wi0iIiLcNmGLiXNlvK7mRPS/AyXaUoRoglHwwzGPSKpyNQaDAS3v/x+qaqoR8+wTePXNGbzMS0lA9sNHgpuzz10KbXrF5LF23QEAAQEB+PHHH0W2RloMHDiwUZKQq5OBLK3JN/bcg6WxlBBFWEKSCVsGgwFJSUn48ssvsX379iaFKwD07t0b27ZtMzm2detW9O7d2+o1vr6+0Gg0Jj9ywl7PqL31XpkQg16de5AH1kEUCgUSnn0JAPDD519Dp9PxMq9UttHFxF4vZmT3zlCqlIjs3tnhNW21nB3auS+Gdu5L8a8cMHYyuAN8eE0ted9c3YJUCI+fPZ5cS/crZMkvij11PwQVr4mJifjkk0+wevVqBAUFobCwEIWFhaiqqmLHjBgxAqmp13vHjx8/Hps3b8bChQtx4sQJvP7669i/fz+Sktw3m90RMRqiCUZ5ZYXF0ICZY1MQ0botZo5NAXA9xGDP0X0W1/lo3SpE9L8DEf3vsCpsKfQAeG30FAANX8qqKyp5mZNEkP0C/viBo9Dr9Dh+4KhLbCkv1aK8VEvxrx4IHyLTkkhzdQtSR4UhI/LS09N5FXtib597SuypJ4l0QcXrkiVLUFZWhj59+iA8PJz9+fzzz9kx586dw4ULF9jfo6OjsXr1anzwwQeIiopCXl4eNmzYwLnGq5BwEXRcxthbbuqlwcMRqA5AibaUFaKMAJ61JNNqSEGvzj2gUqrQq3MPk+MLV2SjRFtqMp+53dTdC/Dy8kKzwAavftqzY3iZU24iyBWxnvYKeHvG22vvkIR4BAZrEBis8egvFJ6Kq0SXs+LSXJzwZefixYtRWFiI77//nlexJ3ZjBbHFs1B4ikgHRAgbsPRj/IHauXNnowc/ZMgQ/Pnnn6ipqcGxY8fw6KOPCmk2Z7gIOi5jLCVf2fKGfrRuFcorKxCiCWYF7+SRSVD7+aNEW2rVu7r+h2+g0+uw5+g+k3OMJ9d4PnO7PaGeKxc+mf8+AODUsROoq61zai5zYcW3MLRnPq5juXhJ7b0PLgLeeE57BL+9Xt1HhsdhzdEdWHN0h2y+UBD8YSy6XOXV4qPcFN/i0NfXVxZij+uzE1s8C4WniHRA5Dqv7gYXQWev6GM8nrOWZFr1hjLntOXXs95fGjwcNbW1Jusaz8l04LLUyOClwcNR8MMxFPxwjBXQ5nZTOa4G7uvWG0plwz+jnV861raYEWIrs3JMhBXf8a/2zGc81pb45OL1tLSus8Lc0WfDxV5PrxxAWMZVXi175o2KioJSqURUVBSvNjAkJCSgdevWmDBhguTEniWh6kmeRi54ikgHSLzyChdBZ82rai2UgPF4ArDqDQUAlVIFnV5n4mF9qv9jUClVGPLQIJP1jDtwMY0MmgpnILFqGS8vL/TtcR8A4IP0DIfmYIQYABNhxXf8qz3zGY+1JRS5FPa3tC4j1Fdm5bj8XizZC1jvGEZJc4QlXOXVsmfeI0eOQK/XW6204yx8ih++PdWWhKoneRoJU0QplSU0Wq0WzZo1k2ypLFulq2yVwWLO9ercAz/s2QkAmDk2pZEwNr/e0jEqn+U4NbU1aH7vTQCAj3ZvRKsbwu26Xurlsbjax7Vc1KZVeVgyfT4MBgMCgzVYc3SHU+s6gi1bpf5+SBGmVFZ6ejqefPJJka0xxZ1KMjlzL0I/B77LdbnT+0hYR5KlsgjL2AolsOXxNK4aUKItRaA6AABYDyoTHlBQeB6Ts6azXlVLc1qqWMBAlQVs4+vji5ahLQAAb014ze7rpZ6kxdU+rt7QtYtzYTAYoFQpMSI50eY4V3lArdmalZSGpTMy2bJbtsIHKLxAHrjT1rK5Z9QV7Vn5gm+vqBBb4p6UrS93SLxKAHu25C11yzJO1jJOrGLCAwBAp9eZCFhLNphXLGDgq7KAO4vgyfENXzz+PPgb5LyZ4Ywgs1fkjpmdYnOsK8uGWbP152+3Qq/T4+dvtzYpnim8QB5IZWvZFcLIHkEq9HOQY/ylO33RcXdIvAqMPQLO0lhzIcmUtSq9WobkhTPwb+F5qP38MXlkElsKK0QTDMCygH1hehKa9boRL0xPsugBtlTJwNF7dOfyWiMfHwoA0NXXo+Dv0yJb4zhcBZk1kctF/HIVuWJ4pO8dEAOlSol7B8Q0KZ6pJq88kIqIsiaMbInapgSvPYJU6OcgRy+mVL7oEE1DMa8CwzW21LgiQIgmGIHqAISFtMChE0fh7+uHeeMb2pHOWpKJ0qtlJt4+lVKFsj1n2bXMYdZ+YXoS1n6/weQaWzYwmMfVcr3HptrYyp07n7wHp8+fRUhYc6w88H2j83KIpXQ2vnVo574oL9XajGUl3A8px7xKBSZmMyoqCkeOHGFjN23FhgrR5tVVyNl2Qjwo5lWicC2VZVwRAAAKCs/j4PEjMBgMqKyuwi+H9mJy1nSUaEsbbVM/1f8xk7W6RUZBpVShW2QUu/ZH61axwtX4GmOvqbkN5qW67L1Hd69Y8O3ihmYbJUVXUK692ui8HLaZ+Y5vJQiiAcbzeeTIERMPrC1vn5CeQL49peTFJFwJiVeBMRZw1rbXjbfqFybPYdu7RrRuy45hGgyY0y0yCh/PuS4uyysrcPr8WSxMnoObIjrgv0uF+OXQXhMBOuShQew1xlv7jAhlbDAv1WXNfncXqdaIaN0Wfj6+AIDP3v6w0Xl3EnzWRO6I5ESEtQ23mYhFEFLDWLi5ervbXNTZ2s4XMkmJ6a4lZFctOYYWENKAxKtAGMeWMliLAWXiWJnqAUw5rP8uFQIAFAoFWxgfABTXjg15aBB++nQTFL4qrN6ynvXMlmhLMTlrOtZ+vwE6vQ55W79ihenbKRkmYtfYa8qIUKAhPAEwDRlw5xhWR1AoFHjkvhgAwLa1Xzc6L2ZVAaEy46VeOYEgLGEcj+rqpB1XCVJHhSBzvwAE95R6coIUCXfnIPEqEIyndP0P37DHrG2vGx9nBCIjPAHA39cPdfUNrUgVCgUe6v8QsrOzMT1rFvQ3BaK6rQ8GvDgYBw8dRE5ODnr37g2dXgfFtfn9ff0aeUcZLyoAVrAahw9YChmgFrGNmT+hIRa5vOwqaqtrRLHBklCVQ8iCK6GyVoQtjL2hct3udlQIMvebkJDgMi+vNaEm12fNB54s3PmAErYE4v6RA3Dw+BF0i4zCrhXfNjneuAGBeYiAQqGAwWBAREQEMjIy8FBMDOrq603OmY/dsWMHUlNTcaXoskmyV01tg8CqqqlumPNaklVE/ztQoi1FiCYYM8emWPS8EpZp3acTyivLcUvXO7DwqxWCr28pmYqvZDFb8zi7hhhNCTatymO7fI1ITiSPsYNQwhZ/OFqMX8pF/Cl5qzFSfr/EhBK2JEZRyWWT/wLc2sLmbf0Kvj4+UCgU7DmDwYD7770PW7ZsQfv27Vnhao6frx98r8Vgtm/fHlu2bEHkbbcBABtSUFldhcrqKhgMBqiUKquNEgp+OIaCH45ZFa7uXMPVXpbPfhcA8Nfh31FfVyf4+pZia/nazrflwXXWuytGU4K1i3NRXqpFeanWY73ShLRw1CPnTDiC0HG+hHRKuMkVEq8CYWmL3VbM6OSRSVApVWx1AYPBwArY9je2x5fr1mPfr7+ib9++yM6+fr2x11Wnq0d1TTWys7PRt29f7NmzBxu+3ICPvvmU9eR6e3lD7efPJocx4pRJEps5NsVmYplxaIGU4l/FFNMPRfeDUqEEDAb8uu0nXua0Z9vblXGntpLOnE1IE6MpwZCEeAQGaxAYrJFkIh2FO0gfd8jSl2ucL+G5UNiAiJjXPX1hehLW//AN2rRsjf8uFSLq1jtw6MTRRmEAP/6wA/5BAYi+Jxp11zx7qampSEpq7DXNzs5GRkYGAMDb2xvHDv+GI8eO4ulnngYAhGiCUfDDMZu2McLUuG6rcQ1Y4/hcqdRw5VpPlw8s1a/tNqQP/jp7Em063oj3d653eg1r296EeyOX992TwwbE3hLnY/vZlVvYzsxt6Vq+bKVte2lCYQMywDibP6L/HWxSVkHheej0Ohz58xgWTZ1nEjLQq1cv3NTpf2jTtg2mTJnCHs/IyEBOTg7UajWCgoKgVquRk5PDClcASJk6FYHBQejT5wH06tULAFBRWcGet+ZJnTwyCSGaYJRXVrCeTOMasOaVCaQQPiBkMpklr/PKeUsAAP+dOgudrnFJM3txpzJbBHfofZc+jnhK+fTWOuM1ZewAYNMz6oy9zthn6Vq+vMSUMCVvSLwKjLFAZMpnpb0zGyXaUnaMUqmESqlC1K13YOGKbMTFPMEK2Oeff54dl5SUhNTUVPb3efPmYfHixVCr1Vi8eDHmzZvHnktNTcXYhAQAQF19PTtPbX0dWt7/P0T0vwOzlmQ2qvHKCNNAdYBJtQHm/FP9H8PCFdkmolYK4QNC1pq1JJTv+F8k29whO2WO02u4ewmqprbHhdg+53MNvuZy9/fdHXBkS5xP4eRMmAFXO5yx1xn7LF3LV1gFxeHKGwobEBhmOztEE8wKVoVCgeCgZqiprYGvjy+b0W88FgC05VocPHQILVq0YK8DGkIDjIVqcHAwSktL2d/T0tKQlJQEL5WKTe4qKipCly5dGtmnUCgQF/OESe1XoEF0W6o4YL49z4Q+RN16B4pKLksmjEAMZi3JRNbH78HL2xvrT+abeNAJU5raHud7+9xSZQM+15DLdj9feHLYgCOItWVtvi5XO2iLnRAKChsQCHsTgxgvnTFxMU9g5tgUNA8ORf9efTBrSSYi+t+BXp17IKJ1W9TU1qBEW4oWYWEmwtVgMMBgMGDq1KmYO3cuO5+xcJ07dy6Sk5NhMBjQPLg5ezwsLAzt290Iby9vGEsqg8GAPUf3NbLbkvfV+H4Yr+Oeo/vYkAcpeGDFZNKIhi5T9XV1KPqvUGRrpE1T2+N8b59bqmzA5xq03e/+OLOVLlYCk7kHlasdUky4oiL/ng15Xp3E0cQg8yQfZh6VUsVWAjCvudqxY0f89FPj7HVNUBDUAQFo3ry5iXANDg7G5cuXUVlZifLycrRq3hIXr1xiE8Duu+8+nD59GgaDgfXuAkD/Xn2w5+g+VpAyHlfj4+beVOO6tHuO7mP/68meVwBo2+82lJVr0e2BaMxa9Z7Y5ngc1mrHurKmrKuQss2e6HkVO1HLEdzJgyrH5080DXleBcLRxCDzBCfGy/pU/8cQoglGiCYYk0cmsR5dtZ8/Kisr2ev9fP3YbWidXo/58+ebCFegwQObmZkJvV6PoIBAVFY3XK9QKKBQKFBeXg5/Xz+2JBZTy3XP0X0oKDyPWUsyTVrM7jm6z2ocKRPryoz5eE62YDGnUuatqQ0e8UO78uEB3xMlh7XasXKMJfX0LmliYsnLFx8fD41Gg8rKSkl4/7h4IqXoQXUUiln1bEi8OoBxqIAziUFMuSlz0TdzbAoC1QEAGryeTGxsTWU1SopLoAkMQnBQMwQFBEKlVGLBggVIT09n5w0ODmZfp6en452334baT42Kygo21KCmqhq+Sm/MGz+jUZvY8soKqP38UXq1zKQerHG1AXOoVaxlHu/zMICGcIzNn64T2RpuSDFxyVHcafvene5FblhKWIqLi4NarYZWq5VExrqnZc+7kxAn7IfEqwM4klFvHhv70bpVmPhmGnR6HRQKhcXmBZOzppu0by3RluLA/v0AgKLiIgDA8mXLMWfO9Wz2efPmobi42CQGdubrr+ONeXMQcE0QA8B3mzehV+cebKUAxj5GLNfU1pp4CvV6faN4V2OEzO6XE/5+/ujf6wEAwMo3c0S2hht8evjE9hY64mEVW3BbQ47eYnfBmpfP1d4/S95Uax5WvmyhWFJCDpB4dQBbXkZLCVzGHlZG/C1ckc2Kw+CgZgCu10ft1bkHAECn18HXxxcRrdsiLuYJRLRuix3fb4e2/GpDqEDmfKRNv+5xTUtLw9ixY1FRUYGEhASkpaWx516f9Tree+89NuZ31apVWP/DN6xNjGAGwIYvRLRuiyEPDUKIJhi+Pj5sKANhHznpWQCA8lKtyJZwwxWJS5HdO4siCB0RomILbsI5XCG+rHn57PX+2WubPXVO+fJEepoHl5AnJF4dwJaX0ZJX1rygPwC28H+IJhgzx6aYXPfDnp0AGmJT+/fqg/LKCvywZycmj0zCqs8/xY4dO3D69Gm8mZXFrpGamorExERUVlZCq9WisrISiYmJJnVgZ856Hf/9+x+0F4tx/kwBK1Anj0xCWEhDFYMObW/E8a/34p6uPQEA93TtiUB1ACqrqxCoDiDPqgO0bdUGXiovAMCy2W+5ZA0+vYWMhw+A03Mycx0/cFQUQeiIEKXteXkjZfFlr22urHNqz5oEITVIvPKMJa8sc2xh8hxW/L00eDibIPXS4OEm1zGhAv6+fthzdB+bMDUhMxUqpRJpaWlo0zocH37wAby9vRu1hlUoFGxC17hx4zA9PR3e3t5Yvmw5bu7QEe2ULTB5ZJJJNYBDJ44CAPtfRkzPWpLJxsDainklbDN6yEgAwHefuCbu1RXeQj7nFEsQOrIubc/LGymLL8a2qKgoTuEAlrypro71lGosKYUzEMZQqSwJ0vL+/6GyugpqP3/MGz8DE99MM4k/DdEEo+PNN2HVJ6tQXFyMyE6RqKmtYceolEqEhYahqLgIOr0eAHD69GmEhoaivb45Pli1HJOzprPe4IXJc5C8cAbq6uugVCrRtmU4W+qqvLICJdpStoSXvSXBiAb+u3QBtwxsCAdZ+PVK3NLldl7nd0UZJSmXZiKkhSeWynIGS2We+C795GhDAqlCpbE8AyqVJUOYeFkGXx9fvDR4OBZNnYcQTTDUfv5sPdYDBw8gNjYWZ86cQXVNtck8eoMBldWVCFAHsOW0Tp8+jSlJE6GvrGPDGICGuNqFK7LZ6gYGg8Gk+sHMsSkmMbBcYl7tbdzgCbRpGY72bdoBABZNmsn7/K7wFpIHkuDCX0f+YF/b+mNDXEeIcADzEAVXhlMI4RWVskedEB4SryLz0bpViOh/ByL634FZSzJRUHieTdKaOTYFwPUQg3njZyBQHYD+vfogonVbhAWG4vnnn8fgwYPx7cZvUVpSynbeqqisgL+XH77fvAWDBw/G8OHD8dsfxwAAvTr3gEqpQrfIKES0botenXugprYGCoUCXTt1RogmmA0RYEIa7Gk44Eg1BuZZuLPoXf3mhwCAf0+ehu5am16CkDvffLyGfd2vXz8RLZEPrgoHMBaR5mLP+He+xaaxME5PT8fdd99tUr6RD5p6PhRW4FmQeBWZhSuy2ZjW0qtlbAKXcQMDRswxonD9D99g8sgkFJVcBgDs+3UfKv65jBsrm2HGhFT0eaAPpo+fhurfL6H0z0L8+ccJk0oBTAvXopLLOP71Xuw5ug+V1VUwGAw4ff4stOVXTcpi2StGHa356qjolQt3/u82KJUN/+RclbhFSAOplttyJd7e3uxODyEOxiLSXOwZ/863F9ZYGG/duhV6vR5bt27lZW6uSDlRj+AfEq8iYOxhZKoOMB7TQHUAfjm0F0E9IzAxM9VEzE0emcTGnk7Omm7SlYup17pl1w/4++Tf2LLrBwANXlvjpgfMPMbi0rjyAQCLlRHsEaOO1nx190YHCoUCLwwaBgDYvWWHyNYQroTKbUkfMTx1rl6T69Y631vwxsI4JiYGSqUSMTExvMzNFbmHFZDn2D4oYUsEIh/viYLC8ybJTx+tW4WFK7IxeWQSm0wFgE2oMu6AxZwP0QQjUB3AJlUpFAr4+/rB18cXM8emsNdE9L+DPb9o6jybotLYDq7i05FrPJUDvx/GAy8MBAB8dmwnAjTifx4J/mGS3SK7d8bxA0cbJb25UzLcwgmvYef67+Dt7Y38/HyxzeGMGAlAlHREWIM+Gw1QwpaEseRhNPZWPtX/MSgUCqj9/LEweQ5Wfv0ZAu++AZGP98TCFdls8hQAtrGASqmCwWBg67ECaBQ/ajAYmtySd8Rr6u7b/XzS/fYu7Puz4NXpIltDuIqm6tuSZ1Z8XOWps+VBE2NNV0GeQn6Ru+dYaMjzKgMC777B5HfGGwuA9XgCwKwlmQBg0vSAEcnG54y9uHx4TMnzah+rv12LV2ZNhEKpxFenf5V0nKA7eQjFwNrzc6fnKlfPq6vwFI9uv379oNVqodFosH37dkHWJNwf8rxKCGtZ9FyPe3t5A2io38rEvJp7Oa01PejVuQcWrsjGzLEp7DkGvjymjsa4eiqDHmwIGzDo9di5/juRrbGNHD2EUkqWslZujMqQuS+WPGhSiXUlCHeBxKsAWBOJXI9nTZ59LTHrcWgCg9jKAcy4yVnT8dG6VSZltwCwlQSYTlnmQtk8fMFekU04htrPH90iOwMANn26XmRrbCPHVqlyFNyE+2CppJOrM+HF6IqVkJCA1q1bIyEhweE5KPSAcBQSrwJgKcb1o3WrUF5ZYVLCytp4xpO6/odvUKItRaA6gD3GeGJnLcnE5KzpbNktJkyAqSRQerXMRBBb2uo3F8MMFNPKP5NGJAIAju8/LK4hTSBHDyEXwS0l7yzh/sjVM2pLXPIhmKm8FeEoFPMqEpYqDnAZb6n6wMIV2WzFAQamXuzCFdm4UlqMyuoqk2oDxuszXtxenXtg/Q/fNGoDSzGtrqFZr3bQ6fUYNPp5jEqfKLY5HsWLvQei6PwFhLUNx/J8z83s5QOKeXVfXB1LK/eWtQT/UMyrxLG3pikz3li4AtfjTWeOTUGIJhjeXg2Fwvv36sN6TKuY9rEGA2YtyWTryxoLV6b5gaU2sBTT6hpefPJ5AMDmVetEtsTzkGM4BEEIjS2PMR9b/mKEOxDuAYlXF2MtXtRYEDYVU/rC9CS2KYG5gGSuBYBAdQDq6utgMBjYdq4RrdsiLuaJhlJaAEq0pZic1VCiiVnfOPxgz9F9JFQFYnJ8Q+hAdWUVzv31j8jWeBZyDIcgCKGxJS5py58QExKvLoZLvGhTY5it/PU/fNPo3KwlmWxClnGnLGaL//jXe/HxnGwsTJ4DtZ8/ADSqVvDS4OFYmDyHU/IWwR83tGqL5sGhAIBvPv5cZGsIgiC4445xvIR8IPHqYriEBzCis7yywqJYfKr/Y1ApVXiq/2M21zIvl2V+jhFKxq1fjc8be1wpSUsYkp57GQCw9bMN4hpCEARhB3Ld8iePsXtA4tXFcIkXfWnwcASqA1CiLbUoFhnP6Z6j+xqJ25ljUxDRui1mjk1p0hZrcbO2xnKNySUc49XnXgEA6HQ6/PDF1yJbQxAE4d7I1WNMmELiVSI0JRatlbFiYlYXrshucovfnsQrStISBl8fX/SO6gEAWL3oA5GtIQjC0/C0bXS5eowJU0i8SoSmxKJxUpW5d9Y47tUcil2VPvPGvwYAKDp/ATXV1SJbQ7gaqjFLCElT4pS20Qk5QuJVIBwRkcbXvDR4OBv72qtzD4vjS6+WNZqfYlelT487urEtgLetbZyUR7gX1AHM/bx9UrwfxqbFixfbFKdRUVFQKpWIiooS1kCCcAISrwJhr4j8aN0qTM6abnLNnqP72HJWxswcm9JQCstgaDQ/xa7Kg0H9HgUArMikLxnuDtWY5ebtk6IgtIYUvZeMTQBsxngeOXIEer0eR44cEdA6gnAOEq8CYa+IXLgiGzq9jvW0Rj7eE70697A4h6VSV8bnKHZV+sxOTAMAVGrL8dfhYyJbQ3DB0e1/qjHLLWlGCoKQq4CWYhIQY1NCQoLNGE8p2k4QTUHtYSWKcUvWWUsyUaItRYgmGAU/HGt0noSpe3DLwLvw36VC3NGrOzK+oOQtqUMtZhtwVXtYV7UOtWdeV7dHlTLUupUQA67tYb0EtEl0SrSlqNfVi20GAMBgAL776XucKjhjdcyzDz+F8xf/Q1V1FQCgqroKsxY3JGW9vzYX2oqrmJE9D+cv/ieEyQCAP8+exO8nTwi2Hl9culKEq5XlYpvBiWN7DuDff87ghpvai20KYYMhCfFYuzjXo7f/XUlcXJxLRJOxR7ep+ePj41kB52nY85wIQmg8yvNK8IOvjy9USnlFnHj5+KLbLTfASyVtu89eKMLf5y6gWfMQrDzwPZQye86E5+Eqz6urII8iN+g5cYOeE79w9bx6lHgd0r83vL2k42z+343hGNS3JxQ8zrl2624s/2o7XnyiH4bERPM4cwMB/r7oeENr3uclGjAYDLj58USc+e8S+gx6BJPfnSO2SQRhE7mJV7njiFgigeU6PDm0xBVQ2IAFPnhtLDSBarHNcCmPT5yPC5dLsOq7XZidMFRscwg7USgU+Hn5HLQfMBY7N2zCrd3uxMD4Z8Q2iyAIieDIdj6FALgOTw4tERPB9yR37dqFxx57DG3atIFCocCGDRtsjt+5cycUCkWjH6YECGFKSvwgtAsPQ0r8IIvnl+ZtQYeBY7E0b4uwhhGcCQ8Lxe4V8wAA7894E1dLtSJbRBCEVHCkOoAUKwrIqRSaLahjlzgILl4rKioQFRWFnJwcu677888/ceHCBfanZcuWLrJQfhgL0jFxsTi9cQkAWBSpmbkbcO5CETJzN4hgKcGV7pE34eWn+gMAXrnvCdRW14hsEUEQUsARsSRFgSWFUmiEfBFcvD7yyCOYM2cOnnzySbuua9myJVq3bs3+eGIiizWvqbEgZcZMz1ljUaRa8sySN1aaLE0fg7tvvxnlZVrMHJ4EDwhPJwjCQ5CiN5iQD7JRgF26dEF4eDhiYmLwyy+/2BxbU1MDrVZr8uMOWPOaGgtSZowCYI9Z8syOiYttcl5CfDbnvIZAtR+O7T2I5XMWiW0OQcgWd9mmdhek6A0m5IPkxWt4eDiWLl2KdevWYd26dYiIiECfPn1w8OBBq9dkZGSgWbNm7E9ERISAFvMPIz6jO99iMZ7VWJAyQvaNxKHssabEaVNxsoR4NAsKwNEv3gIAbPjwU/x78rTIFhGEPKFtaoJwHyQvXm+99VaMHj0a3bt3R3R0NJYvX47o6GgsWmTdC5WamoqysjL2p6CgQECLncPSFj4jPncf/auR19QcYyHblOi1dI29thGu58bwlsgYNwwAMOHhobhaWiayRQQhP2ibmiDcB1mWyrr77rvx888/Wz3v6+sLX19fAS3iD3MvaWbuBkR3vgUA7PaMMnMBYJO4+LKtKaFL8MvU+Cex/49/sG7bHiQPegGLt+d5ZNw3IT3KrhSLbQInXNWxiyAI4ZHlX7/Dhw8jPDxcbDNY+PRIWopf5eJxtWSH8Vx82EjhBeKyas54tG4RjPOnziJz7DSxzSEIAMChH/cAAEJCQkS2hCAIT0Fw8VpeXo7Dhw/j8OHDAIDTp0/j8OHDOHfuHICGLf8RI0aw499++2189dVXOHnyJI4dO4YJEyZg+/btSExMFNp0q/CZ8GQpftVWzdawvvFo0TceS/O22LSDqT6QlPEhO94Z2wjh8fXxxvF170KhAHZv2obDP/0qtkkEwfLNN9+IbQJBEB6C4OJ1//796Nq1K7p27QoAmDRpErp27YoZM2YAAC5cuMAKWQCora3F5MmTceedd+KBBx7AkSNH8MMPP+DBBx8U2nSruMojaUksGntQM3M3oFhbjhJtOV7LWdPIDmMxy7SgNQAo0ZabCNxhaYvg0+NpDEujbHapowlUY9Wc8QCAmcMTUXjuvMgWEUQDKpVKbBNkC1VCIAj7UBg8oHikVqtFs2bNUPLjStm1h2VEakr8IIyJi0WHgWNx7kIRK1KT5n8Eg8GAEE0gLu/INbkmuvMt+D7/CAwAbrqhJQ4cPw1vLxUC/P0wJ3EoK4p9ejwNnV4PlVKJ2n1f2G0TITxTFq3Aok++gSY0GCv2bYaXt7fYJhEeymPtugNocEwQjjFw4EAUFhaidevW2Lhxo9jmEIRolJeXo0+fPigrK4NGo7E6TpYxr3LGWuyppeNL87Ygaf5HOHehCK/lrAHQ4OUN1QTiakUVfjr4B/x9faBUKBDbO4q9zjhWlvG07v/jFAwGA1q3CMHlHbkmonNITG+olEoMienNyV6qCys+b44fjjtvbgdtcSmmPjVKbHMIgnACqoRAEPZB4tWF2CP8LB3PzN3AdlVi3ONj4mIRGOCPEm051m7NR2V1DfQGA77PP8KuFd35FqiUSrQMCULZ1Qp2PqVCgasVVY2E86fzJqJ23xf4dN7ERnZbsstYQFPZLHFQKpU4uGYBfL298PeR37FtLcUb8smmVXl4sfdAbFpF27iE66GC/QRhHyReXYg14ReiCUS5mfCzFDfLjA3VBGJO4tBGY7t2ag+FQgG1ny8MALvWlvwj0On1OHD8NPQGAxQKBUI1gfDz9UGJthzTc9ZY9fR2GDgWr11L7pqeswblFVUI0QSydjGC1lLsLCEsSqUS3y+ZCQB4e/Lr+PvoHyJb5D6sXZyLovMXsHZxrtimEARBEGaQeHUhlkpVMRRfE5EMlpKzxsTF4vKOXBSZbfMz/PPvJRgMBvj5NMQ7hl4TmUxylr+vD9qFhyF72kt4I3EoqmtqAQAKXBfW03PWoMPAsRiWtgivZi7DuQtFMKChtazimp1BAf7s+pbazwLUwEAs7u0aiXdTGsIGpsW9hIqr5SJb5B4MSYhHWNtwDEmIF9sUwkOgpC2C4A4lbAmEcaJVeUUVirXlCNUEouhakpUlhqUtwtqt+RgS05vd0geAsL7xKNaWQ+3nCz8fb5RcrYDBYIBKqWSFjHlCFbM+ANx1W0ec+vcSDABqautQWV0DBRpCExQKBbKnvcR26DKfx1qylvH98dEQgbCPh8bOwrZff0NYm1ZYlv8tFApF0xcRBA9QwhY/UNIWYU5eXh5yc3MRHx/vMSElXBO2SLwKhLHoAxqLS0swVQAUCgVCggJgABDbOwqfbfkFAODt5QW9Xg+dXs9eo/bzha+PNxQAHuodhd1H/2LXZCoTKBUK6K9VKCi9JnwZjKsWuOKeCNdgMBgQHjMKRSVaPPPqKDw/JUFskwgPgcQrP3iiUCFs44lfaEi8GiEF8eoIjOfV18cbldU1AACVUsmKVUaEqpRKdoxCoWDFKPOaEb/V17ysaj9ftAjRsB5gptIAI3QteV0teVzJ2yotThZcwK2DxgEAXlu+CHf3v19kiwhPgMSrMNgjbkkIuwee+D6SeDVCruKVYVjaInz+/W74+/rg8Qfuwpb8IxY9q6/lrGEFKgD4eHtBp7vumQ3VBCIwwJ8dPz1nDRQA3jCq+cpgLkwtCVWq9yo9tuw+hEfHzYVKqcTyvd8htFWY2CZ5BJtW5WHt4lwMSYjHI8M9448MA4lXYbDHC+eJHjvCPaA6rzLDVsLT7qN/wWAwoEWIBp/Om4g5iUNhAPB9/hET4VhWXonK6hq0atUKHTt2RNs2bfBuyii2YsEbiUPZpLDM3A0o0ZYj0CgZy9iO6M63mCRkWaqGQO1ipUdsdFeMGPgAdHo9xvYdDJ1OJ7ZJHgFVJyBcjT21YKluLOHukOdVItjagjf3cLboG48SbUNWOTM+7vXl6P/wANx7771o0aIFgIawglbBavx+5BCmvDYHv/66F7HXvLXG3beMu20xdhh7aUmcyo874sbj+Onz6PvUo5j09htim+P2kOeVPK8EQTgPeV4ljrmn1ZJnk4HxcP508A943TWEFa5KhQJvjH8RivY9sOqTTzFo0CAT4RoU4A+ofBDWvhOWr1iJ9xYvxe7jBWz3LabZwauZyxrZYVw3lus9ENLhx4/mQAFgx/rv8O2Kplv+Es7xyPA4LM/f6HHClSAIQgxIvIqEpQYG5RVVbAMBY5bmbUGLvvH4bMsvbDKWUqHAvff0xn1xL2H38QLU1tXD20vFXsM0L2A6cAFA+/btsXnzZnTp0gXnLhShZUgQmwA2bv5HaNE3HgDYWrHGzQm43gMhDZoHB+HQmoUAgKWvZaLg71MiW0QQBEEQ/EDi1cVY806ae1ozczegWFtu0rWKEa1J8z9iva1AQ5OBm2++CUs/+Ag7d/2Evn37ImvhW6irvx7f6OPthUvFZdBWVMJgMCA7Oxt9+/bFnj17sHr1akRERODQiTN4N2UUVEol9AYDSrTlSJr/EZLmf9SoOYElbHmLCfG585YbMf3lBk9gUswzqKutE9kigiAIgnAeEq8uxpp30jzZKSV+EEI1gSbeztdy1qBEW86Wu2LOGwDMmTMHxcXFGD16NOrq6pCRkYHs7Gx2/tq6euj0ehgMQHZ2NjIyMlBXV4fRo0ejuLgYGRkZGBLTG2PiYvFuyiiEagLZ0lpMwwPjzmBL87ZgWNoi+PR4GsPSFlm8BwZzwU7hBeIxa8yz6N+zM/R6PWY+nyi2OQRBEAThNCReXQxX7+SYuFgU7cjFnMShyMzdgKV5W8Bk0ikABAcF4I3EoZiTOBRPPvYoHn04Fh06dMCUKVPYOTIyMpCTk4PgZhqEhAQjICAAOTk5yMjIYMdMmTIFHTp0QN++fRH3xEB0GDgWPx38A4EB/njmoWhWIL+bMoqtSsCI7y++3w2dXo8vvt8NwLpIfS1njYlgp/ACcfnmnVT4eHnhtz0HkJvxnqBrb1qVhxd7D8SmVdTykiAIguAHEq8uxt5yUozQezVzGWJ7R6FdeBhCNIEo0ZZjes4aZOZuwOy0ZKj9fOHv64OkpCSkpqay18+bNw/vvPsevLx9kJOTg3nz5rHnUlNTMW5cEgDA39cHFf4tce5CET7b8gubxFW0I5ftsGVcMiu68y1s7Kyfr08jW5mKCOcuFMEANFlmixAOH29v/LmhQbSuW5KLM3+eFGxtKiFFEARB8A2JVwFhPJPD0hZZ3EZfmrcFVyuqoFAooNPrsfvoXzi9cQneSByKduFhUKChAoBK0xJAQ2gAAIwbNw7p6ensPGlpaWjevLnJsfT0dLw6bhw0AWqEtwhBcFAAHo2NgUp5/SMQ3fkW9jUjRBkbdh/9C0BDh6+siSMANIhSJuGLKeXVLjwMc4zqyQJUD1YKtAsPQ+b44QCA5CfiUVleIci6QxLiEdY2HEMS4gVZjyAIgnB/vMQ2wJNgBOH5i1dYwcccj+58C9ZuzYdOr2fjW6M738J6PwGg4w0t4VcShNatGsRroNoPVyuqYIABycnJUKvVrGAtLS1l1507dy5SpyajsrIC5ZXVAAC1ny+aN2+BD9+YgFHTF8FgMGBL/hH2mpT4QawgZX5nOni9lrMGAFgxajyOkC6Thz+Ojbv246dDxzFxwPNYunM9FAqFS9d8ZHgclY8iCIIgeIWaFAjIsLRFWLs1H107tcelkqusQDx3oYj1YDK0C29o63nuQpHJHB07dsTuX35By9BmqKyuQVl5JQAgKCgIarUaLVq0MBGuwcHBuHLlMurr6nD5SjGAa80LmgejsroGD8c+hIOHj6KyugahmkAUXQsZsATTwICxz7yZgq1GC4Q0MBgMCOsbj5KrFbj/8VgkZ89r+iKCsAE1KSAIgi+oSYEE2X30L+j0elwqucpuozNb7V07tYdKqUS71i2gUioR3fkWi97MqspK+Hh7ofBKKStcAUCn0yEzM9NEuAINHtj58zNRVV3DHmO+rWgrqvDXqbOoq9dBpVTiod5RNu1PiR+EEE0g1H6+KK+oarL8FyE9FAoF/v46B95eKuz6eguO/LJPbJMIgiAIwi5IvAqIJXHHxINeKrkKnV6Pc4WX2XjXMXGxeDb2HqiUSnh7NUR4VFeWo/DiJZg7zLOyskxiXIODg9nX6enpePPNN03Gl16twKVLl3Dp0iXodDp2zaV5WxDWNx4t+sY3Eqdj4mJxeUcuWoRoUGxUj9b8Xii2VdqEaAKxJmMiAOD15xNwpfCSyBYRBEEQBHdIvAqINXE3LG0RCgovmxxrGRKEpXlbsCX/CHx9vOHtpUKIJhA33dASO3/80WTsksWLTaoKzJ07F5cvX8bcuXPZY/PmzUN2djYUioat46qaWvz8889QKZV4+qFoqP18UVB4GcmLVrLNEqbnrEGLvvEIMxKyTFKZcUwu1W+VH0/264WnY6JRr9NjXOyz0Ol0TV9EEARBEBKAxKsEWLs1HwaDAUqj5Jn9f5zC9GtNCiqra1BZXYOgAH8cOnEGq1atYsdlZ2djjpFITUtLQ0JCAiorK5GQkIC0tDT2XEZGBpYuaYhFVSmVWLN6NYbE9Man8yaiuqa2QdRei30N0QRCAaBEW47ia0IWaEjOKtGWIzDAH7uP/kX1W2XMmvmTcNMNrXC1pAzpz4wW2xyCIAiC4ASJVwkwJKY36wF9NvYe9nhNbR0UCgW8vbwQeq3z1pCY3tiTn48dO3bg9OnTWLBgATs+NTUViYmJqKysxNWrV1FZWYnExESTOrAZ8zNx+vRp/LBtG37ZvRvf5x9Bh4FjoVKpADTEwzLNEIxhZLVx6APFuMqf39a+jUC1H37/9RA2f7pebHMIgiAIoklIvAqMpVapn86biNp9X+C+brdh99G/8GzsPWy1AYPBgEC1H4p25GJMXCw+nTcRSqUSqampCA0Nxfvvvw9vb2+kpqYiKSnJ4ppMIwNvb298vGwZWrUMQ/aiLADA1cpqnLtQBG8vFTueaYZQrC1HqCYQ7cLD8IaZmAVsx7g2VdOWkAa+Pt74adkcAEBO6lwU/H1aZIsIgiAIwjYkXnnAkiC1hq1WqcaNAVLiB6G6phYAUHa1wmTuITG98e+//2LkiBG4Jzoa+b/8bFW4MowbNw47duxAj7vvxojhw7HnQENN13qdDu3Cw1BXfz3mUYHrHtY3zBoOcG31yoxbuzWfQgskTudb2mPS8McAAK8+PBR1tXUiW0QQBEEQ1iHxygNcBR1gu5wUcy668y14NXMZ245VbzDg1cxlrBfzZMEFKBUKHD58GP1jYnDir7/h7+sDhUIBhQLsfxmaBaqhCfDHpcILGPzUIOTv/ZU956VSISV+kEnCzhuJQ616VbmGCjDjhsT0ptACGZA1YSTu6dIJ9XV1GPfQM9Ab1RwmCIIgCClBTQp4YGneFrbLFB9lophi/yqlEkNierOdt8wbGRjz0IP9MOz5YYjq2h1hYWHw9/VBbV09VIY6NPeqhaH4HFBRjKV5W5C8aCUqjeq+qpRKdO3UHodOnDFpoMDcC9/3R0gTvV6Pdo+MxoXLJXhq9Ai8kD5ebJMIGUBNCgiC4AtqUiAgXOubcg0vYLyW76aMwn3dboMmUA21ny98fbwbYlBbtwAAtGvdgq1QsH/fryj/aw+6d+uGrl274q67e2LmhJcReuFXGAoOs8L1tZw18PXxxrOx9yBUEwiFQsE2Tng3ZRQOnTjTyItsj2eZkC9KpRK/fNxQuWL9+yvxx74jTVxBeDp/HfldbBMIgvBASLwKxLC0RUjM+JCTCDQWw0xpqqqa2uve0muC9VzhZXSL7MDGpo6Ji8WQmN64dOkSTp06hfc/32gilI1ruG7JP4LAAH8881A0u62fmbuB9fAy2/xL87agvKIKIdeqHRDuzY1tWuLtKS8AAFIGv4jyMq3IFhFSZvqzYwA0hCoRBEEIBYlXgVi7NZ99bY8IZLyw/r4+ABpKWRlff+jEGROv7/f5pt6y13LWsB5f41CBiqpqNqGKCQeI7nwLG6owJi4WS/O24NXMZSjWliMowJ9CBjyEcUMH4LlH7gMAjOnzFOquJQ4ShDm6a4me0dHRIltCSJm8vDwMHDgQeXl5YptCuAkkXgWCqeX6bOw9dolAxgubNXEE2oWHYc41DyvTNnZITG+T8eYBzAZc3/ZX+/myx+vqdWwMLeMJ3n30L7ZNLK5dZ+6JJTyDlW+8is7/uxFlV0rw1sQZYptDEISMyc3NRWFhIXJzc8U2hXATSLwKBFPL9dN5Ex263jyu1rg2rHEc7ZzEoVApr7+tsb2jkBI/CKGaQACAj7cXFAoFnnkoGu+mjGKrG3QYOBbRnW8xqQxgHHtLXlfPQqFQYNv7r0OlVOLnjVuRv3m72CYRBCFT4uPj0bp1a8THxwu+Nnl93RMvsQ0gnINpIWvcvrVrp/bY/8cpAA3hCvd1uw2BAf4o1pYDANqFh5mI6Fczl7FVDJjYV6BBMJNo9VxCmwVhdcZEPJOyEPNeScb7uzagTfsIsc0iCEJmxMXFIS4uTpS1jb2+YtlgD3l5ecjNzUV8fLws7BUL8rzKEOOqBUyahALXwwMOnTjDjmXCAlLiByFEE8i2mWUwDw2gygKEMXH9eyN91GAAwKuxz6K87KrIFhEEQXBHTK+vI1CIBTdIvFrBnq5Zjoy3dU1TcxkLzDcSh6JdeBge6h2FqxVVCNUEso0Bno29ByGaQJRXVOGng38gKMAfD/WOQmbuBnZu89AAS00IHLk3wn2YnTAUj97bDTVV1Zg5YpzY5hAEQXAmLi4OGzdulI0XU25iWyyoSYEVmEYB7cLDcHrjkiYL9ZuPN8fS9dauccVc5g0OrM3N5VkQnkd1TS3C+sWjsroWY+akYMCIp8U2iZAAg/8XjdqaGtxzzz145513xDaHIAiZQ00KnMTcA9nUdnpTbVNfy1mDcxeK8Nq12FRb13BtwcrlGuM2rUwil73VAxyxh3Av/Hx98P3imQCApdMz8ce+QyJbRBAEQXgq5HnliLMtUlv0jUeJthwhmkBc3pHrkA0MXLzClo4NS1uEtVvzMSSmt8NVDwjPZtmXP+CVOUuhVKmwbPc3aBHeSmyTCBEhzytBEHxCnlee4doC1hpzrsWmzkkcanOcrfhS5px5SSvGK/xq5jL2OuZYYsaHGJa2CEDjOq4EYS+jnuyPl5/qD71Oh5TBo+AB330JgiAIiUHiVSDMxa81kWorPIE5t/voXyZzpcQPatRwILrzLex1THcv2v4n+GBx6itoGdIMl/69gGVvvCW2OQRBEISHQeJVJKyJVEZgMo0DjMWtNfE5Ji6WbTjAnDP2rjJduJz1HhMEACiVSvz88RwoFAp89dFq/PT192KbRBAEQXgQJF5FwpYQPb1xCXYf/auRuLUlPs3PtQwJAgDcdVtHm/GtVAaLcISbIsKxJWc6ACDr1TRUV1WJbBFByAvq/EQQjkPiVSSaCiNwdoufaVRg3LDAEtSUgHCUB3tGYVDfu2HQGzD6gSehq68X2ySCkA1UjJ4gHIfEq0QwF5GObPEbC2CmNBYTMmANioMlnGHdgqm49cY2KC4sQmbCNLHNIQjZQMXoCcJxqFSWRHC2FBdAzQQIcbhUXIr2A8aiprYOI1LGYUhivNgmEQJBpbIIguATKpUlMyx5Wu2NR7XmRaW4VsKVtAwNxq+rMgEAKzPfw5XCSyJbRBAEQbgzJF4ljL3xqNZCDSiulXA1d9zcDpOHPw4AGNs3DlUVlSJbRBAEQbgrJF4lDF/xqBTXSgjBmxNGoM9dd6CqogKvPTeWGhgQBEEQLoFiXgmC4I2Kqmq0e+QVlF6txMPPPYXE+elim0S4EIp5JQiCTyjmlQBA8a6EsAT4++H3vAYRs3n1epw8+ofIFhEEQRDuBolXN4fiXQmhad0iBO9OHQUAmDooHsWXLotsEUEQBOFOCC5ed+3ahcceewxt2rSBQqHAhg0bmrxm586d6NatG3x9fXHzzTdTUWc7oHhXQgwSn3kEIwb2QV29DsmD4qHX68U2iSAIgnATBBevFRUViIqKQk5ODqfxp0+fxoABA9C3b18cPnwYEyZMwEsvvYQtW2gbnAuONDsgCD748LWxuDE8DJf+vYBZI18V2xyCIAjCTfASesFHHnkEjzzyCOfxS5cuRYcOHbBw4UIAQGRkJH7++WcsWrQIsbEkyAhCqnh5qfDHuncQdO8wHPwxH3u37kTPmD5im0UQBEHIHMnHvObn56N///4mx2JjY5Gfn2/1mpqaGmi1WpMfgiCEx8/XB+sWTAUAzH1pCv7954y4BhEEQRCyR/LitbCwEK1atTI51qpVK2i1WlRVVVm8JiMjA82aNWN/IiIihDCVIAgLPP5AD8x4ZQgMBgOmPBGPuto6sU0iCIIgZIzkxasjpKamoqysjP0pKCgQ2ySC8GhmvPI07rrtJlRor2LCo89RAwOCIAjCYSQvXlu3bo2LFy+aHLt48SI0Gg38/f0tXuPr6wuNRmPyQxCEeCgUCuSvyECg2g/n/jqFjR9/JrZJBEEQhEyRvHjt3bs3tm3bZnJs69at6N27t0gWEQThCEqlEjs/nA0A+OD1Bfhj32FxDSIIgiBkieDitby8HIcPH8bhw4cBNJTCOnz4MM6dOwegYct/xIgR7PgxY8bg1KlTmDp1Kk6cOIHFixfjiy++wMSJE4U2XZJQBy1CTnTt1BG5s8YBAKYPHYPyMkqmJAiCIOxDcPG6f/9+dO3aFV27dgUATJo0CV27dsWMGTMAABcuXGCFLAB06NAB3377LbZu3YqoqCgsXLgQH330EZXJugZ10CLkxvCBD2DgfXehrrYOY/sOpvhXGVNbUyO2CQRBeCCC13nt06ePzT9Wlrpn9enTB4cOHXKhVfIlJX4QMnM3UActQlZsWJSC9o+Owb+XruDDWQvxyutTxDaJsJMjP//Kvn7xxRdFtIQgCE9D8jGvhG2ogxYhRxQKBX5aPgcA8M3yNfjxq80iW0TYS1VlJfs6KipKREsIgvA0SLwSBCEK7cLDsPOjhgSuha9Ox+ULF5u4giAIgiBIvBIEISL3db0NY4fEwmAwYPT9T0Kn04ltEkEQBCFxSLwSBCEq2dNeRrdOHVFbU4PMsSlim0MQBEFIHBKvBEGIzvYPZkGpUCB/8w6sW7JCbHMIgiAICUPilSAI0QkK8MeRL94CAORmvIvCc+dFtoggCIKQKiReCYKQBLd1jMDUkYMAAJMeG4662jpxDSIIgiAkCYlXgiAkw7xxw9D5fzfiakkZpj75gtjmEARBEBKExCtBEJJBoVDgwOos+Pl44+Rvx7H0tUyxTSIIgiAkBolXgiAkhVKpxF9fZUOpUODbFV/gryN/iG0SQRAEISEEbw8rBkw7Wm1FlciWEATBhSC1P96e+iLS3/sUu7/9ATd0bCe2SYQZPr4+AAAvLy+Ul5eLbA1BEO5ARUUFgOu6zRoKQ1Mj3IB///0XERERYptBEARBEARBNEFBQQFuuOEGq+c9Qrzq9Xr8999/CAoKgkKhEMUGrVaLiIgIFBQUQKPRiGIDIR3o80AYQ58Hwhj6PBDGeNLnwWAw4OrVq2jTpg2USuuRrR4RNqBUKm0qeCHRaDRu/+EjuEOfB8IY+jwQxtDngTDGUz4PzZo1a3IMJWwRBEEQBEEQsoHEK0EQBEEQBCEbSLwKhK+vL2bOnAlfX1+xTSEkAH0eCGPo80AYQ58Hwhj6PDTGIxK2CIIgCIIgCPeAPK8EQRAEQRCEbCDxShAEQRAEQcgGEq8EQRAEQRCEbCDxShAEQRAEQcgGEq8i8fjjj6Ndu3bw8/NDeHg4hg8fjv/++09sswgROHPmDEaNGoUOHTrA398fN910E2bOnIna2lqxTSNEYu7cuYiOjoZarUZwcLDY5hACk5OTg/bt28PPzw89e/bEr7/+KrZJhAjs2rULjz32GNq0aQOFQoENGzaIbZJkIPEqEn379sUXX3yBP//8E+vWrcM///yDuLg4sc0iRODEiRPQ6/V4//338fvvv2PRokVYunQp0tLSxDaNEIna2loMGTIEY8eOFdsUQmA+//xzTJo0CTNnzsTBgwcRFRWF2NhYXLp0SWzTCIGpqKhAVFQUcnJyxDZFclCpLInw9ddfY9CgQaipqYG3t7fY5hAik5WVhSVLluDUqVNim0KISG5uLiZMmIDS0lKxTSEEomfPnujRoweys7MBAHq9HhERERg3bhymTZsmsnWEWCgUCnz55ZcYNGiQ2KZIAvK8SoDi4mJ8+umniI6OJuFKAADKysoQGhoqthkEQQhIbW0tDhw4gP79+7PHlEol+vfvj/z8fBEtIwhpQeJVRFJSUhAQEIDmzZvj3Llz+Oqrr8Q2iZAAJ0+exHvvvYfRo0eLbQpBEAJy+fJl6HQ6tGrVyuR4q1atUFhYKJJVBCE9SLzyyLRp06BQKGz+nDhxgh2fnJyMQ4cO4fvvv4dKpcKIESNAURzug72fBwA4f/48Hn74YQwZMgQvv/yySJYTrsCRzwNBEATRGC+xDXAnJk+ejPj4eJtjOnbsyL5u0aIFWrRogVtuuQWRkZGIiIjAnj170Lt3bxdbSgiBvZ+H//77D3379kV0dDQ++OADF1tHCI29nwfC82jRogVUKhUuXrxocvzixYto3bq1SFYRhPQg8cojYWFhCAsLc+havV4PAKipqeHTJEJE7Pk8nD9/Hn379kX37t3x8ccfQ6mkTRF3w5n/PxCegY+PD7p3745t27axiTl6vR7btm1DUlKSuMYRhIQg8SoCe/fuxb59+3DvvfciJCQE//zzD1577TXcdNNN5HX1QM6fP48+ffrgxhtvxIIFC1BUVMSeI2+LZ3Lu3DkUFxfj3Llz0Ol0OHz4MADg5ptvRmBgoLjGES5l0qRJGDlyJO666y7cfffdePvtt1FRUYEXXnhBbNMIgSkvL8fJkyfZ30+fPo3Dhw8jNDQU7dq1E9Ey8aFSWSLw22+/Yfz48Thy5AgqKioQHh6Ohx9+GNOnT0fbtm3FNo8QmNzcXKt/mOifp2cSHx+PFStWNDq+Y8cO9OnTR3iDCEHJzs5GVlYWCgsL0aVLF7z77rvo2bOn2GYRArNz50707du30fGRI0ciNzdXeIMkBIlXgiAIgiAIQjZQYB1BEARBEAQhG0i8EgRBEARBELKBxCtBEARBEAQhG0i8EgRBEARBELKBxCtBEARBEAQhG0i8EgRBEARBELKBxCtBEARBEAQhG0i8EgRBEARBELKBxCtBEARBEAQhG0i8EgRBSJTevXtDoVAgPz/f5LhWq0WXLl3g6+uLrVu3imQdQRCEOJB4JQiCkCiZmZkAgOnTp7PHamtr8eSTT+Lo0aNYsWIFYmJixDKPIAhCFEi8EgRBSJT7778fAwYMwPbt27Fz504YDAbEx8dj+/btWLRoEZ599lmxTSQIghAchcFgMIhtBEEQBGGZ3377DV26dEF0dDTuvvtuvPXWW0hNTcW8efPENo0gCEIUSLwSBEFInJEjR2LlypUAgBdffBHLli1rNGb9+vVYsmQJDhw4gJKSEpw+fRrt27cX2FKCIAjXQ2EDBEEQEicsLAwAEBQUhJycHItjKioqcP/992P27NlCmkYQBCE4XmIbQBAEQVgnOzsbCxcuRKtWrXDx4kWsWLECo0ePbjRu+PDhAIBjx44JbSJBEISgkOeVIAhConzxxRcYP348+vbti0OHDqFZs2aYNWsWKisrxTaNIAhCNEi8EgRBSJBt27Zh+PDhuPPOO7FhwwaEh4dj4sSJuHDhAt555x2xzSMIghANStgiCIKQGAcPHkSfPn3QvHlz7N69G+Hh4QAamhN06NABOp0Op06dQmhoaKNrjx07hjvvvJMStgiCcFvI80oQBCEh/vnnHzz66KPw8fHB5s2bWeEKABqNBikpKSgrK0NGRoaIVhIEQYgHeV4JgiDcCPK8EgTh7lC1AYIgCDeguLgY586dwz///AMA+OOPP1BaWop27dpZDC8gCIKQK+R5JQiCcANyc3PxwgsvNDr+8ccfIz4+XniDCIIgXASJV4IgCIIgCEI2UMIWQRAEQRAEIRtIvBIEQRAEQRCygcQrQRAEQRAEIRtIvBIEQRAEQRCygcQrQRAEQRAEIRtIvBIEQRAEQRCygcQrQRAEQRAEIRtIvBIEQRAEQRCygcQrQRAEQRAEIRtIvBIEQRAEQRCygcQrQRAEQRAEIRtIvBIEQRAEQRCy4f9PuHoko3bX2wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code\n",
    "plt.figure(figsize=(8, 4))\n",
    "plot_decision_boundaries(kmeans_rnd_10_inits, X)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Kw01ETLp3EdY",
    "outputId": "29ca66c4-b683-4463-819e-67ed1c28ae24"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "211.59853725816836"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_rnd_10_inits.inertia_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5CngluWd3EdY"
   },
   "source": [
    "### Centroid initialization methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PmkLNY4X3EdY"
   },
   "source": [
    "Instead of initializing the centroids entirely randomly, it is preferable to initialize them using the following algorithm, proposed in a [2006 paper](https://goo.gl/eNUPw6) by David Arthur and Sergei Vassilvitskii:\n",
    "* Take one centroid $c_1$, chosen uniformly at random from the dataset.\n",
    "* Take a new center $c_i$, choosing an instance $\\mathbf{x}_i$ with probability: $D(\\mathbf{x}_i)^2$ / $\\sum\\limits_{j=1}^{m}{D(\\mathbf{x}_j)}^2$ where $D(\\mathbf{x}_i)$ is the distance between the instance $\\mathbf{x}_i$ and the closest centroid that was already chosen. This probability distribution ensures that instances that are further away from already chosen centroids are much more likely be selected as centroids.\n",
    "* Repeat the previous step until all $k$ centroids have been chosen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rLkaFl-H3EdY"
   },
   "source": [
    "The rest of the K-Means++ algorithm is just regular K-Means. With this initialization, the K-Means algorithm is much less likely to converge to a suboptimal solution, so it is possible to reduce `n_init` considerably. Most of the time, this largely compensates for the additional complexity of the initialization process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CJa9-3Yv3EdY"
   },
   "source": [
    "To set the initialization to K-Means++, simply set `init=\"k-means++\"` (this is actually the default):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0ib8qwXL3EdY"
   },
   "source": [
    "### Accelerated K-Means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n33_e98j3EdZ"
   },
   "source": [
    "The K-Means algorithm can sometimes be accelerated by avoiding many unnecessary distance calculations: this is achieved by exploiting the triangle inequality (given three points A, B and C, the distance AC is always such that AC ≤ AB + BC) and by keeping track of lower and upper bounds for distances between instances and centroids (see this [2003 paper](https://www.aaai.org/Papers/ICML/2003/ICML03-022.pdf) by Charles Elkan for more details)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zN06Hk1E3EdZ"
   },
   "source": [
    "For Elkan's variant of K-Means, use `algorithm=\"elkan\"`. For regular KMeans, use `algorithm=\"full\"`. The default is `\"auto\"`, which uses the full algorithm since Scikit-Learn 1.1 (it used Elkan's algorithm before that)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KIOkK_XC3EdZ"
   },
   "source": [
    "### Finding the optimal number of clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rkp2Hrw93EdZ"
   },
   "source": [
    "What if the number of clusters was set to a lower or greater value than 5?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 314
    },
    "id": "8tSRp0JC3EdZ",
    "outputId": "c1395801-a629-4acb-8ec0-5ee9321b72ef"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates and saves Figure 9–7\n",
    "\n",
    "kmeans_k3 = KMeans(n_clusters=3, random_state=42)\n",
    "kmeans_k8 = KMeans(n_clusters=8, random_state=42)\n",
    "\n",
    "plot_clusterer_comparison(kmeans_k3, kmeans_k8, X, \"$k=3$\", \"$k=8$\")\n",
    "save_fig(\"bad_n_clusters_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XYXOphcY3EdZ"
   },
   "source": [
    "Ouch, these two models don't look great. What about their inertias?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zX92l9RT3EdZ",
    "outputId": "8406bac2-ec52-493d-e5ca-0e3b02b4d0c9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "653.2167190021551"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_k3.inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "__w2JjE_3EdZ",
    "outputId": "d5dd694e-c07e-42e3-b844-0f3a3b849ef3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "127.1314188046183"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_k8.inertia_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3Ls8bmnH3Eda"
   },
   "source": [
    "No, we cannot simply take the value of $k$ that minimizes the inertia, since it keeps getting lower as we increase $k$. Indeed, the more clusters there are, the closer each instance will be to its closest centroid, and therefore the lower the inertia will be. However, we can plot the inertia as a function of $k$ and analyze the resulting curve:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 345
    },
    "id": "wlmaHWeS3Eda",
    "outputId": "f2c426aa-3f66-4ef8-96e9-72dda9e08f50"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates and saves Figure 9–8\n",
    "\n",
    "kmeans_per_k = [KMeans(n_clusters=k, random_state=42).fit(X)\n",
    "                for k in range(1, 10)]\n",
    "inertias = [model.inertia_ for model in kmeans_per_k]\n",
    "\n",
    "plt.figure(figsize=(8, 3.5))\n",
    "plt.plot(range(1, 10), inertias, \"bo-\")\n",
    "plt.xlabel(\"$k$\")\n",
    "plt.ylabel(\"Inertia\")\n",
    "plt.annotate(\"\", xy=(4, inertias[3]), xytext=(4.45, 650),\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1))\n",
    "plt.text(4.5, 650, \"Elbow\", horizontalalignment=\"center\")\n",
    "plt.axis([1, 8.5, 0, 1300])\n",
    "plt.grid()\n",
    "save_fig(\"inertia_vs_k_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L2cuVcYI3Eda"
   },
   "source": [
    "As you can see, there is an elbow at $k=4$, which means that less clusters than that would be bad, and more clusters would not help much and might cut clusters in half. So $k=4$ is a pretty good choice. Of course in this example it is not perfect since it means that the two blobs in the lower left will be considered as just a single cluster, but it's a pretty good clustering nonetheless."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 454
    },
    "id": "eopMCF_v3Eda",
    "outputId": "cb90352c-7dde-49b3-b2cb-4b03c96f960e"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code\n",
    "plot_decision_boundaries(kmeans_per_k[4 - 1], X)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NBDX_L5W3Eda"
   },
   "source": [
    "Another approach is to look at the _silhouette score_, which is the mean _silhouette coefficient_ over all the instances. An instance's silhouette coefficient is equal to (_b_ - _a_) / max(_a_, _b_) where _a_ is the mean distance to the other instances in the same cluster (it is the _mean intra-cluster distance_), and _b_ is the _mean nearest-cluster distance_, that is the mean distance to the instances of the next closest cluster (defined as the one that minimizes _b_, excluding the instance's own cluster). The silhouette coefficient can vary between -1 and +1: a coefficient close to +1 means that the instance is well inside its own cluster and far from other clusters, while a coefficient close to 0 means that it is close to a cluster boundary, and finally a coefficient close to -1 means that the instance may have been assigned to the wrong cluster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k2L-6pqn3Eda"
   },
   "source": [
    "Let's plot the silhouette score as a function of $k$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "kBwhjoOx3Eda"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ybraNJXL3Edb",
    "outputId": "e777aa8a-2593-4788-9f15-3282b1219e40"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.655517642572828"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_score(X, kmeans.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "NwqzUWF53Edb",
    "outputId": "9250f63e-37b5-4467-dd03-2ce62b4b6e1d"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates and saves Figure 9–9\n",
    "\n",
    "silhouette_scores = [silhouette_score(X, model.labels_)\n",
    "                     for model in kmeans_per_k[1:]]\n",
    "\n",
    "plt.figure(figsize=(8, 3))\n",
    "plt.plot(range(2, 10), silhouette_scores, \"bo-\")\n",
    "plt.xlabel(\"$k$\")\n",
    "plt.ylabel(\"Silhouette score\")\n",
    "plt.axis([1.8, 8.5, 0.55, 0.7])\n",
    "plt.grid()\n",
    "save_fig(\"silhouette_score_vs_k_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DG22wlbi3Edb"
   },
   "source": [
    "As you can see, this visualization is much richer than the previous one: in particular, although it confirms that $k=4$ is a very good choice, but it also underlines the fact that $k=5$ is quite good as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DD5ruc7F3Edb"
   },
   "source": [
    "An even more informative visualization is given when you plot every instance's silhouette coefficient, sorted by the cluster they are assigned to and by the value of the coefficient. This is called a _silhouette diagram_:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 858
    },
    "id": "Cc9AJ7sh3Edb",
    "outputId": "0167c073-ace9-4ccb-86e4-a16357ab684f"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1100x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates and saves Figure 9–10\n",
    "\n",
    "from sklearn.metrics import silhouette_samples\n",
    "from matplotlib.ticker import FixedLocator, FixedFormatter\n",
    "\n",
    "plt.figure(figsize=(11, 9))\n",
    "\n",
    "for k in (3, 4, 5, 6):\n",
    "    plt.subplot(2, 2, k - 2)\n",
    "\n",
    "    y_pred = kmeans_per_k[k - 1].labels_\n",
    "    silhouette_coefficients = silhouette_samples(X, y_pred)\n",
    "\n",
    "    padding = len(X) // 30\n",
    "    pos = padding\n",
    "    ticks = []\n",
    "    for i in range(k):\n",
    "        coeffs = silhouette_coefficients[y_pred == i]\n",
    "        coeffs.sort()\n",
    "\n",
    "        color = plt.cm.Spectral(i / k)\n",
    "        plt.fill_betweenx(np.arange(pos, pos + len(coeffs)), 0, coeffs,\n",
    "                          facecolor=color, edgecolor=color, alpha=0.7)\n",
    "        ticks.append(pos + len(coeffs) // 2)\n",
    "        pos += len(coeffs) + padding\n",
    "\n",
    "    plt.gca().yaxis.set_major_locator(FixedLocator(ticks))\n",
    "    plt.gca().yaxis.set_major_formatter(FixedFormatter(range(k)))\n",
    "    if k in (3, 5):\n",
    "        plt.ylabel(\"Cluster\")\n",
    "\n",
    "    if k in (5, 6):\n",
    "        plt.gca().set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "        plt.xlabel(\"Silhouette Coefficient\")\n",
    "    else:\n",
    "        plt.tick_params(labelbottom=False)\n",
    "\n",
    "    plt.axvline(x=silhouette_scores[k - 2], color=\"red\", linestyle=\"--\")\n",
    "    plt.title(f\"$k={k}$\")\n",
    "\n",
    "save_fig(\"silhouette_analysis_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q3IzZv0k3Edb"
   },
   "source": [
    "As you can see, $k=5$ looks like the best option here, as all clusters are roughly the same size, and they all cross the dashed line, which represents the mean silhouette score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yK3rbOOm3Edb"
   },
   "source": [
    "## Limits of K-Means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QG2jxFa53Edb"
   },
   "source": [
    "Let's generate a more difficult dataset, with elongated blobs and varying densities, and show that K-Means struggles to cluster it correctly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 454
    },
    "id": "gRVFf1Fz3Edb",
    "outputId": "25cf9a31-d8d9-4cc5-ae28-e42a98b59960"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjIAAAG1CAYAAADjkR6kAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAUMZJREFUeJzt3Xt0VdWdB/DvJQmXkIYEGKREY4oZxQcgKRZBOzRahdYsla41duoyrViXbRkUrWUGMq1N01Zjp1QdHyvarpnqDIp2plXaZipaaGBVG7VIivigQ9RASVFaSIJpesnjzh9xX84997zveex97vezFgtI7r1nn8c957f3/u29E+l0Og0iIiIiBU2IugBEREREXjGQISIiImUxkCEiIiJlMZAhIiIiZTGQISIiImUxkCEiIiJlMZAhIiIiZRVHXYAgjY2Nobe3F+Xl5UgkElEXh4iIiBxIp9M4duwYqqqqMGGCdZtLrAOZ3t5eVFdXR10MIiIi8uDAgQM45ZRTLF8T60CmvLwcwPiBmDJlSsSlISIiIicGBgZQXV2deY5biXUgI7qTpkyZwkCGiIhIMU7SQpjsS0RERMpiIENERETKYiBDREREymIgQ0RERMpiIENERETKYiBDREREymIgQ0RERMpiIENERETKYiBDREREymIgQ0RERMqSOpD5xje+gUQikfXnzDPPjLpYREREJAnp11o655xz8Mtf/jLz/+Ji6YtMREREIZE+KiguLsYHP/jBqItBREREEpK6awkA/u///g9VVVU47bTTcM0112D//v2mr02lUhgYGMj6Q0TubOzswYV3bsPGzp6oi0JEZEvqQOb888/Hww8/jKeffhptbW1466238Hd/93c4duyY4etbW1tRUVGR+VNdXR1yiYnU19bRjYN9Q2jr6I66KEREthLpdDoddSGc6uvrQ01NDe666y5cf/31Ob9PpVJIpVKZ/w8MDKC6uhr9/f2YMmVKmEUlUtbGzh60dXRjVX0tGhfXRF0cIipAAwMDqKiocPT8lj5HRquyshJnnHEG9u3bZ/j7ZDKJZDIZcqmI4qVxcQ0DGCJShtRdS3rvvfceuru7MWvWrKiLQkRERBKQOpBZu3Yttm/fjrfffhvPP/88PvWpT6GoqAhXX3111EUjIiIiCUgdyPzhD3/A1VdfjTlz5uDTn/40pk+fjs7OTsyYMSPqohERUR44Oo78InWOzOOPPx51EYiIKADa0XHMyaJ8SN0iQ0RE8bSqvhYnV5ZiVX1t1EUhxSk1/NotN8O3iIiISA5unt9skSEiIiJlMZAhIiIiZTGQISIiImUxkCEiIiJlMZAhIiIiZTGQISIiImUxkCEiIiJlMZAhIiIiZTGQISIiImUxkCEiIiJlMZAhIiIiZTGQISIiImUxkCEiIiJlMZAhIiIiZTGQISIiImUxkCEiSxs7e3DhnduwsbMn6qIQEeVgIENElto6unGwbwhtHd1RF4WIKAcDGSKytKq+FidXlmJVfW3URSEiypFIp9PpqAsRlIGBAVRUVKC/vx9TpkyJujhERETkgJvnN1tkiIiISFkMZIiIiEhZDGSIiIhIWQxkiIiISFkMZIiIiEhZDGSIKBY4cR9RYWIgQ0SxwIn7iAoTAxkiigVO3EdUmDghHhEREUmFE+IRERFRQWAgQ0RERMpSKpC58847kUgkcMstt0RdFCIiIpKAMoHMSy+9hIceegjz58+PuihE0uHQYyIqVEoEMu+99x6uueYa/OAHP8DUqVOjLg6RdDj0mIgKlRKBzOrVq9HQ0IBLLrnE8nWpVAoDAwNZf4gKgdXQY7bWEFGcFUddADuPP/44Xn75Zbz00ku2r21tbUVLS0sIpSKSS+PiGjQurjH8nba1xuw1RESqkrpF5sCBA7j55pvx6KOPYtKkSbavb2pqQn9/f+bPgQMHQiglkdycTBTHVhsiUpXUE+I99dRT+NSnPoWioqLMz0ZHR5FIJDBhwgSkUqms3+lxQjwiZy68cxsO9g3h5MpSPLf+4qiLQ0QFzs3zW+qupY9//ON45ZVXsn523XXX4cwzz8S6dessgxgicm5VfS3aOro5vT8RKUfqQKa8vBxz587N+llZWRmmT5+e83MimW3s7MkECjLmqVjl2BARyUzqHBmiuODwaCKiYCgXyHR0dOCee+6JuhhErrhdmdlN8i0TdYmokCkXyBAVAjctOGztIaJCxkCGyGdGLSRugw03LThuW3uIiOJE6uHX+eLwa4qC0VBm2ZN9iYhk4ub5zRYZIp8ZtZA0Lq7Bc+svZhBDkWI+FcURW2SIiAoEJz4kVbBFhoiIcjCfiuKILTJEREQkFbbIEBERUUFgIENERETKYiBDpACONiEiMsZAhkgBnL2XiMgYAxkiBXC0CRGRMY5aIpIQZwImokLGUUtEijDLfWFXEhGRMwxkiAyElVxrFrCwK4lkw4RzkhUDGSIDQbWI6B8GZgEL12Yi2bCVkGTFQIbIQFAtIvqHQZgBC2vUlA+2EpKsmOxLFKIok3i5YCARqcLN87s4pDIREcZbYKLqLlpVX5sJooiI4oItMkQWwmhBEdtYWDMVO3uOZv4Os9WGw72JSCYcfk3kkzASHMU22nf3Zv0dZlIlEzmJSFUMZIgs5Jvg6CTBVmyjYX5V1t9+dQGZlUH7cyZyEpGq2LVE5JG+O8aoe0aGBFuzMshQNiIiI+xaIgqBvjvGqHtGhpYOozJs7OzBYGoElaUlbIUhIqVx1BKRR/pRQEajgqIcpWRVhraObvQNDePkytLIy0dElA92LREVACfdYEREsmDXEhFliXJGYSI7nHWa8sFAhihAstygZcjVITLD4f+UDwYyRAGK4gZtFDzZtcDIEnBRYWKgTflgIEMUIC836HyDCi/BE2vEFCV2dVI+GMgQBUjcoAE4Dk7yDSq8BE+sEcuBLWNE7nHUElEI3Ew+xxFFhYuTFBKNi82opba2NsyfPx9TpkzBlClTsGTJEvziF7+IulhErtm1eGhr4o2LazJz0rBmXljYMkbkntQtMj/72c9QVFSE008/Hel0Go888gi++93vYteuXTjnnHNs388WGVKFviYu/l9ZWoKyZDFbZ4iooMSmRebyyy/HZZddhtNPPx1nnHEGbr/9dnzgAx9AZ2dn1EUjRaiSc6CviYv/A2ASLhGRBakDGa3R0VE8/vjjGBwcxJIlSwxfk0qlMDAwkPWHCtuGLXtxsG8IG7bsjbQcdgGVftSG6F4CwPWQFKBKwEwUR9IHMq+88go+8IEPIJlM4ktf+hKefPJJnH322YavbW1tRUVFReZPdXV1yKUlMuZ1SHTf0DDKksXsVpIch68TRUf6QGbOnDno6urCCy+8gFWrVuHaa6/Fa6+9ZvjapqYm9Pf3Z/4cOHAg5NKSbNYun4OTK0uxdvmcvD/La61bv9K008/RdjcFUeNnK4J/mKRLFB2pk32NXHLJJaitrcVDDz1k+1om+5KfvA6NNUvkdfM5Vu8xGq7tZAg3h/qSirgAamGITbKvkbGxMaRSqaiLQQVoVX0tKktLMJgacdWKYZbI69eEdUbdGk66OtiKQEEKqsVPf22zW4+kDmSampqwY8cOvP3223jllVfQ1NSEjo4OXHPNNVEXjQqIuCEDQFmyGH1Dw65umkaJvG6nY7d6j1FA4iRI4bTwalKlSzCoAMOPigHFi9RdS9dffz22bt2KP/7xj6ioqMD8+fOxbt06XHrppY7ez64l8kLfVK3tghET1QXZjO2mqZzN6tGI8rir0iXIa5Py4eb5LXUgky8GMuSEVeDy3PqLQ78huwmcVHmoxU2Ux50BAhWCWOfIEPlN3wSub6oOqwtGdBksrJmaFcRYNc+zWT0aUR53dgkSZWOLDBU8WWq4RssSAAilbLIcA6JCxu/hCexaeh8DGVKJuIkNpkbQNzQcarcFu6iIosfv4QnsWiJSkOgyEJP4hdltwS4qoujxe+gNW2SIfMJmYbXwfBHJiy0yRBHQJuaqMtdHnNmdg6DmOeG5JwoXAxkqWEYPnHx+pm0WzvchyYdh/vwY8eXlPHCmWaJwMZChguV0an+nP9MOizV6SG7s7MGClmewoOUZ2wcjH4b5swtUnAxj9nIemOdAFC4GMhRbdrVpp1P7e1kCwOgh2dbRjb6hYUdLHPBhmD8/5lvxch44zwtRuJjsS7G1oOUZ9A0No7K0BF3NyyIpg0goXVgzFTt+fxipkVEki4uwdvmcnAed9rU7e44yCZWIChaTfYk8sGvBySdfon13L/qGhjGtLImu5mWGAYr2texWij/Z86BkLx+RwECGYkvMx7J2+RxHr7fLh9D/3smNXnRNNMyvsu2icPNaUp/seVB+lM/qO2L2OwZQ5Ba7lkh6Qc/34bRLx25xSSI3ZJ/Hxo/yWX1HzH7H7xUB7p7fxSGViQqQXzdqbc3Qyee43a74fACWN07xWdrFJcV2iNxqXFwjZQAj6K93N2XVVg4AGH5HzL4//F6RW2yRocD4VbNyG5i43a7Z52t/DiBrHSTtwo4yP4yI8iG+S0UJoOXKuY6vdbaqUL64aOT7GMhEK6qmcz+2u7GzB82b92A0DZxcWQoAWStThxHQyN71QPGn/x44DUp47VK+GMi8j4EMeaWviQLIujEHtVK1tkm+fXev6weIavJ54KkcKKuk0PaX5MDh11SQ/BztoB1BJHIEtJOc6VeqXlgz1dG2na7/I4KYooRxfkFc5DMyJqpRP7KPNvIbJ/gj2TGQodjw8wEjbt47e45aDrk2e53XMuqHYLvJS1BRPjMYBzX7sZcZock/HH5NbrFriWIjiCbwNZt2oX13LxrmV+Heq+tMkxidbpvN9NFycvyZqBotq+PP70/hYNcSFaR8msDNaoE7e45iND3+N2BeG3e6bTbTR8tJqx1bXKJldfwLrVuPnGEgQ7HitVna7Aapv6laBSJets1mdP+4mWnZKkhhsOmen9ex1fFnkElG2LVE0vLSjOy1W8Dttoxe73Tb2u4qkVvDboz8+dElxK4Lb9gdR35j1xKFKqhWBbtmZKPteq2xua2FG5XN6bbFiKT23b2sYfrIj2PJrgtveB1TlBjIUN6Cuvnb3Ry12xVBDYCcgMQo4Mk3+NKXzU1NvmF+FYoS43/HqRsj6m4yP47lqvpaVJaWYDA1UnDdffmcvzhdx6Qedi1R3mSYmEwENdqmbatJ6+ymXg96WYQ4ivIYeLkGzd5TqOeyUPeb5MSuJQpVmLUxba1Ru12j1hvtYpD6362qr0VRAhhNw7AlSby3efMe0xqqtixmrUdRt1KEKcruBS+tgk4TvAtFoe43qY8tMhSpIFs+9HPAuNm21RozbpYn0JZXu6ovm+CdMzpP+p8ZLfDJOX2I1MUWGVKG25q0m1qjfg4YPdGiAyCn1aRxcQ1arpxruC2rlh6r8jKR1Buj46b/mbZ1zulxZl6HHAqp1ZKCURx1Aaiw6GvB2lYKJxoX1zh+8CysmYpD/UNYWDPV8nXaB5/2s8W/9Q9Q8XnamrxZ64++vG72lcYZXSNW143ba4qiZfb9IzXI0LLJriUKRVCrRVtxOtU5YN4VIT6jsrQEx/46bLoadW1TO0bTQAJA1fstMLwpU5jcPFBkePjIWBZyL6gkcXYtkXTcdMcY0Tc/5zuLqyjPhi17DW+i4vMX1kzFyZWlAJCzGrW2DGJI9aSSCY66NYJoTi/EJvpC3GczThLU9a+VoZuTXXxqkyFJXOpAprW1FR/5yEdQXl6Ok046CStWrMDevXujLhZ5IC72tcvn2N60xMNpzaZdmYeU/sbr5EbsZKrz1MhYJqDREp+/s+conlt/MdYun5OzGrW2DPdeXYfu1gZ8teFsR6OXgniQyPRwyoeb4MRoLqFCDWrsRuLpXxv1wycMql4TKpVbhkBU6kBm+/btWL16NTo7O/Hss89ieHgYy5Ytw+DgYNRFI5fcXOzi4dS+uzfzkNLfeLX/9/KlF+VJFht/BZyssSRes7Bmqu329UFGEA+SuDyc3ARkTKY+wSpB3ei1UT98wqDqNaFquaOiVI7M4cOHcdJJJ2H79u1YunRpzu9TqRRSqVTm/wMDA6iurmaOjGI2dvZgw5a9SI2MIllchLXL5+QMsc1nAjOj/JiFNVOxs+eoYReTVf+9fpg2AMOy2A31LtQcASdDq/P5LHIvTsdR1X1Rtdx+cpMjo1Qgs2/fPpx++ul45ZVXMHfu3Jzff+Mb30BLS0vOzxnIqMcoODGbjdfsS2/2kDSaH8YsGLILkvRlApzNYWK3ryoKc5FP8s7uPDlNklcpoZjUE8tk37GxMdxyyy248MILDYMYAGhqakJ/f3/mz4EDB0IuJfnFqJtEDHvW5wDom8lFV9OGLXsN5x/RJ+2abU9ssygB0yHc4n0isPLSZK9Kl5BdF56X5nBV9j1O7M6T1TXv5hzHpXtEpXyVQqVMILN69Wrs2bMHjz/+uOlrkskkpkyZkvWH1GQUEIiJ7fRBiJ7ZCKmNnT0YTI2gsrQkZ30lo+1t7OzJrFStnVTPbJkEL1Sqtdo9mLwEJVaTEqpAxYec3XmymkjSzTmOMkj187zEJSCLMyW6lm688UZs3rwZO3bswOzZsx2/j/PIRMvvh7TTz8s3l0Y/5422K8tq6QIvVOpaCTLo8us4hB0YqnT+nFIpuDbj53mJw/FQkZvnt9Qz+6bTadx000148skn0dHR4SqIoeht2LIXfUPD2LBlry83AKez+pq9zumMr6IGVllakqlRaodcG3VNuSVujtpZgrU/j8tN0+n+OJ2F2W47IvgMa5bYOM4i7Gb2bFn5eV6cHo+4fXdVInXX0urVq7Fx40Y89thjKC8vx6FDh3Do0CEMDQ1FXTSSgNvmY6NuDKPPsJrzRvyuYX5VZu4SL/Tz1BjNTSMbL2Vz+h67dbGcbgfwNuGilpvrKqqV383KqFJXV5BljWJ4uczf3biTOpBpa2tDf38/6uvrMWvWrMyfJ554IuqikQNiErm1y+cE8vluZjIFTtw4b29/LWtWX/FvJ3kv4nc7e46a3rTsbtDaXB39A1fm5FcnZdM/bM3208tnO3m/kwkX7cj6QNKWy6yMUZfd62SGcSDzdzfulMiR8Yo5MvJyMj+L3ZBqAFn5KtrmZKthpQkAaQCVpSVYesYMtO/uxcTiCRgaHvM0F43Z+kx2Q7bjlFchWK1NFWXTexyGDTtZHyzqsru5tjlfEFmJ5fBrihe72piTGmfj4ho0zK9CAsCRwZThcGstUWOad3IFihLA0jNmZLo0ksVFrmpTTpY/MPusONfcxL4B48PkgRPDeKOsgbvZdtDdEl67VLTlMitj1DP22k1X4MeIv3yuI5W63sg5BjIUCa8P+1X1tagsLcFgagQbO3uws+co0gCGhscAWOdHiBvnnwePZ/IxtF0SC2umonnzHqzZtCuvfbO7QUf9sPHK6CGg/5nYt7XL56AoMf4akfcSZgCnL5fbbQf5wIsioAvrAW6X62S1707L6PU6EiMO/Tj2DIjkwkCGIuHkYS+6ivQ3i2N/Hc6MTBGBjegmckJ7I9SWQ8wZ0767N+v1+ptWHBItvTB6CJk9mERrmbZ27iaAy/dY6svlNngMMtiIokUurOApn9ZIp2XMpyXHjxGH4rPilN+jOgYyJC2zB6f2ZtS4uAZdzcvQ1bzMNAHXqNXAKEgSD96G+VWW5ZA10TJoRg8hqwdTPiOR8j2WRuVyExwF2YITRYuc2B+xwKl2ZXk/5dMaGXSAp5+F24/PimP3sIqY7EvScrugoJuJ8LRJqWXJYsvEQf3nulnbqZDlczyCOJZBJlmHkeTqB+3aYH5N6kgUhNguGukWAxm5hXVDtwqIxARqhXRDL9SAy8/9dhrcGoky6NFOxGi02rubzyi064fCxUDmfQxk5CbDMORCvCmHcdzXbNqF9t29aJhfhXuvrnP0HpXOhdExzHcJDafbiZqMZQLUun6ciNv+uMXh1xSafJIy8+1n3tjZgwUtz2BByzOWff5WZVR1BFE+wujfF4nTP/tdr+PzolKOkdExDCJZVZ/bYvc9CyPhPMjrx25knNX+qXT9OBG3/QkSAxnKi5cvm7gZAcgriBBrOfUNDeNnv+vFwb4h3N7+es6NzqiMogxrNu3KBENmD1ur3wctiAdTGMGbSJyeVDLB9PrQnxeVEiiNjmEQ5Xcyk7RWPt9Hp7NjB9lKYDcyzmr/VLp+nIjb/gSJgQzlxcuXze8JrRIYf2ACwF+HR3M+26r2/LPf9WaCIbOHrdXvg6ZirUzM79Ny5Vx8teFs0+tDf15Ubx0LsvxOv2dBfx+Dvh7tRsZZ7Z/q149e3PYnSFKvfk3yc7tSrpv1d4xob6Rrl8/JmbJdm8RoVUYx/PrIYCozmZ5+NlJR1tKSIiSLJ0RSM9Iuu+BV2H3t2nNkVe44rLIcFifHyut5dnONub0e3ZbJaD/1P+M1Q3pM9qVQOU0UtBvi7GTUhZPXitk+jYaiLmh5Bn1Dw6gsLUFX87I89zw6YSdnas+d6P5T/RiqQMYkXBnLlI9CT8ANE5N9SQpG3UB2Td/iPWLdJLOVrXf8/rBpE/eaTbtQ29SeWeW6fXev6WsbF9eg5cq5rlZ1Vk3YSwMEdaNX+RwIQeyD+MyFNVMDOc9OZ7Y2YnXtqXg+VezqLQQMZMgTJzchL1968Z7UyCiA8Um7jBL/APN1lX72u/ERM0PDY6gsLcHE4iLLriyzmX7XLp+TWYdJ5RtYmH3t+uOkPYZ+f7YqRGC9ZtOunH3w42EuPnNnz9FAzrPTma2NWF17Kp7PfCoFKgZuqmAgQ544uQnZDVE1GlYpapUjo+msz9F/5trlc0xvkCLxt7RkAsqSxRgaHkVZstjyBm+0P9qbcNxqlkHxmsDr5BiqOopDu4bXqvrsRU/9eJiHNbW/+Hy/tqfi+cynUqBi4KYK5siQJ0ZdCE4mQdO+T3yxT64sBYCsvvTZ69uRxviIpLfubLDc3jlVFfjz4HHDWVYB+D5JmV7c8gCi4OUY+tGNFUbOg/57od1XbfIscy7ijfk17gQ+s++SJUvQ2dmJ559/HkuWLMna8NKlS/H666/j5z//OS699FL3pfcRA5lw1Ta1YzQ9HnxUaVaXNmMVcFjd/MWDTmxPiCqQkOEGJUMZ8uGl/H4EkFEEobKdKz+WLZCBrMdVlvKoJvBAZseOHfjYxz6Giy++GFu3bgUAHD9+HJ/85Cfxq1/9Co899hg+85nPeCu9jxjIhEsEHxMSCQyPpR2NVHH6ZdcGNotmT0NbRzeml03Eq739WS0ywPhEecB4fkbYN5Cobl4yLFoY9r6r0iIju3wWkpTp+MnWMipbeVQT+KilpUuXoqGhAdu2bUNHRwfS6TRWrlyJbdu24e6775YiiKHw3Xt1HbpbG1CWdD49kdN+4509RzGaHv9bvOfPg8fR3dqAn9700Uy/tXYCOxHQhCmqfnA3+QZuy+g0ByjsffcjiTmIRGgVcqa0ZRTXTsP8qlAnt/SbbDk3spUnzjwn+7a2tmLChAm47bbbsHbtWmzatAlNTU24+eab/SwfBcjqhpvPzdjpSBU3k+M5nd1zVX0tEibbslrDxS9R3by8rN/jtIxOH1a8cY+T6eFuRltGce3ce3VdpuXAy/DqqAM42WbCla08ceY5kJk3bx4aGxvx61//GnfddRc+//nP44477sh5XWtrK8477zyUl5dj5syZ+PSnP4233347nzKTT6xuuPncjO2+wNq5YvqGhgGMdwdZrWek/UzxbyD3htu4uAbfWjE3J5CyW8PFL2HcvLw8MPxc3NNsXhHA+dpZUT/0gmQW0Mm0z1ZBp9fh1X4vPeL0fVGuhUZyyGv49YwZMwAA5eXleOCBBwxfs337dtx000144YUX8PTTT+PIkSP45Cc/iZGRkXw2TT6wa9nId2Vqu1Vq+4aGkQCQGhnNWs8o364MqwX9tKsIq9qC4OWBISYY3LBlr+v364+neP+GLXuzJi/0Ml+Q0/lUZAoC7JgFszK11FhVBrx+L9y+T3tOvR4bbVeyDMeVouE5kLn//vvxve99DzNnzsSxY8fwyCOPGL7u6aefxrXXXouzzz4bdXV1+MEPfoA33ngDr732mudCkz+sWg/ybVkwujFp54oper//Jw0g+f6EdaKLya+uDO2NUr+KsHigy5Ck6JRfM7jmG8CJ9wOwnZzQaRnsznkQE8mFTcbA2W7+JDesgiO7becTPGnvHVSYPI1a+tGPfoSrr74aH/vYx/Doo4/irLPOwuTJk7Fv3z5MnjzZ8r1dXV2oq6vDgQMHcMopp3guuBMctRQdo9EM2rWL1i6fYzq6aGNnjy8jj8SogcrSEpQli7MemoOpEfQNDSs1osDtKAiz4e3i3/kOt/UyYsVuDS2zz9L/Xn8sZBo9kw8VR37p5bueGhEQ8PDrrVu34rLLLsNZZ52FHTt2YMqUKWhpacE3vvEN3HHHHWhqajJ97+joKJYvX45kMon29nY3m/WEgYxcRCBTWjIB08qSWTcwuweVGaubofidUdDi5000rHk43JbZ7Bg6GW7rZltWr3UyH5AXXq8X2RkFaBu27EVqZAzJ4gmWgb0sgYEs5SC1BRbIvPzyy6ivr8f06dPx/PPPY9asWZkNzp49G6Ojo3jzzTcxbdq0nPem02l84QtfwPbt2/Hcc89l8muCxEAmekatAtrAQnQliZ+J1hOjoMDoBmnV6iI+I+gAI595OIJk1/qhPS4AXAUG2gBFdNdpz4HYnpi0sCgBdLcaz9Ac5L4G8d4gH9RmAZpgdW3FJZgjAgKaR6a7uxuXXXYZJk6ciKeffjoTxADAlClTsG7dOvT396O1tTXnvel0Gv/4j/+IX/7yl9i6dWsoQQxFxyyJT/Shi+HZ08sm4mtP7cnKswBgugCeUX++Pl+jraM7k3z6s9/1BrqYnr4MXubhCJJZroP4+aLZJyoc2mRgwD6fQ79+kP4cCA3zq1CUGP/bqkxu6XNk8vlc/b7bCTJpV78fIgektMR64VPxWpmuP7+Y5UOpmCelYplVEPhaS+l0GqtXr8bPf/5zbN++HbNnzw5yc1nYIhMNp2vJaJcY+PaKuYZLE2g56UZaVV+bGdZt1IVV6MzWutK2iJnNxqx974tvHck5T2F2KfjZ+qDN3bKbiRrwfz+NPo/dMyfYdZGq1AKlYpmjEvjMvm6sXr0amzZtwmOPPYbS0lIcOnQIhw4dwvHjx4PeNEXEac1Q1NavOLcqc7PWzuCr53SUlWjx+WrD2ZkHNmtA48xGiugnMTSqOWrfK2Zx1gabZudH9okHnU7gKPg9V5DfcxzFrdZvdq5VbIFSscwqCLxFJpEwmmcV+NWvfoX6+vogN80WmQhY9fFfca79qthe8ln029SOegJgOTqp0Gq+TvfXqJXiROLpKJLFRY5HlBnlMRXCsXYqnxYZq7wxvxOqaRyPSzgCXzRSFQxk3Mv3S2o06uJrT+0BcCLh08n7rMqnHZoNAM2b92Ql2WqDJ7uHJ5t6x+nPu1l3i/bYuh0GbpTk7XYYNmUz6sb1K7md341sVqMgyX9SdS2RWvJNZNQ3nTYursH8kysAAOdUVTh+n55+WQMxk2dbR3dmVIx4r3aSrLXL51h2A7iZVC/O9Od96RkzUJQATp02OWv/V9XXorRkAhIAFtZMzfoMs2OlT/K2mvQwytlvVTzX2utXHDu/ktvj2g3i9TyL4wu4nwCSgiV1ILNjxw5cfvnlqKqqQiKRwFNPPRV1kWJPBAGDqRFPN3Sj/IH9R/4CAPi/d495flCIm0hqZBQJAKUlRVhVf2IByZYr52a22bi4Bl3Ny9DVvMz2Zm6X7yDTtPJ+MLuJ6x9aIlfp1d7+rP1vXFyDaWVJpJGbx2R3rLTH2irvIZ/rLx8qnmsnxzSfz45jjpnX8yyOr13lyCkVA2dZSR3IDA4O4txzzzVdx4n8o134ryxZnPfaJUZf0r8OjzlepFL/fnETSRYXIQ1gWtnErAUkgx5aHZfalzjOYp2kNZt2GS74aDWkPN/kS6vuo8bFNZnrr3nznlBv8qqf6yC+C04f+qo8lDd29mAwNZIzlN1J+cNI8iZvlMmRSSQSePLJJ7FixQrT16RSKaRSqcz/BwYGUF1dzRwZB5wOmRbMpr83SjjU993r+/CNliQwSxDNJwnSze9llW+5xfuPDKYwNDyGBMbXuwpzOnm74c4bO3ty8p68UPUcy8TpMVQln0amody8Pq0VbI5Ma2srKioqMn+qq6ujLpIytLVRJzUPbW3CaqI67efde3Vd1sKN4vVtHeMr2JYli3NaBADkdG04qRXZ1XZUrQ35Xe5JJRMctULYtZjp5VNDb1xcg5Yr57puHdFvU9VzLBOn3zer1iyZWmtkGsoddGtyIWGLDDliNMRZ3yIzvWwiXu3tN5zMTvs5t7e/hr8Oj+Hy94dju1mrx015rRaeVLU25NeoMrdDoc2G1RclgJYr5wKwX+JAP8Te76UjnCwkKdN5l6ksQVKltYbkEsvh104CGT0Ov/aPk5uRfl0dq88BnHUbOOmGMHsY8Aaay8+uOW33DwDbIEJ/Prys5p1vcCrTNSFTWfziJHgslACO8lOwXUsUDLMEOT39ujr6z7jwzm1YWDM1MzTaTTNuamTUsHnaqstD9eTNILjtmhNJwdrjLh5E2kRgo2H3+u3oX+P2/IguSLNEdCf7JtM1IVNZ/GLUnac/L+zyI7+xRYZs+VFz9PoZVpNQWSUJx6mWGxbRjXdOVUVmyDwA05XKrY5xELVuuxaZuFC5xcJuEID+NartH4UnNi0y7733Hrq6utDV1QUAeOutt9DV1YX9+/dHWzBJBZVUt7BmKooSuROgudmmqH0urJnqqoxiLgsAOa04RknCVmWVXdRJkWJF690H+zPH1WgSO8B+QjCjWrfV/jkd/up0fiCZuD2vKrdYaFtfzPbDjyRXo2Ma9feHoiN1IPPb3/4WdXV1qKsbT/K89dZbUVdXh69//esRl0xOQd0ArRZydLpNcfMSI5bEHCH6m4/R/5s378kJWADjoMWqrLKL+gEmugZPrpyUOa5GE66ZTQhm161ntX9R73uQ3O5bXLqcgtwPo2Ma52uIrEkdyNTX1yOdTuf8efjhh6MumpSCunFoP9dsojqn2xRBx2gahkO3N2zZm8nNAMZfo1+CQDAKWlR+CERRdu35FCtaAwnbYNCo9qs9d05yZLRUPm923M5W7LXFQrYWCSf74bXMRtdLnK8hsqZMjowXzJHxn37Yrdubrd2wXTFKKQHgWytyf6/lV1973Pvs3Y7sMnu99rUAct5nN8KskOWbI+bk2sw3PyyK74EKOW1xvz/IKjY5MhQMt5OZif+v2bQLg6kRJHCiRUX/e7valag1iSBIX2tbu3wOihLjs802b96DF986YlpGv24uqjVJ+5lzYVSL1Z8T7Ygzkec0mBpBackEDKZGMud96RkzUFlaknkPneC2tUAcc9HK5eTadLMNo2vIbV6TH1RoRbG7P8jWElaI2CJTgOxqQWbzfRQlxgMY/WRq+t/nW7vSzlGi/0z9KCazid3cBDoq1bispu832w+7uT0A81YvwP560J4jILelhtzzOnGh28+3a4lTocUkaHb3Bx6jYLBFhizZ1YLM5vtomF+VqXFrv9Qi6facqgpfalfaKer1ixbqR84AMKxFNm/e47gWJVogAEhfs7LKGbIbJQKc2D+7JSa0rK4H/TlyUsNmDdaeVWK1H8fP6XnSzx9ViOfOLtdHhValuGOLTIFzMoOr3dTzYdZInMwS6iSPJ+r98Mpq/19864jlcg5mC4MC1i0yfvOaS6NSy5kX2nl8/jx4PPQWACczL4f1HYn7uSZ7bJGhHBs7e7Cg5RksaHnGtl9c+x7RsiFmeBV5Ek5GC6zZtAu1Te1Ys2mXL/uwZtMuNG/ekxkWLCZIG0yNZL1On4cj9sVutJVMNSuzmq8YBt3W0Z3TsmI39Fy7f9papl+L1+U7T4wd2XOZ8t1H7Tw+Vgt0hjE60Ww7YX1HZD/XJBe2yBQIszWOnIxoKUoA5ZNKbGdz1dOuvdRy5dy81/jRr+XkZt0mFVpbtKzKqz0vDfOrMgsvArBtrQqzzNrtiweTvjXITblkr6Xne42Ztciodu36QfZzTcFjiwzlEHNZOFnjSD9KpeXKuZkZXt3MzKtde8lpDUv7On1NVL+Wk5t9kqm1xQm7OVdEgu3OnqOmLSt2rW3i2LoZdeamzNrtm7UGueFXy1FQ8r3GxDw+P73po1n7Kcu167bFKZ8WKtnPNcmFLTIFzm0/uNfRFKIbCLBeJ8esFs/++GxOyux0/hgAvo46c1NGUofbliEV57UhebBFhhxz2w8ufgfkjhbS048MKksWo29o2HBFZcFoSny/a6L6mqKK/fFOaqziNS++dQS1Te244r5fGy4hoB+FZHS8vdSu41KrLsSROkbcfh/z/f6q+L2kaLBFhmxZzUOysGYqdvYczfxtNHpItN6I1zhZPdlsu9rf3d7+Gv46PIbLzzUepWPGKpdDtoeuH2UTuUWCUY6U0fnTCjJPQ+bjD6iXXxUXsl8XFCy2yJCvjGpG+kUgf/a73qw1koAT88sMpkZwsG8IO35/GM+tvzhrRWUr+nWX9GUaGh5DGuOjPdzQ1xRlbjnwY1ZRkVs0/+TceX7E57fv7nU1l4wVP2cejkJYo4SCEKfWo6C+l3E6RjSOgUyBcvNldtLVNKkk91ISw4FHxrIb/fy4Qa2qr0VpyQQkcCL51ymZAxc9u4eoWRCgPb9mSaTi8ytLSzCxeIJl0rSbY+YkMNGWT7ZAQV/+MK+XfB+yboJCr9sye58qAYJsgTPlj4FMjLi5kTgd0QJY38hFLouQGhnNqclefm5VZpZSN2UULTdrl88x3O7r3/ok3rqzwVW3kgqMZh12O6uo05u1yF0aGh5DWbLYsAvP7dww2jKZvUZbPrO1nbTvCfMhGWVgle9D1k3ZvW7L7H2qBAhezq8qQVqhYo5MjLjpy3c6osXNfDOC1VwhXCHZnja3SNCO9HIyAsxNfoHZa8WEiGYjmZyMYDO7Jt1ef4WSpxJmXojXbVldL3HNaSmU608mzJEpMPp5X5zUNKxq+kY1FrsVlCtLS1BacqJ7QpXamRB0jctLVx4A9A0No29oOOs4tnV0G/5cy+z8GpXjxbeO4FD/UM5K49p1nYzmD3Iygk1/LYntA3B1/fnVShJ1zdquhctrYOFln/yez8fq82T6fnkhW/cnZWOLTAyEMV/D+Cih1/HX4VHbUUJWI4q83KzDqOkFXePyMlJKfxwXzZ6WGWG04/eHAeS2yJiNMNMmTOtXDdeuNN7d2mD4WRu27DVtSXNzfqKu2bptbQxj+4LX1sqoj6mT4xf298uNOLckqYwtMgUmjPkaGhfX4PjIqKNRQtoRRTt7jrrK+fBavnwFWePa2Jm7irB+n4xqlI2LazCtLJk5juI9O3uOoqt5Gbqal+UcR6NjpW3BAXJXDdfPmKzdvpNz5eacRl2zddvaGMb2ZfxMN5xcy0GXMZ/PV631mHIxkFGYk2Z6J5zeBMweeEafp106IMwERq+CHJkiAgltMq1+n8yOkfZ1Yjj7wpqphtsxCpjEZ4jzsXb5nMwQ+NKSIvS+n9vU3ZqbNK19IFklXrsR9Ygxo+2HGQhY7b/XYxzGMbXqurG6lv26R9nJ5xhEHQhS/ti1pDCr5tSwmku12wFguE0/y6JiM7DTbiSryf/aOrozEwkaJdfaJeYabUO/CKfZ9sSin2blUulcxFmQ58PrQIKwlhmh+GHXUoGwqkn41Vxql0Sn3Y7ZNv2sMarYDOxmOQGj14h9BsaDlNTIaM5EgdrEXKdzzjTMr0ICwMTiCVnnV7s9sf6S0fFW8VzEWZDnQ9xrnCwaq72W7VoRifzAQEZRdrUvv5pL7W6O2u3o5w9Z0PIMFrQ84+tIgkJsBhZdQ+LfyeIiw9eIlcqdzjmzaPY0TEgAQ8NjWedXvG7t8jlouXKu6QMszuci6tFNXgR5PvQzeTud7FBMirmz56jvZfKDiueZcrFrSVFBjAIwG/HiprtI3y0BeF9NudC7Lsya6M3m6HFLXENmXUdeXxsHUY8EkpXbEUp+XatB4XmWF7uWCoDT2le+s/2azboq1kEym90TQFbCr5dyFXrXhXb/tefbr6460dJTPqnE0WuNupniWqNla5MxJ9eethtK5iAGkP88x/X75TcGMopy+jBzEww4+VLr8zX0r9V2S5gNEXZaLllvMmHdXIyCFwB5bXvNpl2obWrHmk27MssTWE2sJzQursl0M0U1dDlMUY+uClI+58zJte+mGypqsp/nuH6//MZAJubcBANualtiGK+b2T3dlkvWm0xYNxej/Rfb3rBlr+0Dxeih0767F6PpE3MB5Xt9GL2ftchw2R1v/e/DmnNF1oqISkSr6WBqhN8nC8yRIXIpytwdfQ6SVd++Ua7C9LKJeLW3Hw3zrWdnzgfzDtzJ93qyO95+ng+nZZU1v03Wclkp1O8Tc2QIgHlNjTXm/ATVUmQ10ks/sZiYPM1qOKy2RixymvYf+UvO5Hd+Xw+sibsT9ISRUZwPr/sU9L1Jxa4afp/sMZCJMbMvrdHMmzIENTKVJQrapQT0CbXNm/dknUu7PASxvtJgagQvvnUE/e+PIDPbrp83d22gV+jn1Il8H1R2gXUU8zh53aegAw0VgwJZu9hlwkAmxsy+tNqfu7lxsLYULP3SDsKGLXsxmgYSyJ3szmiiMhH4iKCofXcv0hif3M5o+vsgb+6Ffk6dkPlB5TW/xus+BR1oyHysyTvmyBQ4mVYuVrH/OgxOVkXWnhtgfEHIBICK0hIsPWMGdvYcjTSnh+dUTTLlZ/BaKixunt9KBDIPPPAAvvvd7+LQoUM499xzcd9992HRokW272Mg456TNX/E73hjCYfZcV9YMzUToAAn1rnS/pvnxT9xvt7dTnyZ7+d6IVNQRcGLVSDzxBNP4HOf+xwefPBBnH/++bjnnnvw3//939i7dy9OOukky/cykHHPzc2iEG4sUTy8RH4LMN4VpA8aRXeNmKAu6OMf5we4G3G+3oPatyhGTFE8xGrU0l133YUbbrgB1113Hc4++2w8+OCDmDx5Mv7jP/4j6qLFkps+ahUT59wyyvHQ5g3kkzdk9l6jpF9RjubNezC9bCKKEsA5VRWhHH/muYyL8/Ue1L75+bnMbyEzUrfIHD9+HJMnT8b//M//YMWKFZmfX3vttejr68PmzZuzXp9KpZBKpTL/HxgYQHV1NVtkXAii1rNm0y607+4NdO6SoBgdD6N8lMrSEpQli10dN7Pcl42dPbi9/XX8dXgUl587fsxEAq9Y4dqsJUbfmgO462Zys94WxRvPO0UpNi0yf/rTnzA6OoqZM2dm/XzmzJk4dOhQzutbW1tRUVGR+VNdXR1WUaXjtaXAr9q3dvv62WRVYjebrfg3gKzjlm+rzfGRUaRxYtVg7RIBDfOrTGu5+tYcs/NpViYx34wIhsyOAcWP/ppgSxypQupAxq2mpib09/dn/hw4cCDqIkXG603Ir6Zg7fYb5lehKAE0zK/K6zNloX2wi3+LCepW1ddmzfty21N7cNtTezLdQtrAQbxHOyRa3/IiPk87Gd69V9cZLuS5sbMnM4S7tKQIg6kRLKyZang+9XMJiYn4UiOjIRxB8pNf0yLo7xmcHp9UEauuJb1CTvY1GtkSZo06Ts3SbqdlF8sHGLFLehTdVgBwxfvdSvqESf25NVquwC7JUtsFBSBTXi9dZBQOs+vQr4Rau25UkWjOa4PCEJuupYkTJ2LhwoXYunVr5mdjY2PYunUrlixZEmHJ5KWvvUe1Am2cuiOctm7pVwa/4tyqzAR3V5xr3h2ktaq+FkWJ8X+LbiXtpHcLWp7JtPC07+41XYncrmVNu/I1gEw5zRYDpeiZXYd+taLadaOyq4lkVRx1AezceuutuPbaa3Heeedh0aJFuOeeezA4OIjrrrsu6qJJSXuzaVxck1WLCkOcWmIEp8fQjxqreJ92e6ILSyQHA8h01Zm1ton3BF1eCo/ZdejkXHul/+ww7yVAPO8n5D+pu5aE+++/PzMh3oIFC3Dvvffi/PPPt31fXLqW3HyZo/7ix3mujaA4nYRww5a96BsaRgLAt1bMzetaiPo6IfVEcc3wflK4YtO1JNx4443o6elBKpXCCy+84CiIiROnTboyPJziPNdGPtZs2oXapnas2bQr53dWc9WIUURtHd1YesYMFCWAy8+tcnR+ra6buHQTcFHK4Mgwion3E3JCiUCm0Dn9Mgdxo3H7oIhTboyfrIagG51ffb7Nqvpa7Ow5itH0idwZO1bXjZsHhMzBgswBWRjHLchtGI1iCjuo4P2EnGAgowCnX+YgbjQyPyiC5PcDwmoIuji/wHhT+ppNu3Bk8DgSAJaeMSNz7qOaJVXma0DmGrv+uAURdAR5bvTHVoagQjtVgIyBNUWDgUxEgripBXGjsXpQyFxTz5ffD4h7r65Dd2uD5czGYpvtu3sxNJw9IR6QG/CI42611IHTLkmr8yhzsCDDw9WM/rgFEXR4OTdOv7dujm1Y9wKj5TuiEuf7n2oYyETE75taUF8qq5uZzDX1fPn18LY7L/rJ7MTMvaUlRUgAWFgzNef1YrI9/TpMXofl2p1HmYMFmemPWxABoZdzE8T3Nqx7gZikr7K0JPLAOs73P9UwkImI3zc17dTyYdUUZK6pu2F0vPx6eNvd7PTD5Z9bfzEWzZ6Ws0SB9vXaWX+B8WAnAeDIYMrTPiysmYqiRG7QZIc1UndkCQiD+N6GdS9oXFyDruZl6GpeFsvjSN4oMfzaq7gMvzaiHaEEALc9tQdpnJiZlUMWnQtyiKfdSDKjBTVFeYoSQMuV2cOsrWZfBexnDjbidf85NHacDKMFieImdsOvKZe2Jt/W0Y00xmvpa5fPybumUGg1bf3x8nP/tZMSGn2e0UgkUR59EGNV/nya271eL6yRjmMXA1G02CKjKH2LjNG/vU6YVug1bb/33+rz3Nbm9Z9l1KLDFoJw8XgT+Y8tMgXAaAXmxsU1rmqHQa/doiq/99/J57341hFHScFiNWux7tJPf5c7P43ZeS20lraw5Jv7wvOiLp47OTCQkZTXL4iTh6b+oWi0dovbG3OcvtB+J2U6GfklFoC0Swre2XM0sxiodoVt7fw0ZtcAu0DcC/q6NhqFFta2yZzTY8/vlBwYyEjK6xfE6KFpNtV4++5eT83hRl9yfqFPWLNpF2avb8dZt/3CcQtLw3zr1bH1I4u0eTHfXjE3a34as8DJKMDhw9Ja0Ne10Si0sLbtViFdK+LYb9iy13aOpcrSEgymRgriuMiKgYyk/OzeMJpqPAFgND0+bDvfz/O7vKpr392LNICh4bGc9ZMWtDyDs277BW57ak9WC8u9V9dZtgLpk4K9DEM1CnDYDWUt6OvaKrFbtqR92QKrIIljD8B2jqWyZLEUE/QVMib7BkiGJMCNnT2ZYGXt8jmZcixoeQZ9Q8MoLSnCtLKJrsoY9H7JcNzsmB3XjZ09uL39dQwNj6K0ZAK+2nC24TBpAJklC3b2HM3aV7H/C2umYmfPUSysmYodvz+M1MgoksVFWdvzYz+Y8B1Pfp9DFb6XfnOyz4V4XMLg5vnNQCZAMjwMzMogvnyDqRH0DQ1L9cCS4bjZMZu7xW6E0oYte7MCkg1b9r4fUE7AtLJkZqi2mEdGdDto/xZzBQV54+TNOXx+H3OeQ1IZA5n3RR3IyHAjsSuDDGXUU6HFx6pFxs1ni5YxobK0BGuXz8lpkdH+fWQwhaHhMVSWlqCreZmn8pN8VAjgicLCQOZ9UQcy+ZIxyIgDJw+MoI69/nPF/90EJyL48RrI8LqSE88L0QmcRyYmrJLr/EjkK9SETidJlH6tHK1/nVgTS3yumPk3WVyUaY2xs3b5HJxcWerotUYKKWlTJbKsxUSkGgYyErN64GoXifRK/0ALK7CJOoBy8sCwC3bMAhMz4lgDyPncto5u9A0NoyxZ7Ogh5vSBZ3acOcKMiOKEgYzEgq6h6R9oRjX1IIKOOLQI6AOT6WUTUdvUjivu+7Vl8LB2+ZyccxrUXBRmx5k1f1JB1BUeUgcDGUXl270A5D7QjGrqQQQdKrQI2O23PjB5tbcfo2lg98F+18FDUHNRqHCciczEocJD4SiOugDkjVhjKejPFMOB/XwYBlF2v7nd74b5VWjf3Ytzqirw58HjtktE6JM6rbann1fGaTKoCseZyEwQ9x6KJ45aIkL+q1C74fa94vVhziNDRBQljloismG2/lTz5j1Ys2mXYd+89j36tY/c0L7XSR6A6CIS6zEB1tOm22HuARHFCVtkqCDpW0XEKsTaGXT1LSba9wDwpUVGfI6bVpZ85xvhxGtEJDu2yBDZEK0cC2um4sI7twEAWq6ca7kStTZ5VrSqTC+b6Lp1Q/s5RovT6VtM9P/Pd9QRk4ApDFG3/EW9fQoPW2SooGlbJ7TJhXZBgphdNwEgjdzWGy2zFhTtzwFk/q1da6nlyrmZ/7MFhVQSdctf1Nun/LBFhpQRZa1pY2cPBlMjqCwtyQogjHJP1mzalTVPTGpkFAAwqWSC51mCtT/XtrKsqq/NdG+J4Ea7DdY0SQVRt/xFvX0KD4dfSy7u66/oH+Zhb1us/C22bTbcs313b2aeGGB85FCyuAgAbM+N2TBSs5/ry6IfRh3lMSNyKurh/1Fvn8LDriXJxb15NMpAzc2212zahfbdvfhgxSQc6v8rGuZXYWfP0UjOjZtyxz0QJqJ44urX74tDIMMHUXicHGuvOTX5lEmsp7V2+RzX24l7IExUqOL+bGCOTIxwXRx/OMkrcTIlurbf3e7cuM1lMXq96P7yunwB8wQoLMzdCheXcDiBgQwVBLdBihmr4MVskj2nNxqj14sFJUVCslsMhCksfLCGi5WUE6QOZG6//XZccMEFmDx5MiorK6Mujicy1FJkKEPY9PucT5BidPzMWk+0N3KrbRq932i24MbFNehqXoau5mWeg5FCPP8UHnF9LayZygerj+y+t6yknCB1IHP8+HFcddVVWLVqVdRF8UyGWooMZQibfp/z+dJv2LIXB/uGMrkqRp8P5AYuVts0ev/OnqMYTY//7ZRfXWZEXonra2fPUT5YfcTvrXNSBzItLS348pe/jHnz5kVdFM9kaP6ToQxaYbQQBL3PRp/vpkXH6P1Oy6z9PL+6zIiccHotU/54XJ1TYtTSww8/jFtuuQV9fX2Wr0ulUkilUpn/DwwMoLq6WulRS3Ek60gaJzPwyrC2Udgjp4zEfcQEGZP1u0vxU7CjllpbW1FRUZH5U11dHXWRyICsNQ1t64a25hnE2kb5tEq5GTkVFDZ7G4t7PpKs310qbKG3yKxfvx7f+c53LF/z+uuv48wzz8z8ny0yFAZtK0PQ6xuJtZoqS0vQ1bzM988PGltkjLHFgsgfblpkQl+i4Ctf+QpWrlxp+ZrTTjvN02cnk0kkk0lP7yUyWgpAX/N08gAvhIc8p383ZrbsBBEFJ/RAZsaMGZgxY0bYm1VKITwIZSce1KKrQJwL/TpHRufKyVpIa5fP4QMvhhjgEYVP6hyZ/fv3o6urC/v378fo6Ci6urrQ1dWF9957L+qiBcos/yDu/e9ObezswYKWZ7Cg5ZnAj4X+XOjnenEyDNsI54AgIvKH1IHM17/+ddTV1aG5uRnvvfce6urqUFdXh9/+9rdRFy1QZg9CJliOy3fafj2rAFF/LvRzvbgZhk1ERP5TYvi1V3FYNFKLXU7j8l1IUU8kaBYlgJYr51p+nt054DkiIsofV79+X9wCGb8F/dBV5aG+sbMHzZv3YDSNrNEmTsuvfd2GLXuVHo1ERCSDgp1HhtwJuqsqqq4wt7lEjYtr0HLl3JwuIqflZ5cfEVF0GMgUsCim8feL1UKOYm0kN4GFUV6L0/JrE4DXLp+DkytLsXb5HPc7RURErrFriZRkNPGY+FllaQnKksW+dWlpu44A5HQ35TsJmipdcEREYWHXUgg4FDpaVosurl0+x9dRQ9quI/HvDVv2Zs6/k5Ybq+uFXVMUFt63KI7YIuMRpyIvHEYtMoOpEfQNDTs+/1bXC1tkKCy8b5EqpF6iIC44FXk8GQUV+tla9TP6OmF1vXA2WAoL71sUR2yRodjSByVOWj6CyrMhIiLnmCNDhNzcEye5KCLfBYDrvBXmHxARhY+BDMWCURChXxfJzRpIYhi1myZ4Ju0SEYWPgYxCWOM3ZxRE6NdFcrMGkpPX6s+HX/Pm8DwTETnHQEYhrPGbsxqOHVRio/58uF0s0ixg4XkmInKOgYxCgn4wq8woiAh6Fep8z4dZwMLzTETkHEctUSypMDeLCmUkIooCRy1RwcuneyasHJWgW4yIiAoBAxmKnY2dPRhMjaCytMRT9wxzVIiI1MFAhmKnraMbfUPDKEsWe2rtiDpHhaOWiIicYyBDSjN66OsDESeBgfY1UXf5sEWIiMg5BjKkNKOHvj4QcRIYyBQ8RN0iRESkEgYypDQnD339DL9ePycsUbcIERGphKtfk9KcrBytn+HX6+doOR06zSHWRETBYosMxV4QrS1Ou6Jk6rIiIoojBjIkLb9G7wTRVeM0oVimLisiojjizL4krQvv3IaDfUM4ubIUz62/OLTteukOiqqsRERxxJl9KRactGYEMeeKl+4gtrwQEUWDLTKktCBaQqJI0GVSMBHRCWyRIaW5aWXxqyUk6gnxmBRMROQNAxmSjpuHul9BR9SBBLumiIi8YSBD0onioR51IMFJ8IiIvGGODMWWKnknqpSTiCgszJGh2HKTPxN1d5FTqpSTiEhGDGRIKW4e+lF3FzmlSjmJiGQkbdfS22+/jW9961vYtm0bDh06hKqqKjQ2NuKrX/0qJk6c6Ogz2LUUP+yGISKKPzfPb2kXjXzjjTcwNjaGhx56CH/7t3+LPXv24IYbbsDg4CA2bNgQdfEoIm4XdyQioniTtkXGyHe/+120tbXhzTffdPR6tsgQERGpJxYtMkb6+/sxbdo009+nUimkUqnM/wcGBsIoFhEREUVEmWTfffv24b777sMXv/hF09e0traioqIi86e6ujrEElJQglhPKYjPJCKi8IUeyKxfvx6JRMLyzxtvvJH1noMHD+ITn/gErrrqKtxwww2mn93U1IT+/v7MnwMHDgS9OxSCIIYnc8gzEVE8hN619JWvfAUrV660fM1pp52W+Xdvby8uuugiXHDBBfj+979v+b5kMolkMulHMUkiq+prMyOV3LAa4eT1M4mISC5SJ/sePHgQF110ERYuXIiNGzeiqKjI1fuZ7Bss2YdCB7EyNhERBS8WM/sePHgQ9fX1OPXUU7FhwwYcPnwYhw4dwqFDh6IuGr1P9u4ZTjRHRBR/0o5aevbZZ7Fv3z7s27cPp5xyStbvJG5EKiiyd89wzhkioviTumspX+xako9f3VGyd2vJXj4iIpnFomuJ4knbHZXPEGjZu7VkLx8RUVwwkKHAGAUq2ryVfB72sue/yF4+IqK4YNcSBcZu1BC7X4iIyAi7lkgKdq0SjYtr8Nz6i9G4uIYz7RIRkScMZALCB3N2oGKHOSVEROQFA5mA8MHs3MbOHgymRlBZWsKcEiIicoWBTEAKOdnTbWtUW0c3+oaGUZYsZq4MERG5wkAmIG66VeLGbWtUIQd9RESUHwYy5Du3gUkQQR9zlIiICgMDGfKdVWASVoDBHCUiosLAQIZCFVaAwe4qIqLCIO2ikRRPYS00yQUjiYgKA2f2pVjgLMFERPHBmX2p4DAnhoioMDGQoVhgTgwRUWFi1xIRERFJhV1LRDqcV4aIKJ4YyFBBYA4NEVE8MZChgsAcGiKieGKODBEREUmFOTIkLeaqEBGRnxjIUKiYq0JERH5iIEOhYq4KERH5iTkyREREJBXmyJAp5qgQEVGcMJApMMxRISKiOGEgU2CYo0JERHHCQIaIiIiUxUCmwKjQtcQ8HiIicoqBTIFRoWtJhWCLiIjkUBx1AShcjYtr0Li4JupiWFpVX4u2jm6pgy0iIpID55EhIiIiqcRmHpkrrrgCp556KiZNmoRZs2bhs5/9LHp7e6MuFhEREUlC6kDmoosuwo9+9CPs3bsXP/7xj9Hd3Y2///u/j7pYREREJAmlupZ++tOfYsWKFUilUigpKcn5fSqVQiqVyvx/YGAA1dXV7FoiIiJSSGy6lrSOHDmCRx99FBdccIFhEAMAra2tqKioyPyprq4OuZREREQUJukDmXXr1qGsrAzTp0/H/v37sXnzZtPXNjU1ob+/P/PnwIEDIZaUiIiIwhZ6ILN+/XokEgnLP2+88Ubm9f/0T/+EXbt24ZlnnkFRURE+97nPwaw3LJlMYsqUKVl/iIiIKL5Cz5E5fPgw/vznP1u+5rTTTsPEiRNzfv6HP/wB1dXVeP7557FkyRLbbXH4NRERkXrcPL9DnxBvxowZmDFjhqf3jo2NAUBWQi8REREVLmln9n3hhRfw0ksv4aMf/SimTp2K7u5u3HbbbaitrXXUGkNERETxJ22y7+TJk/GTn/wEH//4xzFnzhxcf/31mD9/PrZv345kMhl18YiIiEgC0rbIzJs3D9u2bYu6GERERCQxaVtkiIiIiOwwkCEiIiJlSdu15AcxsnxgYCDikhAREZFT4rntZIaYWAcyx44dAwAuVUBERKSgY8eOoaKiwvI1Si0a6dbY2Bh6e3tRXl6ORCKRWUTywIEDnCAvIDzGweMxDh6PcfB4jIOn8jFOp9M4duwYqqqqMGGCdRZMrFtkJkyYgFNOOSXn51y+IHg8xsHjMQ4ej3HweIyDp+oxtmuJEZjsS0RERMpiIENERETKKqhAJplMorm5mTMDB4jHOHg8xsHjMQ4ej3HwCuUYxzrZl4iIiOKtoFpkiIiIKF4YyBAREZGyGMgQERGRshjIEBERkbIYyABIpVJYsGABEokEurq6oi5ObLz99tu4/vrrMXv2bJSWlqK2thbNzc04fvx41EVT2gMPPIAPfehDmDRpEs4//3y8+OKLURcpNlpbW/GRj3wE5eXlOOmkk7BixQrs3bs36mLF1p133olEIoFbbrkl6qLEysGDB9HY2Ijp06ejtLQU8+bNw29/+9uoixUYBjIA/vmf/xlVVVVRFyN23njjDYyNjeGhhx7Cq6++irvvvhsPPvgg/uVf/iXqoinriSeewK233orm5ma8/PLLOPfcc7F8+XK8++67URctFrZv347Vq1ejs7MTzz77LIaHh7Fs2TIMDg5GXbTYeemll/DQQw9h/vz5URclVo4ePYoLL7wQJSUl+MUvfoHXXnsN3/ve9zB16tSoixacdIH73//93/SZZ56ZfvXVV9MA0rt27Yq6SLH2r//6r+nZs2dHXQxlLVq0KL169erM/0dHR9NVVVXp1tbWCEsVX++++24aQHr79u1RFyVWjh07lj799NPTzz77bPpjH/tY+uabb466SLGxbt269Ec/+tGoixGqgm6Reeedd3DDDTfgv/7rvzB58uSoi1MQ+vv7MW3atKiLoaTjx49j586duOSSSzI/mzBhAi655BL85je/ibBk8dXf3w8AvGZ9tnr1ajQ0NGRdy+SPn/70pzjvvPNw1VVX4aSTTkJdXR1+8IMfRF2sQBVsIJNOp7Fy5Up86UtfwnnnnRd1cQrCvn37cN999+GLX/xi1EVR0p/+9CeMjo5i5syZWT+fOXMmDh06FFGp4mtsbAy33HILLrzwQsydOzfq4sTG448/jpdffhmtra1RFyWW3nzzTbS1teH000/Hli1bsGrVKqxZswaPPPJI1EULTOwCmfXr1yORSFj+eeONN3Dffffh2LFjaGpqirrIynF6jLUOHjyIT3ziE7jqqqtwww03RFRyIudWr16NPXv24PHHH4+6KLFx4MAB3HzzzXj00UcxadKkqIsTS2NjY/jwhz+MO+64A3V1dfjCF76AG264AQ8++GDURQtMcdQF8NtXvvIVrFy50vI1p512GrZt24bf/OY3OWtQnHfeebjmmmtiHb3my+kxFnp7e3HRRRfhggsuwPe///2ASxdff/M3f4OioiK88847WT9/55138MEPfjCiUsXTjTfeiJ///OfYsWMHTjnllKiLExs7d+7Eu+++iw9/+MOZn42OjmLHjh24//77kUqlUFRUFGEJ1Tdr1iycffbZWT8766yz8OMf/ziiEgUvdoHMjBkzMGPGDNvX3Xvvvfj2t7+d+X9vby+WL1+OJ554Aueff36QRVSe02MMjLfEXHTRRVi4cCF++MMfYsKE2DUChmbixIlYuHAhtm7dihUrVgAYr31t3boVN954Y7SFi4l0Oo2bbroJTz75JDo6OjB79uyoixQrH//4x/HKK69k/ey6667DmWeeiXXr1jGI8cGFF16YM2XA73//e9TU1ERUouDFLpBx6tRTT836/wc+8AEAQG1tLWtgPjl48CDq6+tRU1ODDRs24PDhw5nfsQXBm1tvvRXXXnstzjvvPCxatAj33HMPBgcHcd1110VdtFhYvXo1HnvsMWzevBnl5eWZ3KOKigqUlpZGXDr1lZeX5+QblZWVYfr06cxD8smXv/xlXHDBBbjjjjvw6U9/Gi+++CK+//3vx7o1vGADGQres88+i3379mHfvn05wWGai6578g//8A84fPgwvv71r+PQoUNYsGABnn766ZwEYPKmra0NAFBfX5/18x/+8Ie23alEMvjIRz6CJ598Ek1NTfjmN7+J2bNn45577sE111wTddECk0jziUJERESKYsICERERKYuBDBERESmLgQwREREpi4EMERERKYuBDBERESmLgQwREREpi4EMERERKYuBDBERESmLgQwREREpi4EMERERKYuBDBFJbcmSJUgkEvjNb36T9fOBgQEsWLAAyWQSzz77bESlI6KoMZAhIql95zvfAQB87Wtfy/zs+PHj+NSnPoXdu3fjkUcewaWXXhpV8YgoYgxkiEhqS5cuRUNDA7Zt24aOjg6k02msXLkS27Ztw913343PfOYzUReRiCLE1a+JSHqvvPIKFixYgAsuuACLFi3CXXfdhaamJtxxxx1RF42IIsZAhoiUcO211+I///M/AQCf//zn8e///u85r/nJT36CtrY27Ny5E0ePHsVbb72FD33oQyGXlIjCxK4lIlLCjBkzAADl5eV44IEHDF8zODiIpUuX4pvf/GaYRSOiCBVHXQAiIjv3338/vve972HmzJl455138Mgjj+CLX/xizus++9nPAgD27NkTdhGJKCJskSEiqf3oRz/CzTffjIsuugi7du1CRUUFWlpa8Je//CXqohGRBBjIEJG0tm7dis9+9rOYN28ennrqKcyaNQtf/vKX8cc//hH/9m//FnXxiEgCTPYlIim9/PLLqK+vx/Tp0/H8889j1qxZAMYnwps9ezZGR0fx5ptvYtq0aTnv3bNnD+bNm8dkX6ICwBYZIpJOd3c3LrvsMkycOBFPP/10JogBgClTpmDdunXo7+9Ha2trhKUkIhmwRYaIYoctMkSFg6OWiCg2jhw5gv3796O7uxsA8Nprr6Gvrw+nnnqqYRcUEamPLTJEFBsPP/wwrrvuupyf//CHP8TKlSvDLxARBY6BDBERESmLyb5ERESkLAYyREREpCwGMkRERKQsBjJERESkLAYyREREpCwGMkRERKQsBjJERESkLAYyREREpCwGMkRERKQsBjJERESkLAYyREREpKz/B/XyIKKlX8aGAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates and saves Figure 9–11\n",
    "\n",
    "X1, y1 = make_blobs(n_samples=1000, centers=((4, -4), (0, 0)), random_state=42)\n",
    "X1 = X1.dot(np.array([[0.374, 0.95], [0.732, 0.598]]))\n",
    "X2, y2 = make_blobs(n_samples=250, centers=1, random_state=42)\n",
    "X2 = X2 + [6, -8]\n",
    "X = np.r_[X1, X2]\n",
    "y = np.r_[y1, y2]\n",
    "\n",
    "plot_clusters(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 314
    },
    "id": "M2ugGS7b3Edc",
    "outputId": "aeced248-8d68-46d3-877d-1560a38c60ae"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9IAAAEpCAYAAAB/WRotAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAxkZJREFUeJzsnXl4E8Ubx79Jj6RpG9pyC4igqCiCSrk8uOQQOYXigRSKokKpimIptMhlC5SKeJSKilKoiEhBTpFDLlGKCNL+EFDkkEOqXG1o0/RI9vdHnGWz2U02yebsfJ6Hh3azOzubJjPzzvc9FAzDMKBQKBQKhUKhUCgUCoUiCaW3O0ChUCgUCoVCoVAoFIo/QQ1pCoVCoVAoFAqFQqFQHIAa0hQKhUKhUCgUCoVCoTgANaQpFAqFQqFQKBQKhUJxAGpIUygUCoVCoVAoFAqF4gDUkKZQKBQKhUKhUCgUCsUBqCFNoVAoFAqFQqFQKBSKA1BDmkKhUCgUCoVCoVAoFAeghjSFQqFQKBQKhUKhUCgOQA1pCiXAmDlzJhQKBXbv3u3trlAoFAqF4rfQ+ZRCodiCGtIUighnz56FQqHA448/7u2uWLB7924oFArMnDnT212RlYsXL+K9995Dnz59cOuttyI0NBSNGjXCsGHDcODAAavzq6ursWbNGowePRqtW7dGREQEIiMj0alTJ3z00UcwGo2S7vvEE09AoVBArVZbvXb16lV88sknGDRoEFq2bAmVSoV69eqhX79+2Lp1q1PPWVVVhXfffRexsbGIjIxEZGQk2rRpgwkTJjjVHoVCofg6dD71LLVlPpV6bwrFXQR7uwMUCkVekpKS8Mwzz+DWW2/1dlcc4sMPP0RmZiZuv/129OnTB/Xr18fJkyexbt06rFu3Dl9++SWefvpp9vxTp04hLi4OEREReOyxxzBo0CCUlpZi48aNSExMxLfffosNGzZAoVCI3vPTTz/F1q1boVarwTCM1eurV6/G+PHjccstt+Cxxx5DkyZNcOHCBaxZswbfffcd5s+fj+TkZMnPeP36dTz++OP4+eef8dBDD+Hll18GAJw5cwarVq3CokWLHHjHKBQKheJO6Hzqu/OpI/emUNwGQ6FQBDlz5gwDgOnbt6+3u2LBrl27GADMjBkzvN0VWVmzZg2ze/duq+N79+5lQkJCmOjoaMZgMLDHL1y4wCxatIgpKyuzOL+srIyJjY1lADBff/216P3OnDnDREZGMm+++SbTvHlzRqVSWZ3z/fffMxs2bGCMRqPF8RMnTjB16tRhQkJCmIsXL0p+xiFDhjAKhYJZsWKF1WvV1dWS26FQKBR/gs6nnqU2zKeO3JtCcRfUkKZQRBCb+EePHs0AYE6fPs28//77zF133cWEhoYyt956KzNz5kyrSYKwbt06pmfPnkxUVBSjUqmYe++9l8nKymJqamoszlu6dCkDgFm6dCmzYcMG5qGHHmIiIiKY5s2bMzNmzGAACP47c+YMwzAMe86uXbss2v3ss8+YQYMGsRNNdHQ006dPH2bnzp2yvWfuok+fPgwA5uDBg5LO//LLLxkAzIQJEwRfN5lMTI8ePZg777yT0ev1Tk2+L730EgOAWb16taTz9+/fzwBg4uPjHboPhUKh+Dt0PvUdAmE+lfPeFIorUNduCsVJkpOTsWfPHgwYMAB9+/bFunXrMHPmTFRVVSEjI8Pi3KlTp2LevHlo0qQJhg4dijp16uCHH35AcnIyDhw4gNWrV1u1v3r1amzbtg0DBgxAYmIidDodunfvjrNnz2LZsmXo1q0bunfvzp4fFRVls78TJkxAu3bt0KtXL9SvXx8XL17EunXr0KtXL6xduxaDBw+W421xCyEhIQCA4GBpQ5a98z/88EPs2bMHe/fuRVhYmEf6tGrVKgDA8OHDceXKFWzYsAH//PMPmjVrhn79+qFu3bpO9YNCoVD8HTqfeo5AmE/lvDeF4grUkKZQnOTw4cMoKipC48aNAQBvvfUWWrVqhQ8//BAzZsxAaGgoAGD79u2YN28e+vbtizVr1iA8PBwAwDAMEhMTsXjxYqxZswbDhg2zaP+7777D1q1b0atXL6t7L1u2DN27d3coQcqxY8fQokULi2OXLl1CbGwskpOTJU/869atw5EjRyTf9/7778eQIUMkn8/n3Llz2LFjBxo3boz77rtP0jWff/45AKBPnz5Wr508eRJTp07Fq6++iocfftipPul0OuTn50OtVuPRRx+VdM2hQ4fY+48cORI6nY59LSIiAkuWLLGIWaNQKJTaAp1PpUHnU3nvTaG4jLclcQrFV7Hnivb5559bXUNeKyoqYo8NGjSIAcD89ddfVueXlJQwCoWCGTZsGHuMuKI9+eSTgv2yF9Ml5oomxiuvvMIAYM6ePSvpfPKMUv+NHj1aUrtCVFVVMV27dmUAMMuXL5d0zccff8wAYHr27Gn1mtFoZLp06cLcfvvtTHl5OXvcUXewZ599lgHAzJ49W/I1d911FwOACQoKYkaNGsWcOnWKuX79OvPFF18wWq2WCQkJYQoLCyW3R6FQKP4CnU+FofOpc/OpXPemUFyFKtIUipO0b9/e6ljTpk0BACUlJeyxgoIChIeHs7u6fMLCwnDixAmr4x07dpSno/9x+vRpzJ07Fzt37sTFixdRWVlp8frff/+N5s2b220nNzcXubm5svZNCJPJhISEBOzduxcvvvgi4uPj7V6zadMmJCUloXnz5vjiiy+sXs/KykJBQQF27doFjUbjVL+mTp2KlStX4vHHH0dqaqrk60wmEwDgvvvuQ25uLpv99LnnnoNOp0NiYiI++OADLFmyxKl+USgUir9C51P3EmjzqRz3plDkgBrSFIqTaLVaq2Mkvodbc/HatWuoqanBrFmzRNsqLy+3OtawYUMZemnmzz//RMeOHaHT6dCjRw8MHDgQWq0WSqUSu3fvxp49e6wWAt7EZDLh+eefx5dffomRI0di8eLFdq/59ttvERcXh4YNG2Lnzp2siyDhjz/+wIwZM5CYmIhu3bo51a+33noL8+bNQ8+ePbF27VoEBQVJvrZOnToAgIEDB1qVEBk0aBASExPxyy+/ONUvCoVC8WfofOo+Am0+lePeFIpcUEOaQnEzWq0WCoUCV65cceg6W/UaHWXhwoW4fv068vLyMHLkSIvXxo0bhz179khuy90xXSaTCWPGjMHy5cvx7LPPIjc3F0ql0uY1mzdvxrBhw1CvXj3s2rULLVu2tDrn2LFjqKysxKJFi0TrNZP3/Pr161bJZt566y2kp6eje/fu2Lhxo8OJTe666y788ssvgklsyLGKigqH2qRQKJTaBJ1P6Xzq6r0pFDmhhjSF4mY6deqELVu24OTJk2jVqpXL7ZFdW+4uvT1OnToFAFYJUBiGwY8//ujQ/detW4dly5ZJPn/06NGSJ37upP/0008jLy/P7i41mfRjYmKwa9cu3HHHHYLn3XbbbXjhhRcEX1u1ahUqKiqQkJAAAFCpVBavk0m/W7du2Lx5s1OuZD179sSKFStw7Ngxq9fIsdtuu83hdikUCqW2QOdTOp+6cm8KRXa8HaRNofgq9pKjkDqTXIQSk2zZsoUBwDzyyCPMlStXrK65dOkSc+zYMfZ3bt1LIY4ePWoz6YhQH0iNxm+//dbi3Dlz5rBJTKQmU3EXRqORfW+HDx/OVFdX273m22+/ZVQqFdOoUSPmxIkTTt/bVoKSt956iwHAPProo0xZWZndtkpKSpjjx48zf//9t8Xx0tJSpl69eoxarbZInlNZWcn069ePAcAsWbLE6WegUCgUX4XOp54l0OdTZ+5NobgDqkhTKG7m8ccfx1tvvYW3334bd9xxBx5//HE0b94cV69exZ9//okffvgB6enpaN26taT27r77btxyyy346quvoFKp0LRpUygUCrzyyitsHC6fcePGYenSpRg2bBieeuop1K1bFwUFBTh8+DD69++PzZs3y/nITjF79mwsW7YMERERuPPOO5Genm51zpAhQ3D//fcDAE6cOIEnn3wSlZWV6N69O1auXGl1/m233cbuTDtDbm4u3n77bQQHB6Njx47IysqyOqd79+4W9Ue/+eYbjBkzBqNHj7ZIIqPVavHpp58iLi4OnTt3RlxcHKKjo7Fjxw789ttveOKJJ1zqK4VCoQQ6dD6VRqDPpxSKr0ANaQrFA8yePRtdu3bFBx98gO+//x4lJSWoW7cuWrRogZkzZ+K5556T3FZQUBDWrl2LlJQUrFy5Ejdu3AAAjBw5UnTif+CBB7Bt2zZMmzaNTerx0EMP4ccff8SGDRt8YuI/e/YsAKCsrAwZGRmC59x2223sxF9cXMwmdPnqq68Ez+/WrZtLEz/pU01NDRYsWCB6Hnfit8WQIUOwZ88epKenY8OGDdDr9WjVqhUyMzPxxhtvOJS8jEKhUGojdD61T22YTykUX0DBMAzj7U5QKBQKhUKhUCgUCoXiL9hO3UehUCgUCoVCoVAoFArFAmpIUygUCoVCoVAoFAqF4gDUkKZQKBQKhUKhUCgUCsUBqCFNoVAoFAqFQqFQKBSKA1BDmkKhUCgUCoVCoVAoFAcI6PJXJpMJf//9NyIjI6FQKLzdHQqFQqFQZIdhGNy4cQO33HILlEr37Y/TOZVCoVAogY4jc2pAG9J///03mjVr5u1uUCgUCoXids6fP4+mTZu6rX06p1IoFAqltiBlTg1oQzoyMhIAsPTAt9BEhHu5NxSKf/H0vd0AAO+88w5iY2O93BsKhSJGeXk5+vfvz8557oK0v3nzZoSH0zmVQpGK0WjEY489BpUqBGe2FHm7OxQKxQY3ystw18AOkubUgDakieuZJiIcmsgIL/eGQnGdLXn5WJ2Ti+GJCegXH+eRe3bv3t0j96FQKK7hbndr0n54eDgiIuicSvF/8vPzkZubi4SEBMTFuW9ONRqNAMzfIW2Eeze8KBSKPEiZU2myMQrFj1idk4vLFy9hdU6ut7tCoVAoFIpfk5ubi+LiYuTm5rr1PqdPn3Zr+xQKxTtQQ5pC8SOGJyagfpPGGJ6Y4Nb7kN1zCoVCoVAClYSEBDRq1AgJCQluvU9WVhYA4JZ6t7j1PhQKxbNQQ5pC8SP6xcfh8/2b3O7Wnf7CJLe2T/EN8vPzMWDAAOTn53u7KxQKheJx4uLisGnTJre6dQPmjPcAkPTsWLfeh+JdlqzJQ+tBnbBkTZ63u0LxENSQplAoVtwoLQUAxMTEeLknFHfiKbdGCoVCoVACnQXLsnG++CIWLMv2dlcoHoIa0hQKRRRaKzaw8ZRbI4VCodRmKioqvN0FigeYNDoJzRo1waTRSd7uCsVDBHTWbgqFQqGIExcX53aXRgqFQqnt/P777wCApg2beLknFHcydlg8xg6L93Y3KB6EKtIUCsWK3w+Z61yGhIR4uScUCoVCoQQGT3Tt7e0uUCgUGaGGNIVCEeXjjz/2dhcoFAqFQvF7lEoaKkWhBBrUkKZQKKI0aULd0CgUCoVCoVAoFD7UkKZQKBQKhUKhUNzAzz//7O0uUCgUN0ENaQqFYkHJ1Wve7gKFQqFQKAHBDz/8AABocUtzL/eEQqHIDTWkKRSKBVtW5Hu7CxQKhUKhBBTt773f212gUCgyQw1pCoViAVPDAKAZuykUCoVCoVAoFDGoIU2hUCgUCoVCobiBI0eOeLsLFArFTVBDmkLxY7bk5eP5LgOwJU8+d+x/LxXL1haFQqFQKP5Afn4+BgwYgPx8ecOb/vjjDwDAoO6Py9ouhULxPtSQplD8mNU5ubh88RJW5+TK1ub3qzYAADQajWxtUigUCoXiy+Tm5qK4uBi5ubluaX/IYwPc0i6FQvEe1JCmUPyY4YkJqN+kMYYnJsje9tKlS2Vvk0KhUCgUXyQhIQGNGjVCQkKCt7tCoVD8hGBvd4BCoThPv/g49IuP83Y3KBQKhULxa+Li4hAXJ/98ajKZZG+TQqH4BlSRplAoFAqFQqFQZKawsBAMw3i7GxQKxU1QQ5pCobDcKNGxP8tV/spdCVwoFAqFQvFlrl69CgC4veltsrS3ZE0eWg/qhCVr8mRpj0KhuAY1pCkUCsvli5fYnxs3bixLm+5O4EKhUCgUii+jUYfJ0s6CZdk4X3wRC5Zly9IehUJxDWpIUygUt0ITuFAoFAqF4jqTRiehWaMmmDQ6ydtdoVAo8DNDet68eVAoFJg4caK3u0KhUCQSFxeHTZs2uSWJC4VCoVAovsq3334ra3tjh8Xj+IYDGDssXtZ2KRSKc/iNIX3w4EF8/PHHaNu2rbe7QqEELH8UHvN2FygUCoVCCQh+/vlnAED8oKe93BMKheIO/MKQLisrw3PPPYdPP/0U0dHR3u4OheIwW/Ly8XyXAdiS59sJtz56ax4AIDQ01Ms9oVAoFApFGH9LYhk/8Blvd4FCobgBvzCkJ0yYgP79+6NXr142z6usrIROp7P4R6H4AqtzcnH54iWszsm1OO5zBvZ/9S6Tkmj8FYVS26FzKsVXEUti6W8GNoVC8W983pD+6quvcPjwYcydO9fuuXPnzkWdOnXYf82aNfNADykU+wxPTED9Jo0xPDHB4riYgc3F54xtCoVSK6BzKsVXEUtiaa9KhKcN7aqqKo/ch0KheAefNqTPnz+P1157DStWrIBarbZ7/tSpU1FaWsr+O3/+vAd6SQkU3Gmw9ouPw+f7N6FfvGXCLTEDm4sUY5tCoVDkhs6pFGdxt8EqlsTSXpUIT5ZjvHbtGmpqagAAapXK7fejUCiex6cN6UOHDuHff//Fgw8+iODgYAQHB2PPnj344IMPEBwcDKPRaHG+SqWCVqu1+EehSMUbBquYgc1FirEtBwzDwGQ0u3bXqVPHrfeiUCi+D51TKc7iSYOVi70qEZ4sx1hZWQkAiKkTjZDgELffj0KheB6fNqQfe+wx/O9//8ORI0fYf7GxsXjuuedw5MgRBAUFebuLlACidfu2UAYp0bq9/czwUtVrOVRuKca2HDAMw/7cv39/t96LQqFQKIFLQkICtFot9Hq9JFVaqoLtqtLtjXKMSoXCY/eiUCiexacN6cjISLRp08biX3h4OOrWrYs2bdp4u3sUP0bIwD1+qAgmownHDxXZvV6qek3dsikUCoUSyAgZt3FxcdBoNNDpdJJUaakKtreUbgqFQhHCpw1pCsVdCBm4jrhQSz3XU27ZFP+DZpelUCiBgJhx64gbtdRzPema7SqZmZkAAKWSek96giVr8tB6UCcsWZPn7a5QahEKhuvPGWDodDrUqVMHq37bA01khLe7Q/EhtuTlY3VOLoYnJrjdZdpf+HLhx1i58BMAwC+//OLl3gQ+AwYMQHFxMRo1aoRNmzZ5uzsUP6asrAzdu3dHaWmpW+OYyZy6e/duRETQOZViJj8/H7m5uUhISPCoy7SvM3DgQFy6dAnZaZlIGPyct7sT8LQe1Anniy+iWaMmOL7hgLe7Q/FjdGU3cEvP1pLm1GAP9YlC8Sn6xcdRA5rHpqVfAQBCQ0O93JPaQUJCArv4pFAoFH8lLi6OGtA2uPf2e7zdhVrBpNFJWLAsG5NGJ3m7K5RaBDWkKRSKBffff7+3u1AroItPCoVCoVDkYeyweIwdFu/tblBqGTRGmkKRAXfWoKZQKBQKpTYRCDkkLl++7O0uUCgUN0MNaQrFCfiGcyBk59aXlXu7CxQKhUKphfAN50DIzl1TUwMAuOf2u7zcEwqF4i6oIU2h2EBMaeYbzoGQndtYYwQAtG/f3ss9oVAoFEogIqY08w1nf8rObYtQVSjCwzTe7gaFQnET1JCm+C3ucKeWqjTzDed+8XH4fP+mgEhg9sILL3i7CxQbBILLI4VC8T3cMbZIVZr5hnNcXBw2bdpE80hQ3A4tm0VxBWpIU/wWd7hTS1Wa5TacaYw1RSqB4PJIoVB8D3eMLVKVZrkNZ29vOFZXV3vlvhTHWbAsG+eLL2LBsmxvd4Xih1BDmuK3uMOd2ltKcyDEWFM8Q6C4PFIoFN/CHWOLt5Rmb284pqamAgBCVbQ4jq8zaXQSmjVqQstmUZyCGtIUv4UYuQBkU3OFDGeiFmclpVr8L6d6bGtTwBNq9dK577utbYq8UJdHCoXiDsjYAkA2NVdovCJqcVpamsX/cqrHtjYFPKFW63Q6AEDa82+67R4UeRg7LB7HNxygpbMoTkENaYrfI1XNFTNI7RmqpP19m7db/C+HekzuDcDCgOf2yRNq9YlD/wMAhIaGuu0eFAqFQvF9pKq5QgapFCOVtL99+3aL/+VQj8n9AbAGvDczgisVdJlNoQQy9BtO8XukqrliBqk9Q5W0/0j/3hb/y+FSLnbv5VmLcPniJSzPWuTRjODh4eFuvweFQqFQfBepaq6QQSrFSCXt9+7d2+J/OVzKhe6fk5OD4uJi5OTk2H0+CoVCcQQavEHxe/rFx4nGMHMN1eGJCez/XMSOS2nfVYYnJmB51iJUlJdjS16+4H3ceX8KhUKhULjExcWJho5wDdWEhAT2f4LQMUfad5WEhATk5ORAr9cjPz9f8D7uvD/ht99+c2v7FArFN6CKNCWg4aq5YonDvFm6ql98HMLCw1FWorNQpUclT0D9Jo0xKnmCR/rx++H/eeQ+FN/F21luKRSK78NVc4Xin72dwyEuLg4ajQY6nY5VpRMTE9GoUSMkJiZ6rB8GgwEAMLzvYI/dk+Jb0LJatQNqSFMCGilGsidLTwndS8h1m9tvT/TPWFMDABg7dqzb7kHxbbyd5ZZCofg+UgxlT27KCd3LXqZwT/VPoQAaxNR36z0ovgstq1U7oIY0pdZD3L8XT8+UZKy6YtgKxUTbM/Y9WRrr6aefdvs9/IXaptDSuEEKhSIHZFNu/vz5ksZPV8ZaoQ1Ae8Y+3TT0DrVNoaVltWoH1JCm1HqGJyZAGaSEyWiSZKy6Ytg6kzjMk8nGKDepbYstb7tkUiiUwCAhIQFKpRImk0nS+OnKWOvMBqC7Nw2rq6vd0q6/U9sUWlpWq3ZADWlKradffBzGzU6xaaxyVejhiQmIiNKiorzc4ZrSzsRjuzuGm2EYt7Tr71CFlkKhUBwnLi4OkydPtjl+clXohIQEaLVa6PV6h2tKO7MB6O5Nw88//xwAEBIc5Jb2/RWq0FICEQUTwKtonU6HOnXqYNVve6CJjPB2dygibMnLx/KsRQDMSbZ8MUP1810G4PLFS6jfpDE+37+J/Z1Ajvsj365Yg4+mzgEA/PLLL17uDYVCcZSysjJ0794dpaWl0Gq1brsPmVN3796NiAg6p/oq+fn5bKmnxMREn/QyGTBgAIqLi9GoUSNs2rSJ/Z1AjvsjH3zwAZYvX44nHxuAvLmLvd0dCoXiILqyG7ilZ2tJcypVpCleZ3VOLspKdFaZq53BlfhlW9fy3auJOzgAKIOUfu12rS+9AQAICgqc3fPaFt9MoVAohNzcXOh0OovM1c7iylhq61q+xw9xBwcApVIZEJ5ACii83QXZqG3xzRSKVKghTfE6xFU6IkrrskHqTPwyMaCXZy3C5YuXsDxrkZVBzXev5rqDj5udAgCiRjjfQJdi7Hsyk3ggUtvim70N3bigUHwH4iqt1WpdNkidGUvJeJCTk4Pi4mLk5ORYjQ9892quO3jv3r2Rm5srOp7wxxt744+nxyeTyeSR+3iS2hbf7G3oxoX/QA1pitfpFx+HlUW7sLJol9Nu3cTwbN2+LRu/LNUIJcY3YHbRBiDJGOca17YMeP5rUox9T2bqDkRofPNN5F5ECrVHNy4oFN8hLi4OO3fuxM6dO5126ybf83bt2rHxy1LHEDIeAGYXbQCSxgdiXBcWFto8nz/e2Bt/PD0+kfepTkSkR+7nCWh8803kNnKF2qMbF/4DNaQpfgtXtSWG5/FDRQgLD3fITZy4bY9KnoDP92/CqOQJkhOP8dvgX7MlLx8V5eUWaruULNyezNSdl2WOpSNudf4I37jzVAZqf1Bi5V5ECrVHNy4oFP+GP5aR73lhYSE0Go1DbuJkPEhMTMSmTZuQmJgoOfEY93qh8/Pz86HX6y3Udnvjj6fHJ5K1+93JGR65nzvgG3eeykDtD0qs3EauUHt048J/oMnGKH4BMZaHJyawqjU3AdjwxAT2dQBW58rZj8XTM2EymqzuK3QvfpIyX2Tgre0BAO+88w66d+/u3c44CT9xTaDf1xHy8/ORm5uLhIQEWTYW5G6P4jo02RjFUfjfY/5Yxn0dgFu+8+Qeer0eOp2ONXZt3csfxtxOnTrBaDSi7OcL3u6K07Qe1Anniy+iWaMmOL7hQMDf1xGWrMnDgmXZmDQ6SZaNBbnbo7gOTTZGCTiEXJ25qi3Xzdqd5aJW5+TCZDTHP7Vu39auC7YcyrKn4qVvvfVWt7bvTryliPqDEiu3Oh+I9ab9wbOAQpETvmcJfyzjfs/d9Z0nfaiqqoJSqUS7du3setC4OubS77o0vKWI+oMSK7c6H4j1pv3Bs0AuqCJN8QuEFGlv9cMRRVoO3K1qE0X666+/RsuWLWVvn0LxdfxB5bIFVaQpjuILniXOKNKu4u7vemlpKR577DEA8GtFmkJxBX/wLLAFVaQpAYccKjNX2XVW5eVm6+Yr4e7CnfHSNTU1srdpC1tqAFUKKN7CHzwLKBQ5kUNl5o7ZzozfpA/cGGp3e7y4+7teWFgIAAgNCXVL+1zsqX61SRWk+Bb+4FkgF9SQpvg0Qgavs8e4btiuZMXuFx/HKtGeKE/lTmP90pnz7M+ecO225bYnJSmWM4s1aqBT7BGI7uoUihBC46Gzx7hjtr9k7vfUd/2OW1u4tX3AftIrKUmxnDG2qYFOsUcguquLQQ1pitexpQ4LGbzOHuMqu2Iqb1ZSKga36ICspFSbfQ7E8lTBwcFuv4ctNUCKUuDMYs1fFngUCoUiB7Y2D4XGQ2ePccdssfE7LS0NHTt2RFpammh/6RjtHPZUPymqoDMZqGlpJgrlJtSQpnid5VmLcPniJSzPWmT1mpDB27p9WyiDlGjdvq3Vea3bt2WNcv61QgnJAHMMclZSKp7vMgA/bNwGk9GEfZu3W/WFGPxZSalWJa0o0rClBkhRCpxxy5N6ja8q177aLwqF4nvk5+dj/vz5ooYpfzwUKifFPa9du3bs+CMlIRlgjkNOS0vDgAEDsG3bNphMJmzfbj2nkrEtJiaGTTbm75w+fdpj97Kn+klRBZ1xwZV6jS8q177YJ4p/Qw1piqzInWFayK35+KEimIwmHD9UZHXe8UNFrFIsxQWbKMv7Nm/H5YuXEKpWQRmkxCP9e9s8t6xEh7DwcK8mPpODC6fPebsLDuGMW57Ua3xVFZGjX2LGuKPHKRSK53Dme5ibmwuTyQSlUim4ecgfD3Nzc6HT6aDRaCzGSHJeYWEhO/7ExcWxycDE+kTGq+3bt6O4uBgqlQpKpRK9e1vPqeTcEydOwGQysfHF/gwZp9u2ute7HZGIMy64Uq/xReVajj7ZMsbFXqMGfOBCDWkKAPkMYEdcnsk9H+zaBfWbNMao5AmSzm/dvq1o8q3hiQmIiNKiorwcWUmpWDw9U1J5qkf690b9Jo3xwrTXsf7MQSRnz7F7biCo0fMnpADwjFu3r+OrCacSEhKg1Wqh1+sdNmzJQjwnJ0fQGBcz0n11U4FC8Rfk2Ixy9HvIVZcnT55sc/OQ9K9du3Y2x7127dpBqVQiJibG5lhCIONo79690ahRI0ycOBE///wzMjIy7J7ra2OvM5BCOO8kv+3lnngfX0w4RfrUuW0Hp2PDZ32UKWqMixnqvripQJEHWv6KAkC+EkuOlKly9J5C52/Jy2ddwh/s2gXHDxWhorwcZSU6KIOUMBlNUCgUCK8TiVHJE2RVkH2lJJcrDG7ZCaaaGiQlJQXEIiZQISVblEql3QWy0HVarRYajcaqpIxYCRxfKI1DkQ4tf+V7yFFmydHvoSP3FDo3Pz8fOTk5AIDExEQAwPz581mF22QyWXy+EhMTZR0fAmHc6datG8rLy3Hh+98QFVnH292hiEDKMwUpg7AgOV2SIk+uidZGIUITjkmjk6yuW7ImDwuWZVu9Jnac4pvQ8lcUh5GrxJK9DNNc5dvRewqdvzonF2UlOpSV6LB3w1ZcvngJACyU4/A6kSgr0WF51iK7qrs9ZZ77uq3Ybn/HFTXFG27Bge6KnJCQwC5kHVGJieKTmJgo6N4u5vZOs1hTKK4hh4eLlO8hd+xz5J5C5xI3b51Oh/nz5yMnJ4c1oolqnJiYCI1GA51Oh5ycHLvjrr2xmbyelpZmM7bbXxDTppx17fWGS3BtcEOeNDoJQcogGE1GySoxUbNnjE8RdW0Xc3uvTVmsaxvUkKYAcG+JJS5c129H7yl0PnHlVigUAABlkBIPdu0CAGjT6UF8vn8TRiVPQP0mjQHArtu5Pdf0wMvWLTzpu+La64w7opSFlq3FWqC7IsfFxWHy5MkOL8ydNYhpvW8KxTU8tRnFHfscuafQuSSMRKFQwGQyAQDrel1YWMgqxcQIB2B33LU3NnNjqm3FdvsDer0eer0eABASHGLxmrOuvc5c52p96drghjx2WDwWJKc75HruijHsTFw1xT+ghjTFo7iifAupxf3i4zAqeQLC60QiIkqLcbNTLBKOkXO4BrWte9vrH/d10p692G5fxlRjBABERkZaHHdFTXH0WqkLLVuLNV+Nb5YTT6rEtt7zQN+0oFD8CVfGPv6mWFxcHHbu3Ik+ffpAqVSiS5cuVgnHyHmbNm1CYmKi3Xvb6x8/TtqR0BVfo6ysDACgDlUhPExj8Zqz8cLOXOdqfWlfjG12B55UiW2957Vh4yKQoYY0xaPwy045ktxMTA0m7t0ki7ZQKSxH+sfN9M033oVKaPlrfDSXoUOHWvzuitHGvVaKemlvoUWS3dgqjUJdkc3IpRa7Wu+bQqF4Bn7ZKUczfAttihUWFlpk0RYqheVI/7iZvoWM902bNiEjIyNgxnC+EQ04b7Txr5OiXtoyhJesyUOZvhzR2ihRQ5m6Id9ELrXY1t+ktmxcBCrUkK6FyF2iSkp7/HOccZEWU4vF6kXzlWmp9yTnLZ6eycZB+7srt72/EXGNlxsp6qU9I5i/qAt0XDGG5VKLXa33TaHUFtwR6uBMuIsz332xTTGxetF8ZVrqPcl5JO7a3z1avBneIkW9tGUIL1iWjeu6EkRowmuNoeyKMSyXWmzrb0I3LvwbakjXQuSO85XSHjmHJPzilrAiRl5WUqqFscc/DsBCASav848TuAb2lrx8VJSXIyJKa9etfHhiApvxG0BAlLryVmy3FDWZi9ACxZXyT/6IK8YwVYspFM/ijlAHR8Jd+GWsiGqclpZmNZZyE3sNGGCeO7mbYuR1/nECd3zhltqyN95wkyUC8PsxSuzvc/ToUbfeV4qaLHQN14icNDoJ0doolOnLa01MrivGMFWLKfaghnQtxJk4ZVuKplB7/PPJOYA54dfxQ0Ws8UtU3x82bhNUkPdt3i7q0k2UY6F+cd20l2ctsnD/Fuon1zAfNzuFjX8OBPdt/t/IaDQi+ckxst+Hbwhz1WRnk4bFxcWxWWL9WcWQiivGMFWLKRTP4uz31dZ4yG+Tfy73dTJmFhYWYtOmTdi/fz+Ki4uxbds2q7GUm9jLVv34+fPnC/aL66adk5MDnU4HjUZjVTqP9JVrmJNkiWIVBPwJsb/5woULAQCtW94py334RjBXTQYgSWXlG5Fjh8UjQhOO67qSWhOT64oxTNViij1oHWmKJOSo+QwI115+tm0PlJXooApTQxsTzb5Gzm3dvi2OHypi/+e+vnh6JkxGE5RBSoybnWKhVq/OyWVrSkdEaREWHm5V85nbTwCy1NL2ZSoNBsyMfwVHDxy2OJ6VlYUePXo41JZQzU9+bVLuOWSRZqvGKa1r7D3oe+w/8P9WtI60/+FqzWcC/7PQs2dP6HQ6qNVqREVFWXyfybnt2rXD/v37AVjWgs7Pz7eoG81N/EWu1ev10Ol0orXpuX0F4HItbX/gwIEDeOWVV1jF/bZbmuHouv0Ot8OvNUzqFjdr1ATHNxyweJ0YyOQ1qW2KHaPIC32P/Qv+3ytg6kjPnTsXHTp0QGRkJBo0aIAhQ4bg999/93a3Ap4tefl4tm0PPNu2h5Wi7ErNZ8C6hBVpPyJKixemvS6o/pIyVkLZuMfNTmHdsFfn5CIrKRWDW3TAZ+kLLWpKP9i1CyrKy7E8a5GFCzm3n3LV0vZVvnjnI8Td+TBrRLe/px3uveNuAEBKSorD7Qmpx2KxddySKbaUm0Cpa+xIDJ2vlJOi2bj9B/q38i/y8/PRs2dP9OzZU1BVtoetc/ljY5cuXaBUKtGtWzfRMfOBBx4Q9PIhZfaIG3ZOTg7bbxLbDJhds7t06QK9Xo/33ntP9LkCPdREp9MhNjYWEyZMYI1oADj793kcO3XC4fb46jFfSeWqo1JVViFF1R9VVkfinH2hnBTNxO1fuPL38mlDes+ePZgwYQIKCgqwfft2VFdXo0+fPigvL/d21wIakgW7rERnVUKKb+TyjW5+3DJgOzs3P+M2/zUSV/1s2x7QXbtuEeNMVOdH+vdmDeB9m7fDZDShylCJiCjzLtLwxAQcP1TEPhPXVdxW5m1vJGWTG5PJhAWvvYVBzWOx6oMlAIDmjZuh5Kez2JO7GfuWb2HPmzJlis22bLkXEvgZZLlxev5mDLuCPRdJoXO9bRS5a9HrKxsFgUSgGyiBRm5uLnQ6nYXhKjQe8mOYiXs0V3G2932ylZyRjDU5OTkoKSmBQqGwyF9B7kVKUQFg+w2AVWYSEhJQWFgInU4Hg8Eg+FzkflL77QzeGlvOnTuHhx56CD179mSPtXu4IzaeO4QGTW8BADw08nG77QjFLwsZzsBNN25yDQC/M4ZdYdZHmThffBGzPsq0e64vGLHuiq32hU2CQMSVv5dfuXZfvnwZDRo0wJ49e9C1a1e751PXbufYkpeP5VmLAACjkifYjA8mbtkALNyjiSs1ca0mhi7frVvI1ZvbD657NrkH10Dn3mt4YgKOHjiMfZu345H+vVkFm5TC+mHjNoSqVWjWqiVO/3YCj/TvjeTsOez9+O7ojrqz20Pu9uzxff5GvPfGTPb3Fk1uxeacr3Fr46YW5326OhevZ00DAGzcuBGNGzcWbM8ZV0SibAS6ax8froukvWcPdHd2Rz43voA/vu/Utdu3yc/PR05ODgBLV2o+5LtC4LpHE1drbogMCZkRct8W+vzy3bPJPbibn8XFxazbNt8FnNybfMaqqqpgNBpRU1ODPn36ICMjw+pZSPvuGAc8PbacPHkSzz33nIX6HF5Hi8/2b0T4f9+H6qoqDL2jCwDgmX7DsGTW+6Lt8V23pZwHQNI1gUazXm1wXVeCaG0Uzu+wndRNzK06ENytpX5mfAl/fN8DxrWbT2lpKQAgJiZG8PXKykp295S7i0pxjH7xcVhZtAsri3ZJTrKlUCgs3KOrK6tw+eIlVFdWWRjRUjJHE5V7edYiDE9MwKjkCYiI0kIVpkZFebloArPVOblIzp6D9WcOIjl7jkU96X2bt4NhGGhjolF69TpMRhOOHyqyuC/ftZuf9dtVNdlTruMrFizG0JYdWSNaow5D04a34LWR462MaAB4cXgCIiLNdS/j482DnFj2bEddEYmyUduUM+Iiae/ZbS16bSnVUpUYX1CD/U099RUPAV+AzqnyEBcXh507d2Lnzp02N2dIdmsAUCqVrHs02ZCcP38+m52bm3fC3meVuJbn5OQgISEBiYmJ0Gq1UKvVFtUQyHcVAJvAjNtv8npVVRV0Oh1CQ0NRt25dMAxjpYDzv/fc3z1R715OysvL0atXLzz77LOsEa0MCsK49BR89b9drBENACGhoXhm4ksAgK+2rIG+Qg9AWEmUqoJxz6utWaRnjE9Bs0ZNMGO8eBiaPbXellItVen1tiLsj39/X/AQcCd+o0ibTCYMGjQIJSUl2Ldvn+A5M2fOxKxZs6yOU0XafQgpylvy8vHRtHlgGAYRUVqsLNoleC43WRhXpeWr3HwFmntsS14+PktfiCpDJR4d2IdVmLn3IgY8SUgGQFQFF3s+rrLui4nItuTl48uFH6PkynUA5q903agYfP3O50iYNsHuDubJv07jgeFmL4+ZM2di8eLFfqUi+iv2kgiJqVhSlRhvqcFyqbreUIepIn0TsTmVKtLuQ+jzl5aWhq1btwKwVJCF3L6FPGFIAjL+9ULjQ35+Pt577z1UVlZaqMzce5GM3VqtllWqpX5fHPHW8Sb5+fn47LPPcO3aNRiNRvZ4aJgKdWKicflisc31wJO3d0JNdQ0a1q2PU1t+9Usl0d+w9x6PmZaEtTs2YmivgVianu3QtY6eJzdyqbreUIepIu0jTJgwAUePHsVXX30les7UqVNRWlrK/jt//rwHe+jfkCRdWUmpDl0nFGO8OicXDMNAGaTEqOQJoueuzsllM24LqbRE5SYIKcTLsxahssIAhmFYhZkY6ESlJteRrN624qL5ECMc8N160t/nb0RO2lyUXLkGgEGYSo3f1u3HX9uK0KltrOAOJn9XtVXzlujVuTsA8+I5Pj7eoiapq8qBp5RRX1BgHcFeEiGxsl9SlRhvqcFyqbreUIdrUyy/Peic6hxCycWkIvT5I2ovUanFzs3NzWUzbgt95xUKhcVxIZU4JycHBoOBVZm5x8l3MTExkS1l5ej3xV4ffQGi/l++fJk1osO1kfjqt91Y8/tPGJ44xm7Jz09+2AAA+OfqZeRvWy9r/WZPqqLeVmAdwZ5aW1B0EEaTEQVFBx2+1tHz5EYuVdcb6rA/JrdzBL8wpJOSkrBp0ybs2rULTZtau6YSVCoVtFqtxT+KNEiSrn2bt7vcFt9wFXOL5p9HGJU8AfWbNMajA/tgdU4um8SMqyJzDdyIKK1FEjJioCsUClT8l5jO0VrQpM+t27e1qCcN2E6e5kn+Pv2XhQs3AISrNbj8w59ofksz9pjQICY0mK774AvWrfCrr76yqElKlFFH4Bq1rhhEjhjHnjK8hGql8vsnpd/2FqFihrDUxau989y18SCXAS+1HX/bQPEX6JzqHELJxVyBfA9IOSqxzzv/PAJx5Y6MjARw8/sCgB0fyNgJgP1bc93Hq6qqoFQq0a5dO6c2m8g9iWu6vWfxBsSFu2PHjhZx0OvO/Iyvju5G+H/vn5iAwA1dq39LQ7R9KBYAkDBtAl4YOpKt3zwpa5pThikxakniLWeNIUeMY08ZXtw+ifXPXr/tGWy2jGCpxp6989y18SCXAS+1HX/aQPE2Pu3azTAMXnnlFXzzzTfYvXs3WrVq5dD1NNmYdLKSUtkkXdwEXM5iy7Vaijv18qxFKC+9AYZhUL9JY9a1mtSa5teUFrq3LXdse0nOhFzOAc8nDBPitwOHkfHSm7hx3ZwzICgoCBOeGYtvvt8k6jpDXGs6t+2AHQW7UVlVCVWoCjPGp1icf/TkcXR+rjcAIDs7G6mpqawL386dOx3qJ9dtUCgpjjPt2HMD9JRbrpRaqd5MsiX1fXC2j77m/uxvCc3khiYb8y2kJhdztE3ynSPGLb/Ws63rSkpKYDAY2M8H3zW7Xbt2KCwsFE16SBKUiX3H7CU5E3Pn9pXv7muvvYYff/yR/V2pVCKqfl088+pY0aoeq3Ny0bp9Wxzeux/VlVUIUYVaJGhlGAaDb+sAhmFw7x2t8eKwUZiUNQ1Gk9Ep12DiVhytjUKEJtxpV1lH3JM95ZYrJaGaN93jpb4PzvTRF12fa3soQsC4dk+YMAFffPEFvvzyS0RGRqK4uBjFxcWoqKjwdtcCDm6SLj72Em0JvU6M58XTM9G6fVs0atQItzW/DWs/XGpRx1kIUhKLYRgr9+4qQyUuX7yE44eKRFVmsltMlG0hd2xbic9suZx7s9a0rqQUw1s/iinDX8SN66UICQ7B9HEpKN3/F+a89pbNXVKyq7x2x0Zc15VAbzB/h8YOi7fYeWzTqjVat7wLgHlhwXXhcxSummir1Is9JSIhIQFardYiKY4Yrigljigi3GcTU0295VZNFq1SEpU520dnlX+51CcppdgoFG9hK7mYve+A2Ovcknrdu3fH7bffjvr16yMzM9OiXBYfcp3BYABgzrQt9HphYaHg2EnGVDIXiH3HbI0Jtty5vfndNZlMGD58OGJjY1kjWqFQYOXRXVh/9iCWHfxOdOOfrCH2bd6OshIdKivM7y/XC++7L9ZgxrIPAAC//XkcPTo+ggXJ6U4ri0RNnDE+RXC+l6oiOuJm7qhbrrNKppSEat5yq16yJg+TsqZJSlTmTB9dUf3lUo7tlWKjiOPTirRCoRA8vnTpUkmDLlWk5cGeCkuSg3ETi2UlpaL6sg4jR45E9x7dER0VDfJRu3LlCvbt24e167/BxCXWNQGFkpWR3V+AweWLxbij7T1YuCnP4hp+IjNbpbYA8YRjttRqb3Dp7HlMe3Y8/v3PlV2hUOCzWR/gqcefBCCt1AMAVpHO37YODMzZvP/de9Jq5/FGeRka97gbADBo0CBMnz6dbdMVJZJ/rTPltNyhWnhaEXG3msstPSakVtlLcCalb848g5xJhnxFxfIVqCLtP9j77Ap9f/Pz8/HLL79g6NChePjhh9GgQQMwDAOGYXDlyhX8+OOPyMvLw2+//WblOSSkSHMThG3YsAHHjh3DPffcg+XLl1tdx01kZqvUFgDRMcHXPFgAs6fAzz//zP6uUCqQ+tm76PxYV9E1gNAaonX7tti7wZwIThWmRv7vP1qtmUY+2BulV64hNCQE1348Y9EPV9RI/rWOqIjuUhy9oWS6W9ElzxSkDMKC5HSre9h6Zil9c7b/xMB31sNB6jPURgJGkSYTBf8fVR08i6MqrDooBK8mvIT8/HwMGTIEtzRtirCIcCgUCigUCtSrXx9DhgxBXu5yPBBzG9RBIRaqdr/4OIxPn4L6TRrjwa5d8HyXATh64DAA4Mrf/wAATv92wuKefIVZSHHmHrOVcMyRZGTupKa6GuN7DMVLXYfg34uXoFQqET/waegKzrFGNCC+m8k9TnaVl6ZnI0obBQBQhaoAWO88RoZH4K1xkwEAGzZswNWrV9k2XYlB5l/riNLsqGrhiPLpaUWEvA85OTkYMGCATTXJGcTiJPmvCz2v1L+vM8q/nEmGqAJN8VfsfXYTOOWucnNzERISgg4dOmDZsmUYMmQIGjRoAI1Gg/Bw85xa/7/5ND8/H59++ilCQkIAWMdBT5w40WJBSFzEjx8/DgA4ccJyTuWPBUJjA/eYrTHBlxL4TZw4EbGxsawRrVAqsWjnamw4+ws6P2auXCHmsSa0hkjOnoOIKPP7GqIKBWC9Zsr9eQsAoKq6GinvzrBo0xU1kn+tI0qzo4qjI2q3p5VM8j5MypqGMdOSZI/tJc8kZERzXxd6Zil/X2eTcS1Ylg2jyYggZZDHYqcp1vi0IU3xDfrFx7GxzkLu2MSFelTyBGhDwtCpfiu0aNECSqUSmsgIqDVqqDVqRDeoi+gGdaGJCIdSqURYRDhKLhSjU/1WOL7nF4uJi0xSxw8Vse5Tly9eQqhaBWWQEo/0723RB1s1oMXO8UW25OVjTKcnMKbTE3jy9s64cOovAEDSsy9CV3AOH721wMpTw1E3KH49RqFBPOX5V6FRhwEARowYwR53xYDhX2srKzUfR5Nmcd0g5UxiI4drMnkfAHNs9fbt22V1k7b3Xtl63ZW/r733xp6B7wi+tDCnUByBfHYBiH5/SQ36qVOnolWrm/NpeHg4YmJioFaroVarUbduXdStWxfh4eY5tVWrVmjVqhXCwsKsDF/+eEteV6lUUCqV6N3bck7ljwVCY4M/bGiRcenJJ59EbGysRfnUkZMTseHsQdx6R0uLa8TWCmLHuWsgwHozPjg4GHHjRwMAFn31Gaqqb7rXu2LA8K8dOyyeTWhmzzB3NGkW11i1ZaSOHRaPSaOTsGBZtsdqMk8anYQgZRCMJiPW7tgou5u0vffK1uuu/H3tvT/2DHxHCPTM2u7EKdfuLl26oKCgAD/99BO6dOnCHtfpdOjatSuOHz+OTZs2WQ3Mnoa6dlviisuylCRb6qAQdKrfCt9t2oTnnh6Bt2bOQHJKMgx6AyrKzNmzQ1ShqKmqhjpcgw8//BBvz5yFFau+xKOPdkWPHj1w4cIFi2Qd3IQeYsnFAgWGYTCs1UOo5sSw9ejwCPLfXQaVSiXbfUgtxXZ3tcHl61dEXcJHPBGHzM/fBwBER0fj5Zdflt1wkcvlj+8u6YgbsTMu5o4k+RF7NvK6WJIfR59ZLpx14Qwkd2tfdEW1BXXt9g6ufE7sfV9CQkLQqlUrbN68GSNGjMCMGTOQnJwMg8EAvV6PkJAQNt45PDwcH374IWbNmoUvv/wS/fv3x/vvv4+5c+cCuJnwTKordiDRrVs3lP9XvQP4z4V7ybvo3KurLO2TRK0t770bpVevi7qDxyWOxsfT57MVRRZOnuMWo0UuN2e+q68jbsSO1mSWmjzN1rNxE6oWFB106vnd5d4s1G+h0Dt+nwPJ3doXE6rZwu2u3ZmZ5rjWadOmsceqqqrw5JNPoqioCMuWLfO6EU2xxlaCLTH4ZaBsqbmt6zTBudOn8dzTI1BdXY3padMwN30OKsrKWbf8KkOluUbj3HmYnjYN1dXVeO7pEbh69QrSZ7+NshKdYP/+PnseV4v/YV28+X18tm0PPNu2h8fLUtlLxCaFmpoazEp4FYObd2CN6Pta3YMbB85j46KvZDWil6zJw+pt62A0GXH4eKFNl/Avv83Hw/d3AgBcv37dKXdue8ilLAqp3UTVsaeWOKKq8N0ubWHPTZo8e0ZGBvseSFW88/Pzodfr2RI1cmLPhVPsufxBnZKKKyEMlNqDM58Tfhkose9LkyZNcObMGYwYYZ5Pp02bhlmzZqG8vBwmkwmVlZXsvDpv3jxMm2aeT0eMGIEzZ87gxRdfFPX4+fXXX5GTkwO9Xi/aR2frYLuKXAkJZ82ahdjYWAsjut/IYWYXbpmM6C15+di7YStMRhP+LDpm0x08P2cZlvy0EYB50/ztj7Nk6QMfuZRFIbVbaqI0R2syA5CkIttyk+aGr3GfX6rqvWRNHsr05YjWRsnu3izUb+4xsecKJHdrb9Sv9hROGdJdu3ZF//79sXPnTuzevZuNW965cycWLlyIZ555Ru5+UmTAUddmUgbKXpZsAIgODUdddQRuv+MOvDXzZgzQ7JmzkJ2dDY1GA61Wi/DwcCxatIjdKQeAt2bOwO133IHHevVCv4EDLPpHJqE/i47BZDSxST34fSwr0Yka4e7Emc0JLm8MGoUnW3bCLzt/BAMG995+N/7aVoT9K7aJJttzBf4gplGH2XQJ37J4NXtcToNeboQMcrlqLfPPlWKgO2voSl2Y5/5Xp1aj0Ujqt6sx49xjYgazu92tPVlvNpA2BSjuw5ncDSSjvliWbMCsMEdGRuKOO+7AjBk359M5c+aw82lkZCQ0Go3VfJqcnIw77rgDkZGRmD59ukX/yPiyfft2ts71e++9J9hHOetgO4Krm1gzZ85EbGwsNm7cyB4bOTkRG88dQuKcVJl6aYY775M46Tp1oy3O4a676jduhIbNbgEAXC25Bh/O9StokMtVa5l/Hgk3s2UwOmvoSjXgFizLxnVdCSI04Xb77ahLupBBLCVLubvdrT1ZKzqQNgX4OJ21+3//+x/uv/9+PPTQQ+jYsSPeffddTJ06FXPmuF6DWC6oa7d0hNy+iTu3WP1nck2dutF4Y1wShj/9FCLqRAIAMmalI/3tt9lzMzIyMGXKFGRmZiI19eZklpaWhjdTJrOu33/8/RduNAi1usfl/zJWA8DGc4fYn0nGcMCcMZNfx9HdOOMuzzAMPpo2D1tXrIXJZAIAhIaE4NLOE241VpesycOsj8zeJCU3SsEwDIKUQSgt+MvmdR+vXoZJWWkAgO+++w716tWzeN3f3GC5yN13qTVXuQQHB7Mq91dffSUp8y0Ah+rUirmR+tPfLpBcx+WGunb7FmLfK3uhIeS6hQsXomvXrlCpVIiMjERWVpaFB2BGRgZSUlIwf/58i/l06tSpmDhxItRqNcrLy7Fp0yacPn3aKkSjXbt22LrVvCmtUChw8OBBto2ePXtCp9Oxr/Xp0wcZGRnyv0kiODsmffjhh1i2bJnFsQe6d8bs5Yvk7iIA89y/PMvc9qjkCVg8PZMtm7n+zEGb1w68tT0AoHPbDtix5Bur1/3NDZaLnH3ntkUMYiluzopgJRCkAIwMPl21zK47ODHuyPpoxvgUp2tF+9PfLpBcx+XGI1m777vvPowcORL79u3Du+++i+eff17QiJ47dy5iY2MRGRmJhg0b4qmnnsLZs2edvS3FSey5IAspq63bt2UTewkZiVy1+OGHH0aVoZJ9bUJiopXBXLduXYtjqampSExMtHD9jlGFW9yDJO3oOqgvFAoFVGFqwWeIiNJCGxPtcVXa0QzfK95djEHNY7ElLx8mkwlRkVo0rtcQ89+Y7XbFl7vjGtd7MIKUQRjaa6Dd614ePhp1os0bJE8++aTV6/7kBst3WczJyWEzaJPXXVE9yXsBwKZSFR4ejmbNmqF169Zo3bo17rrrLrRu3RpvvfUWCgsLMXr0aNG2SYZcqYnaAHHlzJ/+du5MhEahOIK9z5PY96pdu3ZsYi8hI5Fcd8899wAAKivNc2pycjLS0tLY89LS0lCvXj2r+TQpKQk1NTUoLzfPqR07drToAzecpG/f/+ZUlUrwORQKBRiGQWFhoeT3RQ4c9Wz57bff0LFjRwsjWhWmRmLGVLcZ0YB5/VNWokNYeDj6xcfhkf69BROhCvFW7nsAgIKigzh78ZzV6/7kBrtkTR6a9WqDZr3asJv154svskapK6on932wp2gqI0IR2rwOwu5tAPW9DaC+uz7U9zbAK9Mn4VThCbw0+gWb7TuSqM3VLN2+gjsTodUmXMraXb9+fQBAZGQkFi0SHrD27NmDV155BQcOHMB3332Ha9euoV+/fqipqXHl1hQH4RvK/LhiIbfv44eKYDKacHjvfgsjnBs3DQANGzY0q5T/uSIb9AYwDIPJkydbbK6UlJSwP2fMmYPk5GSrftarWw+hymCr48nZc1DvlkaorDBYGMrcbJlirutyxDG7yjeLl2PY7Z3w1XufAgBaNLkVZ7cW4sL3x3Dy20Me2bnkDppL07NRWvAXlqbbHuzJYJkyaiIAoKKiAp9++qnFOVwDx1WDxd0GD3GJFjNAXTUsyXuRmJgouBgMCQnBbbfdhpYtWyIqKgrBwZaf9eDgYERFRaFly5a47bbb2FI23LZtZdEVQ2xx6k8uzK64jvvThgHF9xH6PKWlpaFjx45IS0sT/V4VFhbCZDJh//797DjHHfMSEhJuzqcwG7MGgwHXrl3D1KlTLZRh7nw6Z84cC6OaOBrWr18fSUnCi+SMjAw0bNgQBoPB4jkSExPRqFEj9OnTR3Rs8IWNqW+//RYdOnTA6NGjWc+u9t0fwsZzh5D/+49u90rjrzeSs+dg/ZmDSM4W98oka5GrF/9hvfc6PNPT6jzuXO2qweJug4ds0IsZoK4Yltz3QczNWREaBFXLaKhuj0FQVBgQzDNrgpUIigqD6vYYqFpGQxEaJNi+0O9iuCtLt6dxxXXcnzYM3I3Trt3Z2dl45ZVX0LBhQ/zzzz9YvHgxXn75ZbvXnTlzBi1btkRhYSHatm3rzK0lQ127b8J3QSZu24Sug/paTQDkmorycpSV6BARpUVYeDj7e/0mjdG6fVtc/O1P7P3hB2gizGqy/kYZAECr1SIsLAz16tWzmPSjoqJw9epV1v01VK1CcEgIDOV6qMM1OHzjHPTGKvBxNus4P+O4K9nLHWXH1+vxQfLb7MImKrIOls/JQc9O3QTPd4dbkCttcl1/OrR5EGt3mOPOCgoKrIxAwHX3W3e77xIVGjAvGAHLzLXudHUOCwtDixYtcObMGdxxxx1Wr5NsvBqNBoBZabn99ttx5coVVFRU2H0ub7to+0IfxPriS31zB9S127MIfZ46duzIGnREdea7RPNDP5RKJSIiIizCQLKzs5GRkQGFQoHw8HBWXdZqtdBoNKhbt67VfHrlyhXU1NTg2jVz3G1wcDBMJhM0Gg3++usvNru3lOeQAnecTvivHrWnvlvXrl1Dnz59LI7d0bY13t2YJ5hXxB3zvRxrkeztq/D0PeakZ4nPvID5b8wSvMZV91t3u+9yQ8ZISU3uesOdrs5KTQhULaPx55nTgnOq3qBHub4cISGhqK6uQvGlYtx3972oPH0dJn21zWfyBfdsX+mHUF98qW/uwO2u3V9//TVee+019OjRA7/++ivq1KmDWbNmiWaA5FJaWgoAiImJcebWtRoxZVWK4sp3QR6emMAmxwCAfZu3i17zYNcuUAYpUV1ZxRrfZCc2OXsOPtqzFjEN6kKtUcNQfvMzYDQakZmZaTHpA+ad9Hnz5rFeCdWVVVBr1Iiqb/5MvPLECItnIc8HwCE3agJ/59jVBGFS+GnLTgxv/Sjef3M2a0Q3qtcQF77/TdSIBhzf5ZOy2yzXjvCyjBwoleaFSny88MDpqsLpDoWUq57ExcVh586d2LlzJ+Li4qxUTnclzAoJCUGLFi2wefNm3H///cjKupmxlShOJBuvXq9HZmYmunXrhs2bN6NevXoWyrQQXIXMW2qRL6m+/L7QutMUPra+J/a+Q0Kfp969e0OpNC+pTCYTtm+3nlPJdaRsKDG8uWPexIkTUa9ePdStWxdqtZq91mg0Yt68eYLz6fz589ks3uRcUm96w4YNVs9Cng+AU98L7jjtqe/9iRMn0KlTJwsjWhWmxsZzh7Bw0xeiyTkdne+lrKecXUNw1yKaiHAMSHgaAJDz1WeoMAhvlrqqcLpDIeWuO8YOi8f5HUdxfsdRjB0Wb6VyuithFlGi12/ehDb334fMrEz2Nb1Bj8vXLuNGeRmMJhMMlQa8/8EH6NCpI/JWrUBFfaWFMs2Hv2bylhuzL6m+/L7QutM3cdiQ/v777xEfH4/77rsP69atQ+PGjfH666/j0qVLeP/9921eazQa8eabb+KJJ55A06ZNne50bUVs8HZmUO8XH4eVRbvQdVBfm3E9W/LysW/zdpiMJoSoQqEKU6O89AZat2/LGrSVphpUGY0AAHW4BkqlEprICCx87z2LuK6oqCj257S0NAtjgnDx/HkcK/yfxbO4avgKbSI4kr3cEb5e9DkG3toec19ONivsKjWe6vskmjVqgikvTBS8hjtITxqdhGhtFMr05ZIGbe7gRtoZMy2Jbc/RTJf8CYM7WCoUCmz72JwY5eTJk4Kxc64aLO4weMQWep40OIVK2ZDPv16vh8lkAsMwUCgUWLx4MebMmYPq6mq8/PLLOHXqFJo0aWKzfTkWts6+H8SlNSYmxmfcxP3JZZ3iHWx9T5z5DmVkZODnn39G3759WUWaD/mO7d+/H4DZdbuqqspChKipqbEIfQsPD4dSqcR7NubT1NRULFy40Op+NTU1yM7OtlnOzhm447S7v2tfffUVYmNjMXLkSBj/W2doIiNQ/5ZGeGHa64LXcI1hkuuFhKLZg7veIO1kJaWy7W3Jy0dFeTkiorSS1hDcvvDXIi/PnozgUPMmaZsnHxK83lWDxR0Gj5iB50mDM7SpFn+eOY2nRzyN6upqpE5LY43pcn05jLw5de7cuaiursaLL72EP06dRGhTcZWRv/ngrEHrzPvBjTnv3LaDz7iJ+5PLuqdxyLX78OHD6N69O+rWrYuffvoJjRs3BmB292rRogWMRiNOnz4tqDYzDIOXXnoJe/bswY8//sjGV7uTQHPtFnMnctV1iX8993duxmxVmBpVBvOuN8lMSc7NX78WHe9/kG0zKzML09Oss4xmZmZaLAZSU1Px+qQ3UFNVDXW4BoV/HMPwwUPRun1bHD9UhNbt2+LwXvOiw5PZuB3lz6LjSH36JVRwFPn1H6zAY53F1WdCs15tcF1XgmhtFM7vOOqQK5ZQVssgZRCMJqNFfUapbl38ewu577Qb+ghOXTgL4KaSws8K60sutPYy6Lo7C3R4eDhatmwJAFbZd9PT0zFp0iR28fzOO+9YuIOmpqYiJSUFarUap0+ftqiJKgY3M29hYaHdv4Uzmca5EJdWpVKJn3/+WfJ1FPmgrt2OY2uscnUcEwstIN8xkuSLqyJzN8K++eYbPPggZz4VydrNn09nzJiBcePGISQkBNXV1Thz5gw2bNjAjgfEgO/SpYukscGbXLt2Df3790d19U0X3JZt7sZ7m8XVZwKp5kHC0bihXfYQWv8og5QwGU2o38S85nWkPXuhZcV/XcCLjw4GAIwZ8hx2FOz2efdZsX55Kgu0MiIUqtvNdkZmViZSp938DsxJz8Abb7yBaqPwnJqWmoopKVOgUWtQ+ec1mMqFwx64kOft3LYDCooO2vx7OJtpnEDeQwA0m7YXcYtr96lTp/DEE08gNDQU3333HWtEA+ZY2JSUFJSWllrUMyQwDIPExETs2LED33//vUeM6EBELEO0o5mj+fAVX/L74umZFjURKysMCFWroFAoEBIaypZ/uHzxEmanvsWexzeiZ2ek49XXXoO+Qo9XX3sNszPS2dfmzJmDhQvehclkQkVZOTJnZWB4YgKOHyrC5YuXsG/zdovMmM4itLMsBxX6Cjxzbze8PmAka0QrFAq8lzLXphEttlNJFGSNOkxUlea7VZHdZrJjOLTXQLv1Cfn3J7/zd0CFdmIPfb2b/dme0uELSWmcTbQl1ncpz8Q9h7uxmJycjPT0m5//adOmYeHChdBoNMjJybGY8NPT0zFjxgzWvVNqOAx53sLCQsk1qaVkGheDuLQKKXAUiq9iy/vFVc8Y/jhIfr9x4wYA85qIa0QrFAq0a9eOPe/NN9+EwWAAILz59tprr0Gv1+O1116zGE9mzZqFzz77DNXV1TCZTHjzzTcBgB0PSKJFWzWspULGuLS0NFnHeKPRiC5duqBPnz4WRvToKa/g/W9XiBrRYi7ZRJGuUzdadO4XU42J99oj/XuzXmxSk5pyk7LaCi1r1Lwpbm9zNwBg6boVFvOtr7gY8xFTue2plrbWPfaei3tOcN0w9nhKcgrmpHM2n6el4d33hOfUOekZmD1jNjRqcx4SbjtSnreg6KBdZdqRTONCEK9ER+tlU7yH08nGpMIwDCZMmIBNmzZhz549aNGihTtvZ0GgKdLuguyQclVg4s5NdmIBIDgkGOpw8wBUVqKDMkiJkNBQVFYYEBGlxYkLZ1ByoRgP3Hc/OwHOzkhHckoySi5fg8lkgkKhgEKhwEcfL8bsmebkGiEhIdi9ezfOXTiPZ59+hp10uH1yNVEItyY22VmWspssxuVL/yDtmXG4dMZcukKhUCCuz2AUFNrerSRwd27JzqWYqsx9feyweJu7vmOmJWHtjo0Y2msgHn6gk+guqlQVXGznOeerzzD53RkAzDF9I0eOBGB2892+fTubaMcZ1Zffhj3cpYLz+04Sld24cQMMw9h8Ju61p06dskrMxl8cR0VFWcQ+pqenW2W1r6mpwfHjxyX3X+r74sr754seCLURqkj7FnyvkHbt2mH79u3sHMg1oFUqFQwGA/t3I+PLmjVrcPfdd+P++2/OpzNmzEBqaiquXbvGeoFoNBpkZmayFTLIfHr69GnEx8dbjV+AtNrz9uDWxDaZTC579lRXV2PIkCH4559/2GOayAiER0Zg+IQxdud/rvJL1g9iqjL3dW7yVaF1QVZSKvZt3o5H+vdGm04Piq5LuCr4yqJdom0KeRAyDINBzWMBAGqVGvMmzsDYYfEW8/nS9GynFV9+O/ZwlxIu5PE266NMlNwoBcMwNp+Le+3ZU2essnPzlWn+nDonPQMpySkW1zA1Jhh++1dy/6W8L668d77qgVAb8UgdaalMmDABK1euxJdffomwsDAUFxejuLhYNIskxbNwB3WiAh8/VIRxs1PYnVhlkPljUlNdg7ISHQCwk1KVoRIRUVqMSp6A46UXcWvLlvjkk08QEhKC1NRUJKeYjQESOw2YE6yMf3kcpk6dipCQEHz88ceoV78+jv57lp3kyK5wcvYcl9R2gtDOsjMYjUaM6fQEnu/0BC6dOQeFQoGhjw1AyU9nsfTtbMmxSEJlHQCw8czt7mqDIGUQOrftYFWXUWiXk+zW5m9fD6PJiLU7NrJG+dodG+3uoortnIrtPCc+8wK7q7tkyRL2OCnvQuKnnYmfIwtOoWQ9QsiZ7IZfioZb1mv+/PnQ6XTm0Aal0uYzkWuTkpIEs5unpKSIloabM2cOUlJSrK4JDg4WbEsMqaqalPPEVHhfSjBmD1/wjqAEPtzNJeIVUlhYiMmTJ7NlpchcyHXrBswbFYDZy++PP/5AixYt8OWXXyIkJARTp07FuHHjAAAajYY1ovV6PSZMmGAxn0ZHR2Pu3LkWYy8/0aKrkDGud+/eLsVIMwyDnj17okuXLqwRrQwOwqKdq7Hqtz34vGCzpPmfqxST9QMANp655b13s7HSxJNuedYiq2sJRFH+YeM2mIwm7Nu8nTXK923ebjdvi5hyLeRBqFAo8NoC88a0odKAPg/1AGCuM200GVFQdBCA83Gqa3dsZNcFUpAzyRU/Bwy3rNekrGm4risBwzAIUgbZfC5y7VsTUqxLXAGYkjLF5pw6JWWK1TWKYKVgW2JIiTeXGpMupML7UnIxKfiKh4S3cbsiLeaGs2vXLnTv3t2dt6aKtAS4u6at27fFDxu3IVStwgvTXmcH+i15+Vg8PRMmo3k3PbxOJB7s2oVVrbk7rtqQMNwZXBdnTp5C6zb3Qq25mXXUoDegoswc4xkWEY7KCgP+PHkSMTExSHh+DGIH9vTZGOgKfQWmPzcBJw7dTLA1ceR4pL+aZuMqx+DuuAI3Y5vL9OUW6rHQ7jJRmIH/1PHeg20q0tySFb06d7cb9yNEia4UTXvdCwB488038cwzz8iiUHpTkRZT0MlxhUKByMhIQUVHKC5y165d2LZtm0X2XQAICgqCUqlETEyMVSkbojaRxDrcsli2Stm4E7H3xRuKtBwle9wZE+8NqCLtO3A/Z+3atcO2bdugUqkwceJEi1wSmZmZYBgGarUaUVFRFqo1+YySknknTpxA48aNodFoLMYSg8HA5k0IDQ3F77//jujoaIwYMQInTpywuKevwTAMxowZg6NHj7LHVJowfLJ3HWIa1JPlHtz1DXAztplb0nNl0S4L1ZmUASUKM2CeUx8d2MemIk1C3QDgwa5dnPKke/rebtDfKEOdCC0u7jwmm0LpTUVaTEUnxxUKBaIi62DG+BSrewmVXMrfvRFbtn3HbuQTFEEKQKkQnVNhYsAYzeYOKYsVrgmH8mw5mEqjS8/oDELvizcUabnKowZaLLdPKdIMwwj+c7cRXVuQUqrB1rXc7JPHDxWZY7cqDOyEAJh3UIlCHV4nEmUlOgvVmrvjqquuwNGqf3Br27tQU12Na/9cQVnpDRj0BuhvlLFZFNUaNRiTCWfOnEHfvn3xc8EB0d1de8/oyntgD4Zh8Fq/EXjq7kdYI7pT21j8u+cPWY1owHK3mftzr87dEaQMQq/O3QHY311u2vAWLE3PZndGl6ZbK+Vjh8UjQhOO67oSC8XakRimKG0dDOzWFwDwzjvvgGEYWbJuk+y3GRkZklREOTN9iyno5HhKSoqookPU2ZycHAwYMAA5OTk4e/asYFlAhmFES9nMmzcP3P1NktWb/O8NxN4XZ957Z5Rh7jXOquA0kzdFCq54LuTn50Ov10Or1bKKNMMwMBgMrFs1YP7epKSkoFGjRggNDbVSrclntKKiAidPnsStt94KjUaD8vJyXL16FQaDAQaDAWVlN+fUyMhIXLp0CX379sWRI0dgMBhEvyP2ntGd3hvV1dXo378/OnTocNOIVgCzVuQg/8Q+2YxowFIV5v5MSno+2NVchoyIAkJlQAGg3i2NkJw9x6anXL/4OISFh6OsRGehWAutT8TWLB9uWwUAKC3TYfK7M2TLuL00PRulBX9habr4HM9FzkzfYio6Ob5w8hy2dBYfotDO+igTrQd1wqyPMnHy7CmU6wWSbjKwOaeCIxmSrN7l+nLWuPY0Qu+LM++7s6owuY54PLpaHrU243ZDmuJe+C5KjrA6J9cikRfXINbryiwGejKBjEqegIgoLSr+2wUXcrv+Jncl7m7aAgP7D8A333yDi+cvWNSXrlEwKNaX4out32D8KxPw75XLNktJ2Ct/5Y660EajEZmJKRh8Wwec/u13AMCTj/VH6f6/8P2SddCEaey04Dw//nrAYoeQ797V7q42AAClUskOnjPGpyBaGyWYpExsoBVKTibmWkSOT8qaZtFO3tyP2Z/HjBkj7xsBz7sOixmGUgxGYqgBYJN3ceMhuWRmZiI1NZX9nV/KJjPzZk1MjUYDhUKBy5cv46uvvnLquVxFzs0KZ/6m3GucNYi5z0DdvCli5OTksBtijpKbmwudTgeNRoO4uDi0a9eOfU2v11t85rg1pZVKJdq1ayf4PVu5ciXatGmDQYMGYe3atfj333+h1+vZDbrLly/jzJkzOH36NH755ReUlpZCrVazxrxYP219B9017k6fPt3ShTsoCO9syMXGvw7hwUc7yXovLkcPHLaISz5+qAgmownHDxUBAFrea072pVQq2TUPWeuowtSoKC+XZAgLhZAJrU+4CV25bTRo0gi9nxkCwHZtaVfwtPuwmHEoxWgkaxQAbCbrUEUwghhr02Ve5jybc+q8zHns7+Ga8P/m1CtYsmqZU8/lKnJtVjj79yTXAXDaGOY+Q21286aGtJ9CBvLqSvtuntxBn/szGfRbt2+L57sMAHDTFd9kMrEGOvdaMhmUlehEd1uJgV5QUICkpCS8Oj0F7636HEOGD8MHq3NxpOoSjpacx909O2FU8gRoY6IFS1uJZb3kI3dd6JmjX8WQFh2xb9MOMAyDO5q1xJ+bf0He3I8RFBRk81pXBhMysOVvX4/zxReR+v5swUzal69fAQBU11Szg+fYYfE4v+Mo6kbF4LquxGJQFRto+Yr1j78ewIV//oZGHSa4e0wSoHGN6eDgYHy9IBcAcPToUZw8edLmMzpqwPiTikgWwYmJiWjUqBESExOxadMmq88MP9HYnDlzcO3aNYv4Lm6dabVaDYVCgb1799pd2Pq6gchX7LjHbfWb+zmQw6j3p9huimcgn0EpoRPcz6tQXoV27dpZ1IsGzJuzxcXFmD9/Pvs5z8/PZ925SV4J/neBfFb37t2LpKQkPPDAA/jss8+Ql5eHvn374quvvkJoaCjKy8sRFxeHxMREREVFiYafDBgwAO3atbM5rso57hIX7tjYWHz77bfs8Y/3foP1Z37GXfffZ/N6VzzOiMH6w8ZtuHzxEj5LXyi4pii9eh2AOQ8MWeP0i4/DyqJd0MZEs+sdfrv8zXu+Yn30wGFc+bsYqjC1xfpkeGICm2eGu8YCgFfnv4Xg/+aMDs88ZvcZHV1z+JOKSNYoM8anoFmjJpgxPgXHNxxAnSBLIcOqBJbAnMqtM61Ra6BUKLBn7x67BqivG4id23Zg8+lwsddv8jkg76m3DPpAwO0x0t4kkGOkSewPqZNoq7Y0GfS5cULc67ivk4zdLe+9G8XnLqC81JxB1N613GMAWIXcXu1nW9kybb0mNwzDIHfuh1j/6QoYjTUAgE73tcfG7JUOqc+uxIyQOCalUonqmmpWzRTKpE3im2eMNyelIgr2j78eQP729QhTqTHntelWtSi55/IHzjqdm8NoMkKhUKBpw1usziHJQUg2cW6fWvWPxaXLxVCpVPjxxx/Z4/x41kCOUxXDXh3plJQU9m+dmZlp9XpycjIMBgNGjBiB++67z6YBaev99YUM2/Zi0D31ufCF90JOaIy065DPoFarhUajsfhs2BrHAFhdR4xf7t+iS5cu2LZtGxiGgVarxc6dOy0yX0+ePNmibdIeN/O3lNrPtr5Lnv6e5ebmIjubs7BWAK9kzkCfZwZJbsOVdQCJfVYqlaiprmHHWaFM2mLxzVlJqVa5Y7jrKwBWWbgJg1t0YKueJGZMFVyjkXhtbp8u/HkW43sOAwDkL8jF44/2Yq/jx7QGcpyqGPbqSE9JmQIoYHb3zpxn9XpKcgr0Bj1GjngOPdo8ZNOIdLSiiaexF4Puqc+Fr7wfcuFTMdIU90BU2FHJEwTdq7k7pkJxQtWVVazizH09OXsO1p85iIWb8hAWHs66pbZu31bwnuSYoVzP7viSndyVRbssEnGIuUIRV3ExNym5lGYxVmUvweDbOmDt4mUwGmvQIKY+Dny5A99/tt5hF25XdnuJC3eEJhzNGjVBXO/Bom1FaMLZ5BzcncCCooNgGAZ6QwUb88wd3IR2DcnOJckUHqZSiyrYC5LTBTOGVxjMboaVlZX45JNP2Nf4yp+Q0uErKqq7+lFeXo4bN27gzz//xKxZs9jjxEgmicVMJpNVnelZs2bhzz//RHV1NWbNmmXX6EtISIBWq4Ver7eZYdtb77m9GHRPeR7I6apOCQzIZ5B4knA/G7bGMfJzVVUV6xLObYtkys7IyEBkZCQAc4krroJNjGhu23q9HsXFxdizZw82bdqEjIwMi36JfYfbtWvHuoqLPaO7v2fTp09HbGyshRGdtd7swu2IEQ24tg4gLtzqcA3qN2mMRwf2EW0rLDzcXH3kv+olRG3m5o4hXnhcw1lInSbrHeIyDkBUvR6VPMGqT//b/wsU/2V2H/6mZcgUfw63VcXDmyqqO/tgKquC6UYl/vzzT8yYNZM9Toxk5r/EYoyJsaozPWPWTPz5559QVwdhxYyP7Bp9pK4zP2TOV2p824tB95TngZxx9f4GVaQDDH5NaLGskfyah2JtCGXmFmJQ81jW6OaWoCDtkazfpB0xxdzdyjOX775ci09nvIOqykoAQLNGTbFzyTo0btDIY33gYmtHj6jV7e5qg8Lfj8JoMiJaG4UITbhFVm6+Ik0Ge27dRv49hGo7OrKzSK7nsm/fPqjVaknKn6+o1EL9kEu5DAkJQatWrbB582aMGDECM2bMsKoTzSUrKwuzZs3Cl19+if79++PkyZNsLVlHnoOoY2ThTH4mhoG333OKPFBF2n1Irb/cs2dP6HQ6Vm3mt0HqSvMzc4vRoUMHi/JYfIV8/vz5bDvc77k3v9t79+7FG2+8YXFs8NgRGDt9kkf7QRCq2UwganXLe+/G6d9OwGQ0ISLq5neHeNPxFWn+ekXoHmI1raVm8CbXE55+/El8NvtDANKUP19Qqd2t5CpCg6C+sy7Wb96Ep0c8jVkzZlrVieaSmZWJGbNmYtWXqzC4/wAY/rgKpkpatm7us5BcMvxqKL7wnlPkgyrSfoTcGadJ8rHDe/dbKdXce5Fd0FHJE6za4NZKlFp3+dGBfdj4av7u7PKsRWzprOGJCaxhLaSYe4I9677FkBYdsWhKBqoqKxGhCceKeZ/g+IYCm0a0u3ccbe3okUzdh48Xsu7XgDkBx46C3ex5RJGuGxWDscPiJe1K8mNsHE0gQXZso7VRUCrMQ8rTTz8NwLEkXZ5SI8WUHKF+yBVLW11djTNnzqB///44cuSITSMaAJKTk3HkyBH0798fZ86ckWxEA5bPwe0/928hx3su9D76incBpXbijs9fTk4OW99ZLOY4Pz/fIjcCH/I9JGX9pHz3+vS5OadyxyCuEU3q2XMTpHkjr8Ty5csRGxtrYUS/Mn8GNp47ZNOIdmfFDUC4ZjOBiAR/Fh1j1ycA2JJXRH0mirQ2JtrCC8/WeqV1+7ZszWpuH6Q+L/HUU6lVAIBV332DC8V/A3AsSZcn1Eh7yUz5fZArlpapMqLy9HUM7j8AR4/8z6YRDQApySk4euR/GNx/ACpPX5dsRAOWz0L6X1B00OLvIMd7LvRe+oJ3AcU2VJH2MnLHAXOV5lHJEyx2QqXeS6xuNFfh5sYUkZ1bcm+FQoHx6VPYyYuvfpN+KIOUGDc7xWO1o4/8UIB546egXHcDABCtjcL6D1fgwdbWLnBCeGPHkeze1o+uh8LfjyJIqURVTTWitVGYMT4FC5Zl42rJNegNFYjWRqFX5+5W9SK5NSQLig5aPYOt53L0mX86cgB9XjLHdq1YsQJ33XWXzO+I6wjFJYrhrCItdl1ISAiaNGnCunja4saNG7h48aJDRrTUfjh7HhchBd9XvAtqG1SRNuOOzx9XaU5MTHQq5wPX+CWlqrp06WIR85yWlsYa2qT0H9dg5sdQc4/ZUsPdyebNmzFjxgyLYw90ewiz8z6UdL0n86AAlgr10QOH/4ufDkJNdbXFmkl37ToqKwyIiNLiwa5drGpMc+tOE1dw7jOIPZczz/v0PV2hLyuHRh2Gf/faTubpDcgagXjI2VOanVGkbV2jCA1CaFMtlJEqu+2YblSi6oLOISPakb44co4QQustqnR7B6pI+xFSdjcdqaNMSjYAN9VpUmJBqvJL6kYrg5RgGMaqLiJwMzN3WYkOOWlz8fqAeJSXmg3U8DqRFm7dANhJivvMjhjRruxc/3bgVwxr9RDeem4CynU3EBoSijmvTsf5HUclG9GA9B1HR3cQbZ1Pdj8vX7+C0oK/MH/SbDbTIlGcKyoNAIDKqkpWud5RsBvNerVBs15tkL99PVt3WugZbD2Xo7usD93fCXc2vx0AEB/vm7EyCQkJUCqVMJlMdpVmot7m5uYiLS1NstolpmRXV1fj7NmzOH36NEpKSnD16lVcu3YNBoP5b1hTU4NDhw5h6NCheOedd1gj2lmlTWossDPKu5Dy5Q41jKrcFKlI/fzZ+kzxX0tMTGQXUkT5JZm3pd4vLi4OkydPhlJpnlN1Oh22b99u8Z0jLt9bt25Fz5498d5777GGNzGYuVnvuZuAttRwZ94De/zwww/o2LGjhRE9aOwIbDx3SLIRDUiPf3Zk/rd1LjeemeSDeWnmm6LeedWVVRY1prOSUvF8lwH4YeM29pjQM4g9lzPed0t/3gIA0BsqMHPRXMnXeQp+iSp7SjNZtyxYlo0x05IkrZVsqdhEma788xqM1ytw9epVXL52Gfr/8rYwNSb879ARDB36JBZlfcAa0c4qvVI8ApxV3R1dnzkLVbnlhSrSfoC9XUz+6+R34qrEzVIpNYYasJ3JkrxG3KAIRGUGYJGRUkp2cWeze4tx7uQZTB+ZiKuX/gUAhKnUWDLrAwzu+YSk651BLLO1rR1KWzHKP/56wEphFro2SBkEbUQkrutKLH4GAI06DBWVBotM3u6kqroKMQ+bs1Q/9thjFjWRfQVHFFiuAiQlttGR9gcMGACTyYQWLVrg448/Rk1NjVeUXl/OYm3v2X25756CKtKO4Whma3KMO6eS17nxz/ayanPjrYUU6a1bt7LnkgzTRGXmKtRCmcW595A69jg6pqxatYoty0do2qoFcnasZt8bObGV2Vps7SC0VuCuew7vNZckE6oowq+GQu5LICWrVGFqVBkqLTJ5u5P3Js3E96s3AgBKfjqL4OBgt97PGRxRYbnrFqEqIK62XWmqxp0t7sB3H+UDNSaPK72+nsHa1rP7et89BVWkAwx+vWex7NbkdRKfwzAMQtUqNlYHuKlSk7qKRGEW2sXlZt8mdRHJhEFe6zqoL5RBStzR9h4LlZns/AKwKJ3Fvx/pDz+jJb9P9t4DLhfO/IVn7+uOCY/F4eqlf6FUKvHy8DG4/MOfbjWiAfNOpNFkZGOOya7frI8ycb74Ilu2igs/KyQ/C7fRZERB0UHB+5HdygXJ6WytRfIziVue89p0NG14C5vJmwt3Z3LMtCTU6dwcY6ZJ3/kU2tkMDQnF9JcnAwC+//57FBcXS27PU9hTavPz89GzZ0/07NkTMTExUCqVuPvuuwXVJyFlR6oSTNTxHj16oKbGXHaNn3VXqPay3CqtUH99RQm2p/rRmtAURyGfKVLvmfsZ537euHWXiZoMwOK7SFTqbdu22c2KHxcXZ5HFm/udy8jIwJQpU6DVaqHVatGnTx8LlTk3N5d18wZg9Zkn9yT9Efo+2Kp5bet7fu7cOTz00EMWRvQD3R7CxnOH8NH3+W4xogFYrSO48z9ZO5DNfoLQWoG0c/xQEcLCw63qQvOvJZVJSC6ZroP6on6TxmzOmBemvY56tzRiM3lz4a5bspJSMbhFB2QlpUp+ZqG12GvvzGCzeHca0UvsUq9iT6ldsiYPzXq1QYOurXC15BqitVEY2mugpIzjjmSEnjQ6CSplCOK6DwRqzKXH+HlglqzJQ5m+HNHaKPbecqq0Yv31FSXYlspdm+tBOwtVpH0UoRhkW6osN66Zm3kSgMVOLolXVoWpoY2JZndzbcVWi/VP7Jwtefn4LH0hqgyVeHRgH7Tp9KDF78nZc0TrYNvL5m3rPSi5dh3Tnh6Hv37/EwAQHBSMmROmYOLIcc7/IUQQ27VbsiYPqe/PZhVgvaECzRo1QZm+HNd1JYjWRuH8jqNW7QllhbRX99mRvnLrTnPb4d7373+L2U2A0oK/JLVta2ez6WP3ouRGKdRqNfbt2+dU313BFaWSqDUALJRobnZc0qbcajG/PW/FIvtLvDNVpKkiLQWh7Nv26q5zFWDAnLeAq0YDN2Oo1Wo1oqKiLJL8kThmAHY/o7Y+x2lpadi2bRtUKhUmTpyIX3/91eJ3ofrTpB1uu0IZvW29BwUFBXj11VdhMpnYYw2aNsaSHzfKbjwLrSnIsTp1o3H6txMICQ1FZYUB9Zs0tvB241ceAcQzZwPidZ8d6St/fSZ036vF/8BkNEEZpMT6M8Kb4bb6zV3j/HHkN0waNAoAsPXjNXj4gU5O9d0VXFEr+dU9xKqEuEMp5rfprVhkf4h3poq0GapIBwDcGGSy4ylWc5lrRCuDzH9SrjtSRJSWnUDIDusL014XzWYpVBfR1jn8HdTVObmorDCAYRgcP1Rk9Tt5FqE62GL1r8k9WrdvaxVjVHr1OsZ0fgLx9/fCX7//CQWA/l374PpPZ9xiRAPiu3Zjh8WjsqoKDMOgotLAGsZELZ4x3uz2zt+ZtBcH8+OvB5zeyVywLBvXdSWI0IRbZeLm3ndor4EIUgZhaK+Bktu21e/CNT8AAAwGA9auXetwv13FFaUy4b96zFqt1iLLrlCbcscF89vj/y6kUIvhiqpM3gOhmtS+BK0JTZFCbm4udDoddDqdRT1o/mecq+xyFWCdTgeVSmVVo5nEJ0+cONEiKz43D4OUschWnfft27eDYRhUVVUhLi4OhYWFYBgGBoPBqrY1vw42t10xtZ0/fpWXl6Nnz55ISkpijehbWjbH+rMH8dlPm9yiQAutO0jGa1KeqspQyc7//Moj/HWIvXjkowcOO513hazPwsLDrbJxc+/7SP/eUAYp8Uj/3pLbFuv3nfffiya3NwcADJjwtMN9lgNX1ErieadRh1kowVLqYrsKv03+70IKtRiuqMpiNal9idpcD9pZqCLto4jteArtVvKzYAPS4pO5cLNQAmDrK5ZevS6qOospx/y+Hz1w2KIOo61dYG675DnE1Omqykq8PeZ1HNln3tlTKBR4fshzeG/KXLe5mhGk1HwWi2sGLDNdEnp17o6CooOses1N4GErlsjeDqIndnzFiJ/yEr7Z+S0A4ODBg7L9XaSokO5QKn1B/XREKXZVVfYXVbq2QxVp+4jVgxbzAOEqu4DZINXr9Wx2bLEYZaF7/frrr9i+fTvuvvtuXLt2zW5sM1855mf05ivUUjLy82O4hb7b586dwzPPPIOqqir2+rDICKw6utvtc6qUms/czNl8yDpIFaZGdVWVxfqFu34AwK6XTEaTlfIrJW8L/xxPZR8v193AM226AwC6xz6MTTmrZGlXqgrpDrXSFxRQR9ZErq6f/EGVpjimSFND2s+w5f7Ed48mCcW4iTqGJyZYuVkDN0tUBYcEo6baHKspNtHwyUpKFTWUt+Tl46Np88AwjKgLlhhirln94uPw5uAE/P7r/9hzY+99AN8vWYegoCCH3k9XcXYSINcRoxkAayyTMhKd23bAjoLdqKyqBACoQlVWrtmA9IGZ3LNz2w4oKDrokUmTYRjU6XIrTCYGzZs3x5o1a5xqh4+vG3juNLgdadvVfgTqZkSgQQ1p5+F/Hm39Dggb1DExMThx4gRr6HITFN599904duwYAEhOWDhq1CgcO3YM99xzD5YvX27V15KSEhgMBofKXfHHTO5zPfnkk+jfvz+uXLnCnh8RXQdLftyAcA//naUmIBW77srfxeAua7nrB5JsrLrSvFEQogq16Zptzyh2JHmrK8/GZfuqdfgg+W0AwNmthagXXdepdrj4g3HnToPbkbZd7Yfcz+ELGxGBCHXt9iOklnUg5wGwcskm7k989+jjh4osEmaQyYS4We/dsNXqvsSIBsAm1rBXquH4oSIwDCOYeGN1Ti6c3avhujj1i4/DZz9txB+FxzDotg6sEd3noZ4o+eksdi/d6DEjmuvaY8/VyZ4bUK/O3dmkYCTxxozxKTi+4QAKig7iuq4EekMF9IYK1jWb33bnth0kuUKRvhYUHRR13XHk2aSgUCiw/oMvAQB//fUXTp065XAbQu7JcrtTO3JvKTjiVu7oPbglueyV73HV7dkdbtOOutz7StIziu/jyGeFnAvA4jPO/8xzP6/kNeLODZiTfh07dowtXUUSeRGjmRjRACzCRGxx4sQJi/8JpC+VlZXS3hAO/DEzLi4Oq1atwkcffYROnTqxRrQmQoP1Zw9iZeFOjxnR3HWQvdAye2um2+9rbZEAlawfPt+/CYf37kdZiQ5VhkpUVhhY12xu2xXl5RbhcLbgr7WEjGNHnk0KvZ8eArUmDADQ5smHnGrD0dAyuXDFLdqR9Yij9+GW5OJfw2/LVddnuV2nnVmn+UrSs0CBGtJeRurAaus8odggbiw119Am2bu57XJrPd/R9h4og5ToOqgvAOBq8T84euCwzb6R+wlNPtzXhOo02oLb77njJmNQ81js+Ho9GJMJtzZuihMbfsba95Z7vBQEGbhmfZSJqyXXoFAo2GyQwM3slM16tWGzdfMHOa5Re37HUZzfcRRL07MtBthJo5MQpDRvDgQpg6wyW07KmiZoGJNBkl+jkTtZig2k3EFZKI6Ie43UwbhHx0dxSwOzS92IESMce7MhbHh5Ii6WJBtyJs7aEUPfmVhuW9f4ehZrRzdBfP15KL6DI58VsXP5xrhQHDXXoNZqtWwcNWkXACIiIqDVanHPPfdAqVTinnvuwf79+6HX6+32rXfv3lAqlejd2zK2lnx3+Bm9pcAfM3v16oVu3bqhtLQUgFmdXbxnLVYd+8HieTwBWd8snp6JOnWjLSqNALDIfi22FiLHS69ex/ozB7FwU56ocRuqVlmJBCTXDDf2mfva810GsHWkheKwxQx8e3lf+PlupAgry34xl0kr05fjgxWf2H5zBeAbX56Ki7VVvcQejhj7zhiXYtf4ehZrZzZBfP2Z/A1qSHsJW8mzhLBV/ok/sZAJoKxEh4+mzbM4lyT7Asyu23XqRiMnbS47eSzclIf1Zw4iOXsO9m3eDpPRhH2bt9vsGymFRbJ9i5XQcsaVadWHn2Foy4746dvvAQAhwSGY89pbOLa+AE0b3eJwe7bgGoa2jEQycAGA3lABhmEsylOR5F7EZVvIeCVtkBJZY6YlscY3MYABoN1dbdj/uZOcWJkt8tr54otYu2Oj6GTJH0iF1G3+5Mq/xpHB+Ng6c+1Oo9GIt99+mz0upiAJlWlxh/ps6/4kY69SqZR8bzGVyxbOPJ+ta2y95k51V2rb/AW9ves85X1A8V9sJc4Sg3yu+OWf+AZ2XFwcNBoNdDod5s2bh7S0NLYN8hpJyKVUKhETE4N58+ZBp9NBo9Fg+fLl+Pnnn3Ht2jWrZGdiZGRk4Oeff8YDDzxg0Tfy3eGXzpKK0WjEsGHDEBsbi5KSEgBmr6EXpr+BtSf3o0mL5g61Zwu+UWjLSByemMCGkpHEYty1CnctImaMctdIz7btgWfb9mANX/L/g127sDWgW7dva7EmWZ2TC5PRBIVCYZXQlayx9m3ebrXWIsY6fx0mtMazdT73PvaEFU1EOCt2pL4/2yKrutjaRSzJqNzYun/JjVKn23K0BJajzyd2jb223KnuSmlb6H2xd52nPBBqCzRG2oPYK+0kBaH4HaG4GxLzDMDqXG4iMJLtGwASM6YCuJng6+iBw3YTfNjrm7N8OmsBNi39ip0gYrRRCAkJxT9X/5U9jocfr8xN8iVWooFcR0pdxfUejIcf6MTGIO8o2A3AstyUrZILJD4agEViMbGSVNz+zPoo06K0lpRYaGcSkPGvcTQ2Z96n7yH903cAAPv27YNarRaNdfZUDLS9+5PyNVIXrN6M3RaK5eTHIbuzf8627evx7v5AbY2RJp95ErPszGfIVuwwd7Nn3rx5AMzG8s8//2zRB25iMbIBBwBTpkwBADbZ1/79+9nzpIwpcn43hg0bhr/+ujmHKJVKREbXQenV67InyeJWEiFt89cHYrldSKkrbrIwsbWIreSrwM08L9x8L2Ilqcj9SU4ZkrxVKO+MvQSsUhKQScl3Ywuj0YghLTsBDIN2d7XBj3nfARCPd/ZUHLS9+wcpg7AgOV3SmsHbsdvcNQ4gXIrUnX10tm1vv2+BAI2R9lHEXHwA6S49QqUR+DHSgNlI5rtbk0F6VPIEVj1uee/drCs3f5c0OXsOq05LQYqbkz32bPgOw+9+BBs++xImkwm3NmqKwvwfcG7HUUwd+7pbdtGIsgrcVI/5O3ZC6uvYYfFQharAMAx2FOxm3ZY2792KCE24hRG9ZE2eoBs4uc/QXgOhUYdBoVCg3V1toFGH4cI/f+OWBo0sSlKRncYffz2AMn05Zn2UySYj4/Zr0ugkmwnF+LuYUnYo+dc46g425cWJCA4yu+EPGTIEgLja6CkV0t79HTGiudfxVS65yM/PR8+ePdGzZ0+rtrlKGl9Vc0axcxRn/2ZUcaY4C/mcA7D6DEn1kBCKHearvXFxcejbt6+VuzUxuhMTE5GYmIjc3FzcfffdUCqV6Nu3L+Li4tg+FhYWYufOndi5c6fkMYXbN2e9SV555RXExsayRnRIaAgW71mL9WcP4rk3xknyiHMUou4qg5Rs2/y1i5An3ef7N6H43AWYjCac+t9xXL54CR9Nm4fDe/dj3OwUi7VIVlIqrvxdDFWY2qL/JJxMFaZGSGgoIqK0eKR/b6jC1LjydzHqNmpgUZKKrFWOHjiMivJyVFdWQaFQwGQ0WfRteGKCzYRi/HWYvfJbQus2oWNiBAUFYdYXHwIACn8/iv1HzJs7ziqrcmHv/lKNaH5b7lJ+uaF4tkLduD97St13tm2qOHsWqkh7EFslHORQc+3tZnLvAcAiIzZRqR/s2kVS9kl7OPo865Z8geVzPkB1jVmVDVOHYcXcj9Hn4Z5O90EqUpRVsXOa9WrDqsEAcF1XAgUABmAVYuDmDiEA0V1C7i7ihX/+ZpO0vZcy10rV5irYJMs3t2++uiN5+vwZtB32KADgnXfeQffu3b3bITfhLpWVtAvAqm2+Ik1Usi5dumD79u2SsgV7GprBWx5qqyLNLwvFRY7voL3PJ/ceANifExISLL5/3LJTzuLo8zz77LM4efKkxbHnp03Eky+5P7OvMyWkCMSbjpSxIh5zXIUYAAa36CCoLBP4a5BBzWPZOTUxY6pVSVGiWgMQLBnqqRJXjvJytyfx95lzUCqV0BWc83Z33Ia71jS21mZ8RZrEdldWVUJvqLBY4/kKNIu3PFBF2kc5fqjIKvaHYCsGWgghxVcsvkYoVoebkGx51iKUlehQVqITzD4pRV0WSngmKeP3L4V4rt1j+Gz2QlTXmEs/ffvR17i896RHjGhAmrIqFocCmA3ZYf2G4LYWt+GuO+5EmDrM6vpJo5PY7Nxiu4TcXcQwlZo9zlXBuQo2aa9X5+6sOu3pLJyO0rJZC3Rs0x4AkJyc7FBGd3/K3ixVZXX0mUjiI61Wa9U2V0njxnVu3brV4VhvT0ETiVFcobCwECaTCYWFhVavOeodIvRdtJeQjOvhwU1KlpOTw8ZDFxYWOpQTQOwcqWPKggULEBsbyxrRSqUS05e9j43nDnnEiAakKatC53ATn/YbPgT33ncfbr2tuZVCDJirinCVZT78NUioWsW+xm2HnPdI/96sF9+DXbuwayNH1zSeZvHutQAAk8mEkVNecuhaf8reLHVN4+gz2Vqbcdd9Y4fFI0ITzlZS8VVoIjHPQxVpDyJll1bqrqeUWGl+zA+/TdJGRNTN3RZ+TUWpfXJ0t3bft9/j/TdmwKA3D0jBwcF4Z9Lbsu2geWJXbuTsRPTu1xc9uvdATEw0jCZzopJrV6+hoKAAkVWh6N9B+mbAmGlJWLtjI4b2GoiHH+hkEX+9NF18UBTaUfXlXUlDpQH1Hr0DANC9e3e88847kq7zh1haRxVWZ59Jyn34CdMcdVP3BL6qSPtqv8SorYq0lL+T1O+Y0HliNaXFYrJJG9y/AT8eWkp/nBkXXn31Vfz0008Wx0ZPeRVxiaMlXW8LOeofS+GjpJno3/dxdO/RHTHRMTD9N6devXYV+wsKUBpcg7t7dpTcHtcLsE2nB/FZ+kJUGSrx6MA+NkPWuHHWYjHdvsSWFWuRM9XskXF++1FE14mSdJ2veq4RnFnHOPtMUj0TJ2VNY/PWOOKm7il8de3nq/0SgyrSPoqUXVqpu55SYqWJQg1AsE3SxqjkCTYza0vpk9R+/3Phb4x9ZBAyx02GQV+BMFUYPp6+ECU/nZX1y+XOXTlFaBBULaORt+ILDBs6FM2aNkW4JhxB/5UMiY6JRr8n+uGJkUOgahkNRah1fWuhUlKrt62D0WTE2h0bMXZYPOpGxVhlBBdCaEdV6Pl9ZfdZrVLjvRTzImb37t1s/VJ7eCKW1lXV257CKlRax5lnkqLkxsXFWZTR8QWDkP/8nihj5gzk/c3JyfEbL4jaiJTPj9TvmNB5YjWlAeuYbG4biYmJovHQUvrjyLiwfv16xMbGWhjRY6a9ho3nDsliRAPSs0k7izooBA/E3IYVX3yBoUOH4pamTaEO17BluGKiY9C/3xMYO3QEHoi5DeqgEKs2hLKE792wlc323S8+DtqYaDAMI+gVyEWopKfQe+BsLhi56ffcUISqzIp722EPS77OE55r7qwdLdS2s88kZc04dlg8hvYayOat8QWDUO461+6CWzbWF9ahckIVaR9CbMdTSsZIR9pzZ1/FKD53EW8OGoXSayUAAKVCgddHJWLWhKmy9ovgjt2vJWvysPbHb/Hpkk9Roa/AfffeBwDQG/S4UV4GAFCFqmCoNAAAgpRKFF8qhiYiHAe37MPgLn3YdsiuJpvc7L/fAWB4nyFYmm5OaEFicriJy5x9fl/bfW7c/S7c0JcjPDwce/bs8XZ3ALiuejsSU+mKqm4rNtTV+7lTjfUHrwJAnmzQnqS2KtL2EPssk+Pt2rVzKH7Znd8NR9tesGABVq5caXHszQ8y0G3I47L2C3DfWmJLXj6OfPcDPv90Ccor9LjnvnsBAAa9ARVl5QDM9a2rDOaEmgqFAmfPnkWLVrfjj5qr0FVXsO1ws4QPT0ywqEjSdVBfJGfPsapa4sizCL0HvhQ3fbX4MhI6mv/2n858D88+4Rubk66sO+yt4+Ra0ziy1nLmnu5UY31tXSeGUHUcX+4vVaT9FLFdX7EahrZwtxuS1B3qct0NvDEwHi8+Mgil10oQpAxCyvOvQXfgvNuMaMA9u3Ird6zF4k8+xo8//YQOnToiM8s88N4oLwPDMGAYBtXVVdBGRCJIqcTixR+jQ6eO2LdvHx7o3RmrdqwHYE5YQYzm+tH1LFyF3kuZy7pxc2NyHFXWhZ6fv1PrbYX60Ndm47m8vByLFi3ySh/4uKp621PInG2fr+Taig119X621G5XFXt/ydBN/o6JiYl+0V+KMGKfZXJ8+/btkmP03e3uLzVfQFFREWJjYy2M6B7DnsDGc4fcYkQDjmWTdoSf1m3Hko8/wU8//YTOHTshKzMLAFBRVs7OqTVV1dBERkCpVOLDDz9Et27dsGXjZjS6EYyC9dsBAMuzFrFGc5260awRrQxSIjFjKuvG3S8+DmHh4Sgr0Tmsrgu9B3JUKpGLuo3q44GunQEAL86ciJqaGq/0g48rqre9dZyzbfPXPguWZeO6rgQRmnC7a0Zn7mlL7XZ1Hear+XD4kL/ljPEpftFfR/ALQ3rRokW47bbboFar0alTJ4sajoGEmHs0NxkG/3WxwZtv6Mo9yNtz5dZdL8WLjw7CM22642ThMQBAr87dUbL/LN4alyxLHzxN7qdLcf36dbz88suorq5G6rQ01pgmhGvCoVFr8Plnn2PmrJmorq7Gyy+/jGvXriH6rsZWbRb+ftRmvI2cgyR/UvJ2UopbGjTC+KdfAAAsXboUlZWVdq6wxB3Jx9ztauxs+/xFtlSDlNwPgOT3ylbbriYH81VXbjH8rb8US8Q+y+R47969JZfNEispJ9f4Y+87vWHDBsTGxuL5559nj8UlJmDjuUN4Y+HbsvTB03zy+acWc+r0tGmsMU1Qh2ug1qjx6eefYe7cueycevnff/FAk1ZWbZ7+7QRrRHOzfBPkTBrGNa7d7f4uhem577M/D3plhMPXu2Nz3Z2uxs62zV/7OLLOIvcEIPm9stW+q+swX3XlFsPf+isFn3ftXrVqFUaNGoXFixejU6dOeO+997B69Wr8/vvvaNCggc1r/c212xnEXIv4irSnXJBqamowb9xkHNhmVhsVCgWe6vskPpmxEEFB1rHCcuAutxluuy+NfgGq22MAAJlZmUidlsaeN2vmTLw2cSKCgoIQEhSMzPmZmDFzJvv61KlT8dqrr5rjqC8Y8Mnyz1g3ol6du9us9+xIHx293heSPzAMgzoPNYfJaELTpk2xbt06ydf6i5uwowgpX66qYXK9V/6WhKu2QF275UPsu8L/7Htq/Pn333/xxBNPWBy784H78M66pVAoFG65pztduUm7I14Yjfb1WgAA5qbPweyZs9jzZsycidf/m1OhUGDe3LkWISxTp07Fq6++CnW4Bkf1l7Dys2WylfB09tl9JRnZ3vXfIesV8/pk55L16Ni2veRr/cVN2FH4ax051j5yupV7ex1GsSagXLvfffddvPjiixgzZgzuueceLF68GBqNBp9//rnVuZWVlWzJCfLPX3BGMd6Sl4+K8nKLhBgEvhuS1F3YrKRUDG7RAVlJqQ7131BhwKRBo/Bky06sEX1fq9Yo3f8XPpv9gduMaMB+Yq0la/LQrFcbNOvVxqGd1lkfZbLJEYLr3ixplZKcgqlTb7qlz5g5E4sWLUJYWBgWLHzXwohOS01F6pSpqB9THwDw9YHN+PHXA4jQhGPG+BQ8/EAnyf0ZMy0JdTo3x5hpN3c1XdnN9IWdQYVCgZ2fml3eL1y4gF9//VXytf7iJuwoQsqXq8arXO8VVWhrF/48pzqjGOfn50Ov19stLwdI+07l5+ejZ8+e6Nmzp8PK9f/+9z906tTJwojuPtTswr1gfa7bjGjAtkcb+TkrKdXhNcvyrEW4fPESlmctQpPwGPb4+JfHWcypszhz6gfvv29hRKempmLy1CmIqm++/o8t+3H0wGGEhYc7bEQLrXecVZbd5f7uKF0HP476Tcx1zfu8PMyha/3FTdhR3OF9J9d75QvrMIpr+LQiXVVVBY1Gg/z8fAwZMoQ9Pnr0aJSUlGD9+vUW58+cOROzZs0CH39QpJ1RjOVSmbk7qdzYovVnbGeLBsyK4qcz38HGpV+xxx55sDPWvf8F1JxayO7EXmItAILloYgq3KJJc/x6oghhKjXmvDadbaNZrza4ritBtDYKVy79CwQr2cRiDMNg0aJFmDPnZgmNqKgolJSUsL+npaVh9vRZ0Bv0KNeXw8Qw+Pfff/HAAw+AYRhEa6MQoQkX3NXkPhNgnggu/PM3GIZBkDIIpQV/iT67P/LgU93xx9k/oVQqUVBQwGZr9TfkMHrz8/ORk5MDwFw6hxjWvlrCiuJ93KVIi82p/qBIO6MYy+25QZLVAZDc5smTJzFixAhwl2b1GjfApz9uRHBwsNN9cgRbHm0AcPniJSiDlBbJvbiK8OG9+1FdWYUQVahFUq9n2/ZAWYkOEVFaXPynGKFBQTDoDdCXlQMS59Q3UybDUK6HOlwDQ7ke//zzDzunKhQKMAxj10OP/H7l72IwDGOx3vEVZdkVaqqr8eTt5njpsUPj8d6UuV7ukfPIscbhlhZdmp5tsbY7v+OozD2mBAIBo0hfuXIFRqMRDRs2tDjesGFDtgQFl6lTp6K0tJT9d/78eU911WWIYty6fVvJu7xyxfpwd2Af6d8byiAlHunf2+Y1RqMR6WMnYfBtHVgjukmDxri67xS+W5zvMSMaMO/oTRqdhAXLslnFmbtbSMpDadRhKNOXswPzdV0JrutKcPh4IRiGgd5QYbFjSZIiZCbPBoLNX5VyfTm7wElOTraY9LkTfkZGBpKTk1kj2mgyJ0Jp1LAhmje7lT1PbFeTqOGp78/GpKxpOF98EWEqNVt2gfvsgbCbeWCFOWmMyWTCvHnzvNwbaQgpXq7GEANm5Uuj0UCn07FGuVKphMlkQm5urltiwykUIfx5TiWKcbt27WTJD+AI3DJZWq1WUOHmc+XKFTz66KN49tln2TnmzvvbYMNfv2DpgS0eM6IBs7o6PDEBq3NysSUv32Ktwc3ZEhGlRUV5OZZnLUJZiQ5lJTrs3bAVZSU6VFYYrJJ6jUqegPpNGiNpZgpC//NSq/jPiAaA5Mn259SKsnKYTCbWmG7YsCGa3toMABCqVgmuicga56Np8/Bs2x6sMh6qVlmtd3xFWXaF4JAQjHjjZQDAkrV5uK4r8W6HJCAWny2HmlxQdBBGk1G0lKi3E69S/BufVqT//vtvNGnSBD/99BO6dOnCHp88eTL27NmDAwdsxyX4Q4y0t2KZufd2tKzW+s9X4vNZC2D676Nz122tsCVnFRrUsx2z7k6kxKtwz5k0Ogmvz08FwzAICQ5BjbHGSpEmKFRBUN9tds3WG/TQld0AYF4ghYeHIyYmxmLCj4qKwpUrV6DX66EvL0e4Jhzl+nI2Edny9z7F9IXpNndYyY4p2WEXS0YWSGQueQ9vf/IOAOCHH35AWFiYnSu8i5B6JVcMMb8d7u85OTnQ6XTQarXYuXOnXI8juS8U34PGSN/EW7HM3Hs7UlbrypUrGDBggEWWZU1EOL4o/B4hIdb1kj2FlLUIOSciyvyZKy+9wSrDoWqVlSJN0ASF4qGGdwIArv97ld04iKlXF6qQUJtzallZGRQKBZuEDADmfvwBPprzrugahlseCwAiorQICw/3a9VZCkPv6Izqqmo0rt8QJzcf8nZ3bCK2hpNDkbYVIw3Aohypu2PDA8WLMNAJGEW6Xr16CAoKwj///GNx/J9//kGjRo281Cv7SI13JoM7Nx5HzoyS9iC7tMcPFUnagV2WmY3BLTpiycx3YGIYaCMicX77URz6epdXjWhAWrwK95yxw+KxcPIcNGvUBFmTZiOu92BUVlXhx1+tB9EVG1bj8rXL0Bv00Kg10EZEQqFQwGg0Yt68eRYTPmDeRc/MzITJZGKN5/ox9aFRawAAw3sPtpn1kfwerY1CXO/BaNaoiV8Z0c7u7qaMnQi1SgUAVsl1fBEh9UquGGJuO942ZOVQ2SkUV5DqhZGfn4/58+c7leFeDsh3pbCw0O44UFlZiccffxyPP/44a0TXa9wAa08VYNWxvV41ogFpaxFyzqjkCVhZtAvj06egfpPGGJ8+BZ16d4P+RhmOHjhsdd321RtQcvkaDHoDwiLCoVQqEapWocpQibl25tSwiHBE1Y9hjWgAeHhwH9bYF1t7aSIjoApTIyJKi1HJE/xKdXa26srCTV8AAC5d/gdb9m13R9dkQ2wNJ4fXna02FizLZiuneCI23NvVUijy49OGdGhoKNq3b4/vv/+ePWYymfD9999bKNS+htRkFatzctl4ZDJZuepW5MiAK9Vo/3nHD3jqnq7IX7QUJqMRjeo1RMGK7fh753FE14lyqp9ywDXYpAy2/HO4v6/dsRFGkxFrd2y0uu7tRZn4599/caO8DJevXWaPz58/H6mpN5OUREVFsT+npaUhJ3sRazwTmBoTUGPCkjV5rMs2f0Dl1jRcmp5t87l80SXJlYni0CpzorobN27ghx9+kLVf/MW4qy7SfKPZXS7XfEOW1DZOTEyU9T5i8A0R6lpO8TRSN3Nyc3NhMpmgVCrZz6urm1uOfN6lGO3Ehfvhhx/GlStXAJhdcb/6bQ+WHtjiVQOau36Qshbhn8P9fd/m7TAZTdi32dqA+ywrG//++y8qyspZF+3qyipkZmYizcac+sGibAsDGgCqjDWoMtUIChOE1Tm5KCvRQRsTjZVFu0Sfydu1oMVwNgFa87vvQKt29wIAnnrjedlrS/PXH66sR/jrM3eubbhrFGLAe0qsENow8MV1HEU6Pm1IA8Abb7yBTz/9FMuWLcPx48cxfvx4lJeXY8yYMR7vi9RBVqqBSs4TqnXoLLYGXH7/7U2Um/NWY2irLnj7+YmoKCuHKlSFFZmf4M9vD6FNq9ay9NcV5NrZW7ImD6rQUCgUCov4Y8Kk0Uk4cticTdpoMuFGeRk+/PBDi1iuOXPm4OrVq5bZRael4e056ayaDQCmG5Vs38V2QbkDrb0B1hd3N13JZtn8lqbo2OYBAMDrr78OOSNP+ItxuZVWR9uTukDnL87FDAN3Gbj8++Xk5KC4uJhNiOYNqDEfGDj7HbB3npxJ+cS+10J9t2W0MwyDkSNH4vHHH0dFRQUAc43kL37dgW9OFSDcB8LP5KqFvCUvHyH/zalC+VaGJybg0K9mpdpkMqGirNxqTs2YMwdXrlyxODY9bRrmps9h1WwAuFZZzvadL0xw70fWZLbWcb5QC1oIVzwV31mfCwBgwKDnC4Nl7Rd//SHnesSZtqQapHzvRDGxwlN1tbkVYrwFNeadx+cN6aeffhrvvPMOpk+fjvvvvx9HjhzBd999Z5WAzBNIHWSl7ORuycvH8qxFqCgvx9EDh2XbBbU14Ert/+njf2BUbF8sTpuH6soqaMMjkb9gGa7uO4XBPXzH5Vau8gMLlmVDb6hA04a3YGm65aBN4lnUeiUiwyMQpFTiww8/xNy5N7NgzknPwJTkFCgYYNLrb1io1DNnzcT7H3yAG+VlAICaqxUWfRfaBeUOtPYGWF/c3XTVFev7zzbcbGvsWLm6ZbUYl9vl09H2pBreUhW12uSCXZueNZCR8zuQlpaG+fPno127dgAg20aL2Pdaat8rKyvRr18/dOjQASdOnAAAKJVKrD1VgNXHf0CdutEu91Eu5ExgWllhQL1bGiE5e47FayQ3y1XcdOvmz6mzM9KRnJIMRgFMfP11izl19sxZ+OCDD8xJygBc0F+z6LuQMMFdk3FLcEl9fm8r1a54KiqVSmSs+hgAcPh4IX49Xihbv/jrDznLZznTllTjW+oaxReFCndRm55Vbnw62ZiryJ1szNWyCNzkXsTtCYBFGQlHE4w50id75x7cuQ/vvJIG/Q2z0RcUFISMV6ch6dkXHeqTuxAqCSVHwgZbyR+4ZRL+PX0Rp/85jzb334fq6moA5jrRs2fMZs//5+q/YBgG2dnZ7MIgJCQEu3fvRvGlYpw/+IdD/XWmTIOUxGu+zqY9W/FM8gvmnzdtcigngrdjiqUidz899dy+8P76Qh98CX9NNubK35F7LQA2279SqUSDBg2cTjAmtU9SzktLS8PWrVvZ34OCg/HO+lzccZ/3PboAyzUBANnKPtlaa3BLYJ24cAYlF4rxwH33s3Nqamoq3po9gz2fJCPjz6m7du/GpeJL2HXikEP95d5/ZdEuSdd4Mgmsu0jo+ASuFv8DdagKV/adcuhaf0mQJXc/PfXcvvD++kIffAlHko1RQ9qDkMGYGM4ksyUA0eyWXIQmJzkG+OuXr2DqUy/j4qmzAABVqApvJ6Ui8ZkXnGrPXQjVhpbLWBQbRLiG7DtTMvBoXG8cKDiA5194Hm+++SZSp0y1iIMmhrRCocAnH3+CjDkZ+Pjjj9GlSxf06dMHqDaxpbqkDFjODG6BMiA26XkPSst0UKvV2Ldvn+TrPJmll0LxBfzVkHYF7vccAFtu6p577sGFCxcAmPMJiBm5Yoawq+MHwzAYNWoUjh8/bnH8i193+JT6DECwPrQcxqIUQ1oVpkbrNm2w5dvN2LnjeySMGo0333wTk6dOsYiDJoa0QqHA4k8+xtyMORZz6vUbpQ5l4HZGEAmE2tJlpTo8e18PAMAboxIxOynVzhU3CYTNeQrFEQIma7e/Yc/9h1t/kWS21MZEo7LCgLDwcLsJMIhLEtc12xVXrDPHT2J0bF+Mat8XF0+dhUKhQOIzL+DqvlM+Z0QD1rWh5XIhAsTdWmaMT0G0NgoA8Oa8NDz11FPo1LkTjh75H9Jnvs0a0XqDHpevXYYqVAWFQgEAePGlF7Fr1y507twZL7/4koURzb+XmDu2M27SgVJb+o/NvwAADAYDli5dCkBaTKWzLtuk7bS0NBp/62UcydJM/1aBi62/L/d7Tn6eMmUKrl27Bp1OB41GY9OI5mf3FmrXUdLT09GhQ4ebRrRCgTc/mI2N5w75nBENWK4f5KwYYiuM7MGuXaAMMi89jxz8BSOeG4mBw57Er/87gulvz2SNaIPegJLL1xCiMsdaMwBefvEldk594aUXYTCZk2cJ3UtsPeaMm3Qg1JaOqKPFgISnAQDvLs/BtdLrAKSFgjm73iJtj5mWRONvvYzUkD9vhwb6I1SRlhFn1GGhnU7+Me4OrjYm2uWd1A2fr0TunPdRXWV2pQpSBmHcUwmY9/pM1ggMZJasyUPq+7NRUWlAXO/BWJqejSVr8jDro0xUVlVCFapCr87dUVB00MLwjdZGIUITjpmvpmLUSwlQRqrYNrlKtFKhgNFkdtvfuXMnMudmYuzA5yyyUfIVY6Ed30BRll0hMX0Slm9YBQDYv38/nnzySbepzUSJUiqVMJlMbqkPLQe+1Bd3Qf4WWq0WGo1G9Fl79uzp0ZravkqgKtLOqMNC3w/+MfK5USgUSElJceh7JNT+qFGjcOzYMYvzFmxYhjvvbyO5XX9mS14+PktfiCpDJR4d2AdtOj3IhrEd3rsfgNmAPn6oCBXl5axrNVGSn0x4Fq3rNEFdtfkzZdAb2BAzpdJsdJs4c2pG5lz0fHYw+sXHiarFQuuxQFCWXWVIy04w1tTg9mYtULjmB7eqzaTtIGWQVZ1mX1rf+FJf3AX5W5B1rNizOhNOGIhQRdoFXEkq4cyOLn+n01YJh8oKAyrKyx3qE3dn+PrlK0h8LA6fznyHNaIHdn8cpQV/IfONWQ4b0b6+c7VkTR6a9WqDZr3aWPRx1keZ0BsqwDAMW+5q7LB4RGjCoTdU4LquBPnb1+N88UUkL5iOC//8jZBgc0mSSaOTcKNUh5Zt7sRbk9OQtyIP5y6cY7NLMwyDCn0Fvt38LcaMSsDVoxdQkPudxYAlpBgL7fiKqeS+/r7LSXZqFhRK8+dywIABbq0JS9ru3bu31T1cSW4lt2paGxJtkb8FgIB/1kDHlc+/M993obJ0fPW5qqoKgDkPSG5urkN9437/Jk+ejNjYWAsjeljiaGw8d8gpI9rbSa3ssSUvH8+27YFn2/aw6OPyrEWorDCAYRjs27ydXdccP1SEshIdykp02LthKy5fvARDubmCRVj4zZCob3JX4oG72uCNqSn4Mu9LnDtzln2NYRiUVeix6dvNiE8Yhf3njiHr2zzBkltchNZjYkq5r7/vckISj506fwaf5i+T3buPC2l7aK+Bktc3UpB7DVQbEm2RvwWAgH9WT0MVaR7eTirBjaMm2SeJcU2SkzmueC9FVN0YnCy6OdmHqdTIeHUaXhqe4HRf3R034+ouIekfAIs+kh03ABjeZwibqZuo0gBQWVUJvaHCqk2ym8fdZQ1SBqFuvbqIiIhAWVkZynQ3UFlVhaG9BlplAXcEseevbfFKp86fRbthjwAAFixYgG7durn9nnzVyRUVWO6Y7dqgSBPsPWttei9s4cuKtLdzFnA9TUhZLK4izTCMw4p3dnY2ysrKLI5HRtdB/JuJLimd7l5/uKrIkv4BlusQ4jUHAF0H9WUzdZPqJOWlN8AwDJRBSjAmxqKsIVGm+flj6tevj8jISNy4cQOlN3SorqrCI/17W2UBl+P5vb3u8zRvjUzEkb0HoFAooCs45xFPRP56xpX1ndxroNqgSBPsPWttei9sQRVpF3A1TkhsZ9PRGtTcEg794uMwbnYKIqK0iIjSSu5bVWXlf7vAxawRHXvP/bhx4Dwu//CnS0Y0IG+pAyHs7RLa25WcNDoJ0dooaNRhKNOXs+fNGJ+CZo2a4L2UuaKGbv+ufdGsURM82LodgpRBrCJN2uXusg7tNRAqZQja39EWKmUIKioNMJqMrNrtKOS5AAjGOrv7ffc1bm92G9rccTcA4M0335S1trQYfNVXagkqIeRW0V3pi79h71lr03vhr7jy+belZjtag5pbWzoxMRGNGjVCnz59HOpbTk4O5s2bZ2FEd3miBzaeO4QvC3e67C4sZ5yyEPZKYErJ8xIRpYUqTI2K8nL2vFHJE1C/SWMkZkwVNHRD1SpERGkxbnYKHh3YB8ogJYJDgi3a5eaPeXRgHzAhQWhy7x1gQoJQZaiEyWjCvs3bHX5m7jM5ol4HMjOXfQjArPY/Ps4zYyd/PedKLhe510CBkldGCvaetTa9F3JBFWmZEdvZdKbkAhcpsdRcvsjKwaoPP2N/b39PO2z88CtoIyMl39PbO1P27m9vV5JcX6Yvx3Vdid3dSzEFm6tUzxifYve9GDMtCWt3bLSrSFPF2TEiOjYFAHTt2hXvvvuuW+9FlU4z9H3wD3xZkXYFW2q2q/Hxjnid7N27F5MmTbLYxHty3Cg8n/qaQ/f0doyuvfvbU2bJ9STO2Z6CK6ZgE6UagN1qJQCQlZSKfZu321SkqdrsGAVbdyHjxTcBAAe+3I5773BvSTZvryd9Afoe+A9UkfYi7trZFNpJ5h8zmUx4Y9AoDLy1PWtEN4iph6v7TmFP7maHjGjA+3Ej9nbGOrftgCBlEDq37SD4Ouk/YHbJvlpyDc16tRHNIEkU7GhtlFUsz3VdCXRlNyT1e2l6NkoL/rLr1i32/tY2xVkqn8xYCMC8qL18+bJb70WVTjO1IR6b4ru4MycC/7Mt9Fn//PPPERsbizfeeIM1ou9odw82njvksBEN2FeE3Y297NOt27eFMkiJ1u3bCr5O+g+YXbJ1167j2bY9kJWUKqhkEwWb70m3OicXZSU6NqGYPZKz52D9mYM23brF3tvapjZLpXPfHoiuXxcA0POFwW6/H1U6vb+mprgHakjLjNhERVyfRiVPcKpdocmAe2zH6o0Y0rIjTh75DYA5icqx9QU4/d0RqEJVIq3axtcNuoKigzCajCgoOij4OjG0e3XubpFIbO2OjYKD2dhh8Ti/4yhmjE/BgmXZrKE9aXQSGw8tNADySzxILfUg9v7SCUeYEf2HI0ITDgC13sD1FO40ZCgUe9ja0CLu2YmJiU61zf9sc38/c+YMunTpgpycHPb8UJUKG88dwsKNzic48nWj7vihIpiMJhw/VCT4OjG0H+zaBWHh4aisMKCsRId9m7cLGrH94uOwsmgXRiVPwOqcXNbQHp6YwMZD20r8RX4WM9S5iL23gVC6yl0s3LwCAFBeoceCZYu83JvAx9fX1BTnoK7dfs4X7yzG2pzPUV1jBACEhoSgfnQ9JI95NSAMMa4rDGDe0evctgN2FOxmS1Vx3a2558/6KJNN4z9jfArrns0tbWUv/T8xqju37SB6Db/Eg1CpB2eeNxD+fnJz8d9i3DUgFgCQmpqKoUOHerlHFIr3CVTXbk8j5MJdt3EDKJVKDE8cExDGGNcFGoBFmarqyiqEqEIt3K255y/PWsSGqI1KnsC6Z5PSVmIu49zQNmJUt27fVvAaris2AIskZM64Z3vbnd7XeW/STHy/2pzP5d89f0ATprFzBYUS+Dji2k0NaT+laP8vmDFyAmqqawAA9aJi8PWCpeh4X3sv90xeuLXvdGU3LAxVAFbGKje++GrJNegNFVAAWJgyV7JhyjWkSYZubu09ABbGLomJbndXG1y+fsWm0S31eWl8tDjD30jAln07AAA//vgjVCrnPC5qC96Kc6bx1Z6DGtKucePGDTz22GNsrWIAuLVVC3y4/Wu2jnGgwK0MoomMQFmJjjVUAeuqIOT8iCgtm31boVBgfPoUyYYp15AmGbq5daQBWBj3fANdzOh25HlpjLQ4g1t2hKnGiLtbtMIvqxzP4VPb8JbgQYUWz0FjpAOY7/M3YthdDyPt6ZdRU12DkOBgHPxqJ85uK3KrEe2t2sUkbrnkRimMJiMUCgVUoaHQqMOsYpnHTEtiaz6X6ctRXWPeZGAAuzEp3OcjWb1njE9h3cMrqypZd3AS5zIpaxqWrMljXcwvX7+CSaOTnDaiyfNS1x/bfL1gKVuuY8IE50IlahPeinOm8dUUX+fMmTN46KGH0KNHD9aIvrfTg9h47hAWfZ/vViPaW7WLSfwzMZwjorQICQ2FKkxtEctMakZf//cyFAoFqiurWKWeYRibcd78ZyOhbQ927YKK8nJERJkXpsQdnMQ3L55u9hoLCw9HWYmONZ6dNaIB33en9wXeWvoeAODEmZNYvXW9dzvjB3gr1pnGWPsm1JCWGXdNjn+f/gtP3f0o3ntjJqoqDAgP0yA3PQfXfzqL1i3vlPVeQnjrCzx2WDwiNOFgGAZByiBERdaB3lCBulExOL/jqIWxunbHRjAMg+qaalzXlaDGWMO+JpaQjMB9Pm6MMjGSVaEq1sDlx0xzjV9X3ycaH20fhUKBvLkfAwCOHDmCCxcueLlHvo234pxpfDVFDqSWuXKEmpoaPPLIIxg+fDiqqqoAANqYKKw6vhfzVn8q231s4a3EYyT+WRmkxKjkCWysszYmGiuLdrHGKkkIVlNdA4ZhEKIKhTLo5pJRLCEZuZb7bCRO+fihIpSV6BAWHs4a18MTE6xiprnGr6vvE42Rtk9st4fQpvODAIAXpid5pMSkP+MtwYMKLb4JNaRlRu7J8eiBQxjRtgde7j4UFXo9lAoF1r3/Bf7Z8wfi+gyS5R5S8OYXmNx7QXI6enXuLpqpe2ivgQhSBqFZoyYIUgbhgbvbIkgZBACiCcn49+A/Hzk+Y3wKa+COHRaPBcnpaNaoCTq37YBZH2WiTF9usx0xvKX0+ztDej6BhnUbAKCJx+zhrQzkgZr53B2GHUUcOT0bysrK0KNHD3Tu3BkGgwEAUL/pLVh/9iBWHPkemvBwl+8hFW8ppeS+42anoF98nGimbm7NaFWYGgDwSP/erDEtlpCMew/+s3GPcw3cfvFxGDc7BRFRWlSUl+PogcN22xLCWyp/IDBruXnz38QwGPb6aC/3xrfxluARqEKLv6+DaYy0zMiV2EJXUorEnnEovXINABASHILUsW8g+flX5OqqT+Jq7Wj+OUQlFoptlnI/e30Tqz0tFRoT7TwVhgrU79oKgDmD7/PPP+/lHvkGNDbZvdiqbewtAjlGWq7P86OPPoqKigr291BVKBbtykejpk3k6KbP4mrtaP45RCXmxzYLJSeztwbin8uN33YmuRiNh3aN7avW44Pk2QCAo+v247Zbmnm5R96HxiW7H19cB9MYaRdxZVfTVTeiv0//hRFte+C5tj1ZI/rd5Axc/+lMQBrR/J0oe67RYorvmGlJqNO5OcZMS2LP6dy2g8UAKNS2rfuJ9Y3ERpP+CNWeloq9WtgUccLUYRj5xHAAQE5ODmpqauxcEdgQpTQnJ4fGJruAPcWZuqw7jisqviueDTU1NRgyZAhiY2NZI1obbXbhXnNyf0Aa0fz1iz0vOSHFl8RHP9u2B7bk5bPntG7f1sLwFWpb7H5C66rlWYtw+eIlNrkYuc8j/Xs7pdYTFb2ivJyq0k7Q++nBiKgTCQDoMqK3l3vjfZasycOkrGk0LtlF7CnO/u6yTg1pAbwRu1RdVYXpIyfg5e5DcaNEBwAID9PgxoHzeGm499xs3O1ywTdk7X2hxFxb1u7YCKPJiLU7NrLnFBQdtNs2MYTL9OVWzyjUN349aVJ7mhuvzX3P7L1/9mphU2yzeOZCBAWZ3feHDBni3c54ECHDhLjAAqCGngvYcyUOVJd1d+LpxHNVVVV47LHH0LlzZ4scClH162JFoWdduPm42/2Yv36x5xottPlP4qPLSnRYnZNrEeNsr20xY1bKuorcJzl7DtsnobrSYu9dv/g4NlGZp2PPA4UPtn4FALihL8Pn33zh5d54DqG12oJl2WylGH818nwBewKZv7usU0NaAE/GLulKSvFavxEYekcX/Lq3AABwd4tWaNaoCTJefYvNTuwt3J1kjG/c2vtCiRmmJD56aK+BDrVNkpld15VYPOOSNXko05dbKM3c2Ghbgyr3PXNWYXcFf483cZT9X2wFABQXF+OXX36xeC1Q41mFDBOilCYmJlJDzwWo4iw/nnxPx40bh4ceegilpaUAzC7cI94ch/pNGmPExJfcfn97uHujnr9+seclJ2ScEmOYm8VbattixqxQLDZJODYqWbz6Avf9kvLeyb1+q21x1/VvaYSH+j0GAHh17hRUVlWyrwXy2kJorcbNz+OvRp4v4O+Ksz1ojLSXYBgG2VPSsW3lOvZYz06PYvU7uT5VF9fX4kPE4p+5fXO0z0Lnc2tJn99x1KE+ctsDrOOy3Y0vxpu4m/uHPYo/z58BABQUFCA4OBiAb8azygGNg6ZwCeQYaSkYjUYMHz4c586dszj+0a41aHr7bd7plAhy5VGRC7H4Z27fHO2z0PncWtIri6TXKua2BVjHZLub2hh3zTAMhrTsCJPRhGYNb8HxjT8DCOy1ha+tdSnexZEYaWpIy4TUiaamuhoZYyfh0K6fwMD81t/X6h789MVWr6vP/gB3sCM7iHyjmnv8+IYDggOkvUHTFUPa29TWCSGiY1MAQJcuXfDhhx8CoAYnpXYQiIa01O/u/Pnz8fXXX7O/K5VKzMz7AA882sWt/QsUuGsXovhGRGkRFh5ulQSMGJNC6x17ayBnDWlv42sbH57ir99PIan3UwCA9e+vwGNdutXatQWl9kGTjXkBIZcjvktQ/ke5ePL2zvhl149gwKDNHa1xbvv/sH/FtoA1orlJwFyBuBQBYN2zxeo38xN4OZpkDABmjE9hy175G/4eb+IsH0ydBwDYv38/rl+/DoDGs1Io/opQ+AI3VGPChAmIjY21MKJn5GVj/dmDAWtE85OAudrW810GAADrnk3cogFYrGf4btmOJBkjSHHj9kVqax3q5nfdjhb33gkAeHJiPBiGqbVrCwrFFlSRlgmhXUuyi6sOD0NVRSVMJhMAoEmDxihas8+nXLjdRZ3OzdlkDaUFfzndDnEpitZGIUITzu6Ikh3Szm07oKDooF1FGoDV+XRSCBxu7dUG13QlUKlU+PHHH73dHQrFI9QWRZqEavAZO2MSBr8wwq398QXImgKAy67GpC2++rwlL5/Noj0qeYJdRRowG9Gt27fF8UNFtU65DWQqDZWIu/MhAMBTfYfg87dp5mpK7YAq0l5AaNfyga6dAQCG8gqYTCY0adAYh7/ejd83HXTKiHZnogd3tS2UBMyZexL1GYCFkkyM5oKig4JKNQD22IJl2Zj1USbOF1/E2h0bqREdgBStNRvPlZWVWLp0qZd7Q6FQnIXvTZKVlWVlRM9asQgbzx1y2oh2ZyIpd7T9//buPD6q+vr/+DsJJEBIwha2ElnUCi37LiBLURtFEX8K1RYKiIgCGsUiiJZ8RRpERERAFqUBrCgUAUUKSoOAC7gEFFGgRUQwIItI0KABMvP7IybNNsnc5M585s68no9HHg+cZO4cbwInZ87ncz6ehoCV5zU9dZ/zJnZXjY7O/32mpCFjeUvBF0+dpZMZx7Rz2/aQ7NwGs6gqUbpj8gOSpJVvrtX3Z8+YDQgIQBTSPrB6wTLdcmkXvfXyGklSbPUYHXrzU+1/4yP9usll5b6uLydo++ra3dt1UcO69dW9Xe6y7ILFs5XXzFtSlLfkOq9ILnhmtKeivOBZgJKKHWFVlmCeVBlsasTGqX/vREnSvHnzdOHCBcMRAaiI9evXq2PHjlqxYkX+Y9f84SatO5yu9ld1rdC1fTlB2xfXvm7Irfrz+DGqWuD4roLFs5XXzHvzP2/JdV6RXPDMaE9F+YYXV2nB5Ok6mXFM53/OLvb5soTaJGwnu/nOwYqqVkWS1HJAN8PRAIEn5AppX/4D/t9PP9dNTTopNWW2zl+4qBoxcVo3Z7mObt6rOjVrV/j6vhwh76trFy2WC/53eV6z6B6dks6MznuNx+ZPV4v+XfTY/On5y8uT75ng8QgrTwWzr48Ag72WP/mCwsNzZw788Y+BudwzWI/lQmjx5c/x6tWr1bFjRyUnJ+c/NuKv47TucLrumzHZltfw5VGXvrp20WK54H+X5zWLrqYr6czovNdYMHl6fsHuynEpPCJcV914rce9z55+3/L1EWCw13Npud+/zB/P6sm/zzYcTcloeMCUkNsj7YujDN7fsFmzxiXr56xzkqSoyCi9/+JGXdH0cluu72RFpzzaMfWxrCncUm7x++O5rPzJ2wX3VXvi6WgHJlU6z669u3XV0OslSYsXL1abNm0MR1RYsB7LBTNM7ZH2xc/xnj17ip033aJTOz356gu2XN/pis5jsWOqdFlTuCVpweTpcuW4Sj0mqyhPv2+F6iRsJ3v2oce16ZW1kqRT7xxQlagqZgMqIpiP5oL/cfzVL0oqpO38B/zk0W819ppBOvdDliSpSlQVzXhwioYPCMwuWKCyeu6yN2dJF71uec+ThnP99qYr9fWxI6pUqZJ27NhhOpxCOJYLdjJVSNv5c7xr1y7dfffdysnJyX+s76D+uv+p5FKehZJYPXvZ0+AxT9f09ncnCubg4Xa7dVPTznK7XLqiyWVKX7nFdEiF8Psb7EQh/QtfTe3+7+4vNGVYks6cOi1JCgsL08vTn9cNv+zNzMNf7MI83Y+CZzZXrxZd5ruKns6S9uZdSL4noePcT+dUt1fu8R2DBg3SQw89ZDgiwDecPLU7LS1NEyYUPmawbqMGWrhtrSpVqlTocQqzwjzdj4JnNleNji5zFV7edX7KytKPZ85aWrHH9yR0fLbjY00aNEqStG3pv9S+RWvDEQG+wdRuH/kp65yGdkrUuBuG6Myp06oUEaFxfx6tHz44UqyIlthbW5Q398ObfdMF90mX9fVF981Y+Z542nPDXhxnqFa1miYMv0+StHLlyvyzpQGYd/z4cfXo0aNQEd28QxutO5yuxe+/UayIlthbW5Q398ObfdOeBo+VpOi+Zyvfk5L2TDN4zDlade2oepfknp7SZ/gN+Ue6AqGMQtoLJ48d1129BmhQi6t0+vhJSdK0+yfrzPavNWXsJI/P8+VwMCcq6X7kFaM1Y2so+Z4JxYaJlaWsry9aOFv5nngqunmDxDn+es9DioyqLEkaPHiw4WgAfPLJJ+rSpYv69eunn3/+WZIUW7um1h1O14w1fy/1ub4cDuZERe9HXlHavueV+QPASjqa0xNvvrZo4Wzle1JS0c2bI86y4O1XJUk5LpcmPfu44WgA81jaXYqcnBzdl3i7Du//UlLuEu6bf9dPqVPnKSIiwu5wQ1JFBkR4s0y7Iku5PT2X5eHO8sm+z9Tjz9dJkp599ll168YRHgguTljanZ2drRtuuKHQypD4X9XXondeK7H7DOsqMkzV2yXaFVnKXdZQM5aGO0NqymytXrBMknT6vYOKrBxpOCLAXlaWdpO9SvDTuZ80+U+jtS99d/5jI2/9s54e/zeFhYUZjCy4vPDqi/rxXJZqxtYoV9e+YGfYU0F75y1Dyl3sVuS5CBxtm7dS+xattXPvbiUlJWnHjh28EQb4yfHjx3XLLbfkd58lqVqNWL3y6WbyqY02vLhKP2VlqXqN2HJ17At2hksraK8bcmu5C96KPBeBY/ikJK37+8u6cP6CLu/XUV+/tbvsJwFBiqXdBbjdbv31T6M1qHmP/CK6a+uOOrH1P5r1UApJvwJK2lc8c+lcfX/2jKpXi5akMvcdF72GqaXzLO12nq1L1kvK/Ts+aZLn7RgA7OFyuTRw4MBCS7irVY/WPz5N04rdb5NPK6ikvco/njmrqtHR+cdilbX3uODXmFo2z9JuZ8o7W/q7M6f1/KplhqMBzKGQVu4S7umjJ+imJp30yTu5y4sTe/RV5vav9e8X1qpa1WqGI3S+korPgoVw3ucfnPGox2K66DWs7qe2C3vfnScsLEx/u++vknKnBO/du9dwREBwys7OVmJiojp37qyvvvpKkhQRWVlrDn6gFV9sU1zNGmYDDBJl7VVeNmOeTmYc07IZ87y6hpW91HZi37sz1W/cSF1/31uS9MCTk3Tup3NmAwIMCflC+tnxUzSgaWe9+8a/5Xa7ddklzfTfNz7WqqeXsvzTRiUVn0Wnb0eERyjHleOx02u1gPV2urbVKdymCnhUTNLgUfpV3QaSpDvvvNNwNEDw6d+/v7p3765Tp05JkiIqV9IzG5Zr7YEd7IO2WdECtDyFsJUi1tvp2lancJsq4FFxkxY9pfBfVpbcOPZ2w9EAZoTksDG3260l057Va88vV07ORUlS2yta6a1Fr9J9Lge7hm/ZPcSr4CCzvK53SdeuyMAzOMupM9+pybVtJEnDhw/XmDFjDEcEVJzJYWMul0tDhgzR/v37878uqmqUFm9fr7haNX0WSzCzYwCX3UO8ig4y83T9igw8g/O8v2Gzpo0aL0n6bM17avqrxoYjAiqOc6RL8dHmd9S/SSetXrBMOTkXVbdWvN5dtkHvvriBIrqcvNkz7E3X12qnt6xrlrR0vKQYWaodOurUqK1RA4dJklJTU3X27FmzAQEONmLECHXu3Pl/RXRYmP4yd5pW7X+fIroCvNk3XFbn12qnt6zrFe1ee4qRpdqhpdt1v1OtevGSpPYDexmOBvC/kOlIb1v3pp5Pnqnz2dmSpOrVorVr5VY1qFvfcJTOlddB7tq6k3bs/qjUTrIdXd+iHWsr16xot5sjr4JLrW5Ndf7iBSUkJGjNmjWmwwEqxN8d6V69emnr1q35j4eHh2vmGy/qspbNffbaoSCvy9uiQ2vtTd9dajfZjs5vwa5yXmHs7fUq2vHm2Kvgkf3Tz7r1iu6SpPHD7lXy6AmGIwIqxkpHOiQK6bDwcLldLkm5BfSi/5ut/r0TDUfnfP4sZEt6PX8Wtyz/Di7vf/KBrr3rFknSkiVL1LJlS8MRAeXn70K6oD/cO1KDx9/ts9cMJVaKYzsK0YKvl1dM+6uwZQl4cJk9for+veI1SdK+1z9Uo/oNDUcElB9Lu4twu1yKrBypbUvX69st+ymibWJlSXRpy7a9HfZV9PX8NfSrouddI/B0a9tFv212hSTpjjvuMBwN4Dx/mZuidYfTKaJtZGVZdGlLt70d+FXw9fw59KuiZ14j8CTNmKyq0bnbI3sMvc5wNID/BGxH+tChQ3r88ce1efNmffvtt2rYsKEGDx6sRx55RJGRkV5dI+/d87+OekgTRtzn44hRkJVucaB3ewM9PpTPhYsXVLNbU0lSYmKipk6dajgioHz83ZEuOsATvmelAx3o3d5Ajw/lc+TAIY3+Xe5Kr9Sp8zTw2psMRwSUT1B0pPft2yeXy6WFCxfq888/16xZs7RgwQJNmjTJ8rXu+cNwH0SI0ngzgCxPoA/7CvT4UD6VK1XWhOFJkqSNGzcyeAxAwPJmAFmeQB/4FejxoXwSLmuitj06S5KGPzpGFy9eNBwR4HsB25EuyYwZMzR//nwdPHiwxM9nZ2cr+5dhYlLuu+cJCQk6unmvYqvH+CtMiOFccI743pfpp3M/Ky4uTmlpaabDASzzVUfaU06lI+1/DOeCE7jdbt3UpJPcbreuan+lNiz4p+mQAMuCoiNdkszMTNWqVcvj56dNm6a4uLj8j4SEBD9Gh4J7ne3Yv+zt3mmgIj7957uScv99Wb16teFogMBBTjWr4F5nO/Ywe7t3GiivsLAwjZ3+iCTpnZ3bdfzUCcMRAb7lmEL6wIEDmjNnjkaNGuXxax5++GFlZmbmfxw5csSPEcLKcu6CPBXM5b0eYEXD+Pq6qkM3SbmFg+uXCf9AqCOnmmVlOXee0orl8lwPsOra227OP0e+Rf8uhqMBfMvvhfTEiRMVFhZW6se+ffsKPScjI0OJiYkaOHCgRo4c6fHaUVFRio2NLfSB//F1h7e8e4k9FczsTYa//Ou5FZJyl6WNHj3acDRAYCCneuaP7m559hKXViyzNxn+snDbGknS+YsXdG8K50ojePl9j/TJkyf13Xfflfo1zZo1y5/MffToUfXu3Vtdu3bVkiVLFB7ufe2fN2GUPdK5AnX6NPupEQjWbdmg2x/KfaNu5cqVatasmeGIAO8wtdv/AnXyNHupESiWz1qol2ctkiQdeutT1alR23BEgHcCeo90fHy8mjdvXupHXhGdkZGh3r17q0OHDkpNTbVURKM4qx3egh3s8nazvXmev86DLoo92Cjoxt7XKb5mbqIfOnSo4WgABDKr3d2CHeyKdLPLeq4/z4Muij3YKOiPD4xSZJUoSVLX268xHA3gGwE7tTuviG7cuLGWLl2qiIiI/M/Vr1/fq2vQkfZeSV3hgh1sSeXqZgdqF1wK7NhgxrGTx3V5vw6SpH79+umxxx4zHBFQNjrSgaWkrnDBDrakcnezA7UTLgV2bDDj4Bf7lZT4R0nSa7NfUt8rexmOCChbQHekvbVp0yYdOHBAaWlpatSokRo0aJD/AfuVtE+5YAc7789dW3ey1MUN5H3OgRwbzGgQX08PDh0jSVq/fr3OnTtnOCIATlPSPuWCHeyCf7baxQ3kfc6BHBvMaPabK9SiQxtJ0oD7BxuOBrBfwHak7VCejnSo7tf15v/7hVdf1IMzHlWOK4cuLoJarR5NdP78RdWpU0cbN240HQ5QqkDtSIfqfl1v/783vLhKCyZPlyvHRRcXQSsnJ0cDmnaWJPXt0lOvzVluOCKgdEHRkTYl1I5cytsnLKnMfcozl85VjitHEeERdHER1Ha/ul2SdOrUKQppoJxC8bglK28e/PO5JXLluBQeEU4XF0ErIiJCd00ZL0lK+2Cbdu3dbTgiwD4U0kWE2nJfK28c5N2bmeOn2tatZ+AXAlGjeg3UtXVHSdLkyZMNRwM4Uygu9bXy5kHe/bl7ygTbOvYM/EIgunHYbYpvmDvfqO+ImwxHA9iHQroIUxOkTSntjYOiRa4v7k2orQCAc6z/5Wxpl8ulpKQkw9EAzmNygrQppb15ULTI9cX9CcVVAHCGZ996WVLu2dKT56YYjgawB4V0iCutOPZHkRtqKwDgHFGRUZr7yAxJ0nvvvadvvvnGcEQAAl1pxbE/itxQXAUAZ6geG6v+d+ZO8H562XP6OftnwxEBFUchDY98UeT6o8sN2GXYTberZmwNSdLgwUwcBVB+vihy/dHlBuwycvKDiqhUSZLU5parDEcDVByFNDxiKTcgvbt0g6TcycirV682HA0Ap2IpNyA9uebvkqSME8eU/vknZoMBKohCGn5lZU82EAga/ypBN/ZOlCSlpKTo4sWLhiMCgFxW9mQDgeDXbX6rS359qSSpz4j+hqMBKoZzpBEwWvTvoiPfZnBGNQKO2+1W3JWN5XK51Lx5c/3jH/8wHRKQL1DPkYZZd1x5g05mHOOMagScixcv6uZmuUevJnbvq1WzlhqOCPgfzpFGhZnoDjN4DIEqLCxML6YskCTt27ePwWMALDHRHWbwGAJVpUqVNPL//iJJ2vhemr4+esRwRED50JFGiegOA8X9+oZOOnrimCpXrqzt27ebDgeQREfaCegOA8X9qW1fnT19RlGRUfru3S9NhwNIoiMNG9AdBorbtXKLJOnChQt64oknzAYDwDHoDgPFLdy2VpKUfT5b819ZbDYYoBwopAOcqQFcHEsFFBddLVqjBg6TJK1atUqnT582GxAAS0wN4OJYKqC46rEx6jkgd5jn+KeTFcSLZBGkKKQDHMdFAYFl5vipqlalmiTp1lv5pRhwEo6LAgLL+Gf/prDw3HKk1f/rbjgawBoK6QDXtXUnRYRHqGvrTqZDKYbjqhCqPli+SVLuntG3337bcDQAvNWiQ2uFR4SrRYfWpkMphuOqEKryzpY+lHFYu/buNhwN4D0K6QC3Y/dHynHlaMfuj0yHUgzdcoSqpo0aq0+nHpKk8ePHG44GgLf2pu+WK8elvemB98s63XKEqubtWqluowaSpJ5Dr9eFCxcMRwR4h0I6wAXy0K9Ajg3wtdfmLM//87Bhw8wFAsBrgTz0K5BjA3zt+XdflyS5JQ17dIzZYAAvBfXxV5mZmapRo4b2r/tIMdEc1QHAXmvT3tDov41XdHS01q9fbzochKisrCz169dPZ86cUVxcnM9eJy+npn7wL1WrHu2z1wEQmja8tFpLUmarQZ16Sv/llAzA337I+lFX3NjJq5wa1IX0N998o4SEBNNhAADgc0eOHFGjRo18dn1yKgAgVHiTU4O6kHa5XDp69KhiYmIUFhZmOpyAc/bsWSUkJOjIkSNlHjgO73Ff7cc99Q3uq2/4+7663W798MMPatiwocLDfbdji5xaOv4++Qb31X7cU9/gvtrPxD21klMr+SUiQ8LDw3367nywiI2N5S+8D3Bf7cc99Q3uq2/48776ckl3HnKqd/j75BvcV/txT32D+2o/f99Tb3Mqw8YAAAAAALCAQhoAAAAAAAsopENYVFSUkpOTFRUVZTqUoMJ9tR/31De4r77BfQ1NfN99g/tqP+6pb3Bf7Rfo9zSoh40BAAAAAGA3OtIAAAAAAFhAIQ0AAAAAgAUU0gAAAAAAWEAhDQAAAACABRTSkCQdOnRII0aMUNOmTVW1alVdeumlSk5O1vnz502H5ijz5s1TkyZNVKVKFXXp0kUffvih6ZAcbdq0aerUqZNiYmJUt25dDRgwQPv37zcdVlB54oknFBYWpvvvv990KI6XkZGhwYMHq3bt2qpatapatWqljz/+2HRY8DPyqX3IqfYip/oeOdU+TsipFNKQJO3bt08ul0sLFy7U559/rlmzZmnBggWaNGmS6dAcY8WKFRo3bpySk5O1c+dOtWnTRr///e914sQJ06E51tatWzVmzBjt2LFDmzZt0oULF3TttdcqKyvLdGhB4aOPPtLChQvVunVr06E43vfff6/u3burcuXK2rBhg7744gvNnDlTNWvWNB0a/Ix8ag9yqv3Iqb5FTrWPU3Iqx1/BoxkzZmj+/Pk6ePCg6VAcoUuXLurUqZPmzp0rSXK5XEpISNC9996riRMnGo4uOJw8eVJ169bV1q1b1bNnT9PhONqPP/6o9u3b67nnntPUqVPVtm1bPfPMM6bDcqyJEyfqvffe0zvvvGM6FAQg8ql15FTfI6fah5xqL6fkVDrS8CgzM1O1atUyHYYjnD9/Xunp6br66qvzHwsPD9fVV1+t7du3G4wsuGRmZkoSP5c2GDNmjPr161foZxbl9/rrr6tjx44aOHCg6tatq3bt2un55583HRYCBPnUGnKqf5BT7UNOtZdTciqFNEp04MABzZkzR6NGjTIdiiOcOnVKOTk5qlevXqHH69Wrp2+//dZQVMHF5XLp/vvvV/fu3dWyZUvT4TjaK6+8op07d2ratGmmQwkaBw8e1Pz583X55ZfrzTff1D333KP77rtPS5cuNR0aDCOfWkdO9T1yqn3IqfZzSk6lkA5yEydOVFhYWKkf+/btK/ScjIwMJSYmauDAgRo5cqShyIHCxowZoz179uiVV14xHYqjHTlyRElJSXrppZdUpUoV0+EEDZfLpfbt2yslJUXt2rXTXXfdpZEjR2rBggWmQ4NNyKcIJuRUe5BTfcMpObWS6QDgWw8++KCGDRtW6tc0a9Ys/89Hjx5Vnz591K1bNy1atMjH0QWPOnXqKCIiQsePHy/0+PHjx1W/fn1DUQWPsWPH6o033tC2bdvUqFEj0+E4Wnp6uk6cOKH27dvnP5aTk6Nt27Zp7ty5ys7OVkREhMEInalBgwb6zW9+U+ixFi1a6NVXXzUUEexGPvUfcqpvkVPtQ071DafkVArpIBcfH6/4+HivvjYjI0N9+vRRhw4dlJqaqvBwFix4KzIyUh06dFBaWpoGDBggKffdtLS0NI0dO9ZscA7mdrt17733as2aNdqyZYuaNm1qOiTH69u3rz777LNCjw0fPlzNmzfXhAkTSPjl1L1792LHyPznP/9R48aNDUUEu5FP/Yec6hvkVPuRU33DKTmVQhqScpN+79691bhxYz311FM6efJk/ud499c748aN09ChQ9WxY0d17txZzzzzjLKysjR8+HDToTnWmDFjtHz5cr322muKiYnJ3xsXFxenqlWrGo7OmWJiYorth4uOjlbt2rXZJ1cBDzzwgLp166aUlBQNGjRIH374oRYtWkQnMgSRT+1BTrUfOdV+5FTfcExOdQNutzs1NdUtqcQPeG/OnDnuSy65xB0ZGenu3Lmze8eOHaZDcjRPP5OpqammQwsqvXr1ciclJZkOw/HWrVvnbtmypTsqKsrdvHlz96JFi0yHBAPIp/Yhp9qLnOof5FR7OCGnco40AAAAAAAWsGkHAAAAAAALKKQBAAAAALCAQhoAAAAAAAsopAEAAAAAsIBCGgAAAAAACyikAQAAAACwgEIaAAAAAAALKKQBAAAAALCAQhoAAAAAAAsopAEAAAAAsIBCGoBlV155pcLCwrR9+/ZCj589e1Zt27ZVVFSUNm3aZCg6AACcg5wKOBOFNADLpk+fLkl69NFH8x87f/68br75Zu3evVtLly7VNddcYyo8AAAcg5wKOBOFNADLevbsqX79+mnz5s3asmWL3G63hg0bps2bN2vWrFm67bbbTIcIAIAjkFMBZwpzu91u00EAcJ7PPvtMbdu2Vbdu3dS5c2c9/fTTevjhh5WSkmI6NAAAHIWcCjgPhTSAchs6dKiWLVsmSbrjjju0ePHiYl+zevVqzZ8/X+np6fr+++/11VdfqUmTJn6OFACAwEZOBZyFpd0Ayi0+Pl6SFBMTo3nz5pX4NVlZWerZs6emTJniz9AAAHAUcirgLJVMBwDAmebOnauZM2eqXr16On78uJYuXapRo0YV+7ohQ4ZIkvbs2ePvEAEAcARyKuA8dKQBWLZy5UolJSWpT58+2rVrl+Li4vTYY4/p3LlzpkMDAMBRyKmAM1FIA7AkLS1NQ4YMUatWrbR27Vo1aNBADzzwgI4dO6bZs2ebDg8AAMcgpwLOxbAxAF7buXOnevfurdq1a+v9999XgwYNJElnz55V06ZNlZOTo4MHD6pWrVrFnrtnzx61atWKwSgAAIicCjgdHWkAXvnyyy91/fXXKzIyUhs3bsxP+JIUGxurCRMmKDMzU9OmTTMYJQAAgY+cCjgfHWkAfsG75wAA2IOcCpjH1G4APnX69GkdPnxYX375pSTpiy++0JkzZ3TJJZeUuFwNAACUjJwKBA460gB8asmSJRo+fHixx1NTUzVs2DD/BwQAgEORU4HAQSENAAAAAIAFDBsDAAAAAMACCmkAAAAAACygkAYAAAAAwAIKaQAAAAAALKCQBgAAAADAAgppAAAAAAAsoJAGAAAAAMACCmkAAAAAACygkAYAAAAAwAIKaQAAAAAALKCQBgAAAADAgv8P2cVudrccaVAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans_good = KMeans(n_clusters=3,\n",
    "                     init=np.array([[-1.5, 2.5], [0.5, 0], [4, 0]]),\n",
    "                     n_init=1, random_state=42)\n",
    "kmeans_bad = KMeans(n_clusters=3, random_state=42)\n",
    "kmeans_good.fit(X)\n",
    "kmeans_bad.fit(X)\n",
    "\n",
    "plt.figure(figsize=(10, 3.2))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_decision_boundaries(kmeans_good, X)\n",
    "plt.title(f\"Inertia = {kmeans_good.inertia_:.1f}\")\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_decision_boundaries(kmeans_bad, X, show_ylabels=False)\n",
    "plt.title(f\"Inertia = {kmeans_bad.inertia_:.1f}\")\n",
    "\n",
    "save_fig(\"bad_kmeans_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ktr9nlLE3Edc"
   },
   "source": [
    "## Using Clustering for Image Segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LNjLFqo13Edc"
   },
   "source": [
    "Download the ladybug image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "Nh8k3EI_3Edc"
   },
   "outputs": [],
   "source": [
    "# extra code – downloads the ladybug image\n",
    "\n",
    "import urllib.request\n",
    "\n",
    "homl3_root = \"https://github.com/ageron/handson-ml3/raw/main/\"\n",
    "filename = \"ladybug.png\"\n",
    "filepath = IMAGES_PATH / filename\n",
    "if not filepath.is_file():\n",
    "    print(\"Downloading\", filename)\n",
    "    url = f\"{homl3_root}/images/unsupervised_learning/{filename}\"\n",
    "    urllib.request.urlretrieve(url, filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YhNUE3zP3Edc",
    "outputId": "130001ff-d41e-4b86-d550-18e688ad6d64"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(533, 800, 3)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import PIL\n",
    "\n",
    "image = np.asarray(PIL.Image.open(filepath))\n",
    "image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "ReJceBbp3Edc"
   },
   "outputs": [],
   "source": [
    "X = image.reshape(-1, 3)\n",
    "kmeans = KMeans(n_clusters=8, random_state=42).fit(X)\n",
    "segmented_img = kmeans.cluster_centers_[kmeans.labels_]\n",
    "segmented_img = segmented_img.reshape(image.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 428
    },
    "id": "i56l1nv33Edc",
    "outputId": "3ce94ed9-d912-4c97-aab8-b04be2dc090f"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates and saves Figure 9–12\n",
    "\n",
    "segmented_imgs = []\n",
    "n_colors = (10, 8, 6, 4, 2)\n",
    "for n_clusters in n_colors:\n",
    "    kmeans = KMeans(n_clusters=n_clusters, random_state=42).fit(X)\n",
    "    segmented_img = kmeans.cluster_centers_[kmeans.labels_]\n",
    "    segmented_imgs.append(segmented_img.reshape(image.shape))\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.subplots_adjust(wspace=0.05, hspace=0.1)\n",
    "\n",
    "plt.subplot(2, 3, 1)\n",
    "plt.imshow(image)\n",
    "plt.title(\"Original image\")\n",
    "plt.axis('off')\n",
    "\n",
    "for idx, n_clusters in enumerate(n_colors):\n",
    "    plt.subplot(2, 3, 2 + idx)\n",
    "    plt.imshow(segmented_imgs[idx] / 255)\n",
    "    plt.title(f\"{n_clusters} colors\")\n",
    "    plt.axis('off')\n",
    "\n",
    "save_fig('image_segmentation_plot', tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OFrWFiwi3Edd"
   },
   "source": [
    "### Using Clustering for Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3v4DPPwZ3Edd"
   },
   "source": [
    "Let's tackle the _digits dataset_ which is a simple MNIST-like dataset containing 1,797 grayscale 8×8 images representing digits 0 to 9."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "4kKIJb7W3Edd"
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "o_yCF_d_3Edd"
   },
   "outputs": [],
   "source": [
    "X_digits, y_digits = load_digits(return_X_y=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x2ACRviF3Edd"
   },
   "source": [
    "Let's split it into a training set and a test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "L-iJLw8v3Ede"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "ZtEAq4G93Ede"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_digits, y_digits, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GrLbsX5w3Ede"
   },
   "source": [
    "Now let's fit a Logistic Regression model and evaluate it on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "7YkHdjRO3Ede"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "id": "rohL50sq3Ede",
    "outputId": "10316026-492b-4d2a-93ea-b65ea6ba9306"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       "#sk-container-id-3.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       "#sk-container-id-3.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=10000, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
       "            penalty\n",
       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
       "            C\n",
       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('l1_ratio',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
       "            l1_ratio\n",
       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
       "            dual\n",
       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
       "            intercept_scaling\n",
       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
       "            class_weight\n",
       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
       "            solver\n",
       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">10000</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
       "            warm_start\n",
       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling\n",
       "        .textContent.trim().split(' ')[0];\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "\n",
       "\n",
       "/**\n",
       " * Adapted from Skrub\n",
       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-3');</script></body>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=10000, random_state=42)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg = LogisticRegression(max_iter=10000, random_state=42)\n",
    "log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VuJcYo2z3Ede",
    "outputId": "52b181dc-6341-40d9-808f-768dcb086b21"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9733333333333334"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "urUPYrB-3Ede"
   },
   "source": [
    "Okay, that's our baseline: 97.3% accuracy. Let's see if we can do better by using K-Means as a preprocessing step. We will create a pipeline that will first cluster the training set into 50 clusters and replace the images with their distances to the 50 clusters, then apply a logistic regression model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "XPflKl703Edf"
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 155
    },
    "id": "ua8oRFfR3Edf",
    "outputId": "69147134-f2c9-48c7-df99-a91102b761a9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       "#sk-container-id-4.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       "#sk-container-id-4.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;kmeans&#x27;, KMeans(n_clusters=50, random_state=42)),\n",
       "                (&#x27;log_reg&#x27;,\n",
       "                 LogisticRegression(max_iter=10000, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('steps',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=steps,-list%20of%20tuples\">\n",
       "            steps\n",
       "            <span class=\"param-doc-description\">steps: list of tuples<br><br>List of (name of step, estimator) tuples that are to be chained in<br>sequential order. To be compatible with the scikit-learn API, all steps<br>must define `fit`. All non-last steps must also define `transform`. See<br>:ref:`Combining Estimators <combining_estimators>` for more details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">[(&#x27;kmeans&#x27;, ...), (&#x27;log_reg&#x27;, ...)]</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transform_input',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=transform_input,-list%20of%20str%2C%20default%3DNone\">\n",
       "            transform_input\n",
       "            <span class=\"param-doc-description\">transform_input: list of str, default=None<br><br>The names of the :term:`metadata` parameters that should be transformed by the<br>pipeline before passing it to the step consuming it.<br><br>This enables transforming some input arguments to ``fit`` (other than ``X``)<br>to be transformed by the steps of the pipeline up to the step which requires<br>them. Requirement is defined via :ref:`metadata routing <metadata_routing>`.<br>For instance, this can be used to pass a validation set through the pipeline.<br><br>You can only set this if metadata routing is enabled, which you<br>can enable using ``sklearn.set_config(enable_metadata_routing=True)``.<br><br>.. versionadded:: 1.6</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('memory',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=memory,-str%20or%20object%20with%20the%20joblib.Memory%20interface%2C%20default%3DNone\">\n",
       "            memory\n",
       "            <span class=\"param-doc-description\">memory: str or object with the joblib.Memory interface, default=None<br><br>Used to cache the fitted transformers of the pipeline. The last step<br>will never be cached, even if it is a transformer. By default, no<br>caching is performed. If a string is given, it is the path to the<br>caching directory. Enabling caching triggers a clone of the transformers<br>before fitting. Therefore, the transformer instance given to the<br>pipeline cannot be inspected directly. Use the attribute ``named_steps``<br>or ``steps`` to inspect estimators within the pipeline. Caching the<br>transformers is advantageous when fitting is time consuming. See<br>:ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`<br>for an example on how to enable caching.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each step will be printed as it<br>is completed.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KMeans</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"kmeans__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_clusters',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=n_clusters,-int%2C%20default%3D8\">\n",
       "            n_clusters\n",
       "            <span class=\"param-doc-description\">n_clusters: int, default=8<br><br>The number of clusters to form as well as the number of<br>centroids to generate.<br><br>For an example of how to choose an optimal value for `n_clusters` refer to<br>:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_silhouette_analysis.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">50</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('init',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=init,-%7B%27k-means%2B%2B%27%2C%20%27random%27%7D%2C%20callable%20or%20array-like%20of%20shape%20%20%20%20%20%20%20%20%20%20%20%20%20%28n_clusters%2C%20n_features%29%2C%20default%3D%27k-means%2B%2B%27\">\n",
       "            init\n",
       "            <span class=\"param-doc-description\">init: {'k-means++', 'random'}, callable or array-like of shape             (n_clusters, n_features), default='k-means++'<br><br>Method for initialization:<br><br>* 'k-means++' : selects initial cluster centroids using sampling             based on an empirical probability distribution of the points'             contribution to the overall inertia. This technique speeds up             convergence. The algorithm implemented is \"greedy k-means++\". It             differs from the vanilla k-means++ by making several trials at             each sampling step and choosing the best centroid among them.<br><br>* 'random': choose `n_clusters` observations (rows) at random from         data for the initial centroids.<br><br>* If an array is passed, it should be of shape (n_clusters, n_features)        and gives the initial centers.<br><br>* If a callable is passed, it should take arguments X, n_clusters and a        random state and return an initialization.<br><br>For an example of how to use the different `init` strategies, see<br>:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_digits.py`.<br><br>For an evaluation of the impact of initialization, see the example<br>:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_stability_low_dim_dense.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;k-means++&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_init',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=n_init,-%27auto%27%20or%20int%2C%20default%3D%27auto%27\">\n",
       "            n_init\n",
       "            <span class=\"param-doc-description\">n_init: 'auto' or int, default='auto'<br><br>Number of times the k-means algorithm is run with different centroid<br>seeds. The final results is the best output of `n_init` consecutive runs<br>in terms of inertia. Several runs are recommended for sparse<br>high-dimensional problems (see :ref:`kmeans_sparse_high_dim`).<br><br>When `n_init='auto'`, the number of runs depends on the value of init:<br>10 if using `init='random'` or `init` is a callable;<br>1 if using `init='k-means++'` or `init` is an array-like.<br><br>.. versionadded:: 1.2<br>   Added 'auto' option for `n_init`.<br><br>.. versionchanged:: 1.4<br>   Default value for `n_init` changed to `'auto'`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=max_iter,-int%2C%20default%3D300\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\">max_iter: int, default=300<br><br>Maximum number of iterations of the k-means algorithm for a<br>single run.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">300</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Relative tolerance with regards to Frobenius norm of the difference<br>in the cluster centers of two consecutive iterations to declare<br>convergence.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Verbosity mode.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Determines random number generation for centroid initialization. Use<br>an int to make the randomness deterministic.<br>See :term:`Glossary <random_state>`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy_x',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=copy_x,-bool%2C%20default%3DTrue\">\n",
       "            copy_x\n",
       "            <span class=\"param-doc-description\">copy_x: bool, default=True<br><br>When pre-computing distances it is more numerically accurate to center<br>the data first. If copy_x is True (default), then the original data is<br>not modified. If False, the original data is modified, and put back<br>before the function returns, but small numerical differences may be<br>introduced by subtracting and then adding the data mean. Note that if<br>the original data is not C-contiguous, a copy will be made even if<br>copy_x is False. If the original data is sparse, but not in CSR format,<br>a copy will be made even if copy_x is False.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('algorithm',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=algorithm,-%7B%22lloyd%22%2C%20%22elkan%22%7D%2C%20default%3D%22lloyd%22\">\n",
       "            algorithm\n",
       "            <span class=\"param-doc-description\">algorithm: {\"lloyd\", \"elkan\"}, default=\"lloyd\"<br><br>K-means algorithm to use. The classical EM-style algorithm is `\"lloyd\"`.<br>The `\"elkan\"` variation can be more efficient on some datasets with<br>well-defined clusters, by using the triangle inequality. However it's<br>more memory intensive due to the allocation of an extra array of shape<br>`(n_samples, n_clusters)`.<br><br>.. versionchanged:: 0.18<br>    Added Elkan algorithm<br><br>.. versionchanged:: 1.1<br>    Renamed \"full\" to \"lloyd\", and deprecated \"auto\" and \"full\".<br>    Changed \"auto\" to use \"lloyd\" instead of \"elkan\".</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;lloyd&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"log_reg__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
       "            penalty\n",
       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
       "            C\n",
       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('l1_ratio',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
       "            l1_ratio\n",
       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
       "            dual\n",
       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
       "            intercept_scaling\n",
       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
       "            class_weight\n",
       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
       "            solver\n",
       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">10000</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
       "            warm_start\n",
       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling\n",
       "        .textContent.trim().split(' ')[0];\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "\n",
       "\n",
       "/**\n",
       " * Adapted from Skrub\n",
       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-4');</script></body>"
      ],
      "text/plain": [
       "Pipeline(steps=[('kmeans', KMeans(n_clusters=50, random_state=42)),\n",
       "                ('log_reg',\n",
       "                 LogisticRegression(max_iter=10000, random_state=42))])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline = Pipeline([\n",
    "    (\"kmeans\", KMeans(n_clusters=50, random_state=42)),\n",
    "    (\"log_reg\", LogisticRegression(max_iter=10000, random_state=42)),\n",
    "])\n",
    "pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XaC3s8af3Edf",
    "outputId": "b01d452a-7df5-4706-8477-6f7744892ac7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9711111111111111"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GqOcoKCbDlvE"
   },
   "source": [
    "- With 50 clusters we are not yet as good as the original logistic regression\n",
    "- If we increase the number of clusters, say 88, we can get better results than logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 155
    },
    "id": "jworsKG3B5vK",
    "outputId": "d58eae76-d848-4317-9916-d1f347ccff08"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       "#sk-container-id-5.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       "#sk-container-id-5.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;kmeans&#x27;, KMeans(n_clusters=88, random_state=42)),\n",
       "                (&#x27;log_reg&#x27;,\n",
       "                 LogisticRegression(max_iter=10000, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('steps',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=steps,-list%20of%20tuples\">\n",
       "            steps\n",
       "            <span class=\"param-doc-description\">steps: list of tuples<br><br>List of (name of step, estimator) tuples that are to be chained in<br>sequential order. To be compatible with the scikit-learn API, all steps<br>must define `fit`. All non-last steps must also define `transform`. See<br>:ref:`Combining Estimators <combining_estimators>` for more details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">[(&#x27;kmeans&#x27;, ...), (&#x27;log_reg&#x27;, ...)]</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transform_input',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=transform_input,-list%20of%20str%2C%20default%3DNone\">\n",
       "            transform_input\n",
       "            <span class=\"param-doc-description\">transform_input: list of str, default=None<br><br>The names of the :term:`metadata` parameters that should be transformed by the<br>pipeline before passing it to the step consuming it.<br><br>This enables transforming some input arguments to ``fit`` (other than ``X``)<br>to be transformed by the steps of the pipeline up to the step which requires<br>them. Requirement is defined via :ref:`metadata routing <metadata_routing>`.<br>For instance, this can be used to pass a validation set through the pipeline.<br><br>You can only set this if metadata routing is enabled, which you<br>can enable using ``sklearn.set_config(enable_metadata_routing=True)``.<br><br>.. versionadded:: 1.6</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('memory',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=memory,-str%20or%20object%20with%20the%20joblib.Memory%20interface%2C%20default%3DNone\">\n",
       "            memory\n",
       "            <span class=\"param-doc-description\">memory: str or object with the joblib.Memory interface, default=None<br><br>Used to cache the fitted transformers of the pipeline. The last step<br>will never be cached, even if it is a transformer. By default, no<br>caching is performed. If a string is given, it is the path to the<br>caching directory. Enabling caching triggers a clone of the transformers<br>before fitting. Therefore, the transformer instance given to the<br>pipeline cannot be inspected directly. Use the attribute ``named_steps``<br>or ``steps`` to inspect estimators within the pipeline. Caching the<br>transformers is advantageous when fitting is time consuming. See<br>:ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`<br>for an example on how to enable caching.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each step will be printed as it<br>is completed.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KMeans</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"kmeans__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_clusters',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=n_clusters,-int%2C%20default%3D8\">\n",
       "            n_clusters\n",
       "            <span class=\"param-doc-description\">n_clusters: int, default=8<br><br>The number of clusters to form as well as the number of<br>centroids to generate.<br><br>For an example of how to choose an optimal value for `n_clusters` refer to<br>:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_silhouette_analysis.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">88</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('init',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=init,-%7B%27k-means%2B%2B%27%2C%20%27random%27%7D%2C%20callable%20or%20array-like%20of%20shape%20%20%20%20%20%20%20%20%20%20%20%20%20%28n_clusters%2C%20n_features%29%2C%20default%3D%27k-means%2B%2B%27\">\n",
       "            init\n",
       "            <span class=\"param-doc-description\">init: {'k-means++', 'random'}, callable or array-like of shape             (n_clusters, n_features), default='k-means++'<br><br>Method for initialization:<br><br>* 'k-means++' : selects initial cluster centroids using sampling             based on an empirical probability distribution of the points'             contribution to the overall inertia. This technique speeds up             convergence. The algorithm implemented is \"greedy k-means++\". It             differs from the vanilla k-means++ by making several trials at             each sampling step and choosing the best centroid among them.<br><br>* 'random': choose `n_clusters` observations (rows) at random from         data for the initial centroids.<br><br>* If an array is passed, it should be of shape (n_clusters, n_features)        and gives the initial centers.<br><br>* If a callable is passed, it should take arguments X, n_clusters and a        random state and return an initialization.<br><br>For an example of how to use the different `init` strategies, see<br>:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_digits.py`.<br><br>For an evaluation of the impact of initialization, see the example<br>:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_stability_low_dim_dense.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;k-means++&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_init',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=n_init,-%27auto%27%20or%20int%2C%20default%3D%27auto%27\">\n",
       "            n_init\n",
       "            <span class=\"param-doc-description\">n_init: 'auto' or int, default='auto'<br><br>Number of times the k-means algorithm is run with different centroid<br>seeds. The final results is the best output of `n_init` consecutive runs<br>in terms of inertia. Several runs are recommended for sparse<br>high-dimensional problems (see :ref:`kmeans_sparse_high_dim`).<br><br>When `n_init='auto'`, the number of runs depends on the value of init:<br>10 if using `init='random'` or `init` is a callable;<br>1 if using `init='k-means++'` or `init` is an array-like.<br><br>.. versionadded:: 1.2<br>   Added 'auto' option for `n_init`.<br><br>.. versionchanged:: 1.4<br>   Default value for `n_init` changed to `'auto'`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=max_iter,-int%2C%20default%3D300\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\">max_iter: int, default=300<br><br>Maximum number of iterations of the k-means algorithm for a<br>single run.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">300</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Relative tolerance with regards to Frobenius norm of the difference<br>in the cluster centers of two consecutive iterations to declare<br>convergence.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Verbosity mode.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Determines random number generation for centroid initialization. Use<br>an int to make the randomness deterministic.<br>See :term:`Glossary <random_state>`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy_x',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=copy_x,-bool%2C%20default%3DTrue\">\n",
       "            copy_x\n",
       "            <span class=\"param-doc-description\">copy_x: bool, default=True<br><br>When pre-computing distances it is more numerically accurate to center<br>the data first. If copy_x is True (default), then the original data is<br>not modified. If False, the original data is modified, and put back<br>before the function returns, but small numerical differences may be<br>introduced by subtracting and then adding the data mean. Note that if<br>the original data is not C-contiguous, a copy will be made even if<br>copy_x is False. If the original data is sparse, but not in CSR format,<br>a copy will be made even if copy_x is False.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('algorithm',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.KMeans.html#:~:text=algorithm,-%7B%22lloyd%22%2C%20%22elkan%22%7D%2C%20default%3D%22lloyd%22\">\n",
       "            algorithm\n",
       "            <span class=\"param-doc-description\">algorithm: {\"lloyd\", \"elkan\"}, default=\"lloyd\"<br><br>K-means algorithm to use. The classical EM-style algorithm is `\"lloyd\"`.<br>The `\"elkan\"` variation can be more efficient on some datasets with<br>well-defined clusters, by using the triangle inequality. However it's<br>more memory intensive due to the allocation of an extra array of shape<br>`(n_samples, n_clusters)`.<br><br>.. versionchanged:: 0.18<br>    Added Elkan algorithm<br><br>.. versionchanged:: 1.1<br>    Renamed \"full\" to \"lloyd\", and deprecated \"auto\" and \"full\".<br>    Changed \"auto\" to use \"lloyd\" instead of \"elkan\".</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;lloyd&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"log_reg__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
       "            penalty\n",
       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
       "            C\n",
       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('l1_ratio',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
       "            l1_ratio\n",
       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
       "            dual\n",
       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
       "            intercept_scaling\n",
       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
       "            class_weight\n",
       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
       "            solver\n",
       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">10000</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
       "            warm_start\n",
       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling\n",
       "        .textContent.trim().split(' ')[0];\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "\n",
       "\n",
       "/**\n",
       " * Adapted from Skrub\n",
       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-5');</script></body>"
      ],
      "text/plain": [
       "Pipeline(steps=[('kmeans', KMeans(n_clusters=88, random_state=42)),\n",
       "                ('log_reg',\n",
       "                 LogisticRegression(max_iter=10000, random_state=42))])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline = Pipeline([\n",
    "    (\"kmeans\", KMeans(n_clusters=88, random_state=42)),\n",
    "    (\"log_reg\", LogisticRegression(max_iter=10000, random_state=42)),\n",
    "])\n",
    "pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AUuf-NivB-p3",
    "outputId": "2a724844-c525-42b4-c866-07e68cd73b6e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9822222222222222"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0qdwhqt63Edg"
   },
   "source": [
    "## Using Clustering for Semi-Supervised Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DaFDOZ183Edg"
   },
   "source": [
    "Another use case for clustering is semi-supervised learning, when we have plenty of unlabeled instances and very few labeled instances."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Igjgg5wB3Edg"
   },
   "source": [
    "Let's tackle the _digits dataset_ which is a simple MNIST-like dataset containing 1,797 grayscale 8×8 images representing digits 0 to 9."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "EMAPAyj33Edg"
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "\n",
    "X_digits, y_digits = load_digits(return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "qknpSq35CSAy"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_digits, y_digits, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cgoJ1Zv-CRVX"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C4FrSPKV3Edg"
   },
   "source": [
    "Let's look at the performance of a logistic regression model when we only have 50 labeled instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zxnA-B713Edg",
    "outputId": "b21da35b-0f66-432b-f60a-da144d8f3332"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9733333333333334"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "log_reg_full = LogisticRegression(max_iter=10_000, random_state=42)\n",
    "log_reg_full.fit(X_train, y_train)\n",
    "log_reg_full.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "id": "UVlCexq-3Edh",
    "outputId": "67bb26e5-2c95-49f0-9fcc-730e41c7e31e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       "#sk-container-id-6.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       "#sk-container-id-6.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-6 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-6 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-6 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=10000, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
       "            penalty\n",
       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
       "            C\n",
       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('l1_ratio',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
       "            l1_ratio\n",
       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
       "            dual\n",
       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
       "            intercept_scaling\n",
       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
       "            class_weight\n",
       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
       "            solver\n",
       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">10000</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
       "            warm_start\n",
       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling\n",
       "        .textContent.trim().split(' ')[0];\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "\n",
       "\n",
       "/**\n",
       " * Adapted from Skrub\n",
       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-6');</script></body>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=10000, random_state=42)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_labeled = 50\n",
    "log_reg = LogisticRegression(max_iter=10_000, random_state=42)\n",
    "log_reg.fit(X_train[:n_labeled], y_train[:n_labeled])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yn3IW1TI3Edh",
    "outputId": "020e5284-5efb-46db-e3a2-59f21c34b7d1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8266666666666667"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BjTC7ksl3Edh"
   },
   "source": [
    "It's much less than earlier of course. Let's see how we can do better. First, let's cluster the training set into 50 clusters, then for each cluster let's find the image closest to the centroid. We will call these images the representative images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "id": "h_pZfAlw3Edh"
   },
   "outputs": [],
   "source": [
    "k = 50\n",
    "kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "X_digits_dist = kmeans.fit_transform(X_train)\n",
    "representative_digit_idx = X_digits_dist.argmin(axis=0)\n",
    "X_representative_digits = X_train[representative_digit_idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sW2YZYPz3Edh"
   },
   "source": [
    "Now let's plot these representative images and label them manually:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 191
    },
    "id": "qrjrTKEj3Edh",
    "outputId": "4754d956-78e3-494a-cb4f-8a793985952d"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmYAAACuCAYAAACcCMF6AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAn0RJREFUeJzsfWmTXNeVXNa+7/vSKxaSwGhEhzgz0oRFR8x881f/Af9Rj8OasMdjiRIlCgTRa3Xt+/6q6r3a/AGRh7eaIEWgIaJvT2VEBSSxQfWr9969ec/Jk2nbbrdb7LHHHnvssccee+zxwWH/0L/AHnvssccee+yxxx6vsSdme+yxxx577LHHHvcEe2K2xx577LHHHnvscU+wJ2Z77LHHHnvsscce9wR7YrbHHnvssccee+xxT7AnZnvssccee+yxxx73BHtitscee+yxxx577HFPsCdme+yxxx577LHHHvcEe2K2xx577LHHHnvscU/gvMtfXiwW6PV66PV66HQ6ePHiBb7++mu8ePECk8lEfi4SieDZs2d4/vw5nj9/jmQyiXg8jkQiAa/Xe+eL+BCYTCaoVquoVqsol8v48ssv8cc//hFffvnlzrWHw2E8e/YMz549w9/8zd/g5OREPuFw+ANewbtjs9lgtVphuVxiPp/j3/7t3+TjdDpRLBZRLBZxeHiI09NTuV6bzfahf/U7YzAY4OzsDOfn5zg7O8P19TUuLy9xfX2N6XSK5XKJ5XIJl8uFbDYrn1/84hf4xS9+gc8++wzpdPpDX8Y7Qb3vpmnKs1+r1dDr9TAejzEejwEAjx49kvseiUQQDocRDofhdrs/8FW8GyzLkusbjUZoNpvy6XQ66Pf76PV6GAwG8jPj8VieB8uy8PTpU/zTP/0T/vmf/xmfffYZAOD4+PjDXtiPwGg0wvX1Na6vr3F1dbWzzk+nU/m5YDCIJ0+e4KOPPsLTp0/x5MkTPH36FE+fPkU0Gv1wF/CesF6vd6794uJCvpfNZoNf//rX+Pzzz/H5558jGo1q+cybponZbIb5fI6rqyv867/+K37zm9/g3/7t32BZFtbrNSzL2vk7drsdfr8ffr8fPp8P//AP/4Bf/vKX+NWvfoXDw0P4fD74/X54PJ4PdFVvj9lsJu93o9HYue9HR0dyr58/fw6XywWXywWn8050agf7itkee+yxxx577LHHPcGdKJ5pmuh2u3JqaDQaMAwDbrcbwWBQfs7n88Fms8E0TQyHQ3i9XgSDQegW06meJnq9Hm5ublAul3Fzc4NWq4XFYgGv1wubzQa73Q6XywW/3w/gdaXl8vISXq8X8Xgcq9XqA1/Nu2O9XsMwDBiGgeFwiMFgAMMwYJomXC4XvF4vIpEI4vE4gsGgVifGN2G73WK9XmOz2cAwDPT7fdTrdZTLZYxGIwCQE7LdbofT6YTT6YTH44Hb7cZkMsFkMpFKis/nk1OWw+H4wFf347FerzGdTjGZTDAajVAul1EqlVAul+UZmM1msNvtcLvdsNls2Gw2yGaz2G638Pv92j4Ly+USo9EIjUYDjUYD3W5XPpPJBIvFAna7HR6PBx6PB16vF5ZlwWazSaXY5XLB7XbLP9cFq9UK0+kU3W4XtVoNk8kE2+0WwWBQqgQ2mw1+vx9OpxOz2QytVgupVArz+RybzeYDX8G7Y71eY7FYYD6fYzaboVarSadkNBrB6XQik8nA6/WiUCggkUggGAzKO26361X72G63Uhm//QFeV8e8Xu931jj12eb1u91uOBwOOBwO7bol6/UapmnCMAyMRiNMJhNZ3/gscA8kv3mfFbM7E7Nms4mXL1/iT3/6k5Q6fT7fTtmSi9B8Pke/30cgEEA0GsV6vb7bb/8TYz6fy2Jcq9WktF8qlTAajTCfz+H1eqVs6/V65XvodrsYDocIhULI5/NYLpcf+GreHepC3W630el0pG1js9ng8/kQj8eRSqUQDofh8Xi0ezFVrNdraUlNJhN0Oh1UKhVcXFwAeL1YJRIJ+Hw+hEIhhEIhOBwOITCj0UgIbL/fl9K+3+/Xipgtl0uMx2O0Wi00m015/q+vrzEajWBZFkzThNPplA2N7V2v14tkMvmhL+GdYVkW+v0+SqUSrq6u5H4Oh0Os12s5jPn9ftnIVGK23W7hcrlkXeCBTQesViuMx2M0m01Z67bbLcLhMILBIOx2u5BxPvfr9RrZbBaTyUTrQyifeUp21GfeNE0hZMlkEkdHR8hkMohEIvB6vXC73VoSs/V6LZKF5XIpzzOLDQ6HQ9a6YDAIv98vz4DNZkMkEkEgEIDb7YbT6ZT/XSdsNhssFgu598PhEJPJBLPZDLPZTA6obOW/T1IGvAeNWaPRwNdff43/+3//L6LRKGKxGGKx2M7JmDdzNpuh1+shGo1isVhod5KazWbodDq4ubnB5eUlLi8vcX5+jqurq50H1u/3IxgMIhgMwmazYTAYoNPpYDgcIpvN4smTJ9/p0+sElZhVq1V0Oh2MRiOYpikn53g8jnQ6jVAopFV14E3YbDZYLpfyorbbbdzc3OD8/Fy0kiSimUwG2WwWdrsdl5eXME0TlUoFo9FIiFkgEAAA7apHq9VK9FXX19e4uLjA+fk5zs/PMZ1OparodruxWCwwmUwwGAzgdrsRj8dxdHT0oS/hnWFZFnq9HsrlsmhoZ7MZJpMJ3G73ziZFTVkgEBBSxu/F4/HA5/PB5/N96Ev60SA5aTQaKJVKQkIjkQgcDodUT2w2GyzLwnQ6Rb/fx/HxMQzD0JqYqc98pVLB9fU1SqUSrq+v4fP5cHh4iGKxiKOjIyFm4XBYCIluxGyz2XynYkaC5vF44HA4pCOSTCaRTCYRDAaxXq+F1JGYeTweOJ1ObStmXMNIzMbj8U61jJ0Qp9P53ve4OxEzbtC9Xg/1eh12ux3RaBSBQEDKe/w4HA4sl0thnqZpakfMVqsV5vO5tKRY4mSljBt0IBCA1+uF1+vFer3GfD7HYrFAu93GcDjEbDbT7tpV8L53Oh3UajURfAeDQSHmsVgMkUhESvo6g2Xt6XSK8XiMfr+PbreLTqeDcDgMv9+PfD6PYrGIfD6PfD4Pu92OxWKBbrcLt9uNzWaD+XwuVdPb7X4dwMWKp8hOp4N2u41ms4n5fC4/p1YKNpsNBoMBZrOZdhXyHwKrX9vtFl6vF9FoFNFoVCqglmVhNptJ5SQYDCKRSCAcDmv3Tmy3WzmYUKrC1hU3XrvdLhszJR+z2QzL5VI7ycp2u5XPfD7HYDBAvV7H1dUVGo2GdAei0SgSiQQODw/x5MkT5HI5RKNRed8ty8JqtYLNZhNx+H2vkPP3JgFZLBZYLpdysOBAQyqVksGmaDS6Q+T4v7HlqWvFzDRNTCYT4Szz+RymaUpnYLFYwDRN+X7eJ+5EzNhXdbvdUiUKhUIIh8MIBAI7N0Sd4pvNZrAsSztywlIuNXLJZBKLxQIOhwOFQgGFQgH5fB4ul0se0vF4jMFgID13HV7OvwRqjUjMVqsVotEoQqEQDg8Pkc/nEYvFZAPS/XqXy6VoDQaDAebzuSy4wWAQ2WwWjx49kpYGW3bpdBr5fB69Xk/0Z3zRg8Gg1u3s/2jwer1Ip9N4+vQp3G43LMuS6VS32y1VsOVyCbvdDsMw0Gw24fP5EAwGEQgE8OjRI2QyGa3amACkTRkIBBAOh7FarURnut1ud3R0hM/nk41Zx02Z6/dkMpEK+cXFBWazGXw+H46OjpDP5/H48WOcnJzg4OAAsVgMHo8Hq9UKhmFgOp1iOp3C4XAIoWG1/L5iuVxiNpvtaIfZkg8Gg8hkMnL4zOVyyOfziEaj2Gw20gLN5XJIp9OiOdSxYkaCOpvNpFJmmuZPdsi4EzGj0FnVTQSDQdEesMy9Xq/l4sbjsbbEzOFwwO12w+v1IhQKIZlMYrvdyqLLD6sEg8EADodDFmaemnQ8QajggkVixkphIpGQqlE0GoXP54Pdbn8QxIyL1XA4hGEYsgFzsTo9PUWhUEA4HEYkEsF6vUYmk0G/38doNILf74fNZhNipvsAyH80eDweZDIZOJ1OJJNJ2YhUfRnv73Q6RaPRwHq9ht/vRyaTQS6Xw8nJidbEzO/3IxQKieiZ7wHbWJQx8MNWlm5rHaULpmmKdKFcLuPs7Ez2tlQqhaOjI1nzDw4OpIrI9ZFVdZfLhUwmI+T2PoOHUK51s9lM1qlQKIRsNovT01McHR1JMSIej2Oz2UjLnjIeErM3Eff7jtvEjN+DFsTMZrPJaZEai0AgAL/fj0AgIESGugN6ARmGoaXGjAuUz+dDOByGzWaDx+NBPB7HJ598gmfPnuGTTz7BYrFApVJBpVLBbDZDKBT6ziSebg+qCp4Ie70earUaQqEQIpEITk9PUSwWRQCrk2/ND2G1Wu0QM76kdrsdoVAImUwGJycnyOfzUjmxLAupVEqIGV/o6XQKv98vVbeHCpIV9aPzM+92u5FMJhGNRnF4eCj3c7vd7uhw2u02KpUKPB4PLMuSv3dycoLj42Mkk0mt9GUAZNo0EAggEolIJ8AwDHmOLcuSTZj7gNfr1XIykV5d3JQ7nQ6q1SpKpZJUPY+Pj/H48WOcnp7i+PgYmUwGlmVJG3A4HKLVaqFarcoQWDwe/9CX9hdBuQ6nyLnWUT/N1u3JyQmKxSIODg6QSCS0JF8/BJWYsY2ptuXZ6uah5H0TtjsRM5fLhWg0imKxiMePH8Pr9WI+n+Pm5kYIiMPhwHq9FvPFwWCAbreL8XiM6XSKcDgsP3ffX2CPxyMLk8/nk9FZ0zRxcHCAaDQKp9MJ0zQxGAxQq9VQq9Uwn8/h8/nkIWY1UVfwoWW53jRNAJAFSMfF+IegniLH47FMHsfjcUQiEYRCIakQ8NrVF3s0GkmlldVlHb8jao04Rs4p7Nuw2+0Ih8PIZDIoFovIZrMiiNYZt42V+f5zjN4wDHQ6HVxeXorujj83m83+anqUvzacTqdUiYrFIgAI+eCGTM1dIpFAoVDA4eEhCoUCotGoVno64PVQW7/fF0I2GAykZR2JRJDL5fDo0SMUi0UxCR+PxzsWKp1ORz6xWAzRaBT5fP4DX9lfhsvlEtcErm/BYFDsX1qtFl69egXLsqS4YlmWHEh1O3S8C0heOW3PwtT7xJ1WSrfbjWg0ikKhgPF4LPYA7XZbWLbdbv9Oz51TDqyc0fPovm9UJGZOpxORSER0JqvVSl4+ErPhcIh6vY5arYblcgmfz4disYhUKrXj/6MjKIw0DAPj8VgmTEk+XC7Xgzo9qa1MEjPaP1BbxwqBKoTm6XkymSAYDIrnj67EjO27xWKxI3y9fVp0Op0IhUJIp9M4PT1FJpNBKBTS+plnm4bVFA6B8LDJairbXp1OR4gbPxRS6zYEQTlGMplEoVDAbDbDYDDYGeyiv1U8HkexWMSTJ0+EmOl230nM6FnW7/exWq1E/J7L5XB6eopUKiVt6fF4jGq1iqurK1xdXclg2Hg8RjabRaFQwGKx+MBX9pdB781IJCIDXCRmpmmi1WrBNE2Ypon1eg2Hw4HtdivVwP8oxEzV4YXDYVkH39e+914qZoVCAcvlEhcXF2i1WiiVSpjNZvJL0geKEw3dbhej0UiI2fs2Z/trgRWRUCgkPXV+ODLucDi+UzHjQAR1aQ+lYkbNIG0yWBGiju6hQH0R1YqZzWYTYsaKmepbRUI3Ho+laqAzMWPbjveei/NtqMTs+PgY2WxWDjS6QjXepH1Eu91GvV6XWKZ2u412uy22KGz1sZqmTrjpBLVixrWt0WhI65Kbs0rMnj59KtN5ulbMaCQ7GAykVRuNRpHL5fD48WPZkPk8VKtVvHjxAl9++eXOBN96vUa/39eCmDmdTvj9/u9UzHw+H0zTlOd8Pp+LlIkHcZ/P917JyX0F9wMO9iUSifduf3WnldLpdErLAoAsOHa7HbPZTH6ODy4tJhwOh1yc6hR+30GtzJs2GJZ2STz5GY1G4oLPicVIJKLdYqWKndmWMU1zp6zb6/XgdDrF3f62xszr9Uq5W21133eCwk2ZZJzaSWoMeS3qdbCqSE8nVlsBiN5KtwWMmzDbVjQUfdPPeb1exGIxZLNZmdDV+TDC554VMmpIK5UK2u02er2epABYlgWHw4F4PC5icaYe6HhocTqdCAQCIvKmztI0TSEc8/kcHo8Hm81GJrY5JKVbhZAEnJoxFhVUg+Xr62v4/X659m63i1evXuH6+hq1Wg1Op1PagrRI0eFg4nQ64fP5sNlskEwmcXBwIIdRVoWpPRsMBmg2m9ItiMViH/rXf29QBxv9fr/4ldlsNqxWK7HS4FDj+56wvzMxCwaD4nHC9s7p6anojoDXegSGHZfLZYRCIdhsNnHQdblcWpdA6XdD8lmv19FqtcRawePxIJ1O73jd6EbM2MJiC3M+n+/EFHW7XYkoUgXfKpLJJNLpNFKplBjPejyee79RkZCwKsoPACFkt0kWnaNpUBgIBJBIJLSrlqi4bZtAC5jb104TylAohHg8LvdaZ2LGSKZms4l6vY6bmxv5DAYDkWlYlgWPxyM+TtlsVlrebGfr9j04HA74/X7pDFiWJZ0DtVpI/7JqtSpEhjYjOkF93ylLoHdjtVoVQubxeMTPihUzRjUlk0nEYjGk02kcHR1pM/RBs1SbzYZUKoVHjx7B7XYjlUrJ806T4fl8jk6nA5/Ph1gstuNlqDtur3UqMVN9LTkY8L4Hud4LMWM1IJVK4fT09Dv5aOPxGC9evMCLFy+wXC6llTefzzGdTuHz+bQ7Vd0G46ZarZZk6ZGYuVwuIWYsDetwelLBUwIfRlbLttvtDjFzuVzSur59T09OTnBycrJD2liBuc/gQs1KGf162IJ/E7FkxWw0GqHf72ubdqFCtU2gse6brp96o3A4jEQiISfq+07AfwhqLBEjeegAz0GI1Wol1aVoNIpkMolcLifEjN+Dbu++w+GQiJ1AICDrfiaTQblcxtXVFWw2G9rttrRs6/W6kDLdUk6od6Y0RZVuVKtVLBYLNJtN2O32na7BeDyWw3kqlUI0GsXR0ZFW07gOh2PHsZ9TxcfHxyLhYWt3Npuh3W7D5XIhn89jPp8/mFYmJ5G51g0GA5GqcC+k+z8lCu8Td/Yxo8P9D2E4HErOYLfblQkGXlgoFNLWOoAjsyzfl8tl1Ot1jEYjWaiDwSCi0ah42bCVu91uxefrvj/MXJwo/FYn8jjs0G63JeGBLQC1Dahu7NRh0X7kPkMNpScR4T1UW5Jqu5fDLuqABCsO1GXoSlRut3ZvQ7WVCYVC0u6978/4D4FVE9XVXvXz4tg8iVk6ncbh4SEymQxSqdSO679OxEzV0QKv34VwOCwbNg1VGfLM530ymeDo6Ajj8Vi7tZ1VIxqmq61oZqayfWWapvxnvv+bzUYmVA8ODnBwcIB4PK5FNB0PzZQr+P1+MVJX9zhm41J7NxwOtdDQ/ViwShyLxZDJZOS5HgwGOwONw+FQnAk48Pg+ZCo/yQrBhToUCiGRSEhU0Xg8hs/nk0gH3cDNablcYjAYoFqt4uXLl2i329hsNkilUnC73UgkEnC5XDKZxYWOEzBv0mPpBPV7YBYgW1zqBN9ms0G325Uy+Gq1ksrKfQaJmJoJyKrZarWSKhh1B5PJBNVqFd1uV7SWnOhKp9MyAHLfK4W3obbzSqUSWq2WkBLg2++JpFNnInYbqjUQ311+RqORPOMejwfFYhGPHj3C06dPZVo7Go2KtYoO910l37TGGAwGYpZMr8pYLIZEIoFMJiMVFHX6VMcKMYcYVKd3tudVKcN0OpXqkWmaos9yOp1IpVLI5XI4ODhAJpNBNBrVco1X32kSlXQ6je12K2Scgy66VUZ/CGq+r6qpdblckngxnU7RarXQ7/dFa8bD+12NlX8yYsYTSDwel41tNBrB6/VKpppOuD0+PxwOUa1WcXZ2BsMw4PV65aSsEjO2+Uhi1uu1iAx1hGq0xxNDJBKRhYhEZTKZYLPZyEO8WCzkO7rvUFsbrHLxern5bLdbqRy2Wi1UKhV0Oh15iT0ej2TMkZjpMPCiQm3nlUol8SOk+z3bvao+56GAxAyAbL789Ho9ecZdLheKxSJOT0/x7NkzqbYwP5gL/H2HOoU6m83kma7X6zg4OECxWEQikZC8yHQ6jel0isVigeFwKJXj76uq3meQmJGQsKV3u7Lf6/Vk7eekdiAQQDAYRDqdFpuMbDYrh1WdoCZaMOEmGo0ilUrJ0AMzcJkC8VDgcrkQj8flvechzG63o9lsYjAYoNfrycQtByL4jNy1C/aTEDNOaVFzQn+f6XQKj8cDwzC0q5jdHp9nxezVq1dwOBziBn14eIhEIgG32y0VM/oacYG/7zEdfwlq9YiTuvl8HqFQCL1eD71eDzabTfQXfIhTqdTO9O59hUo6+HLSDkOtCqg2KeVyGb1eTwSxHo9H7FJI1HWonKjgVFqz2cT19bW09NjSJXl9COkWt+FyuRCLxRAIBJBMJncqZjw193o9IWaPHz/Gs2fPpI2rWxVRPWwZhoFWq4WLiwtcXl4CABKJhNgjJBIJaV8OBgO4XC7R3OlaMXM4HNLG5HCaao/BZ54mu5vNRmQrJKokZplMRpv7fhvq4YoV0kwmI3Yxqj/pQ6uYMWqQBQe1TcnBRfoZck9jK/iuJPytiZmqOVA35NvEiv+c+qtGo4FWq4VWq7Xzd/jy6naqYvWAN4Ysej6fw+l0iiM4S93dbhdXV1dSGrbb7TKxpgMpVQPcWQFgFJN6GlQtFZiTSg+w+XwuL69OGzeHW7LZrGhKOp2OuH03Gg2Uy2XMZjMxmCyXy5hMJvB4PCgUCkilUvJdvcleQweo02perxfL5VLu4+2DCjVXDwXqc029GQOuWTFhVnAkEkEgEBBSpns+7nq9lnYm3eybzSZSqRQMw0C9Xke9XpeNmjo06rJ0u24139fv9yORSIgelhYJhmGIXpiHEnr3HRwciHefztrK5XIppGs0GuHi4kLIOY1mWQnWsQPwQ+A7Tl0pNdWqobLT6RTPu6urKzm4+Xw+efbVqisAPH78+Ef9/78TMWOVQBWD3xb+scXHU3atVkOj0UCz2dzJ0WM7SLdFXK0e0GRyNBqJ6SYZda/XkySE1WqFSCSCSCQiUVSJREKLEvBtYsagWrreqxUB1Z+McUYs+/Lfxc1KB3KiErPFYoFutyut+G63i3q9jlKphOl0isvLS1xcXKDZbAJ4ffpm4kMkEhGdio4bNadQGStlmuZOugfvLw9sur3TPwS1Isi1j9Y48/lc3otYLLaTjavrvVZx2/ql3W6j0WggFouJhUSlUkGz2YRhGACwQ8x0eMdVcA1T/fhoD9Jut+V55+Qin4tgMIhsNouTkxOJIdOtKq6Cgw6NRgP1el3WtqurK0ynU6zXa/G3oz3MQ4E6icvOgOruzz1uu90KMWO7l8TM5/PJcCRJ61+VmKkGfGopUwXz5BaLhSxgjUZDxms9Ho9M9FBkqhNUvc319TWazSaGw6EQM7ZqeePY6igUCigWiygWiwgGg9IKuu+gzgKAVMyYZqCejNUJRi5g0+kU3W4Xbrcbbrd7Zxxbhw2LvlS0eLm+vv4OMYtEInKqPDs7Q7/fl5YG/2SwO6fydLh2FdyAaBS8WCx2RK7qEIiO7/RfAgkWvbyGwyEajYYM+sTjcSQSCfFt0619+X1YrVbi1dXr9dBqtRCLxcTfqVwui9Eu8Pq5JjnVlZipXowulwvhcBiWZcFut8OyLPG1Ug+jNFs/OTlBJpPRPh/Wsiz0ej2USiUhZFdXV7i8vITD4ZChFl6rrjrpN4F2R3T4Z7eHxEw9pA0GA1xeXqLf7wshYxWRxQtqz/7rf/2vP+r//52emtvkjAnsrAoxioYj5ePxGJ1OB71eD4ZhIBqNiildPB7XsgzKyiE1B2zfsPo3nU6FqDG2iOP0tBHQaXxebeUEAgGkUikcHh5iNBrttLfn87l4vrhcLnS7XRiGISbE4XAYkUgE6XRaxu7vO3jP1AgmtqoWiwU6nY60OVqtFiaTieSjplIpHB0dIZfLIRKJaNnaIZgRyxDndrstBsGcNl6v1zLUMp1O0W635fsKBoPabdIqKLkgUeFUnsPhQCqVEh2S6tum670Gdm1iKH73+/1YrVbodrtwOp2YTCao1WpyMGWFQI1n0/E74O/MyggAqXxwnaOJOqtp8Xgc6XQahUJB2+g9HqiY4NDtdmWordlsim42HA4jHA6jWCwKEQ0Gg1re6zeB/GaxWMAwDPGjZOQahx3W67Ws95PJZOfZD4VCiMViiMViCAaDb/X/fydGwKBmMsvJZCJkjIHmk8lEpnUohA4Gg8jlcjg8PESxWEQ6nZYwWF2gOgOzasRFiO07m80mizS1B8fHxzg5OcHp6akIQ3W4dhIz4PX9KxQKsCwLgUBA4mgYTm+aJtrtNmw2m3gb+Xw+JBIJZLNZufe6XDs3JwA75qGZTAYOh0PGxanJcDgciMVicp0fffSRtokPKvgcP336FG63W5zOQ6GQmA5Td7Jer9HpdHB+fo5MJoN0Or1TLdQNXKg5pcgWB4d46HvE07Gu10mo7zsP0fl8Hr1eDwAkmorklNFMNOZU27gPBZy8Ho1Goi1crVZy4EilUiL6ZytXN2JGV3vLsjAYDNBqtVCtViX/erVawefziensRx99hEePHqFYLN5726O3AYe7yG16vR6q1aro60jOqJnm90aNudvtFnkXu0NvgzsTM/5Catp6r9fDYDCQ/24Yxo6/B3vxjx8/RqFQQCQS0WKDVnHbBV0t21MYTAO+QqEgJym635+cnCCZTErp876DC6zNZhNi5vf7kc1mcXZ2hu12i16vJ4sWK4Q8Qfh8PsTjcRwcHODRo0fI5/NIJBJa3He+VHRAj0QiSCQSSKVSsjFNp1PYbDaZwItGoxLHwsQH3TUnHo8HmUwGLpcLiURCToIejwf9fh/T6VQOX6yqnJ+fY7VawePxIB6Pa/GsvwmqwaxKygzDEAJCYqaju/9tqO87dVa5XA6j0UiGuJrNphy6uQmxUqBO5j6UKsp2u5WWbrvdxng8xna7FWkHJQuZTEZa2boSM5qnttttIWYkGH6/XwyUP/74Y3z00Ufi0/eQ7rXq8E+t3eXlJXq9njzz1Naqelt+FouFJCm8bZv3vRAzDgHQ2Z/5adRVLRYLmeDjZFoymcTh4SFyuZyW1gGqGF6NWmFkA8ugdPePxWI4PT3dIWa6nTC4WFPYzMV6sVhIhYxTqr1eD8vlEtlsViaUqEc4PDwUbx8dBKPUFQGQzTcajSIej0uluFqtCmGJx+OIRCJIJpPIZrM4PDzcCTvXFfT2CYVCUi0EXutJ3W63TCnTTmA4HGKz2SAYDCKVSmmhpfw+3F7rmJFITaka4aJzC08F33fV6iWfz2MwGGAymcjAC+HxeEQgrQrjHxIsy9o5jAGvK8mRSATxeFwMd3W9brXQwrWcMYM8XNI2I5fLSfdHxz38h3D7feeBhFmoP/bfwdSft01F0PPp2WOPPfbYY4899niAsG0f0kz7Hnvssccee+yxh8bYV8z22GOPPfbYY4897gn2xGyPPfbYY4899tjjnmBPzPbYY4899thjjz3uCfbEbI899thjjz322OOe4E52GaPRCNfX1zufy8tLXF9f71gHcMQ+Go0iHA6LyaqOlhFvwmazQbVaRblcRq1Ww/n5OS4uLvDq1SusViv8+te/xueff47PP//8wZovEsPhEM1mE81mEzc3N/jyyy/xxz/+EX/4wx/w93//9/jlL3+JX/3qV3LvOX6tCxaLBXq9nmQG/u53v8Nvf/tbfPHFF7AsS0bKc7kcPv30U/z85z/Hp59+KhlqOpuPWpYllhiDwUDu7Zdffik5sOFwGNlsFs+ePcPz58/x/PlzuN1u7Z/5wWCAs7MznJ+f49WrVzg7O5P/ns/n8Ytf/AKfffYZnj9/LhYx2Wz2Q//a7wXqfe/3+/jNb36D3/zmN/hf/+t/Yb1eyzofCoV2/l4mk0E2mxWLnOPjY5yeniKfz3+gK3l70AqH67v6zDscDrnPJycn+Oyzz+Sj63Ou4vZ9V9e6yWQCt9st3qTqOz6fz8Xj7/Hjx/inf/on/PM//zM+++yzD31JPwg1yaff7wunubq62nnfmQcLAMlkcmd/J8e5a6rNW+8QjGGyLAudTgf1eh03Nze4uLhAp9PBdDqVvDz6vDBTk9EGoVAI6XRaQo91BGOpeK39fh/1eh3n5+eo1+vy4DIziz5nD810Efg2tHq9Xsv3cHNzg6urKzQaDQyHQ3no+bPMU9RhKJieNpvNRmKXmA/Y6/XgdDqRy+VgWZZkgdpsNsxmM3S7Xdzc3CCRSCCRSMhipjtuRxNZliUeSDabDZlMBsPhENPpVEyEdfRw4zPK6LnhcIherwfTNMVI2O12YzqdolwuS36e3+9HJpN5EO/5crnEaDRCo9FArVZDt9uFZVnwer1Yr9dwOp3yHalZqaFQCMvlEna7XaKNdPs+6ADP7OPFYiFebUyBGA6H6Ha78un1evB4PJINrCtJYx40fcw6nQ4MwxDfPqZD2O12yZZUY6qYpRkIBLTwOGNiz2w2Q6fTQbPZFEJOT8ZAICDpPuoexmQQRjLeFW+9QzDAdTKZoF6vo1Qq4ezsDN98881OCjsAyduiCWswGBRzukKhoL3hJAnGfD5Hu93G9fU1/vznP2M0GmGz2cDpdMpJkqasfJB1W6B+CIyuYG5kuVzGy5cvcXFxgUajgcFgIKSM5qObzea9PcR/bagnqdFohGq1im+++QavXr3Cer2Gw+HA4eHhTl4qAEynUzQaDQAQF+iHFPa7XC4lS24ymcAwDDEZzWaz6Pf7YsbIpAydyBkPXiSck8lEDDcXiwXcbjcSiQScTifG4zHOz8/lQJZKpSTwWHdYloV+vy9h1o1GA4vFAj6fT8xk1QgbuqJHo1EhZuqhVCdsNhsh5bfDrJnwst1u4Xa70Ww2JREhEolIVqauxMyyLAyHQ1SrVVxdXaFer2M0GmG1WgkpczgccDqdsCxL1kgmITANIRgMakHMyG0GgwEajQbK5TKurq5wfn4uRtJMbzEMQ/YuNa6NB/i77mvvTMy63a5URs7Pz/H111/vnIzW6zVmsxksy5LMSMYPFQoFjMfjB0HM6JKsErPlciku0LFY7DvE7KFhtVrJibLT6eDm5gYvX77E2dmZbNissvLlJTnTgZixCkAH6Fqthm+++QZ/+MMfpF1TKBQk0Jbu9yRmk8kEdrtdWpwPBbzv4/EYw+FQ3n3TNFEsFtHv9zEej6XVsdlsPvSv/FZgpZSVwfF4jF6vh06nI/FbiURCnot6vY5Wq4VUKoVHjx596F//vcGyLPR6PTlwjcdjLBYLyURlBZiu+CTqmUxmh5jpeCBViRmzoFkpIUFbLBaw2Wwi4Wi1WthsNhLTpQMpeROWyyWGwyFqtRrOzs7Q7XYxGo2wXq93Au5tNhs2m41ElTGCKJFIIJlMIhQK3amt91OBzy+5DYnZxcWFcBfGjbFCCEAOb+q+dle8UyuTizHzMMfjMabTKaLRqEQv8SL5EjNXigvcfD7HarWSjVm3F5bEczgcot/vy0vZaDTgdrsRj8cRjUaRy+UQj8cl0Fa36yTU1q3azjZNE8PhUAjJ9fU1bm5uUKvVJDPz9sPKDVyXhVolZpPJBL1eT05UDCs+PDyEzWbbCXdmha3X60kINBdzQL9nnosxT8k2m02qSWrkiNPphGEY8s7zudEZfP6Xy+WOljAcDmMwGKBWq6HVakmVwTAMaXcyO09n8Pq32y18Pp9svKwScF9gtiC7Jm63G8FgUNsM0dv3XT1Qsn0LAOPxWOQ6hmEgFArt7G86Qm1llstluaeRSAROpxN+v19kCpQxzWYz2Gw2iW1jHJ8OxEzd41j5Up/5dDqNZDKJ6XQKh8OB+XwuzwE7Jep7che89VtCZsheu8vlQiQSQS6XQ7FYlM98PpfTQ7/fl4dabWORYXLh0mmj4imyWq2iUqmgVqthPB7DbrfLQ3l8fIxHjx4hk8kgGAx+6F/5TlDJGCum/IzHY4xGI4xGIzSbTdRqNcznczidTnnAgdcbttfrRSgUkvBrHSqI6jM/n8+xWCxEU0USfnh4CK/XKwszWwCVSgWtVgu9Xg/T6VSIqo7PPDNDPR4PfD6ftCZvXwMrJLpnR7JdA2BHLxqJRJDP55HL5ZDP59HtduF2u0Vj43A4sFwuMR6P5XvSWWvk8Xh2qoCqTpYaw+l0iuFwKG36xWKBcDgs+ZqZTAaRSES7Nj4PI06n8y9KUdSOkc4tTILFh8FggHa7LXmgHNhiFcmyLDgcDsxmMylMxGIxHBwcoFAoIB6Pa5GJ7Ha7RRfJ55oSlFwuJ+97o9GA3W7HaDTayYp9n3hrYsYSrkrMQqEQcrkcnjx5gk8++QSffPIJJpOJTDTUajWMRiPZwNUWwWq10lIQT93Fzc0NvvnmG1SrVUwmEwBAIBBAOp2WKaRkMolgMKjV9d0GKyOGYaDb7eLq6komcDm5MxqNpFKyWCzgdDpFjwBATlnUGupyglYrZqwKmKaJ5XIJn8+HRCKBw8NDRCIRadVSIE1i1u/3MZlMhJjp+MzfJmYkXG/6OYfDAZfLBa/XKwMPOl0r8O2mbLPZvkPMcrkcPvroIzx58gT1eh2maaLf70tLlxNtPDnrPJHqdruRTCaxXq8RCoVkstztdmMymaDT6aDb7aLZbEprbzQaIRKJIJVKIZ/PI5vNyrOgG9Qq8ffdR77LDodDnnfdDl63weGGfr+Pdrst+vDT01PEYjH4fD74/X7RljebTazXa7hcLsRiMSnSxGIxrYiZOsyw2Wzg9XpxdHSEg4MDHB4e4vLyEuPxGKVS6X4RM2qrqC8Ih8PYbrc4OTnB3/zN3+Czzz7DaDRCIBCQh5iLFXvUTG5neVS3cj+JWaVSwatXr9Dv9zGdTmG320X0mM/ncXBwICeLzWaj3WZMkJjNZjP0ej3c3NzgxYsX+Oqrr6SVPR6PYbPZ5IV1u93fIWYejwfBYBDBYFAEwfcd6mGE1TLqIz0eD+LxOIrFIlKplPydVquFSqUCu92OXq8n7V6Kh1lB0emZVwkXSdn3VQZYHVUrZjpdK4Cdd1UlZmxLP3r0CD/72c8QDofR6XRQKpVgmibsdrtoDFlp0bmlxSEHv9+PXC4Hn88Hr9cLn8+HwWCASqUizzqrxaywJBIJZLPZnXdDJ6iE64fIFg8tJKwPgZhRN0a7jJOTE8TjcTx+/BipVAperxderxfj8RjVahU+nw+r1Qper1fekXw+L4e4+w632w2bzSadnM1mIwNbtHs5OTmBy+XC5eUlfD6fPB9qB+R97PFvTcy44AaDQfj9ftjtdplYon1GqVSCYRjodDoYj8eYzWbibTKbzcQ2YzAYSFk0EAhoUT0hVPE/9XK8qXa7XawSKpWKVIhYJfJ6vdq08QhVX+T1euU0zGlEajDUSTS2e/mCqwucTuPzaitztVrJRlUsFpFMJncOIITD4YDP50M0GkU2m4XH48F8Pke9XofT6UQkEpHJLV3A1sZkMsFwOMRwOMRsNvuO7Y26mantH53hcrnEo800TaTTafj9fjlZz2YzTKdTTCYTEbzr3MZVcVtbuF6vpaXfarVEKM2ugd1u3xF9c61UNy5doE6bcoiJzzvF75TzRKNRxGIxJBIJ0dTp9txTM0X5STAYRCaTwcnJCQ4PD5HL5ZBMJuH1emGapmhoWZRIJpPi5UW5CnWW9x1cs6gpi8ViWK/X8Hg8CIVCME0T1WoV9Xod4/EYm80Gbrcbfr8f4XD4vd73t2ZC3JhVYqaKnOv1OiKRiFgnUOivkrPJZCLELBqNCqHRCbeJGauHXq93x8OKrTvqqsLhsIgndSNmXJhJzDKZjGhq+OGAB9sZ2+0W8/kc/X7/jS0BHRZptZW5XC7h8XjE8oWVhNv30m63w+v1IhqNIp1Ow+v1Yj6fiz6BJXIdiRk1J8PhEIvF4o3VID4PqrGsDvf6+8DOQDabhc1mQyqVErsIy7JkXTMMQ1rV1JfpTsyA3Xae+i7wME5zbdM0xSaINgnssuh0GCOoh+aaRgkDbULY1leJGYe9eEjXCaqQHXgtyyExKxQKyGaz0tYejUZot9toNBoyfc2ht1AotCNX0WGvU/WEXLt5IKN8hX5+JGYejweBQED0d+/rvr9TxYztKL/fD5vNBtM0MR6P0el0EA6HpaR5m5ixWkahaL/fRywWk8kdncDFZrFYYDabYbvdymZst9sxn8/R6XRkGIATXOzB6+R2D3z3oY1EIkin09hut+JRFwwGsVgsZFq31WphPp+j1+vJv0MlZros0CoxW61WMpHGE2IwGPzOi8iWdiwWQyaTkYpZrVYDACn364TVagXDMDAYDNBqtTAcDjGfz78zcamW9n+o3akTWBVZr9cyNn+7YkZixoqZqsPT5Vl/E9R3f7PZyJo3nU7RbrfFNqnZbIqM4U3EjP8encADOMk3JTzAt4cvv98vxCwej4u3nW5SBeDbyUTeL1bMptMpDg8PhZiNRiOYpolWq4VqtQrDMOBwOBCLxYSY+f1+KVTo8PyrhrnUClI3TJ18tVpFo9EQPzdWzNi2f1/3/Z2IGX8R3gRaBqzXawwGA5RKJRG/UvTPxSoQCEhlhUa14XBYC08zdZyWlTKSTdUJmfqSfr+P7XaLwWAAj8cDr9eLw8NDYdqqSd99f4FZtucJIR6PS8mXbdpAIIDJZCI+duPxWFq3Xq9XW4NdtZVJXz6v1yvPPbUJKrhoh8NhpFIpuc9c4NWWiI5gleyHtFO3dRc6g/eT1W5WgjkMM5/P4XA45D2n8/tDqBau12s5UFNvxE+lUsH19TUajQa63S5CoZBohweDAbrdLlqtlmzyjCbTBaqP2XQ6xXw+l4oZn2/ec07f6jyBC3xLxKmfPTg4gMPhQDqdlgMmBwJqtRqq1arIWdj6nc1mojPnd6KLVEnVjQGQKvFgMEC5XEa73cZqtRL5FeVY7/O+v/U35XK5pFI2m82QSqWQTqfR6/Vgs9kwGo0wmUxkA6IWYblcwuv1IpFIyIvJh32xWGixSbFywgfPMAyZSFQzw7hgu1wuiSmhbYRhGLJIsS2sQ8mbbSlCbdXxGngdk8lEqgQkdPwZHUXgasXMNE1pY7Ai8n2WEdQmJBIJrcXfBKsmFMJ/n13GQwSTC7ghD4dDDAYDiSCbz+dSSVUNpR8CMaM3G70aVSPVdruNTqeDTqeDyWQiXn88wHHwabVaIZ1Oy3ujC97k/K8SM677D6UybLfbZX3z+/1IpVKw2+2IRCIiwWi325IfSs2VitFoJCat/LvhcFgbYvYmzOdz0Yx3Oh2s12spSsXj8fcuxXqnilkgEIDH44FlWUilUshkMjJ5xg/bPjRd48bNFijwLTFTy8P3GWrlhL/7dDrFaDSSUxMAmKYpY7T8GVYHgdeCymw2K+3M26TnPoIES7VBYJn3tsas3+/Lhs0qm85tndtTmZvNRjZd/vl9FbNgMIh4PC5VBBpS6gjVLoNaivv+3L4vkJjxnvd6PQk6JjFjW+t20oduz/tt0AGe0TzMiS2Xy0JWWE1WCQqnN3lgY3VBJ3wfMeOEvUrOHsK9Vtt51JBTW9nr9dDr9cRwls8BB5pYJR6NRqI95PpAoq4jttutDDPe3NxgPp/vyFSoLXuf9/6dxP9q/zWVSiGXy0kLyzRNDAYDbLfbnQ2bLS/+SW0aY5t0IGaq/xpd7+ltxYUb2BU+k6zQD4any1arBb/fLzlr9/0UqbZdSczeBJ6W1YoZ/w4rB5z80WURu9224+//l0AyyzgiRpnoCnXS8i+16dj2Vx20dYbqaeZ0OrFYLNDtdlEqlcRAmzrLaDQqAyG6V1AAiK6M8gzqisrlMmazmfyczWYT0mK32+Ugw8EvXdb52+BzzMMVn2WubSw6PJTIPbby+I5zgI22SDTObjQaYp4dDAal0sYpVurK2TXSCdTZMQu73+9LW95utyMajSKZTKJYLCIajcLtdovMRU124TPytuv+nWqLdD0/OjqC2+1GNpvF8fExut2u+JPxIunyT58TdbPSJcz6tnhd/cJdLhd8Ph/C4TAymYx4uPj9fsnX63Q6SKfTWK/XuLm5kZtPLdJDwO1TpKo5YCuQPmC6uN+zfRcIBKT6SSNdVnxvLzybzUYmGGm8+R8FqmCaES2snuoKVk64ULNyQHNZtn2SySSSyeR7P0F/SDgcDvj9fqkQsAtADSnBNiXblycnJzg9PcXp6alWDvAqeOCmrpprmarDInHx+Xxat+tuQ41bm81mqNVquL6+xtdff41yuYxOp4PFYrHzHYVCIRmAYISRLiHmKmj1xGIKD2Dr9Vp8So+OjnBycoJoNIrNZoNer7djN0JS+y4+bnd6ilwuF+LxuPw5mUzkpVXF/DRj7ff76PV6wiJZfdIlzPpNpWtVIMhprUwmg8ePH+Pp06dIJBJot9tot9totVpCTsvlMizLgtfr1dZ88U247VUGQPSG6rj5arWSqtp938BY8Q2FQtKS5vQpidnt55fEjNoct9stJ+uHDlaWOSCjknRdQWKmGm62Wi3UajU4nU4Eg0Ekk0kcHR0JMXsouE3MmIvLTFCCju/RaBTRaBTHx8c4OjrC0dER0uk0IpGIlsSMuupwOCwh3aycUq4QDofFGuK+r2c/FrQJoW9hvV7H1dUVXr16hUajIfpw7onqdGoymdwhZrolPpimKRXx6+trIWa0S2Lay/HxsXifkbjx4/V6xUbrJyVmNNrkhN5to1HCMAy5QLYw1axMXSpmP+TDpeZAptNpPHr0CP/pP/0nFAqFHbFsqVTCzc0Nbm5uMJlMkEqldtoBukMlr3xg2cplO4P3XZfx+dsWMQAkAYEk7XbFjIsabUO4eD+EVsdfAtcCEhkSUh3e8e8DiRkHfnq9HprNJiqVCmKxmJCPw8PDB1cx46Ybi8Uk2Jp2RyrR8nq9yGazyGQyyGQyODg4QLFYxMHBASKRiJix6gRWxQKBAMLhMEajkcgyVE9PVVf4UKA6/3e73Z2K2WAwkJ/j+03yGovFkEwmkclkkEgktLzvlmWh2+3i8vISX331lejraJeTSCRwcHCA4+NjOaiMRiPhQOrU5rto6+5EzBaLhZT7eKO4GKmajPl8jpubG5TLZdzc3IhORbebddvLKxQKySmSnkY0zuWNdLvd0vKo1+vi7Ubyqku18MdCjexS44toxMmXXE18uO/6OnUBVieKqTmgweZwOJR7OhwOUSqVUK1W0Wq14HA4kEgkZFhGx0BnVQjNeClOqKm4nS3JCU6diYppmuh0OiJ6v7y8RK/Xk4MlNTXj8Vh0pYZhyKbldrtlUEQXw02CQv5wOIzNZiNJF5Sr8B2ORqMS7p7L5ZBKpRCPx3cGIXQ4iN2GehhXp8pVHaVOnZ8fC3WAablc7uSk0pszFAohGo0il8shk8kgm83i9PQU2WxWWpg63ndaYnG4b7FYAHhNQjebzY4t2Gg0wnA4FHsQmsnfxYXgTsRsPp+jWq3i1atXuLy8lAWIokHeEEYZ0PNENVzVCWzTMamAVgiZTEZeUp6mVf8eiiUrlQpGo5GY0z1EqNYSJGasJqnRXVzkdRh8IBHn+D/wbcWs2WyiWq0ilUohGAxKGVslZo1GA7FYDB6P58EQM0at3a6OA9/qcriZk5jptjiroJnm2dkZXrx4gVqthk6ng+VyKdXR6XSKwWDwHcNtrnd0Quc6ogt4MAEg03rsfACvK2XpdBrZbBb5fB6FQgH5fF42KGqvdNCT3sbtASaVZLAboHZ+dBO5/xDU+26z2Xb8utxuNwqFgnyy2Syy2SzS6bRoLf1+v7b3XV3r1P2a7UlW09rtNgaDgVjnHB4e4ujoSFrb7xpHdWdiVqlU8OWXX+K3v/2tTCWxp8wTomVZaDQa8slkMgCgnQ5DdTRna4siR8ZMkXhQU7bdblGr1VAul4VhU2f2EKESM1ZUeK2MtGDAsdvtRigU+sC/8V8GFyi6WQPfEjNOqMViMfj9frleErNKpYJWq4Xj42O43W45VTJHUCfcbud938QV9TfUnOjqgq7CNE20222cnZ3hiy++wGQywWw2E2LG72UwGEjVzOFwIJlMIpVK7cg1dOsUsHJCnaTP55MJfKaYpFIpaV3m83kcHBwIkVGrBrpt0N83WQ58t2KmiyTnx0K973Rh8Pv9ork7ODjAs2fP8OjRIyFm2Wx2x2iXBxDd7vttY2FqoVkx4yS2zWYT7Xy/38dqtUI4HMbJyYlwoJ+cmN0+QVNTMp1Od8r2q9VKIhxYFmekTyqV0mqT4gNGs9zDw0NMp1O0Wi0hYtvtVnx/xuMxarWaOGPT14uao4c2yQN8ayfBRYqLs+qRo7a87ztoD7PZbBAMBnfiV4DXhoqlUgkul0uI2XQ6FU0Cs9SYlsFqmW73nffP7XbvmArzXefGxA2LBF21T9Hhfn8fbsfV0CqBaR+TyeQ765jqeZhKpSQFgtVSXciqeu1qRjCro7FYDOl0WjTHkUjkQ//K7wXqIZzVTz7zrJRyGGQ0Gkk6ADdkHatFBCuCjB7kc89uUSqVwuHhIU5PT2UaOZlMfuhf+71Afd6Xy6UQdLasee9tNpu0Of1+/048IQtU71Idv9POcHtaR53GYpitGlXEDK3j42M8evQIp6enODg4EI2WTvB4PEin01gul/D7/bi5uZGXFni9IJdKJdjtdmlfGoaBWCwm47bZbFbLltYPQZ3KpI6QBF0dp6bXkw7khJNZABCJRJDP5/H06VNYliVaG9qfkJjR7ygSiSAUCuHw8BDpdHpnYdepnQXsvu+maSKZTCISiSAYDEolmBW0+XwuE6mqnlCH+/0mqCHmJycnMp1Lc2mmetzGfD7HYDBAo9FANpuFZVmy0QPQ4t3nJsSg9uFwKBP4rKhQa6TjBN4PQX3mc7kcBoMBQqGQZIAahoFWqwWn04lisYh+vy82Ijy46ErMeMCcTCaSf2xZ1s79ZvKPuvc9BKipNUzwYVYq31/mRLNt6/f78fjxYxwfHyORSOyEuL8t3isxm06nkqmmmqzR44si0ePjYzx+/Bgff/wx0um0eH3oBBIzv9+PTCaDcDgsD+ZgMBBx+Hw+l1PHcrlEPB4XSw1dtUY/BHVAQnXH5zMQDAalaqQLMSOBcjgcQsxY3mYsDQPbOY3ocrmQSCTkFHlwcCDVYQbd61ItIThhFIvFsN1ukUgk5D4yaowaO3q40SKG+aI63O83gYJnejWyfcFNd71eYzKZiO6KGA6HclgZjUZSaU+n0wD0IGY0mGVliMSMa5vT6ZQDFzVIDwXqHjefz9FoNETYbRiGfA8AxGx1PB5LMsC7trLuA9brNabTqRiiD4dDCe5moglb9WxdPhTcbmEzXnI8HsuEucPhQCgUEqPZYrEo7VzahFDS8La4MzGjwJeWGSx5qtEzHDemWL5QKIgHCNtBuoHebfF4XESfnOIwTRP9fh+1Wg39fn9Hb0fxbCKRQCKR0NJ87y9BtRVR0x/U54AERYeNmpopAHJCYvnaNE3U63U0m00Mh0MZl6bpZDAYxOHhITKZjOjQdF3A1GB2m80m1TKK+9frteTpcdCDk8mBQEBL13fC4XCIprRYLMLpdMp10mCYTucqeCCjTUw2m/2Oz+N9B6/TMAzR0dJgmUaatJN4aJYR1JeGw2EsFoudYRYScU6n9no9aWfSlFrnYQDaHA2HQ7TbbUwmEyyXy53uRywWQywW+9C/6l8F3MccDoccTvgdMJKS6+Dh4SGeP38uuaDhcPhO67yeVH6PPfbYY4899tjjAcK2fUhjJHvssccee+yxxx4aY18x22OPPfbYY4899rgn2BOzPfbYY4899thjj3uCPTHbY4899thjjz32uCfYE7M99thjjz322GOPe4I9Mdtjjz322GOPPfa4J/irmUidn5/jiy++wG9/+1tcXFyIv9NqtcLf//3f45e//CV+9atfIZvN/rV+hb8qBoMBzs7OcH5+jlevXuHs7Ez+u+oA7vP58OjRI5ycnODk5ATPnz/Hs2fP8OzZM2093G5Ddbtvt9uoVqsSWt9sNuUzm80wn89hGAaOj4/xX/7Lf8Hnn3+OTz/9FAAQjUY/6HX8GCwWC/R6PfR6PbTbbfzud7/Db3/7W3zxxReYz+fw+/3wer3IZDL49NNP8fOf/xyffvrpe/O3+ZBYrVaYz+cSMcTn/ezsDPV6XUxX6dfFD5/3h/LMbzYblMtl3NzcoFwu4+XLl/jqq6/wpz/9CcPhEH/7t3+Ln/3sZ/jbv/1bHB0d4eDgAMfHx+L2rwPW67Ws2d1uV+71+fk5xuOx+JktFgv5OQDigO7z+fCLX/wCv/jFL/DZZ5+Joa5uUN/3TqeDFy9e4Ouvv8aLFy/QarXku9hutzvP/EO49mazid/97nf44osv8Pvf/35nLVd9SsPhsLzff/M3fyN73cnJCcLh8Ae8gvcHdY979eqVcJtGo4FisSg5sfyzWCze6X1/r8RMDXOdTqcYj8fy4DJ3arvdSvCvjthsNmKkOxqN0Gw2US6X0el0xAGa2YB0C6cJJ3MiAT0yIn8IzERcr9cYDAbo9/viCs5Fe7vdwufzIZVKwev1ot/vYzAYYLPZ7EQ13XeTWXWTGo/HaDabaDQaqNfraLVaYirKDEm/3w+Px4P5fI5Wq4VXr14hn88jn89rZzDLe8z4mU6ng3a7jU6ng0ajsfPcq9FVpmliMBhguVwinU5jOp1q986rma+WZUkM0Xg8lvvfaDQwGAwAAPF4HD6fT8Lsu92uxNbodu1cr2ksOx6P0e120W63YbPZxChaDfJmjiLjiNLpNMLh8L1/v29jsVjAMAwxV1XTPZrNphzA4vG4vPOr1QperxfL5RLD4RDT6RSWZWkXaq6+78y9HgwG6PV6EieWSCR2cnG9Xi+A1ykXNzc3YsKt2zP/fdhut/I9DIdDXF9fo16vYzAYYD6fY7vdwuPxIBqNIhAIwOPx3Dnt4b2+McvlEqZpCmkZDoeSug58S0aYDqCbK/Jms5FFaD6fo9froVKp4OzsTByfGT3DQFO6I3NDphu+zsRMXYxZJbu+vkapVMJsNttZjEhMs9ksqtUqbDabhPyqkU33GcvlUlzdO50OKpUKSqUSbm5uUKvVMBqNYLPZZJEOh8Pwer2YzWYol8sYj8cwTRNut1ty1XSBSkqHwyHK5TIuLy9xfX0tFdD5fI7NZiPpDtvtFqZpotPpoNvtolAoYDwea+V2D+w+59PpFPV6HdVqFfV6Hd1uF91uF71eD9PpFACQSqUkP8+yLDSbTcTjccxmM+02qfV6DcuyhJj1+320Wi3UajWkUilkMhmkUimJlGJAPd9ph8OBQqGASCRy79/v22D+JYlYrVYTEs49gJmgXOOZEbter9Hv96WaqON95/vOpAd2B5j0w5xIkji73Y7tditEhbF1rKLqju12i9FohEqlgnK5jOvra5TLZXS7XUm/8Pl8Erfo9XrvnIP8XokZWx3T6RTD4RCDwUCqKawWuVwumKYp1TOdwAeRURUkZhcXF3KCsNvt8Pl8iEQiiEaj8gkEAhJcrWt2mgouQpZlodPp4Pz8HF9++SVWqxWi0ai07hhLFAwGYbfbsVgs0O/3v5OjeZ+hLlLtdlvu+fn5uVRRgNcVMy5cXq9XKg2lUkmyM9UWgA5gMPtiscBwOESlUsGf//xnfPXVV5KPx0Bnu90ugd6M7ZlMJnj8+LG2xIzv+3Q6RbVaxcuXL/Hy5cvvhJgzM49VE0ZSZbNZGIah3Qat3ndWC0jMGMXFVpUau8aMQZvNhmQyiWg0eu/f79uYzWZotVq4uLiQA2e5XBZSmkwmkU6nZT3j+jAejzEcDjEcDjEej2XT1gm37/twOES320Wr1UIikUA4HEYikYDT6ZQMaMuypOU7n8+Ry+Xw5MmTB0nMXrx4gZubGzSbTXQ6HdhsNqka8vthFvJd8N6IGdt7vJHtdhu9Xg/D4RCGYcDj8cDj8ciLqyt4iiYJnUwmGA6H8Hg8cLvdEnibzWaRyWQkDzMYDCIUCknW2l0Z9YcEQ6pnsxnG4zFqtRpubm5wfn4Oh8Oxk6fG1jYXKLYub+eH3meomXFs4dVqNZTLZXmeeY8jkQhCoRDcbjdM05TFutfrwTAM7ciJulBPJhN0Oh1Uq1VcXV1J5YSbM98LwzDQaDQwnU53tEi6bVLccLlR1+t1XF9f4+rqSn7G4/FIbmA0GoXH48FoNMJiscBsNpONW7dr532nppCf+XyO9XotQdasiN2WJjidTnkndFvrVquVEJPxeCzXvVwu4Xa7EY1Gkc/nAbwmcZPJBDabDYvFAna7XdY73QoPBKtht5+BdDqNYDCIXC4Hj8cjxMwwDDSbTSHvg8FgR39ot9uFrOsCXjvXvmaziUqlgvPzc3S7XZGvcF9nZ4ikTG3vv8v+9l6J2XQ6RavVws3NDUqlEjqdjpwWWUkKhUKiwbnvG/IPwW63w+l0ysLM0OpAIIBcLoejoyMcHR0hm83C6/XKJ5VKIRqNaqe7UGFZlrQ2Go0Grq6u0Gg0hKAahgHTNDGbzWST3m63cppkMLDL5dLiGWArczgcio6OOhK+kOFwGIFAQD5sbepwfT8EtjZINPhZLBbw+/3IZrP4+OOPpT3L0+U333wj7Uy2tnTDbDZDu91Gu91GuVxGtVrFYDDAYrFAOp2Wdp7T6ZSgclZE2R3gwUOnTQn49r6bpilhzWzTu91uqSI6HA4hZh6PR/S0bOno+A64XC74/X5Eo1Fks1l4PB7E43FMJhMUCgUUCgUUi0VMp1PUajVZ74DXww+JRAKRSOS9VE5+atjtdtHA+nw+KaiQaKdSKRSLRfh8PpEjMdibmlJq9NhNYNFCpz2P0g0eqq+vr1GpVNBoNLBcLuHz+ZBIJOT7iEajcLvdsNlsWK/XWCwWO4eUt13/3isxm0wmaDabuLq6EkH8dDrFarWC0+mE1+sVYqbjC6vCZrNJ5cfv9yMSicjn8PAQT58+xccff4xisSg3hlUktjV1BYkZq2SqGJKiWNM0MZ/PZRhgOBzCZrNJK5fETIcNi5UTEjOKQCeTyQ4Z5311Op3YbDZwu91aEhIVasWMerL5fI7FYgGv14tCoSDTlna7HQ6HA/1+H6vVCp1OB5eXl9qSExKzq6srlEolVKtV9Ho9LBYLRCIRPHnyBD/72c9gWRbq9Trq9bpoLHlwU1t8OkGtltwmZi6XS3R3qq6M05hut1tE/zpUxG/D5XIhEAggGo1ivV6LBGG9XiOdTiObzSKdTsvgT71eh2macDgcUnQgMdXt2nmgYCGF5JqkOx6PI5/PIxAIAIAcuPv9Pjwej5ASVhINw9h5H3QBiVm1WkWlUhFO02w2pW15cHCAo6MjFAoFxGIx2dNZSVytVlKA+smJGcueq9VKphRVYjabzUR/whfb7/drUy1RoZZ4t9stHA6HCL4DgQBisRji8TgKhQJOT0/xySef4OjoSISxDwUkZuVyGd988w2q1SparRZGoxEASEWFIlraZ8RiMaRSKSQSCVnAdXhZ1+s1TNOU1gYHPSaTCXK5HILBIJLJpIhiAciJ6fYLqdtzwA2aQz38mKYJr9eLdDqNx48fI5vNSuWk1+uh2Wzi7OxMdJU6kpP5fI5ut4ubmxtcX1+j2+1iNBrBNE2EQiGcnJzg7/7u7zCZTAAA3W4XpmnK2sBNTcdr32w2Iv5nFZBid8oVJpMJ1uu13Pf1eo1oNAq73Q6/36/tBDpJJvVx6icWiyGRSCAej8PpdKJSqciwi8vlknseDAa17Aqx7chCCj/UTadSKeRyOUQiEVnr/H4/yuUyPB6PVI6pNTcMQ4aCdAHvZ7/fR7VaxdnZGUqlEmq1GrrdLjKZDPx+Pw4PD/HkyROk02kEAgHRpFKX6nQ6pYDztoWYO+2KHCGnjuby8lIm1brdLiaTiZy22GvVWQBPzdB4PEa9Xken08F4PBb9AcWOlmVhPB6j1WqJIJwfXaGSUk7d0rOs3+9jsVjAZrNhs9lgOp2i3W5jPB7L1NpyuZSTxunpKY6Pj5FKpbT4Tnw+H5LJJCzLkpNUo9GQzWg2m2EwGGA2m8nJmq3c1Wq1Q0J126T4zqqTxtQOmaaJWq2GP/3pT2g2mwgEAvD5fPJ9rNdrqSDoeO2s8FKyEAwGRV/l9XpFvtHr9UQY32w2d4Z+uInpcAD5PrAKwvcZgFTTfD6fVMxCoZCsg8vlEsFgUJ4Zna7f7XYjFAphvV4jFArJwYLDLaZpiiCek8f9fl+sUgCI5xX9r1gx1ukd8Hq9yOVyePbsGVarFU5PT5HL5eDz+URLp1pkccBFLViwM6JD50CVI3Q6HdRqNVxfX+Pi4kKGfAqFAvL5PA4PD+Xd5nDMixcv4HK5ZCgqHA4jmUzKIe1tcGdi1u/3UavVUK1WcX5+jpubG/H2oVCULFwViOrY2jBNUwhJo9FAt9vFcDiUcj8nMy3Lwmg0QqvVgsPhEFNNHUjI90E9DajErFarSRXF4XCI9oQnBVaXlsslPB4PUqkUjo+PcXR0hGQyqcV3Qi82t9uN9XqNRqMhm+16vZY2JwD5Lih65Yi51+vVkpxQQ0RiRj1dKBQSYuZ0OmUCLxaLYbVaYTgcihZDV1L6JmJms9lkA+YhpN/vy7vQarVkEODx48fI5XJaenmp4GFsPB6j0+kIKZtOp/Jcs0qktnGSyaRYauh0/SRm1A6q4nXTNOWATv1hr9cTfRXXPZWUrVYrLavGXq8X2WwWn3zyiRxOuWarQw63fUm513Pd06U7xiEvwzDkfS6VSjg/PxedXD6fx9HREYrFIvL5PCKRiGjqS6USwuEwotEowuEwcrkcHA4HQqEQIpHIW/0ud3pblssler0ebm5u8OrVK2lhNhoNEf1vNhvx9eAir3PFbDgcirdNt9uViplpmt8hZjRj5GK+3W61ejFVqNOotytmqnaMWkOajnIhsyxLKmYnJyc4OjoSQ877DmoiY7EYttstrq6uZLOlESOJCCvIm81mR3fo8Xi0W5iBb8XAbGfQt0klZsPhEIlEAul0Gul0Gk6nE8PhEOv1WmtSSmLNUzD1VKz+smLGQZhqtYpOp4OjoyNEo1Gcnp4imUxq6eWlgr6Nk8lEquOsEqhm2sFgcKdixjanDokeKniQuO03uN1upZ2tVsw6nY54dfJdsSxLqmar1UpLNwISM5fLhWw2u2NvxO4QD+vq5L26ZuhEzNQhL7Vidnl5KZWyYrGI4+NjFItFmU6dTqd49eoV/s//+T9Ip9PI5XLI5/Pid/cuyQ9vTcxYulytVjIeWy6XcXV1hVarBcMwpEdts9l2bqD6oOo2Pg58exJQ25MUvAKvqyWDwQDb7Raz2Qyj0Qj9fl9GrheLBQKBgPwdnU6RAGRx4f3ly6f6WXk8Hnl5SdIYWUKdAjUaugxBqJNKLE+znM0Fh8SM1TKHw4FAIIBsNotCoYBcLqelp5PaymRr7vT0VDZefiiU56QeRfLc5HSskNPNO5fLweVyybq32WyQSCSkrUVLDfozAt8KyHUaclHB9ksgEEA4HBZtFafKWRngEBenkN1uNwzDQL1eRyAQQDwe184ihgfN2+237XYr16h6FkajUYxGI5E2qEkJzWZTJhrZ6tcFDodjp0qsgs88Rf6sLNKhgNOculgiARDT+Gq1ilKphGazieFwuDNxG4vFRE/GA3iz2RQzYpfLhUgkIkWad8VbMwOW++iC3mg0xAmdwtdIJAK32y1VE7bBLMsSHY6O5Iwl7mQyKTqqZDKJTqcDl8slMRzD4VBIWyQSkfSDXq8n/mY8gegCVsPU6dpEIiF2IHwRA4GAVIkAiM0ANSf0cqM4VgftgQq3241EIoGjo6MdE2W2MgDIQpzNZvHo0SN89NFHKBaLSCaTWsUxAd+2MoHXWaaHh4ew2WyIxWKiLWWFdDKZoNfrCUGhn52uxMzv94uRaDqdlqrAer2WCjEF8hT8s6qgw0b0QyCxXK1WSKfTKBQKGA6HsCxrJxOS7X22tLg3tNttpFIpLVMPfghs2XJCM5/Po9frybDEarXCZDJBt9tFvV4XGQsryToRMx5Iue6rUKMX+VzcjiDU7VA2m83QbDZxcXGBi4sLtNttLBYLOZjykGK32zEajeR5r9VqGAwGoqfnM0Jfs3fZ496aGfCXGQwGO8Ts+vpaqkhs3QAQ7RVz19j209H5n18421etVgupVEry08i4gW83tEAggF6vJ/EtT548Ed2ZTtE8t60uGDWVzWalXeX1esUVO5PJyEmLMSUUjvPlfZcx4g8Nl8uFeDyOo6MjWJaFy8tLaV8ul0s5LQaDQeTzeTx+/Bg///nPJQlBN2KmDuywYhqJRHBwcCBGu4yr4WY0Ho9lLeAUn45EhcQsHA5juVzuDMBwOpdxW9TSqt+VzmArj12P0WiE2WwGu92O4+NjHB8f4+TkBE6nU8jpaDQSp/xWq4V8Pv+giJm66TocDkwmE+TzeUm14OGbMUbNZlOGB5xOp3aB3iRmPIyrmE6nO++BaZpwOp2IRCI7tinsnOnwPtBF4OLiAmdnZ9K2VzW2vJ/UGA4GA1SrVQyHQ2lZs0DB4sO7FGDeqZXJCAaayPHDKlE6nYZhGEJgOARA00Idq2XA68UqEAhIFEcul0Ov1xPtBSfRqKui3xNF8bPZTL4f3cr7wLd5eGzx5PN5DAYD0R/RdI8VQXrcUBBNDzuSFx1BMT+jSRqNxk40C1scHo8H4XAY6XQah4eHUtbXjYiqbR22r6PRqGgGbTabiP3X67XoM6gr8nq9cjDjVKsuTuDqdBUAsclhxYzvNA1JI5GIiL9ZOVe9DnXapLgZ8zpzuZwM8JyenuL09BSPHj2Cw+EQZ3wK4TebDXq9nraxRD8EtqWdTicSiQTy+TwWi4VUlShnYQpOo9EQ6w22xHQBr/M26EumJv3QLiQWiwk5061yTG14s9lEu92GYRg7EXqq6TK7JCTgo9FIpm9ZnKI12E9CzHhq5osbjUZl88nlcsjlcshms+j3+8Ko5/P5jvhfl57zbfD33263iEajODg4kLYOqybj8RiGYciHxGy9Xktche6nSI/Hg0wmg6dPn8rJiB+PxyMbtnqKyuVy0p/XjZyooJ5qOByKJYhlWVL9IzEnKScZ0dX9/jZUQTwd3uPxuAT4BgIBjMdjAN/a6bDl2+/3hZRTe6UL2KriZ7PZyCGLvm6bzQbdbhderxfdbhe///3vJQGEi/O7OoH/1FDXebrZM6A9m80iEom8UYP1EDojPwQeVACIXIGdAR7I2ClxOBwSS0brHJ3Bg8lms4FhGOj1eqjVajKZzcNJKpXSMoqL95YchS7+6pAjK+eGYYiXZb/fF9kWzYWZqRoMBt9pnXsnYsbTs9frRSQSQTqdxmQywcHBAYrFIg4ODlCv1zEajVCr1b4jGmdrQ4eTowrVFTkajcJms0lbh3linNBj65Kj1PzzoRCzdDoNt9uNTCazE2KsmpFaliXjwplMBrFYDH6/X7sXVoXqW9ZqtUQcyuebFeXbxIwvu86blBpQzc0oHA5jNpvtnJRVGQOraSRmbGWr2jUdsNlsxJtuNpuJyztNhTebDRwOB5rNpozbl0olMRpmjua7OoH/1OA6z/scj8dlqlrNwFTXMVYTScx4/3V+5m9DNc0NhUKw2Wyiq1U3dOajUoNlGIb2od68v8zHpFC+Wq0im81KhFUymdTOuw7Y7Q7wPqvEjLFTdF4wTVOkXYvFAgCkU6ISs3eRr7z1N3fbGZjEzLIsnJyc4OTkBMfHx/B6vajX69KbJqnRuWKm+rHx2qk7UcfEGfRcrVYltqjT6TyoihmtEW4vumxdtlotWJYl2oqHRMxoFdBqtcREmZsUXaNvEzN1nFxn8L3lBBZtM2KxmPib0S6AC9doNJLDCQPPdRJBA99WzCjfiEQiEuLN6h+J2qtXr8RCiIe4YrEowmEdCKlqCs61PpFI7PxzAN8hZjTpZH7kQyNmasWMWlkAyGQysq+xlUu9Gb0uH0LF7E3ErF6vIxKJwOfzoVAoIJlMyuFLJ6gm+CzA8L3v9/vo9/vy3H/fM01ilkwmkUgk5N/1tnhrYsYxak7hcPOlEDybzSKRSKDT6YjwjQyTo7Wz2UxKgrqC/Wi2L1XSyU2IPjcUStLP6SGIg/n7v+k6eGpeLBaycFMIqvu1q0Sck0ixWAxer3cnT5L6I5b7aRHyEPIzgd1s3JubG9TrdazXa7FHYGufkSzqgsaPTuA1tdttWdvYvqfpMmULbPMylmg2m0mLk1E+Omks1eujloj2OIZhSDW01WrBNE2Ew2Ex19U99QD41sNRjaniwAc/4/EYV1dX8uHUot/vRzgc1tIeiddNMsaOz3A4xNnZGZrNJhaLxY6NFKtD9Lqk9k5t39/X7yEYDEr2r8/nk3e92+3uBJIvFguxgWLiDdc4WqKomuJ3KUK99TfE05PKLiORCPL5PMLhsHzUrDBuZPP5XPrt1GnoCtM00Wq1UKlUUKvVEA6HRfBH/xoGG4/HY+3NNn8s1GxF9ZSocwtbhbpYrddrGaPmlFa/35fqmGEY6Ha7qFar4oGk22TW94E+PvV6Ha9evRLxP0lqp9MRneFDuO80D2632yiXy/IMMKyek2iM7aHg2+FwYLFY7BxUGQCtC5jmMRgMMB6PpTIYCARELF2v19FutzGfz4WY5fN5Lb37VNwmJxx0o3aaQy0kK/ywvUm/t3cVgX9IUDNIMsr9rlwu4/LyUog4D920iqEh8W2TaZK2+/o9hEIhFItF2Gw2xONxNJtNNBoNtFot+P1+cR4YDAaSdtTv96VSSnkDr/UuViHvVDEjG+RpgKcDlVXSXFHV3RiGAZvNJmaMOhOzxWKBdruNs7MzvHz5UoYestmsTGrUajU0Gg2ZQtU5N/DHgsSMJ2zVcFPXFvZtMAFhtVqJt004HMZwOJSBF8uyMJ1OhZixxK17O4NgxaxWq+Gbb77BZrNBPB6XbDgAMrX1EO67WjErlUo7Az5sVRYKBUmJCAQCO8Ss2+2KjYraEtQBvHa63EejUdFLMQmlVCqh0+mIADybzaJYLCIajd7bjfjHQjVH5zvd7XYxnU5FstDv94WUvXr1CgcHBzKBTmKmG0FlQYUmyrSSePHiBWq1Gtrttuhn1Y+ahsLvDvi2u3JfZQyhUAgHBweIx+PI5/Oo1WpIpVJoNBrStg4Gg2g0GuLTyoMnq2OUcpCYAW/uKv0lvLP4n4vv95XkPR6PiIPD4bCYkKoMUmdycns6j3lyPFlWKhVxDqadhNfrfRA6qx8CT5fUVrGU/VA8nlgRYR4iqyS3Uy14WCGBeygaMxWmaUqVkOJ3nioB7Bgtqs7nOrZz6emkHkapozIMA6PRCD6fTzYkbtqBQEDeBT4HOrZxDcNAv9+Xoa5er4dgMCit+mq1CsMw5N3I5XJIp9OSOakr1IMmTdUrlQoqlQomk4m0NWkqO5/PZUIxFoshn88jnU6L6bpO4H0fDofo9/uoVqsol8tCwqfTqRCT2WyGfr+PRqOB4XAok9f0u4zFYgiFQgBwb7sGTLFgK5Jh7OoaxqGXdrstVVCueV6vV8x171p8+au9McxOo68VKyZc3Dwej9YbtIrtdovBYIDJZIJqtSovKf1dKAZMpVLIZDLahxr/ENSwc9V0k4RG92ohhxmy2SyOj49lbJri0G63K5VhEpVQKCSEROdrV6Fmp3IBZ+vS7XaLhUgikdj56Fo9YOLD8fGx6E9arRba7bYQ8Ha7jdVqhW63K55enMikvx0Xf52gTiLX63UA32oFqRsejUaypgWDwQc17MMA99FohEqlgrOzM1xcXGA8Hu9MKi4WC4RCITHf5SBcsViUFr9OWC6XGA6HqNVqYiJfrVbR7XZlGp0aM+ZCG4axo7fmcGAymUQsFgMAfPzxxx/4yt4MdbDD5/MhFovBbrcjFArJRLXX6xXbH8pzvF7vjoyLBai74K9OzGKxmMSaqH1YHReo78N2u0W/35dkesMwRAROMhKPx3FycoJsNvvgiZm6YaseZ6yU6HzfqanMZrNYLBZyeqxUKjJxO5vN5OXk9G4gEHhQzzywOwjBkXFqLoLBIPx+P+Lx+HeIGeUOOsHtdiOZTMLn8yGdTqNWqwnhZh5uu93esQYZDAaSGsBJbh1NhknM+v0+ms3mjmfjZrORLkg8Hofdbhd/L+YC63a9KtgJYfZlrVbD5eUlvvrqKzmMAN/G9bFaSGJ2enqKTCYjm7tOIDGrVqs4Pz9HqVRCvV6Xahm7AzabTQxZm82mdEUcDodkzeZyOSSTSQDAf/tv/+0DX9mboVqhsBNCDz9ahNESh1UxtYMSj8dl0OOuz/xfbXVkOTcajUpeGOHz+bQM9v0+bLdbTKdTNJtNNJvNHbdgTiVFIhEUCgUkEgntTVZ/CKpYli8tp9DUvruuoH4ykUjAsizRWXQ6HcnL5EasBkHrGmb9Q1CrBZy05rVzY2ZuKj+0F9ANJOSRSEQE/+pgE8kYWzmj0Qij0QiFQkG+E10rxhRzMwuV6xw3YYrcWRHi9Cm1yDofRuhfx5Zeq9US8ftkMpE1jcNuPp8P+Xx+53MX24QPCVVXWS6X0Ww2pVpG3y7CNE0Mh8Pv/Dui0ai09kej0U/0m787+Kyyjcn2qwrqyEhAGcEUjUYRCATeSzdQ3zdmjz322GOPPfbY44HBttVNibrHHnvssccee+zxQLGvmO2xxx577LHHHnvcE+yJ2R577LHHHnvsscc9wZ6Y7bHHHnvssccee9wT7InZHnvssccee+yxxz3Bnpjtsccee+yxxx573BO8N1OpzWaD6+trXF1d4fr6GuVyGdVqFZVKBYZhSI5kNpvF48eP8fTpUzx9+hTRaPR9/Qo/KWazmXj5NBoNfPnll/jjH/+IL7/8EqvVaifAVjXYfAjX3ul05Fr/9Kc/yX2uVquSCer1elEsFvHrX/8an3/+OT7//HPxNXoI2YnAa5+ff/u3f5NPt9sVL694PI7PP/9crl/X612v13JN7XYbX3/9NV68eIGvv/56574/evRIrvVnP/uZeH7d1/iVt8VoNML19fXO5/LyEtfX15hOp/IdhcNh/N3f/R0+++wzfPbZZ0ilUmKyq5Pzu2mamM1mmM/nuLi4wL/8y7/gX/7lX/Cv//qv3/t33G635KUmEgn8wz/8A375y1/iV7/6FbLZ7E/4278/3F7nz87OcH5+jrOzM4xGI7nvwWAQz549w/Pnz/H8+XPZ6zKZDPx+/4e+jHfCarXCfD6XdAd1rRuPx+Lhls1mH9xaV6vV8Lvf/Q5ffPEFfv/730vyw2QyQbFYxKeffopPP/0UH3300Q63eV+4EzGjmehms4FlWRgMBmg0Gri+vka73cZisdjJjRqPx1iv14jFYjAMA+v1+n1dx0+O5XKJyWSCVquFm5sbtNttjEYjWJYFm80Gm80mpoLr9RqGYWC73SKZTGI0GmE6nUoel25EhTElzAg0DAOWZWGz2WC73WK1Wklo/Wg0Qr/fR6vV2gmC1el6VfBZtyxL3NAHg4GYLrpcLnGBptu/zuDiPJ/PMRgMJG6o1WphNBphtVqJiahpmhiPxxgMBnA6nfc2rPjHwrIsLBYLzOdzSXcol8sol8uYzWYIh8M4OTmR72c2m8l3MR6PUS6Xsdls4HK5tDuE2Ww2SWrxeDwSK1coFGRdczgcshYsFgusVis4nU5YloXhcIjJZALDMLBYLCSiS4esXBonbzYbCW9nRuRgMAAApFIpyUadzWYSVt9sNuF0OsVUOBKJiOGobmvearXCeDxGr9dDp9NBq9XCeDyGaZpYrVbyPTFHtdls4ubmRjJxdUt9WK/XEjPFBJ/pdIrpdArgtbEsTbNN00S9XofD4cBqtYLD4UAgEJDCw12f8zsTM4Y3z+dzdLtdlMtlvHr1SmJpAoGALNp0Tk6lUhiPx5I6ryPo9l2r1XB+fo5arYbBYIDFYrETgOp2u7FcLoVtJxIJpFKpnaBXAFpt4HTDJuniy8oXleHlDHlvtVqoVqtIpVIAXjuD65oAwHiayWQiAfbMRVytVkgkEohEIkilUohEIvB6vfd+I/ohMG5pNBoJKWs0GqjVapjP5zspBzycdTodCf/dbrfaXj8jphjefXl5iYuLC9zc3CAYDCIcDiOfz8OyLHH6X61WsNvt6Pf7ODs7g81mk4gincBIHRJsBnKfnp7C7/fLhxma/X4fk8kE2+0WpmlKmPtkMsF0OpVDC4B7/+6rlZPxeIx6vY7z83O8fPlS1vRcLofFYiEpDzyoVatV9Pt9bLdb+P1+ScAgQdGJnKl73M3NDWq1Gvr9vhBtkhLDMNDtdlGpVBCPx5FKpZBMJrWLH1utVjBNE9PpVCLHxuMxDMOA3++X5BK32w3TNHFzc4PRaITNZgOPx4N4PA6fzwev1ysc4F3xXogZy968OS9fvoTNZkOxWJTKQaPRwHg8RqPRwNHR0YMgZgx4PTs7Q6/Xw2AwkBBnxvG43W45dc/nc6RSKfT7fQyHQwQCAQD3f6G6DTWipdvtYjqdwjRNOWValiUnqcFggHa7jUqlApvNBq/Xq131QAXjhwaDAVqtFlqtFrrdLjqdjsR0RKNRpFIphMNhrQj3m0BiNhwO0e120Wq1UK/XUavVYLfb4XQ6JZzdNE0hZqFQSLLxdMV8Pke/30e1WkWpVNohZk+fPkWxWMTTp0+xXq/R6XTQ7XaFnLG6yEDv5XL5oS/nrcAKj9PphNfrRSwWQzabxWQyQTQalQ+Dvb1eL5rNJqbTqZCxwWCA8XgsOaoAtNioN5sNlsslFosFRqORHL6/+uorHBwc4PDwEMViEaZpyiFzOBzKHjifz+Hz+ZDNZjGbzWSf0+2Awson9zgWH9gxACBVUFaUfT4fLMuC0+lENBrVqn3P6i/jo9i+HI1GUinL5/MwTVNyUxk3GIvFUCgUsN1uYbfb70xK3ysxMwwDk8kEo9EIHo8H2+0WXq8Xfr9fWnp8SVkK1QnMgFyv15hMJtKiq9frck0MrY7H48hkMohEIhgMBkJUlsullEtZXdtsNh/60t4KJJ2hUAixWAxerxc+nw8+n09OU6ZpwrIszOdzecBJ4HS7XhVqflylUkGr1UK/34dhGHC5XHC73YjFYkin0zv5gbpiuVxiOp2i3++j1+vJBrRcLuH3++W+BwIBrNdrjMdjdDodpFIpmKb5oX/9O4EknO1ZtnS63S5OTk4QDAZRLBYBvM7/ZR5sr9eT7+vg4ACz2Uw72QYrZtvtFj6fD4lEAoeHhwAgYd0M5uZayAMmW0KmaWK5XMKyLKxWK6zXay3WfDXv17Ised+r1SpisRjsdjui0ajsBZRuMNydVXSuC3wPSGLuM7bbrXxYEazX6yiVSnLo8Pv9Unyw2+3wer1Yr9fodrvSKYpGo9oWXvjsM5w8kUggk8mgWCzi8PAQ/X5fiNtyuZT/zi4huc9dcCdittlshJjN53MhYvF4HC6XS3rMagJ7MpmUFo9OZV0AUq6ezWbSvhoMBhJmG41G4Xa7kc/ncXJygsePHyMSiaDRaKDZbMrPeL1eIbW6LFYqvF4vCoUCfvazn8Hj8QjpGo/H0u5qt9vaXdePAcv71WpVWtis/jLMmO1qPuc6gxtOt9tFv9/HZrNBIBBAJpNBMBhEIBBAMBiEz+eDy+USLRoF8TqDGyk1I1zLgNeh5j6fD6FQSLRW6obW7/cxm82ElOj2LnBzAgC/349UKoXtdivtaepJufZTi7NcLmG322UASDf9LLDbxqVWiHsdD5X8Z36/H8FgUNr9NptNDt+UPEynU2y3WzgcjnsfZM7rXK1WciBrNpuo1+uyvsViMWnput1u2O12IeOlUgnxeBzFYlG799/lckkXi8H1wOsg9kwmIx+fz4der4dgMIjJZAKbzSb7ufqM3AXvrWLGUjVbVdQm8MX0er2yaTGdXbcXlpsU21jdbleIGbVFyWQSJycn+Oijj/DRRx/JZJrH4wEAEYPyu+MCpxN8Ph/y+TxcLhcymQyGwyGGwyH6/T5ubm7gdDoxmUwwm80+9K/63qG2sM/Pz9FoNERnQF0ViRnvu24tDBWsmPEQsl6v4ff7kcvlpGoSDAYBfHtwsdvtMhCis8ZM3aDZmuCaxfUtFApJu9put2Oz2WAwGMBms2E+n2tNzOx2O2w2mxCzQCCAdDotuqrhcPgdYrZer2G326WCyMEvnaC2cUnMWBnj9XFf8/v9CIVCmM/n8q6vVissFguptk6nU+ky3HewjUut1WAwQLPZRK1WQzabRTweRzabRSQSEZ3hZrNBpVKRTy6X01Kq5HQ6pbvH9zwQCCCXy+207+12O+r1Ovx+P+bzuRD35XIpcp4PWjFTxf8kZpzgsdvtoj3Zbrdwu90Ih8Ow2WwIh8Pw+Xz3vqx7G5zE7Pf7QspYLcpkMohGozg8PMTjx4/x0Ucf4ZNPPkE0GoXL5cJms8FisUAkEpG2rnojdYLH40E2m0UymcSjR4/Q7XbR6/XQ7XbhdrtlIo0nDvWEzSlePri6Ldoc5KAYnAJRTt8Fg0ERwKqEXFcsl0sYhoHhcCjTSZFIBIFAAKFQCOFwGKFQSCaz2O5QWzi6wmaziYbO7XbLgs3Kh9vtljY+K2aLxUIGPigg17EqzslyANKuTiQS2Gw2KJfLWC6X6HQ6mM/nImGZTqeiq3W73fD5fNLyUv999x1qpfQ2Ied18NnweDxCUFg9YmtTrZh5vV4tiAr3JWqtqJWs1+uIx+MIBoM4PDxENptFOBxGOByW6tLV1RXK5TKePHmC8Xgsk/q63Hun0ykHLq/XK92/w8ND+e9erxfT6VRkKny21Une9/Gu34mYqQsXR+MXiwUGg4HoMyico3gul8uhWCwKYdEJvF5eM6uAkUgE6XQaBwcHePz4MQ4PDxGPx6UqqPaq3W43NpsNxuOxMPRIJPKhL+2twZI3bTEo8u/1ephMJlIl4AvO0q/X65WWFxd8ncgLX0AuYDabTSqgqndXKBSSirHO4GTZbDbb0dWorTvdSMePhcfjQSQSkeecJHyxWMBms6HZbOIPf/gDHA6HjNaPx2MMh0N4vV4cHx/LBqb7c8ANh5qam5sbvHjxAqVSCfV6HZPJBHa7HalUCvl8Hvl8Hh9//DGOj48RjUbFw1CHLomqrwsGgygUCnj+/DmWyyUymQzsdjsajQYcDoeQ781mI1WxSCQilhkUj+uyzquDD9QLk1ByECQcDiMSiSAajYp1RDQalX1+vV5jNBqhWq1KuzcQCGhRMSRu6wxHo5EM8F1dXeHy8hKdTgeLxQLb7VYKUiTodyWi74WYeTyeHWI2HA7Fw8rj8SAajcoidXJygkKhIA+vTlBL3NSYqMSsWCzi0aNHKBaLSCQS8Hg83yFm6hi23W5HJBLRrhevPrTq1J4qeGWlYLFYSJVRFY2uVivEYjERWeoEVU/ASVNWhPkJhULi96QzqCez2WzIZDJS8aTQny2thwgSM6fTic1mI5usYRhCzGazGTabjSzatMsgMctkMtoTs9ubVL/fR6lUwosXL1CtVmX60uVyIZlM4pNPPsHPfvYz5PN5aQNRb6ZDl0Rd5wOBAAqFAp49ewa32y3yk0ajsdPqZouTxMXlcsk6PxwOtVnnOVU/n8+xWCyEdHLSkK1bErNYLCadIB5GScw4tZhMJrVp5apQNfStVguNRgP1el2Mtfv9vvys2+1GKBTaqZzeBXdaLXizSMxsNpsQs36/LxqFZDIpfeknT54gkUhoWzHji8hr9vv9Yo9wcHCAR48eIZPJSNmTAxEkZjQiHY/HAF4bFepQ4lbBhZpt7NsVs+l0ulMxm0wm6PV6Ow8sX3a2xHTBbVJKkm6324WQcZHSpYT/Q/D7/UIu1I1lvV6LpoQt64cGVnko8Fd9jQzDQKPRwMXFhUwgW5YFl8slhCSfzz+oihmf+V6vh3K5jK+++gqdTke0V5xE/+STT/DrX/9ahkL8fr9ozXR4H7jO2+12qZjZ7XYkEgkxGK7VagAghtnA62l1n8+3Q8yov9Vlnec9pm6cxAyA7PVc42KxGOLxOEzTlDWPmjO1Yma327Va44HvrvOtVgvffPMNXrx4IWbyk8lE7j0rZmzff1BipmrMOB7ND31OgNd+QDRhC4fDCAQCUk3SCbf1BSRljCCJxWJS0uXpkJUjlod5sqJGRZepJfUeU3M0GAwwGAzEFbtSqaDT6cAwDKkm8e9Si0KRMCfWdLMR4AvL+6oeTux2u7Qv+v2+VMxI3PjRCdRcUCdJ0Ltrs9mIuzsAscdhy1pn8H6p07b5fB6GYaBWq0mVmKJ3Wgmk02l4vV6ZzL2r2eR9gLr2qeSKLc71eg2bzbYzAKN7BBuvJxwOS7tKBU1kSTrYDQAgRqRs/76vab2fAiopUTVT3PM49MP3nMUHHrJVayxqTXUgpSpIzilZ4sFK1YzymR6Px6hWq3j58qW0d1Ud/btUiu9sl0GRI40E6fr+poukaJYsWofT0/eBvfN4PA7LskQYyUkkdczaNE2pGnFsmqVgnibvO6gdpIFovV6XD60UmALA8XBuzOoINkmqrlYhAKSdx4oZyRnbe41GQ/SH/JCg6bZJ2e12bLdbOJ3OnXvFSTXaY3ACl619bkw6v+MqXC4XwuEwstmsbDqNRgOz2UwqxNQZcaOi5kTHQ+htvEnCwTbWfD7/0L/eXw1sT/Le2mw2hEIhZLNZIWsejwer1UrWf5vNpmUk0Q+BEYOcRGZxhXs5deZ0XGB7V1fnAV5TIBDAcrmUicxkMikHcuD199LpdPDixQvMZjMcHBygWCzi4OBAkgDe5Tl4L8TMMAyMx2OJaHnTTVCnmdQpF13B00MsFsNmsxFixioYT5acxqTOiotaIBAQYqZDZUF1QS+Xy7i4uMDFxQWur69FKEpDSQrC1VFzy7JEX6f6vej2wqoVM7ZrefBYrVYYjUZoNBrS+kgkErK4q95QukBt69y+V2oKgmma8lzHYjHJjXsocDqdCIfDMn1L8TfXPn43rKqw5fO+xMAfEqp1htvtFiF7IpEQUqZ2SB4SqI1Sq0XZbHYnycDhcGA+n8t9Xi6XO3vdQ4FqEaN2vW4/F6wU8UCu4wGc2nDuZdFoFPF4XAoxAGRv73a7MAwDpVIJn3zyCQzDkAM4CxRvq6N+J2LGX5aTShynpW0A+8z8OZ/PJ20e1SFdt5t1W/zPsXCW7N9UEaEJI4lZIpFAMBgUY0Jd2hxq1mmlUkGpVML19TUuLi52BOHq9xIIBHYmeNjGZpaYbtWjN0Edk2b1iCJhwzAwm80wn8+lvK2GGuukueHvqU5hqlYam81m58DB9uebpjZ1uW4Vaqi3aqXARZf/nMH1XB852caYGrZHdbx+knSfzyd5sKwWzmaznaxcTq5S9qErWCniIeP7tFKMour3+wgEAnLt6v3W7Z6/Ceo7oK4JqkMDSRmfe53auIQaR2az2RCNRpFOp5HP53cmTYfDoRQtarUa3G63DAPycErT2rfBW78xagwFjTZfvnyJs7MzDIdDuN1uPH78WKonNKR0OBzo9/v4+uuvUSgUkM/ntdOh8PQUDAZlQeJ3kE6nYRjGdzRTrK4wmsjv92O9Xn/HWfq+g4svg4ltNhsikYhEzpCAeDwead8x+Pjg4EAmcUlOYrGYxDnpBHUSmb8747h4GOF31Ww2xRKkWCzi6OgIh4eHMvjC6qouUKdRF4uF+FdNJhM5XPD9UL28SM7oaUTLGZ1I+WKxQKPRQKlUws3NjTihn56ein6WnmaBQACDwQC/+93vcHx8LKQlHo/Lz+m07qlgmy4WiyGXy2E+n8uhbbPZyBrRbDblMKaLTcZdcHudVw/sOq3zPwTVx5MtOr/fL7FbbGuzm7BcLhGJRERbrRtIQt1uNxKJBI6OjuByuXBwcCADQIysajQaQsxUm5RAIPBO+rq33hVUZ2COxH7zzTf44x//KC7gp6enUili2c/hcKDX6wlZozhWJ5BBbzYbeL1e8fSpVCo4PDyEYRjfORnw5EwdXiQSkR61Tlo71RaDTtYkGJw84vei5os9ffoUT548wZMnT8SzTDVl1M0qQz1J+Xw+qQ5wIeJ4NY1WSUjog0RvMx5WdCJmqnedOswxHo/FLoetDrY52Mrghy0CVhN0wWKxQLPZxMuXL/HnP/9ZdIUnJycieuYkbrvdRrfbxatXr4SUsWvACU/diVk8Hkc+n8d0OsVwOESj0ZAUACajABDTZV2v98fi9jrPATA1JUCHdf77wOsjGQkEAqK7U6c45/O5RJSRyJmmqR0xU9v3NptNYiapq2Q1cDAY4OXLl3C5XJhOp3C5XOJ7NhwOEY1G38km5Z2IGdkxR2K/+eYb/P73v8cnn3yCRCKB09NTuN1uqaLwwwR6j8eDZDKpha+LCnpuMXJErZixz/ymihlf2NFotCMepAZBhxeWLyXDWymGjsViO1M4bHMkk0kUi0U8efIEP//5z/Hzn/98RzCp/qkTbnv3kZgxgoqLExMQmAxBR3hGGVFcqhMxVSPYOHE1nU4lIxD4tmKm6m1YZaP2hpuVTlCJ2b//+7/j+PgYR0dHODo6kqiaRCIBy7Lw7//+7zg/P8cXX3whh9BEIiHaG5JYXcGKWT6fx2g0QrPZhMfjkXWekgfKGXTblN8Ft9d5Rlk9FGIGfLsHsF3LQ5Yay0h9Fa1jOLGt21QmANmvHA4HEokE4vG4yDL4J6fvZ7OZVNGpNR4Oh5jNZj9NxYyEgmHGsVgMmUwG+XweoVAI2+0Ww+FQxkUpjq/X61JJ6PV6aLfbqNfrMsFEk877DFa5gG89XfhZLBZotVq4uLhAp9ORvzMajVAqldBoNNDv95HP5wFAetS6aMxCoRAKhQIMwxABJINr+TJuNhs4nU7EYjEUi0Wcnp4inU5LlUD3hQmAWCckk0kUCgUR/dN0lDoLVXNos9lgWRam0yk6nY5Elum2QdOXqdvtotVqoV6vYzAYSLpDq9XC9fU1VquVGO3S54cIhULIZDJIp9PakVJWCmmPQcJFq5xYLCZDEKwOUyDMfElOa+oMXk+r1UKn08FkMoFlWTu6Kp26AT+E1Wq1U1xQoV7naDRCp9NBp9PBYDCQaXsKxtVM1fsMFh9YBWZXw+FwiDzj/Pwco9FIZCnr9RrffPONmK7G43HEYjGkUikcHh7KoeS+g4dHdVCNGjlKT1wul0g5FosF2u32zv7OtB9W0N81/eWdiRnHhrlBnZyciOi/1+uJJUQ+n4fT6YRpmvIS9/t9tFot1Go12O12RKNRmey4z6D4lb8rPU68Xi9M00S73cbZ2Rn8fr/8nel0iuvra9RqNfR6PbEVoPhfF51RKBRCsViE2+2WTES6WtM8drVaCTE7PDzE6ekpstnsdzZnnUFilkqlUCwWpVKoel5xuME0TamKrlYrTKdTdLtdRCIRiW7RCSRm1WoVpVIJtVoN/X4fpmliu92i2WzKJqVmy1Fj43Q6kU6nsV6vEQgEEI/HP/Ql/WioLuDcoL1er0xr0b9osVjI1Brbl9Rg8cCqY/WA2G630q6s1+totVoYjUbS/eDh5KHYIq1WKxlwGwwGO/9MHQKbTCZisN3tdpFMJuHxeBCPx5FMJsXf676D69d6vZYhNe5R0+kUjUYDANBsNhEIBGTPv7m5wc3NDXq9nmgLWVFOJpNaEDMOM/HDrsBsNpO4qUAgIAcTBrxfXFzI/h6Px8Vs/C52WG/9N0hMVNPFQqEgjvamaUqp0+PxIJ/PIxAIoN1uw2637xAz9m1Z9r3vUKds1MQDr9cr7Hmz2ewQzNlsJmLhbrcrizqJmS5aG+qG0um0tC+azSZsNpvoKWglEI1GcXBwgJOTE/G00nlxVnG7YsYqmOrnw+diNpvB4/EIaeXP0hNHt1Y+iVmtVsP5+bnE8VBL2mw2MZ/PpaTPTZnviMfjEU0WK8e6QG3jMh/P6/WK+zlzUg3DkOlUtrLYKRgOh1pKOFSQmPX7fdTrdbTbbUwmE8mNVadWdRvweBMo5G42m+L2T9C7i23cdruNTqeDbrcrrbxEIoFkMimH8PsODrix8EITWbViNh6P5X3m5HWv15PP8fExQqEQDg8PcXR0hEQiocWQlzrgRuI1HA4xHo8lfioWi2EymUg8U71eF3P1breLg4MDsda5ix3WO5Vq+LL5fD4kEgkcHBxguVzKhfBkQVNSeh3R64ztzHq9LpuYLqdnEgyKWuPxOHK5nDy49XodwLc9aNM0RRSYTqcRi8V2TPh0AVtzgUAADodjZwqLZDQSicgJMZVKIZVKCTF5KHA4HFIxY7QWI8j4PdDtW81JpU0Ix8l1rCSoVT/qKlVzVZvNhtVqJZEkPMSxpRkKhSTiRTerHNUmIplMioa22WxitVrJ4BNJC6uC6odxLTpIF4BdWyQmdoxGI5yfn8thczgcwjRNyYylRINtHN0nMmkBQv0YK6amae44wzNDdLPZyPQ5W3081Opw31X9JztilKWoViimacrPUqrBg3s6nUY2m0U2m0UqlRILmfsOdXrcsixMJhN0u12JYOr1egiFQrIGcshnNpvB7XYjmUxKClAymfxpK2YqOEa6Wq3g9Xqlxx4MBrHdbmXhXi6XuL6+RrPZFKLS6/XkRU4kEjBN8y6/yk8OTmjQLmIymcjNsyxLvL2oJSoUCnj8+DGePn2qnb7mNhjOS/uA6XQKr9eLQqGAYrGIVCqFcDgstgA6LEg/Fk6nE6FQCKlUCtvtVuKlOCYPQCbxAMjJkkQ1k8nICVqH1oYK6qXG4zH6/b4MNaiZcsBrSx0SEFYV2PLT9dq51h0fH6PX68Hv96Pb7eL//b//J5YoNKTs9XqiwUkmk8jn8zg6OkKhUNDGIkb15xuPx7i+vsbV1RWur68lL1INcWdbl/c5kUjIGqAzMVOHffx+vyS4tFqtnel64PW66Pf78eTJExwcHCCTyQgp02UdVE2wmRP6/PlzOBwODAYD9Pt9DAaDnaovZUuctH/27JlUyngg1aEIwU4YD9CbzUZIWK/Xk8M0hx85ncnrLhQK+Oijj3B0dIR0Ov3TV8wIt9uNeDwuIthGoyEnpVarhV6vJ2JIlgQplOaFBgIBFAoF7Zyj3W43otEoCoUCNpsNSqWSZCSyikDCenJygnw+j5OTExwfHyOdTmtxgvg+UHfRarVQKpXkYS4WizvEjBobnRfm22ArE3jt7k4NAoc/WAZfLBbSzvL7/Ugmk0in08hkMkgkEtqIgVWs1+udFIvpdCr6Mp4yeSDhQkxrEJIUXa/d5XIhHo+LLQ4HmPr9voiD1VY2K6XJZBLZbBZHR0c7G/V9B+8pJ8wuLy/x7//+7/jiiy8wmUwk0B2AVI3YvonFYjubsg6E5PvAqhD95xjifn19DcMwhMCyWsQD2OHhIVKplHRHdFkH1cl57s0AEIlEcHNzg1KpJHs4nw9Wi/L5PPL5PI6Pj3FwcCBm6mxv33fQxoYFhfV6Le869WaUIrFVz64hDyOnp6c7xIw/97a4EzFjL5VRJRyR3263YrB2fn6OSqWy8/dYXaDpIqd6dAIrJ+l0GjabDf1+H8DrKUyeKJbLJcLhMJxOJzKZDJ49e4ZUKiUWE7pCbWk1Gg3ZcOPxODKZDGKxmDaaircFXZ+ZG0fDXVaG2e4wDEPauEw+oB5Jp4xUFcx9pW0Ks3Fph6CSM957Ll7hcFiu/V0nlT4kaA9DU1XDMDAYDPD1119jOp3Kz3m9XsnLi8ViiEajSKVS0tZRXeTvM9Qga8MwUKlU8NVXX+F//+//vfNzbM/SGoPTaEy4oIZYV7CCRE2xqrNU13maKheLRRweHiKbzSIWi4l4XieQnPn9fqTTaXi9XhG1z+dzdLtduW4ewqgrfvr0qbQxGc+kC1QSTucJ2r+oGjqn0yntaurnMpkMTk5OcHBwgFwuJxGN7/y7vMfr2mOPPfbYY4899tjjDrBtdVPh7rHHHnvsscceezxQ7Ctme+yxxx577LHHHvcEe2K2xx577LHHHnvscU+wJ2Z77LHHHnvsscce9wR7YrbHHnvssccee+xxT3CnmXU6XzebTTQaDXz55Zf44x//iC+//BKHh4f49a9/jc8//xw/+9nP4PP54Pf7tRqf/bFYr9c4OzvD+fk5zs7O8OrVK5yfn+PVq1doNpvf+/cKhQL+9m//Fp9++imePn0KAPjv//2//0S/9duB5rm9Xg+Xl5f44osv8Lvf/Q5/+MMfJNDcsixxRw+HwygUCvjHf/xH/PKXv8Q//uM/yhiyblEtNE9dLpeo1Wr43e9+hy+++AJ/+MMfvvfnXC6XjI1ns1kcHx/j5OQEJycnYpeii+nkm3A7huXq6goXFxc4OzvDYrHYue863evvw2QyQbVaRbVaRblc3nnfe72ehFxvNhuxi4hEIvjP//k/49e//jV+/etfaxdD9X2gofJsNsPvf/97/M//+T/xP/7H/8Cf//xnCXp2Op34h3/4B/zyl7/Er371K5ycnIi1UiAQ+NCX8KPRbDblff/9738v+12z2YTf75f3++TkBJ999pl8HsIzr177l19+KffV5XKJNdB8Pofb7caTJ0/w0Ucf4enTp0gmk+LrpYOZ8l/CarXCb37zG/msViuxhyoWi3j27BmeP3+O58+fvzdrGP2fnj322GOPPfbYY48HgjtVzJgFGAwGJZLB4XCIMdtoNJJMqVgsJvFEDwHr9RqmaUp2WqvVksrhfD6H3+/H0dERYrEY1us1VqsV1uu1nKwNw5DA2FAohGg0+qEv6QexXC7FVHM4HMIwDKzXa7hcLjEWZiC70+mEZVliREqDvmAwKFl6Op0oeV2MGGIAfTQalXy17XaLxWIh95nZscDrzFjmwZqmifV6DYfDoV1epIrtdovZbIZer4dKpYJarYZms4ler4flcol2u41Go4GbmxvJieT6oCOYf8p3d7vdwuPxIBqNwuFwyFqwWq3E7ZuRLTqHlt/GdrvdCWXv9/sSYg5ATGkBiPnwarXSKiOVjvbr9RrT6VQiyHq9HkzThNPpRCwWk3VvPp9jMBhIGkSr1ZKILo/Ho9VadxtMNmAe7mq1krQP4HX6idPpxGw2Q6VSwXw+x/HxMVarlRhxc/3UCbxWvvOTyUSc/51Op5juplIpMVN+n7gzMSOxWK1WCAQCEmVAx9xWq4VYLAYAD6KsSdD9nk7/bHPUajWYpikB5zabTXK1TNNEp9ORzctut8Pv9yMUCiGRSHzoS/pBMNS11+tJcCs3Ia/XKx/e+8ViId9Np9NBs9lEIpGQsGOdnN9VwunxeBAIBCSwnRFEq9VK7rVlWZjP51gul5hMJmi325ISMZ/PsVqtHgQxm06naDabuLq6ws3NDarVKtrtNlarFer1OkqlEiKRCFKpFJLJpNatW6YazGYzjMdjrNdr+Hw+id1hO980TSwWC3kHSMQ3m82HvoT3BhKRRqOBVqu1k9yyXq8l/YUHFG7q6/Vai2d+tVrJvSP57HQ6aLfbsNlsErvF7EQeUNrtNprNJqrVKmKxGCKRiBzmdIQay7VcLnfuncPhkJgqp9OJ8Xgs0V2z2QxOpxOpVGrnMKYTOWMusGEYmEwmkvDCogvf/Vwuh1gs9t4LTnfaHVnxASC5l06ncyfKoNFoSDQDKww6R3QQPE11Oh00Gg1UKhVUKhWUy2UEAgEcHBzg8PAQ0WgU0+lUPh6PB+v1GsPhUOJqotGoNsSMga6TyUSid1RNjWEY6HQ6kovKRa3VagkpC4fDH/py3gqMZWEWot/vRyQSQTKZlExUnrBHo5FkyRmGIf+OdDqNo6MjIWYul0uLTer7QGLWarVwcXGBSqUi1QIAqNfrkpdqWZbEtuh6OFOJ2XQ6xXa7hdfrRTKZFMLBxbzT6chJ27IsrFYrre+1iu12i/l8jn6/j3q9jna7jfF4LBUzVhYZ9kzNJcmZDt8DCTYP3sx8brVaEtQejUali8CKYavVQr1eRzwel8NXIBDQlpgB31ZAGbXGdY4VQZ/PB4fDgXa7jXa7jU6nA4fDgWQyiSdPnkhRRrc9X80F7vV6kvVtGIZU/1OpFPL5vBCz93mNd66YsZwLfJubZrfbZXPq9/totVpIJBJYLBbv5Ze+D1Bv3GAwwGw2k9ZeKBSS7KxUKiXB1v1+H4ZhYDgcSrg18+UikciHvqQfBMkJT0rRaFQIRigUQjgcRigUQq/Xw3g8lo1ssVjs5KrpWjlgq9btdiMcDiOVSmE2m8GyLJimKZtPv9+XZ4PYbreysLH6pttCBezmJ7Ka0G63UavV0Gg0MBwOMZ1OYbfbpdXV6XQQjUaRTqelxaUjbDabBBzfzsBTq2QApIWlDrvoeL8Jvsskm41GA7VaDaVSCfV6HcPhcCfrmGsFW7rqoIsO34P6nJNQct2iJCGXy2GxWKDb7cq7PZvNMBgM0Gq1pJOk+zPvdDolpH6xWEheLgswvJ+z2QydTgelUglPnjzZIes6Qg0wbzQacuBmMYWklAH17BRxj7zrGn/nfhJbPAx5ZXkTeF1lGY/HGAwGEvL8UHD75Q0GgygUCtLCPD4+RrFYRCQSkRtnmqaQsFgsJuXuYDB477V3fr8fyWQSlmUhEAigWCxiPB7vBDjzOrvdLpxOJ9brtSxQiUQC4XBYHmRd4Xa7EY1GUSgU4PF4ZDqNBJQnxu12uzOJzFDjYDAoG7Zu3wMrQupJkp/xeAzTNAFANuWHBLfbjXg8jsPDw+9U/brdLqrVqlST1+s1QqEQ3G43kskkgsGg1t+HZVno9/vo9/vodru4uLjAxcUFzs/PUa/X0ev15CBCAuZyuRAMBiW8nhpkHQLc7Xa7kPBQKIR4PI5MJiPh9IVCAcViEfP5XOQ7nE5cLpfo9XqIxWJCZHSFw+GA3+9HLBZDNpvFcDjEaDQS7eByuZR3nuQU2A1+dzqd2hByFcvlEqPRCM1mE6VSCZPJBE6nE9lsFolEAh6PB5ZlYTQa7egoVVnPXWQbd1otVFEfReBerxc+n0+EsKwoGYYBy7IeTCsT+FYgSH1dIBBAoVBAIpFALpdDPp+Hz+eTU8ZsNpPFiuO24XBYRPH3Geypu91upFIpmKYp1SK2aSmQ9Pl8cDqdWK1WO8QsFAppT8xcLhei0ahs1Lzu2WwGwzBQqVSEoPp8PsRiMSSTSWQyGcTjcQSDQXlpdfseWAlUxdDdbhfdbleqpACEdOqqJ3sT3G63LMipVGrnn11dXaHX64nWyOfzIRgMIp1OI5VKIRgMav1dkJiVSiWUSiVcXV3Jh12AxWIhG7LX64Xb7ZYBmXg8jmg0Khv1fYdKzILBoBCTwWCAw8NDHB0d4fDwEIZhSMdoOBzC6/XKdzWZTB4UMctkMgBeExa28pfLpRByaiyB71ZM2W3QCavVCuPxGM1mEzc3N1gsFnC73chkMojFYnKvWUlbLpewLAuhUAjA6/XigxEzYLfFpZKz1WoluiS2ONRpjocCXg89esLhMGKxmPi4OBwOmepQ9UlcrCKRCAKBwL3X3pBshcPhncVms9nsePt0u135WU7thkKhnWqRzpuU0+kUPd12uxXR62g0QqvVgt/vF/EzJ3eKxSLS6TRisRgCgcB7n+D5qbBerzGbzaRFSe3FaDSSKWO2rW4vyDouzipIyDk9rU7jcoOmdCObzcLv9yOTycjEFsXw/B50+C7UaeNut4tSqYSvv/4apVIJ19fXKJVKOy17dk/UyWWuhzrpSvkcA5AOSC6Xg2EY4kd4fHyM8XgM4DVZ4ZrGaezJZCKDH7qCxIwVQ9M0MZlMYLPZRHe2WCyEpAHYef9ZLdMJ3M9JulgxY+ElGo0iGAzC7XZjsVhIG5+dBOA1KWMl9V1xJ2LGxYYnaRrOzedzaV2apgm32y02C+PxWEZsOUqrI0hSstmsiDzZtmIFjFWj6XQq7Q7DMOByuZBKpXYW7vsOEnAu1gQXntVqJZOIwOuHk+SEppM6Von+EizLwnQ6xWAwwGg0wnw+FzsMj8cjrRxd27i835vNBoZhoNFoSLWk1WrB6XTi6OgI0+kUlmXBsqzvLM46tm1vQ13r+E7z8+rVK1SrVakesZ05m81gs9mkypDP5xGPx+V5uM/gRBrv+fX1tZCxVqsFwzC+Uw1SJ5e9Xq+s87qt8epaRxPZ5XKJQCCAdDotVZPNZiN6I2qubq+POsPpdEq3Y7lcYjweo16vwzAM0Q0vl0t4PB4xFT89PcXHH3+MbDarxb6mgh2gxWKxU2xot9vI5XJIJBKy3y+XS5EuqEUZuhRQvkCt2du+A3cmZqyMUWtDcsbyNhdplZiRwLDSpiNcLhfC4TBsNhtCoZBUC1WtndPphGmaMqlYrVbl59LpNLLZLCKRiBZTO6plhLrwcMNVR8yBb9tZKjFTH9SHAIr66e/GqR1aoTyENq5KSLhJv3r1Cn/605+w3W7hcDhweHiI6XQq7zcAqZyohFzn+66udbS94cJ9cXGBarUqWlp+V/1+X0hZv9/H8fExHj16JGvHfcZisRBN2c3NzQ4xY/t+tVrt/B2KxV0uF/x+P9xut7bEjGsdyRglHMFgEKFQCKFQCKZp7lwn35OHRMzYknc4HKhWq7Db7eLnpU4c0xInkUjgk08+0ZaYTSYTqZS1Wi35JBIJ+Hw+ZDIZrFYr6Q7NZjPEYjEkEgmsVispzFiWJd2hd9HYvRdiRv0UJ5NYMeNibLfbhZiNRiMA3/qg6ErMWDELBAIynaKarPLaWWno9/uoVCpy4kqlUkLMdHiAVcsIFWrFjAarwLclbY/H86ArZtTY9fv9nYqZar6sDj7o9ryrXkaTyUSI2e9//3vkcjkUCgXkcrkdKxhObXGTfgj3/fZa1263cX19jaurK1xfX6NarYrX0Ww2k/ffMAx0u12Z7HK5XEin0x/6cv4iaIlRrVaFlPHD5+FN+ilWzPx+v/YVM/pMcoiDFhj8TKdTqQyybcX35SGAxMzlcsHn8+Gbb76B3W4Xyxjqq/lzp6enePbsmcRU6bCvqaDvZLfbFcN4VswePXoEr9eLbDaLyWSCer2OTqeDTqcjnp6bzUbanaZpioXGu6x9dyZmfBA3m43cIPq48IQJQKpq4/FYNmydH2CeDilmZdmfQw4s8/b7fbx8+RKlUgmdTkdOk7Sb0O2ERbLJ6zNNE71eT4wVh8Mh7Ha7mAum02mEw+EHUzm5DVVDpf5v3Mi5mevqZ0WLBFoBsHw/GAwQjUalnUNPOxJP0zQxHo/RbrdFZ5RIJER3opv+xLIsuX6SMn5arZZMJ7OFwcMIJxJDoZBs8jqQVNVMORaLIZ/PY7FYSKKB+mH7h+/A+7IM+NBQ9dNvgt1ulyQEaq34v79rC+s+gRYQdFZg4gvXMq5ndrsdoVAIqVRKvDtDoZAWgx4qaPnBCjEtgFQ7HNVwttvtotPp7HgccuiJ3TJWV9/WdeG9fXM2mw2BQADJZBKHh4fweDwwDENOFcC3i7XP55NK00PAZrPBYDBAvV5HrVaTYQdWypgK0O12xf+EYd+z2Uw7GxHqZ2jAWKlUcH19jfPz8x3rkHQ6jWKxuDORpftifRsUCtPThlNanMSlrUQ8HsdisdBODMxTJBMcxuOx+Ld9n6ZmvV5jMpmg1WrBNM2dtATGsukWz2SaJtrtNi4uLnB5eYl6vS4ftq8peFc/nMpNJBI4ODhAJpPRIsSbgyu8X/F4HAcHB3j+/PmOTUqn05EJXdXL7D8CWB3mWqiGfD+Etc6yLPR6PdTrdVSrVVQqFQyHQxliYWGCFdJIJIJEIiHDbDq93wCkEk4rmEajIVpKTlzX63WMRiN0Oh0MBgMMBgNZIyld4Gc2myGbzYr32dvgvVJaWiocHR3B7Xaj2+3uPJz0NWOEiW7Vg+/DdrvFYDCQqaV6vY5ut4t+v7/T3jAMQ+xEfD4fksmklEF1AokZN+tqtYpSqYTz83MEAgGEw2EUCgWcnp6iUCggFouJ5kznhepNYEueImAOtNx2js5ms1qOz/Od5elQzUn9vmov256WZWE4HAopo93EdruVqrkuME0TrVYLZ2dn+MMf/rBDTgCIYXQwGBQiFo/H5T8nEgkZ+Lnv1jjAt8SM13N0dCT64XK5jJubG5TLZbkWVlL+I4HvOIdAeNh4KO17dnxubm7w6tUrVCoVISLAt5XB2zF11Nzpdv2GYYhm9Pz8HL1eTyrhjCGr1+vSCWAVUSXl7I7QcJxT/G+LO/uYqZ4lkUgE6XRaTsnM0WIky3w+R7fbFX8b3aoH3weOzLdaLVxdXcnkUqvVwmg0kpMFS7uqDk1HssKptE6ng0qlgnq9Lrl52WxWevFMPtCxrP1jwFOjz+fbST9gNBXw+mVvt9vIZrMYjUaYTqdiJ6LD4s3hBkZrjcdjMdM1TVP0pGxrqfFUHB/P5/MYj8eYz+fib6jboUz1cOv1emK4Sv+qcDgs6x81NtlsFvF4/Dtm0joQUmpEb2Oz2ciwE+OZGL33HwGU7bDNZxgGxuMxxuOxDAuok5q6re0qaLLKSexeryeenTabbec6uZbpuqcB34r/mYtK3bwaQ1ar1USHxq4Y25d2ux3RaFSSfpit+S6FlzsTM+opACCZTAKACODoXzOZTOD3+2EYBq6vr+H1ekVj9VCgpgCouXAcEqD3FT1wjo+PcXh4iFwup0VrQwW9emq1Gs7OztBqtcT5OhQKIRqNIpVKIZVKIRKJ3HuPtrvA6/XKiahYLEpFiaXrxWKBarUqnnXhcFiMCKk7us9YrVbiXUa/MgYaM8ScPkY8QVKLxMEPnqhDodCOwa5O8Hg8SCaTOD09xWKxQKVSgcvlwmq1Eq8nmkrzz3w+j2AwiEAgIOkePKzqDG6+qqu7jhvxu0AdcGu1Wmi32+h2uxiNRgiHw/B4PFoP+6hQDyODwQAA5NpUHz8GuXc6HVxfX4umkgReF6jGwjSQpaaSB2z6+g0GA5imKQMilGfkcjnkcjkUi8U7Dfe9F2LGP5PJpET38IQYCoXQbrdFGN9qtRCLxVAsFh8sMVPbPKwkFgoF5PN5PHr0SD70MdORmA2HQ1SrVVxeXqLT6Yg1CnU1qVQK6XRarEMeImw2m3iVeTweaWtYlgW32y1TO91uV4g5bRIymYwMgtxnMKiZp0ASMwBS5jcMQ6Zy5/M5NpuNXJfa6iUx03Eam5N5p6enoiPiQADbfdlsViJ7+CE55TAA21w6Q7XF0KXy+z5AoT+JSqvV2jFazuVy0gLW1R5HBSPYeL2q9Y8a4M7p43a7jVKphEwmI5F0OhEztqFJzBaLhdy/2WwGAFIBMwwDpmnC4XBIwks0Gt1ZA7i/v4sd1nshZjwBskqw3W6FWfOkyOmlUqmEfD4vUQYPBfR7UgO7bxOzJ0+e4OnTp/JnNBrV8rRJ7VC1WsXZ2ZlM3zLQXK2Y6XZtbwu+xLz/1Nk4HA5YloVarYZqtSpVk0AgIAvAfQ+uByAi1sFggH6/L8J/AKKtuQ0SEJ/PJ0kgasVMl2geFYxicrvdiMVicjip1WoIBAISw8Y8xYODAxwcHLxxald3qJNnumYhvisWi4WIv9vtthy8hsOhRNDpbCit4nb7nsTs6OgIlmWh1WrBZrPJgYw5wcBrLpBIJD7wFbwdblfM1AlqFpZug4MP0WgUmUxGquWsmPHQ/ra4s10GK0U0VTQMA7PZ7DteIKoYnp9er/fGXzoej9/l1/rJwX57LBZDLpcTVj0ej7HdbhGNRpHP5/H06VMUi0XE43F4PB6tXlpqiizLksWIbvfUE9LXjS/t7UkU9VlhpUmHdp4K1QaD+WimacI0TdTrdYmrubm5kQmmYDAowfXJZBLxePzOkR0/FQKBALLZLJ4+fQq/34+DgwOcnp6i3+/v+FmNRiMMBgPRoUQiERSLReTzeRwcHCCZTO5Edem2kdMihm0NNeWEBJQpD6FQSLv3+21AXSUPGqyAqpPINpsN7XYbjUYDlUpFqqjUJd1nqG06xu6wTc8qOC1T6vW6mAqPx2PRY3FSczqdIhaLwe/3axG9p65raoV8u93C7XaLm8B8PsdoNILdbsdms5F0G07l0/dNJ4TDYclAjUQiwln6/T6m06kY6DPEnXq7XC6Hx48f4/Hjx1IpTyQSd5JtvDfnf46T88MLovdPq9WScFc+6IPB4EEQM+B15YR+P7QJcbvdWC6XiEQiyOfzePLkyU6gr06gMJI2CCzfcyKFYbeBQEBIyu0pLVYTLcuSyc1isagVMaNnzXw+l9MkP7VaTbytarWahP3SxyuZTCKdTiOZTGqjvwgGg8jn8xLgy2ebkWucQKpUKjg7OxPDXcoVPv74YxwfH4uvnc7EjIMQ4/EYk8lECFokEpH4GmbC6pDm8S6w2WxSDQ2FQkLM7Hb7zsAHD2fValVC3zmdqgMxW6/X2Gw2mEwmYgdUKpXEGqTf70vFjJv1aDRCvV6H0+ncydDNZDJIp9OSBnKfwRYl5Quctr096ESJAmMHKWeYTqdIJpNCVnUCiZnb7UY+n9+ZvFa5jRpJRWL20Ucf4dNPP5VqKfdCTqi+Le5MzFhJUcV/19fXwjK5mbMXz4eVp+s3bU5Pnjy5y6/1k8Nms4kAmG3aTqcjizOJ2dOnT8VSQbdWDokZDTZZMZtMJohEImL/4ff7hZj1+/2dfwen+BaLBVKpFOx2u+jRdAGrArPZbMdGotvtolwuy/PfbrdFIE3T5WQyiUwmg2Qyqc3mzd8zmUzKCDhPi2xhLBYL/PnPf8ZyuUSj0cBisUA8HsfR0ZE4gadSKamYAfq19lQTSRIzTmWtVitp46sL8kMFBztYIecGTQ0WB0aazSZqtRri8bhokYPB4If+9f8i1LSL6XSKcrmMP/7xj/jjH/8o3R6mfJimKRFsw+FQLBPUAxvNd/l83Geowz63NaUqIae+lpVSHti22y3y+bwkA+iEcDgMt9uNbDaL6XQqpKzb7Uo+MAAMh0NZ99gZ+Oijj/DZZ5/t7O985n9y538+vNyo+v2+pLEPBgMp989mM5noYom0Vqvh4uJCpj1U/PKXv7zLr/VBoIr/+dBGIhGs1/+/vTPoSRyKovCFJtA2tTQBSU3EGBJXbv1D/mYXunJl3BDAR6FSqDKrc3Mx6GRmnJk+cr6NLowRC++dd9+5575py3yWZd5eb+D1oTqKaglK34hLQCTEocHVttW80+n8divxv8ZOuMD7HKdlfJ1MJmoGxvBqnJx6vZ5cXl7K2dnZ3lWXDyZwLDCHqpoQJsj/6fV6el0FP0qe5xo6iYw3H4Egx8D6sizl7e1Nu9JxtYdn69vB61fodDqSJIleWSdJoiOYsAYiYmC5XOoMWTuyzReszwoRCcvlUl5fX2W322nHMYZXw3uI4fVFUch2u9UoDVQLm+6/gvcZTR5RFGks1HQ61VxO7HfwHLbbbf2s+7bXwRtrO+YRg2QnmaBbE3s8Dt2oin4H3zorc7FY6KY1m810lMF6vZayLHXA9cvLizw+Psr7+/vBE9Tt7e2f/Fn/HJtz8vz8rGOJBoOBhGGoG5PP2BZ5nAhQpq2qSj1HMEweuq7CAu7blRb8RXVdi3NOnp6edEYiTPHz+Vw3HnSmonUaPqvRaCRZlumpygdh9hm22QG5ZmgdR/cpMp18HWZtQW6Vc05ms5lWQRCojPd2GIbeP9uvaLVaKsxarZYeOtGBD7+lz2GzEBoionEvYRhKHMd7gar4Hle5WCdQQYJI22w2KlxwvddUYRYEgYRhKEmSSJqmGvWUJImGLH/0oKGRCT97cXGhoeK+gv9DmqZa7cRzRsMLfOV/wy/8bR4zCLPpdKp+ss1mszc3EqcldLDM53OvH57FJgMjVPb09FQ7FH3yUR3CnoogziDQMGrIObeXa/RxI8YAdxuw6sNmjQW3qioVZg8PD3J3d6fX9YvFQrrdrp62hsOhxqKMx2OtmCLXDeLVZ9CFjMpnEAQSx7Fst9s9EY4Kks+vF9EBGL2CVnqcqmGEj6LoKJ7tV2DsWLfbVc8sNm8IFJ+FGZ4d/HToLMasU1QFIdCQ+O+cU28ZrgPh2cLvQbDyzc3N/3yJn4LKH7yxEFsnJydS17U28+EGYbfbaS7p+fm5diT3+32v93b4AdFBb4VZEAQaf/O3rAt/LMzsqXm1Wql/zM6N+jiGBr6zY0mLtvk2k8lEqqrSE0Se55JlmYRh6IUI+QzclVtxBnGFiunPFmPrMfFpyDGu7OGzQ0fW/f29XtevVivp9/u6gA+HQxmNRnJ1dSXX19f6QfZdoFisYK3req+aYDvw7HBzX7Hmf+ecVv/ta0WL/bFjI5KweUOY2gYAn8FnFIdQVMZs44KtDqOSgiaAsiylKApxzsl6vdYKVNPfHxAdWKvxXOM4VtFZFIUeSmwUTp7nMh6PZTAYSJqmXl/nt9ttzR9EJAb+LxCvURRJmqYSx/G32zSOY4cghBBCCDkCWjvfhtYRQgghhBwprJgRQgghhDQECjNCCCGEkIZAYUYIIYQQ0hAozAghhBBCGgKFGSGEEEJIQ6AwI4QQQghpCBRmhBBCCCENgcKMEEIIIaQhUJgRQgghhDSEHxL3Qh1bYi0jAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x200 with 50 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates and saves Figure 9–13\n",
    "\n",
    "plt.figure(figsize=(8, 2))\n",
    "for index, X_representative_digit in enumerate(X_representative_digits):\n",
    "    plt.subplot(k // 10, 10, index + 1)\n",
    "    plt.imshow(X_representative_digit.reshape(8, 8), cmap=\"binary\",\n",
    "               interpolation=\"bilinear\")\n",
    "    plt.axis('off')\n",
    "\n",
    "save_fig(\"representative_images_plot\", tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "id": "Fq8e_dKw3Edh"
   },
   "outputs": [],
   "source": [
    "y_representative_digits = np.array([\n",
    "    9, 2, 6, 0, 1, 7, 3, 4, 5, 1,\n",
    "    9, 6, 1, 0, 1, 2, 7, 1, 7, 2,\n",
    "    1, 5, 9, 8, 8, 3, 5, 3, 2, 4,\n",
    "    8, 0, 7, 6, 2, 8, 3, 9, 0, 3,\n",
    "    1, 7, 2, 3, 4, 7, 1, 2, 8, 4\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZwARUyzU3Edh"
   },
   "source": [
    "Now we have a dataset with just 50 labeled instances, but instead of being completely random instances, each of them is a representative image of its cluster. Let's see if the performance is any better:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8VxwDZVd3Edh",
    "outputId": "792a3bd1-4691-43a4-8a0a-6346f2baf7bc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9288888888888889"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg = LogisticRegression(max_iter=10_000, random_state=42)\n",
    "log_reg.fit(X_representative_digits, y_representative_digits)\n",
    "log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zB0ZWRzb3Edi"
   },
   "source": [
    "Wow! We jumped from 82.7% accuracy to 92.9%, although we are still only training the model on 50 instances. Since it's often costly and painful to label instances, especially when it has to be done manually by experts, it's a good idea to make them label representative instances rather than just random instances."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MN_mxt0-3Edi"
   },
   "source": [
    "But perhaps we can go one step further: what if we propagated the labels to all the other instances in the same cluster?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "63Mj8CVL3Edi"
   },
   "outputs": [],
   "source": [
    "y_train_propagated = np.empty(len(X_train), dtype=np.int64)\n",
    "for i in range(k):\n",
    "    y_train_propagated[kmeans.labels_ == i] = y_representative_digits[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "id": "QdPfbDJf3Edi",
    "outputId": "08701ec9-7b0a-4146-ec86-2696e26fb5fc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       "#sk-container-id-7.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       "#sk-container-id-7.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-7 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-7 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-7 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=10000, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
       "            penalty\n",
       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
       "            C\n",
       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('l1_ratio',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
       "            l1_ratio\n",
       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
       "            dual\n",
       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
       "            intercept_scaling\n",
       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
       "            class_weight\n",
       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
       "            solver\n",
       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">10000</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
       "            warm_start\n",
       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling\n",
       "        .textContent.trim().split(' ')[0];\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "\n",
       "\n",
       "/**\n",
       " * Adapted from Skrub\n",
       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-7');</script></body>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=10000, random_state=42)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg = LogisticRegression(max_iter=10_000, random_state=42)\n",
    "log_reg.fit(X_train, y_train_propagated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Izfhc0xG3Edi",
    "outputId": "32f46612-4bad-4acb-beab-ee7604b51b74"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9377777777777778"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WrQrK9ku3Edi"
   },
   "source": [
    "We got another significant accuracy boost! Let's see if we can do even better by ignoring the 75% instances that are farthest from their cluster center: this should eliminate some outliers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "UIz3zMsx3Edi"
   },
   "outputs": [],
   "source": [
    "percentile_closest = 25\n",
    "\n",
    "X_cluster_dist = X_digits_dist[np.arange(len(X_train)), kmeans.labels_]\n",
    "for i in range(k):\n",
    "    in_cluster = (kmeans.labels_ == i)\n",
    "    cluster_dist = X_cluster_dist[in_cluster]\n",
    "    cutoff_distance = np.percentile(cluster_dist, percentile_closest)\n",
    "    above_cutoff = (X_cluster_dist > cutoff_distance)\n",
    "    X_cluster_dist[in_cluster & above_cutoff] = -1\n",
    "\n",
    "partially_propagated = (X_cluster_dist != -1)\n",
    "X_train_partially_propagated = X_train[partially_propagated]\n",
    "y_train_partially_propagated = y_train_propagated[partially_propagated]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sWmhKBgd3Edi",
    "outputId": "15552a95-141b-435e-f853-56f3369658ff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9422222222222222"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg = LogisticRegression(max_iter=10_000, random_state=42)\n",
    "log_reg.fit(X_train_partially_propagated, y_train_partially_propagated)\n",
    "log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kb8oV1Hh3Edj"
   },
   "source": [
    "Our propagated labels are actually pretty good: their accuracy is about 99.4%:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6fjX-D0Z3Edj",
    "outputId": "8606a2e7-59c5-4a85-f295-01aa7b0384f3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.9943820224719101)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_train_partially_propagated == y_train[partially_propagated]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2neFrkQb3Edj"
   },
   "source": [
    "You could now do a few iterations of *active learning*:\n",
    "1. Manually label the instances that the classifier is least sure about, if possible by picking them in distinct clusters.\n",
    "2. Train a new model with these additional labels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lqeOn2UO3Edj"
   },
   "source": [
    "## DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "id": "FpoogNNF3Edj",
    "outputId": "36f43fb7-f5e0-42c5-dcec-7cdf8ae49a39"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       "#sk-container-id-8.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       "#sk-container-id-8.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-8 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-8 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-8 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-8 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-8 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DBSCAN(eps=0.05)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DBSCAN</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.DBSCAN.html\">?<span>Documentation for DBSCAN</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('eps',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.DBSCAN.html#:~:text=eps,-float%2C%20default%3D0.5\">\n",
       "            eps\n",
       "            <span class=\"param-doc-description\">eps: float, default=0.5<br><br>The maximum distance between two samples for one to be considered<br>as in the neighborhood of the other. This is not a maximum bound<br>on the distances of points within a cluster. This is the most<br>important DBSCAN parameter to choose appropriately for your data set<br>and distance function. Smaller values generally lead to more clusters.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.05</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.DBSCAN.html#:~:text=min_samples,-int%2C%20default%3D5\">\n",
       "            min_samples\n",
       "            <span class=\"param-doc-description\">min_samples: int, default=5<br><br>The number of samples (or total weight) in a neighborhood for a point to<br>be considered as a core point. This includes the point itself. If<br>`min_samples` is set to a higher value, DBSCAN will find denser clusters,<br>whereas if it is set to a lower value, the found clusters will be more<br>sparse.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('metric',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.DBSCAN.html#:~:text=metric,-str%2C%20or%20callable%2C%20default%3D%27euclidean%27\">\n",
       "            metric\n",
       "            <span class=\"param-doc-description\">metric: str, or callable, default='euclidean'<br><br>The metric to use when calculating distance between instances in a<br>feature array. If metric is a string or callable, it must be one of<br>the options allowed by :func:`sklearn.metrics.pairwise_distances` for<br>its metric parameter.<br>If metric is \"precomputed\", X is assumed to be a distance matrix and<br>must be square. X may be a :term:`sparse graph`, in which<br>case only \"nonzero\" elements may be considered neighbors for DBSCAN.<br><br>.. versionadded:: 0.17<br>   metric *precomputed* to accept precomputed sparse matrix.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;euclidean&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('metric_params',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.DBSCAN.html#:~:text=metric_params,-dict%2C%20default%3DNone\">\n",
       "            metric_params\n",
       "            <span class=\"param-doc-description\">metric_params: dict, default=None<br><br>Additional keyword arguments for the metric function.<br><br>.. versionadded:: 0.19</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('algorithm',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.DBSCAN.html#:~:text=algorithm,-%7B%27auto%27%2C%20%27ball_tree%27%2C%20%27kd_tree%27%2C%20%27brute%27%7D%2C%20default%3D%27auto%27\">\n",
       "            algorithm\n",
       "            <span class=\"param-doc-description\">algorithm: {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'<br><br>The algorithm to be used by the NearestNeighbors module<br>to compute pointwise distances and find nearest neighbors.<br>'auto' will attempt to decide the most appropriate algorithm<br>based on the values passed to :meth:`fit` method.<br>See :class:`~sklearn.neighbors.NearestNeighbors` documentation for<br>details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('leaf_size',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.DBSCAN.html#:~:text=leaf_size,-int%2C%20default%3D30\">\n",
       "            leaf_size\n",
       "            <span class=\"param-doc-description\">leaf_size: int, default=30<br><br>Leaf size passed to BallTree or cKDTree. This can affect the speed<br>of the construction and query, as well as the memory required<br>to store the tree. The optimal value depends<br>on the nature of the problem.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">30</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('p',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.DBSCAN.html#:~:text=p,-float%2C%20default%3DNone\">\n",
       "            p\n",
       "            <span class=\"param-doc-description\">p: float, default=None<br><br>The power of the Minkowski metric to be used to calculate distance<br>between points. If None, then ``p=2`` (equivalent to the Euclidean<br>distance). When p=1, this is equivalent to Manhattan distance.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.DBSCAN.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of parallel jobs to run.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling\n",
       "        .textContent.trim().split(' ')[0];\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "\n",
       "\n",
       "/**\n",
       " * Adapted from Skrub\n",
       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-8');</script></body>"
      ],
      "text/plain": [
       "DBSCAN(eps=0.05)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.datasets import make_moons\n",
    "\n",
    "X, y = make_moons(n_samples=1000, noise=0.05, random_state=42)\n",
    "dbscan = DBSCAN(eps=0.05, min_samples=5)\n",
    "dbscan.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uxv9a1AR3Edj",
    "outputId": "0df813d7-751b-4c82-c972-a63b4807adcc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2, -1, -1,  1,  0,  0,  0,  2,  5])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbscan.labels_[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "355eHkIq3Edk",
    "outputId": "63fb6ec5-b7ae-499e-e8aa-fb16c6564a25"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  4,  5,  6,  7,  8, 10, 11, 12, 13])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbscan.core_sample_indices_[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lan5vKoh3Edk",
    "outputId": "da344e0e-a59c-4290-f611-fab2769f1eb5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.02137124,  0.40618608],\n",
       "       [-0.84192557,  0.53058695],\n",
       "       [ 0.58930337, -0.32137599],\n",
       "       ...,\n",
       "       [ 1.66258462, -0.3079193 ],\n",
       "       [-0.94355873,  0.3278936 ],\n",
       "       [ 0.79419406,  0.60777171]], shape=(808, 2))"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbscan.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 314
    },
    "id": "5YEMggZd3Edk",
    "outputId": "4c733e31-45a9-4468-eab2-721d44d57d21"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates and saves Figure 9–14\n",
    "\n",
    "def plot_dbscan(dbscan, X, size, show_xlabels=True, show_ylabels=True):\n",
    "    core_mask = np.zeros_like(dbscan.labels_, dtype=bool)\n",
    "    core_mask[dbscan.core_sample_indices_] = True\n",
    "    anomalies_mask = dbscan.labels_ == -1\n",
    "    non_core_mask = ~(core_mask | anomalies_mask)\n",
    "\n",
    "    cores = dbscan.components_\n",
    "    anomalies = X[anomalies_mask]\n",
    "    non_cores = X[non_core_mask]\n",
    "\n",
    "    plt.scatter(cores[:, 0], cores[:, 1],\n",
    "                c=dbscan.labels_[core_mask], marker='o', s=size, cmap=\"Paired\")\n",
    "    plt.scatter(cores[:, 0], cores[:, 1], marker='*', s=20,\n",
    "                c=dbscan.labels_[core_mask])\n",
    "    plt.scatter(anomalies[:, 0], anomalies[:, 1],\n",
    "                c=\"r\", marker=\"x\", s=100)\n",
    "    plt.scatter(non_cores[:, 0], non_cores[:, 1],\n",
    "                c=dbscan.labels_[non_core_mask], marker=\".\")\n",
    "    if show_xlabels:\n",
    "        plt.xlabel(\"$x_1$\")\n",
    "    else:\n",
    "        plt.tick_params(labelbottom=False)\n",
    "    if show_ylabels:\n",
    "        plt.ylabel(\"$x_2$\", rotation=0)\n",
    "    else:\n",
    "        plt.tick_params(labelleft=False)\n",
    "    plt.title(f\"eps={dbscan.eps:.2f}, min_samples={dbscan.min_samples}\")\n",
    "    plt.grid()\n",
    "    plt.gca().set_axisbelow(True)\n",
    "\n",
    "dbscan2 = DBSCAN(eps=0.2)\n",
    "dbscan2.fit(X)\n",
    "\n",
    "plt.figure(figsize=(9, 3.2))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_dbscan(dbscan, X, size=100)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_dbscan(dbscan2, X, size=600, show_ylabels=False)\n",
    "\n",
    "save_fig(\"dbscan_plot\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
