ML_A3_Step1_K Means Clustering_Dataset2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "99e2adca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset 2 : Wines\n",
    "\n",
    "import numpy as np\n",
    "import time\n",
    "import pandas as pd\n",
    "import io\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# preprocessing\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# model \n",
    "from sklearn.cluster import KMeans\n",
    "# will be used to convert string into float\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder_X = LabelEncoder()\n",
    "\n",
    "#dataset\n",
    "a = io.open('wine-quality-white-and-red.csv', encoding='latin-1')\n",
    "df = pd.read_csv(a)\n",
    "df = df.take([10*i for i in range(649)], axis = 0)\n",
    "df = df.apply(LabelEncoder().fit_transform)\n",
    "\n",
    "#input\n",
    "X = df.drop(columns=['type'])\n",
    "\n",
    "#output\n",
    "y = df['type']\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a1069933",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using the elbow method to find the optimal number of clusters\n",
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "for i in range (1,11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 0)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1,11), wcss)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2468ac54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Distortion Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='distortion score'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the elbow method to find the optimal number of clusters\n",
    "from yellowbrick.cluster import KElbowVisualizer\n",
    "\n",
    "# Instantiate the clustering model and visualizer\n",
    "model = KMeans()\n",
    "visualizer = KElbowVisualizer(model, k=(1,12))\n",
    "\n",
    "visualizer.fit(X)        # Fit the data to the visualizer\n",
    "visualizer.show()        # Finalize and render the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9f734ac9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training the K-means model on the dataset\n",
    "kmeans = KMeans(n_clusters = 3, init = 'k-means++', random_state = 0)\n",
    "y_kmeans = kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "0d2aaff4",
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "# Visualising the clusters in with infinite features\n",
    "\n",
    "import plotly.express as px\n",
    "\n",
    "a = io.open('wine-quality-white-and-red.csv', encoding='latin-1')\n",
    "df = pd.read_csv(a)\n",
    "df = df.take([10*i for i in range(649)], axis = 0)\n",
    "df = df.apply(LabelEncoder().fit_transform)\n",
    "\n",
    "kmeans = KMeans(\n",
    "        n_clusters=3, init=\"k-means++\",\n",
    "        n_init=10,\n",
    "        tol=1e-04, random_state=42\n",
    "    )\n",
    "kmeans.fit(X)\n",
    "\n",
    "df['label']=kmeans.labels_\n",
    "polar=df.groupby(\"label\").mean().reset_index()\n",
    "polar=pd.melt(polar,id_vars=[\"label\"])\n",
    "fig4 = px.line_polar(polar, r=\"value\", theta=\"variable\", color=\"label\", line_close=True,height=400,width=700)\n",
    "fig4.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "99835742",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.32027701369660505\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "from sklearn.metrics import silhouette_samples\n",
    "\n",
    "# define our scaler\n",
    "scaler = MinMaxScaler()\n",
    "# scale down our data\n",
    "df_scaled = scaler.fit_transform(df)\n",
    "kmeans.fit(df_scaled)\n",
    "# compute an average silhouette score for all points\n",
    "silhouette_score_average = silhouette_score(df_scaled, kmeans.predict(df_scaled))\n",
    "\n",
    "# lets see what that score it\n",
    "print(silhouette_score_average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b871f550",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have found a negative silhouette score: -0.0035926238602060324\n"
     ]
    }
   ],
   "source": [
    "# let's see each individual points\n",
    "silhouette_score_individual = silhouette_samples(df_scaled, kmeans.predict(df_scaled))\n",
    "\n",
    "# iterate through to find any negative values\n",
    "for each_value in silhouette_score_individual:\n",
    "    if each_value < 0:\n",
    "        print(f'We have found a negative silhouette score: {each_value}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "665d9d4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Number of Clusters: 3 | silhouette_score_average : 0.36212071318244765\n",
      " Number of Clusters: 4 | silhouette_score_average : 0.32027701369660505\n",
      " Number of Clusters: 5 | silhouette_score_average : 0.2677359514631433\n",
      " Number of Clusters: 6 | silhouette_score_average : 0.28069270029692633\n",
      " Number of Clusters: 7 | silhouette_score_average : 0.24578320422334013\n",
      " Number of Clusters: 8 | silhouette_score_average : 0.2382377158043956\n",
      " Number of Clusters: 9 | silhouette_score_average : 0.22165468614825143\n",
      " Number of Clusters: 3 | Number of Negative Values: 3\n",
      " Number of Clusters: 4 | Number of Negative Values: 1\n",
      " Number of Clusters: 5 | Number of Negative Values: 8\n",
      " Number of Clusters: 6 | Number of Negative Values: 6\n",
      " Number of Clusters: 7 | Number of Negative Values: 33\n",
      " Number of Clusters: 8 | Number of Negative Values: 25\n",
      " Number of Clusters: 9 | Number of Negative Values: 34\n"
     ]
    }
   ],
   "source": [
    "# Loop for different number of clusters\n",
    "\n",
    "# re-do our loop, try to find values with no negative scores, or one with the least!!\n",
    "bad_k_values = {}\n",
    "\n",
    "# remember, anything past 15 looked really good based on the inertia\n",
    "possible_K_values = [i for i in range(3,10)]\n",
    "\n",
    "# we start with 1, as we can not have 0 clusters in k means\n",
    "# iterate through each of our values\n",
    "for each_value in possible_K_values:\n",
    "    \n",
    "    # iterate through, taking each value from \n",
    "    kmeans = KMeans(n_clusters=each_value, init='k-means++',random_state=32)\n",
    "    \n",
    "    # fit it\n",
    "    kmeans.fit(df_scaled)\n",
    "    \n",
    "    # find each silhouette score\n",
    "    silhouette_score_individual = silhouette_samples(df_scaled, kmeans.predict(df_scaled),metric='euclidean')\n",
    "    \n",
    "    # find each average silhouette score\n",
    "    silhouette_score_average = silhouette_score(df_scaled, kmeans.predict(df_scaled),metric='euclidean')\n",
    "    print(f' Number of Clusters: {each_value} | silhouette_score_average : {silhouette_score_average}')\n",
    "\n",
    "    # iterate through to find any negative values\n",
    "    for each_silhouette in silhouette_score_individual:\n",
    "        \n",
    "        # if we find a negative, lets start counting them\n",
    "        if each_silhouette < 0:\n",
    "            \n",
    "            if each_value not in bad_k_values:\n",
    "                bad_k_values[each_value] = 1\n",
    "            \n",
    "            else:\n",
    "                bad_k_values[each_value] += 1\n",
    "            \n",
    "for key, val in bad_k_values.items():\n",
    "    print(f' Number of Clusters: {key} | Number of Negative Values: {val}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "a931eea4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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knC1Dxtly3vZYZ3T4Wk7QE0IIIayclL0QQghh5aTshRBCCCsnZS+EEEJYOSl7IYQQwspJ2QshhBBWTspeCCGEsHJS9kIIIYSVk7J/BXFJKRyMuktcUoraUYQQQojXZnVX0Hvb4pJSqDD1F67HJFCugAsHB7fAYPfiyxEKIYQQWZFs2Ztx7MZDrsckAHDmziNO3opWN5AQQgjxmqTszahUJA/5nZ5cu9jJVk/5ArlUTiSEEEK8Hil7Mwx2Npwf2RafEq7EJ6fyn71n1I4khBBCvBYp+1fg4mDLL30b4J7HifHbjvHH2RtqRxJCCCFemZT9K8rraMfqnnXRa7X0WLGXa9HxakcSQgghXomU/Wt4t3h+/t2mOvfik/D/8U9S0oxqRxJCCCHMkrJ/Tf1re+BfpQShUXcZseWo2nGEEEIIs6TsX5NGo2Fhp1qUL5iL2SGnWXssSu1IQgghxEtJ2b8Bg50Na3rVw8lWz4erQzl395HakYQQQogMSdm/ofIFc7GwUy1ik1LovHQPCcmpakcSQgghXkjK/h/oWrUkn9bx5MTNaAasO4iiKGpHEkIIIZ4jZf8PzWhdjXeL5ePHI5f4/uAFteMIIYQQz5Gy/4fs9DpW96xLXkdbBm04xF/XHqgdSQghhEhHyv4tcM9r4MduviSlGun84x6iE5PVjiSEEEKYZNotbo1GI+PHj+fs2bPY2try9ddf4+7ubnp927ZtLFq0CI1GQ5cuXejUqRMAbdu2xdnZGQA3NzcmT55MVFQUI0aMQKPRULZsWcaNG4dWm7XWU5qXL8ro995hYvAJPvhpHxt610ej0agdSwghhMi8sg8ODiY5OZnVq1cTHh7OlClTWLBgAQBpaWnMnDmTdevW4ejoSIsWLWjUqBFOTk4ALFu2LN1nTZ48mcGDB1OzZk0CAwPZsWMHjRs3zqzob2xcU29CL99l88lrzNx9in81qKh2JCGEECLzduOHhYXh5+cHQOXKlYmIiDC9ptPp2Lp1K87OzkRHRwPg5OTEmTNnSExMpE+fPvTs2ZPw8HAATp48SY0aNQCoW7cu+/fvz6zY/4hOq2VFD1+KuDgwautfhFy8rXYkIYQQIvO27OPi4jAYDKbHOp2O1NRU9Pons9Tr9Wzfvp2goCDq1auHXq/H3t6evn370qlTJy5fvsxHH33E77//jqIopl3iTk5OxMbGmp3/sysXlvZVzYL0D75Mxx92sqx5KfI7ZNowmxUWFqbavHMSGWfLkHG2DBlny7HUWGdaCxkMBuLj/3tnOKPRaCr6p5o0acJ7773HiBEj2LhxI61atcLd3R2NRkPJkiXJnTs3d+/eTXd8Pj4+HhcXF7Pz9/Lyws7O7u0t0GuoBsQ45ONfv4Qx7XgM2z5+D73O8ucYhIWFUa1aNYvPN6eRcbYMGWfLkHG2nLc51klJSS/dyM20BqpatSohISEAhIeH4+HhYXotLi6OHj16kJycjFarxcHBAa1Wy9q1a5kyZQoAt2/fJi4uDldXVypUqMDBgwcBCAkJoXr16pkV+60ZXLc87d4pzu6Ltxm37ZjacYQQQuRgmbZl37hxY/bt24e/vz+KojBp0iQ2b95MQkICXbp0oVWrVnTv3h29Xo+npyetW7cmLS2NkSNH0rVrVzQaDZMmTUKv1zN8+HDGjh3LrFmzKFWqFE2bNs2s2G+NRqPh+y4+nLj5kCk7Iqjlnp9WFYupHUsIIUQOpFGs7BqvT3dlqLkb/1nHbjyg9je/Y2+j48iQFpTM52yxecvuOMuQcbYMGWfLkHG2nMzYjZ9R92WtL6tboUpF8jK3Qw2iE5Pp/GMIj1PS1I4khBAih5Gyt4DeNcrQu0Zpjl57wJBNh9WOI4QQIoeRsreQOe1rUKlIHhaFnmfZkUtqxxFCCJGDSNlbiIONnp971cXF3oZP1x0g4uZDtSMJIYTIIaTsLahMfhd+8K9NQnIanZeGEPs4Re1IQgghcgApewtr905xhtavwNm7j/jo51Cs7MsQQgghsiApexVMbFEF35IFWHMsinl7z6odRwghhJWTsleBjU7LTwF+FDDY86/NYRyIuqt2JCGEEFZMyl4lRXI5sqKHL2lGhS5LQ7gX91jtSEIIIayUlL2KGpYtTFCzSlyLSaDHir2kGY1qRxJCCGGFpOxVNryhF83LF+WPczeZ+McJteMIIYSwQlL2KtNqNfzYrQ7ueZwI+uM428/eUDuSEEIIKyNlnwXkdbTj5171sNFq6bF8L1cfxqsdSQghhBWRss8iqhfLx6y21bmfkETXZX+SnCo3zBFCCPF2SNlnIf19POhapQShUXcZvuWo2nGEEEJYCSn7LESj0fBtp1pUKJiL//x5hjXHotSOJIQQwgpI2WcxBjsbfu5VDydbPR+u3s/ZOzFqRxJCCJHNSdlnQeUL5mJR51rEJaXSeWkI8UlywxwhhBBvTso+i/KvUpJP63gScSuaT9cdkhvmCCGEeGNS9lnYjNbVqFE8H8vDLvHdwQtqxxFCCJFNSdlnYXZ6HasC6pLX0ZbPNxzi6LX7akcSQgiRDUnZZ3HueQ382M2X5DQjnZeG8DAhSe1IQgghshkp+2ygefmijH7vHSIfxPHBT/sxGuX4vRBCiFcnZZ9NBDbxplHZQmw5dY0Zu0+qHUcIIUQ2ImWfTei0WpZ396WIiwOjt4az+8IttSMJIYTIJqTss5ECzg6s6lkXrQa6Lf+Tm48S1I4khBAiG5Cyz2bqlCzA1Percjv2Md2W/UlqmlHtSEIIIbI4Kfts6PO65Wn3TnFCLt1h7G/hascRQgiRxWVa2RuNRgIDA+nSpQsBAQFERaW/qcu2bdvo0KEDHTt2ZM2aNQCkpKQwbNgwunXrRseOHdmxYwcAJ0+exM/Pj4CAAAICAti6dWtmxc4WNBoN33fxoUx+Z6btOskvEVfVjiSEECIL02fWBwcHB5OcnMzq1asJDw9nypQpLFiwAIC0tDRmzpzJunXrcHR0pEWLFjRq1Ihdu3aRO3dupk+fzsOHD2nXrh2NGjXi1KlT9O7dmz59+mRW3Gwnl4Mta3rVw+eb3+i9aj+Hh7SgVD5ntWMJIYTIgjJtyz4sLAw/Pz8AKleuTEREhOk1nU7H1q1bcXZ2Jjo6GgAnJyeaNWvG559/nu59ABEREezevZvu3bszatQo4uLiMit2tuJdJA/zOtQkOjGZLj+G8DglTe1IQgghsqBM27KPi4vDYDCYHut0OlJTU9Hrn8xSr9ezfft2goKCqFevHnq9Hjs7O9O0gwYNYvDgwQB4e3vTqVMnvLy8WLBgAfPmzWP48OEvnf+zKxfW7B0dtC6dm18uPqDHd1sZWaNIutfDwsJUSpazyDhbhoyzZcg4W46lxjrTyt5gMBAfH296bDQaTUX/VJMmTXjvvfcYMWIEGzdupEOHDty8eZMBAwbQrVs3WrVqBUDjxo1xcXEx/TxhwgSz8/fy8jKtPFi7ld6V8P3P72y48JDW71agZ/XSwJM/omrVqqmczvrJOFuGjLNlyDhbztsc66SkpJdu5GbabvyqVasSEhICQHh4OB4eHqbX4uLi6NGjB8nJyWi1WhwcHNBqtdy7d48+ffowbNgwOnbsaHp/3759OX78OAChoaFUrFgxs2JnSw42en7uVY9c9jZ8uvYgJ24+VDuSEEKILCTTtuwbN27Mvn378Pf3R1EUJk2axObNm0lISKBLly60atWK7t27o9fr8fT0pHXr1kyePJlHjx4xf/585s+fD8DixYsZP348EyZMwMbGhvz587/Sln1OUzq/Mz/416bDkj10WrKHQ0NaqB1JCCFEFqFRFMWq7qrydFdGTtqN/6wvN4cxc/cp2noVo1VhHR0b1MJgZ6N2LKsmuz0tQ8bZMmScLSczduNn1H1yUR0rM7FFFWqXyM/GiKv0/eMyNWdvJS4pRe1YQgghVCRlb2VsdFpGNHzH9PjMnUecvBWtXiAhhBCqk7K3QvXKFKSwiwMABls9FQvlVjeQEEIIVUnZWyGDnQ2nh7fGI48dccmp7JLb4QohRI4mZW+lnO1t+cqnKHqthsEbD5OQnKp2JCGEECqRsrdipXPb80W9Clx+EM+k4BNqxxFCCKESKXsrN6bxOxTL7ciM3ac4cztG7ThCCCFUIGVv5ZzsbJjd9l1S0owMXH8QK7usghBCiFcgZZ8DtPEqRssKRdl14TY//XVZ7ThCCCEsTMo+B9BoNHzT9l3s9Tr+9csRYhKT1Y4khBDCgqTsc4iS+ZwZ3fgdbsc+Zuxv4WrHEUIIYUFS9jnI0PoV8HR1YcH+c4Rdva92HCGEEBYiZZ+D2Ol1zGlfA6OiMGDdQdKMRrUjCSGEsAAp+xymkUdh/KuU4PDV+yw+cEHtOEIIISxAyj4HmtG6Gi72Noze+hd3YhPVjiOEECKTSdnnQIVdHJnQrDLRickM33JU7ThCCCEymZR9DtW/tgdViublxyOXCLl4W+04QgghMpGUfQ6l12mZ16EGGg0MWHeQlDQ5WU8IIayVlH0OVtPdlY9qleXU7Ri+CTmtdhwhhBCZRMo+h5vYogquBju+2n6MKw/j1Y4jhBAiE7xS2YeFhfHTTz+RnJzM4cOHMzuTsKC8jnZMfb8aCclpDNkkv1shhLBGZst+6dKlzJ49myVLlhAfH09gYCDff/+9JbIJC+lZvRR+pQqw8cRVfj11Te04Qggh3jKzZb9hwwa+//57HBwcyJMnD2vXrmXdunWWyCYsRKPRMLd9DfRaDZ9vOExCcqrakYQQQrxFZsteq9Via2tremxnZ4dOp8vUUMLyvArnYXDd8kQ+iGPKjgi14wghhHiLzJZ9jRo1mDp1KomJiQQHB/PJJ59Qq1YtS2QTFja2iTduuRyZvuskZ+/EqB1HCCHEW2K27L/88kvc3d3x9PRk48aN1KtXj+HDh1sim7Awg50N/277LslpRj5bfwhFUdSOJIQQ4i3Qm3vDrVu3qFu3LnXr1gWeHN999OgRefPmzfRwwvLavVOM5uWL8tvp66wOv4x/lZJqRxJCCPEPmS37AQMGcP78eTw8PFAUhfPnz+Pq6opOp2PChAn4+Pi8cDqj0cj48eM5e/Ystra2fP3117i7u5te37ZtG4sWLUKj0dClSxc6deqU4TRRUVGMGDECjUZD2bJlGTduHFqtXCIgM2g0Gv7T7l3eOX+LoZvCaF6uKLkcbM1PKIQQIssy25gFCxZk1apVrF+/ng0bNrBu3Tq8vLxYtmwZM2bMyHC64OBgkpOTWb16NUOHDmXKlCmm19LS0pg5cyZLlixh9erVfPfddzx48CDDaSZPnszgwYNZuXIliqKwY8eOt7DoIiOl8jkz8j0vbsUmMm7bMbXjCCGE+IfMlv3169fx8vIyPfb09OTKlSsULlwYozHj66mHhYXh5+cHQOXKlYmI+O8Z3jqdjq1bt+Ls7Ex0dDQATk5OGU5z8uRJatSoAUDdunXZv3//ay6meF3DGlSkbH5n5u09y1/XHqgdRwghxD9gdjd+sWLFmDFjBm3atMFoNLJlyxbc3d3566+/XrorPS4uDoPBYHqs0+lITU1Fr38yS71ez/bt2wkKCqJevXro9foMp1EUBY1GAzxZKYiNjTW7YM+uXORkYWFhbzztoHfy8NmuWD74cSffNSmB9u/fgXjePxln8epknC1DxtlyLDXWZst+2rRpzJ07l6FDh6LT6fDx8WHSpEns3LmTr776KsPpDAYD8fH/vda60Wg0Ff1TTZo04b333mPEiBFs3Lgxw2meXamIj4/HxcXF7IJ5eXlhZ2dn9n3WLCwsjGrVqr3x9NWAPx+G8HN4FH+luNDPx+PthbMi/3ScxauRcbYMGWfLeZtjnZSU9NKNXLO78Q0GAyNGjOCXX35hw4YNDBs2jIcPH9K6det0u/f/V9WqVQkJCQEgPDwcD4//FkVcXBw9evQgOTkZrVaLg4MDWq02w2kqVKjAwYMHAQgJCaF69eqvsOjibZjZujrOdjaM+vUv7sY9VjuOEEKIN2B2y37VqlVMmzaNxMRE03NFixYlODj4pdM1btyYffv24e/vj6IoTJo0ic2bN5OQkECXLl1o1aoV3bt3R6/X4+npSevWrdFoNM9NAzB8+HDGjh3LrFmzKFWqFE2bNv2Hiy1eVZFcjgQ1q8SQTUcYseUo3/vXVjuSEEKI12S27BctWsSmTZuYPXs2Q4YMYc+ePRw9etTsB2u1WoKCgtI9V7p0adPPXbp0oUuXLs9N97/TAJQsWZLly5ebnafIHJ/W8WTp4YssOXyR3jXK4FuqgNqRhBBCvAazu/Hz5ctHsWLF8PT05Ny5c3Tv3p2zZ89aIpvIIvQ6LfM61gRgwLqDpKRl/C0MIYQQWY/ZsndwcODAgQN4enqya9cu7t69y+PHcuw2p6nl7sqHtcoQcSuaOX+eUTuOEEKI12C27MeOHcuuXbvw8/MjOjqa5s2b06NHD0tkE1nMpBZVye9kx/htx7j6MN78BEIIIbIEs2W/ZcsWRo4ciVarZc6cORw5coQPPvjAAtFEVpPPyY4p71clPjmVL345onYcIYQQr8hs2e/atUvufiZMelUvTZ0Srqw/foXfTl9XO44QQohXYPZs/Ny5c9OsWTMqVqyY7iI1kydPztRgImvSajXM7VCT6v/+lUEbDnG8TCscbMz+GQkhhFCR2X+l27VrZ4kcIhvxLpKHz/3KM2vPKabuOMn4ZpXUjiSEEOIlXqnsr127xoULF/D19eXmzZsUK1bMEtlEFhbYxJvV4ZeZujOC7tVKUtbV/CWMhRBCqMPsMfutW7fyySefMHHiRGJiYvD392fTpk2WyCayMGd7G2a1qU5ympGB6w/JeR1CCJGFmS37xYsX89NPP+Hk5ES+fPnYsGEDixYtskQ2kcV18C5O03JFCD53kzXHotSOI4QQIgNmy16r1aa77WyBAgVeemtbkXNoNBr+0+5d7PRavth0hEePk9WOJIQQ4gXMtnbZsmVZvnw5qampnD59mrFjx1KuXDlLZBPZQJn8Loxo6MXNR4mM33ZM7ThCCCFewGzZBwYGcvv2bezs7Bg1ahQGg4Fx48ZZIpvIJr5s6EWZ/M7M+fMs4dcfqB1HCCHE/zB7Nv6aNWv44IMPGDp0qCXyiGzI3kbHnPY1aL5oBwPXHSJkYFO0Wo3asYQQQvzN7Jb9rVu36NSpEx9++CG//PJLuvvaC/FUE88idKzkTmjUXX44dEHtOEIIIZ5htuyHDx/Ozp076d+/P+Hh4bRt25Yvv/zSEtlENjOrTXUMdnpG/nqUe3FyZ0QhhMgqXum0ekVRSElJISUlBY1Gg42NTWbnEtlQ0VyOfNW0Eg8Skhn5619qxxFCCPE3s8fsv/76a/744w/Kly9P69atGTNmTLpr5AvxrIG+5Vh6+BI/HLrABzVKU6dkAbUjCSFEjme27N3d3dmwYQN58+a1RB6Rzel1WuZ1qIHf3G0MWHeQI0NaotfJdRmEEEJNZsu+RYsW/PLLL8THx6MoCkajkWvXrjFt2jRL5BPZUO2SBehToww/HLrA3L1nGFyvgtqRhBAiRzO7yTVkyBBOnz5tOhN/27ZtcgU9YdbkllXI62jLuG3HuBYdr3acTBOXlMKJuwnEJaWoHUUIITJktrXv3LnD1KlTadiwIU2aNGH58uWcOnXKEtlENpbfYM+U96sSl5TK0F/C1I7z1t16lMhX28LJP2Y1ff+4TLVZv0rhCyGyLLNlnytXLgBKlizJmTNnyJMnT6aHEtah97tl8HF3Ze2xKLaduaF2nH/MaFTYce4mnZfuwX3COoK2nyDF+ORufxfuxXLoyj2VEwohxIuZLftatWoxaNAg6tSpww8//EBgYCD29vaWyCayOa1Ww7yONdBpNQzacIjHKWlqR3ojd+MeM2PXScpP3USThcGsO36F8gVzMaNVNcq6OpveF7T9WLZdRiGEdTN7gt6QIUO4cuUKRYsWZdasWRw+fJgBAwZYIpuwApWK5OUz33LMDjnNtJ0RBDatpHakV6IoCiGX7rAo9Bzrj18hOc2IvV5HQPVS9KtVFp8Srmg0Gj7yKcvPuw7wy/VUNp+6RvcVf7I6oK58A0EIkaWYLXuA4sWLA1CxYkUqVqyYqYGE9RnX1Jufwy8zZWcE3aqVpEx+F7UjZehBQhLLjlxiUeg5ztx5BEC5Ai708/EgoHop8jqmv8aEwc6GSq6OdGtUmfe/28HGE1f5ZO1BFnWuhUYj9wcQQmQNr1T2QvwTLva2zGxTna7L/uSz9YfZ+lHDLFWEiqIQevkuiw6cZ014FI9T07DVafGvUoKPfTzwK1XAbF57Gx3re9en0YI/+OHQBVwNdkxqWdUyCyCEEGZkWtkbjUbGjx/P2bNnsbW15euvv8bd3d30+pYtW1i6dCk6nQ4PDw/Gjx/Pxo0b2bBhAwBJSUmcPn2affv2cfXqVfr370+JEiUA6Nq1Ky1atMis6CITdKrkzg8HL7D97A3WHb9Cx0ru5ifKZDGJyawIi2TRgXOcuBkNQJn8znxUqyy93i2Nq+H1zk1xsbfl1w8bUm/edqbuPEl+J3u+qC/XGBBCqO+Vyn7z5s1cuHCB/v37s23bNtq2bWt2muDgYJKTk1m9ejXh4eFMmTKFBQsWAPD48WNmz57N5s2bcXBw4IsvvmDXrl20b9+e9u3bA/DVV1/RoUMHXFxcOHXqFL1796ZPnz5vvqRCVRqNhjnta+A9fTNfbDpCU88iONtb/h4LiqJw5Op9FoWeZ1V4JAnJaei1Gjp4F+djHw8alCn0j27PW8DZgd/7NcJv7jaGbQ4jn5Mdvd4t/RaXQAghXp/Zs4hmzJjBnj172L59O2lpaaxbt44pU6aY/eCwsDD8/PwAqFy5MhEREabXbG1tWbVqFQ4ODgCkpqamu97+iRMnuHDhAl26dAEgIiKC3bt30717d0aNGkVcXNzrLaXIEsq6ujC8oRfXYxL4avsxi8479nEKi0LP8e6/t1Lrm9/44dAFChjsmdiiMlFjO/Bzr3o08ij8j4r+Kfe8Bn7r14g8DrZ89HMom09efQtLIIQQb06jKIrysje0bduWDRs20K5dOzZu3EhqaiqtW7dm69atL/3g0aNH06RJE+rVqwdA/fr1CQ4ORq9PvzNh2bJl7Nmzh8WLF5uOiw4cOJAePXpQq1YtANatW4enpydeXl4sWLCAR48eMXz48BfONykpKd2KhchaHqca6bb1IjfjU/ixWSnK5sncr3Gee/iY9ecf8vvlGBJSjeg04FvUmfZl8lCzsBPaTDx34MS9BAbsiEIBvmlQnKoFnDJtXkIIAeDl5fXCm9WZ3Y3/9NK4T4s4OTn5lS6XazAYiI//72VSjUZjuqI3Go1Mnz6dyMhI5syZY/r8R48ecenSJVPRAzRu3BgXFxfTzxMmTDA7/4wWOCcJCwujWrVqasd4zrcuRXj/u53MOx3LngG138rW9LMSklNZHX6ZRaHnOHTlPgBuuRz5slZZ+tQsQ9Fcjm91fhmNczWgsHtp2vywiy/33mDXp02oXFRuKPWmsurfs7WRcbactznW5jZ0zbZ2s2bNGDx4MDExMSxZsoQePXrw/vvvm51x1apVCQkJASA8PBwPD490rwcGBpKUlMT8+fNNu/MBDh8+TO3atdO9t2/fvhw/fhyA0NBQ+fpfNte8fFHaexdn/+W7LDl88a19bsTNhwxafwi3r9by4epQDl+9T4vyRdnYpz4XR7djbBPvt1705jQtV4QlXWsTm5RCi8U7uHgv1qLzF0IIeIUt+759+7J//36KFCnCzZs3+eyzz2jQoIHZD27cuDH79u3D398fRVGYNGkSmzdvJiEhAS8vL9auXUv16tXp1asXAD179qRx48ZERkbi5uaW7rPGjx/PhAkTsLGxIX/+/K+0ZS+ytn+3qc62MzcYseUobbyKkc/pzfbCPE5JY+3xKBbtP8e+y3cBKOziwEDfcvStWQb3vIa3GfuN+FcpyYP4ZD7bcIimC4P587OmFHax7EqHECJnM1v2HTt2ZMOGDaaT7V6VVqslKCgo3XOlS//3rOQzZ868cLoPP/zwuecqVqzIqlWrXmv+Imtzy+3E+KaVGLY5jFFbj7Kwk89rTX/2TgyLQs/z45GLPEhIBqCxR2H6+XjQqqIbNlnsCnaf+npyN/4xQduP03zRDnZ92oQ8jjn7MJMQwnLMln3+/Pk5cuQI3t7e2NraWiKTyCE+8yvH0sMX+e7ABT54tww+JVxf+v6k1DQ2nLjC4tDz7L54GwBXgx1fNqjIh7XKUjq/80unV1tgE2/uxScxf99Z2ny/i98/fg9HW7mulRAi85n9l+bEiRP06NEj3XMajYbTp09nWiiRM9jotMzrUJN687YxYN1BDg1u8cJryl+8F8viA+dZcvgCd+OSAGhQpiAf1fKg3TvFsNXrLB39jWg0Gr5p+y7345NYHX6ZLj+GsL53/Sy3F0IIYX3Mlv2BAwcskUPkUL6lCvDBu6VZcvgi8/edZVDd8gCkpBn55eRVFoWeJ/jcTQDyOtoypF55PqpVFs8CudSM/ca0Wg1LutbmYWIyW09fp+/q/Szxr/PWv5EghBDPMlv2iYmJzJ07l9DQUNLS0qhVqxaff/45jo5ygpF4O6a8X5VNEVcZ+1s4TnZ6zt2JYXnYZW7FJgLgW7IA/XzK0sHbHXub7LEV/zK2eh1re9WlycJgVoRFkt/Jjpmtq2ep+wUIIayL2f2HQUFBJCYmMmnSJKZOnUpKSgrjxo2zRDaRQ7ga7BnXrBJxyan0+/kAM3afJiE5hYG+nhwf1oo9A5vSvVopqyj6p5zsbNj8YUMqFMzFNyFnmLJDLgQlhMg8ZrfsT548yS+//GJ6HBgYKDehEW9ddbd86R5v7NOAemUKqZTGMvI62vHb39fRH/NbOPmc7Ojn42F+QiGEeE1mt+wVReHRo0emx48ePUKns54tLJE1vFM4N+UKPLlKYrkCLlQrls/MFNbBLbcT2z5+j/xOdgxYd4i1x6LUjiSEsEJmt+w/+OADOnbsSMOGDQHYuXMn/fr1y/RgImcx2NlwcHALTt6KpmKh3BjsLH9HPLV4uLqw9aNGNFywnYAVe8njYEsjj8JqxxJCWBGzW/YdOnRg7ty5FCtWjKJFizJ37lw6duxoiWwihzHY2VDT3TVHFf1T1YrlY2OfJ1embL9kN0eu3lc5kRDCmpgt+7Nnz7JgwQK6d+9O7dq1+eqrr7h06ZIlsgmRozQoU4gVPfxISE6j5eIdnLkdo3YkIYSVMFv2Y8eOpV27dsCTy91++umnjB49OtODCZETtfcuzoKONbkXn0SzRcFcfRhvfiIhhDDDbNknJiZSt25d0+M6deqQmJiYqaGEyMk+rFWWSS2qcDU6geaLd3A/PkntSEKIbM5s2efNm5effvqJ+Ph44uPj+fnnn8mXL2ecKS2EWr5sWJEh9cpz+nYM73+3g7ikFLUjCSGyMbNlP3nyZHbv3o2vry8NGzZkz549TJw40RLZhMixNBoN096vRkD1Uhy6cp8OS/aQlJqmdiwhRDZl9qt3RYoUYeHChQDExsZy69YtChWy7oudCJEVaLUaFnf24WFCMltOXaPXyn2s6OGLTis3zhFCvB6z/2qsWbOGESNG8ODBA1q2bMmgQYP49ttvLZFNiBzPRqdlVU8//EoVYM2xKAZtOIyiKGrHEkJkM2bL/qeffuKLL75gy5YtNGrUiM2bN7N9+3ZLZBNCAA42ejb2aUClInn4dv85vtp2XO1IQohs5pX2BxYoUIA9e/ZQv3599Ho9SUlydrAQlpTbwZatHzWidD5nJvxxnDl/nlY7khAiGzFb9mXKlOHjjz/m2rVr+Pj4MHjwYLy9vS2RTQjxjEIuDvz+cSMKOTsweOMRVh6NVDuSECKbMHuC3qRJk/jrr78oW7Ystra2tG7dOt337oUQllMqnzO/9WtE/Xnb6P3TPvI42NK8fFG1YwkhsjizW/Z6vZ53332X3LlzA9CwYUP0erPrCEKITOJdJA+b+jZAr9XSaeke9kfeUTuSECKLk+/wCJEN+ZUqyOpedUlOM9Lq+11E3HyodiQhRBYmZS9ENvV+BTe+71Kb6MRkmi/aweUHcWpHEkJkUWbLPjk5mQULFvDll18SFxfH3LlzSU5OtkQ2IYQZAdVLMbN1NW48SqTpwmBux8p9K4QQzzNb9kFBQSQmJnLq1Cl0Oh1Xrlxh1KhRlsgmhHgFg+tVYGQjLy7ci6Xl4p3EJMrKuBAiPbNlf/LkSb744gv0ej0ODg5MnTqVM2fOWCKbEOIVTWhemY9qleWv6w9o93+7eZwi19EXQvyX2bLXaDQkJyej0WgAePjwoelnIUTWoNFomNehBu29i7Pn4m26Lf+T1DSj2rGEEFmE2e/Q9ezZk969e3P37l0mTpxIcHAwn376qdkPNhqNjB8/nrNnz2Jra8vXX3+Nu7u76fUtW7awdOlSdDodHh4ejB8/Hq1WS9u2bXF2dgbAzc2NyZMnExUVxYgRI9BoNJQtW5Zx48ahlZuBCJGOTqtleXdf3k/YyaaIq/Rfe4DFnX1k5VwIYb7s27Zti5eXFwcPHiQtLY0FCxZQrlw5sx8cHBxMcnIyq1evJjw8nClTprBgwQIAHj9+zOzZs9m8eTMODg588cUX7Nq1C19fXwCWLVuW7rMmT57M4MGDqVmzJoGBgezYsYPGjRu/yfIKYdXs9DrW967Pe9/+wf8dukh+J3umvF9V7VhCCJWZ3Tz+7LPPKFOmDN27d6dnz56UK1eOXr16mf3gsLAw/Pz8AKhcuTIRERGm12xtbVm1ahUODg4ApKamYmdnx5kzZ0hMTKRPnz707NmT8PBw4Ml5AzVq1ACgbt267N+//7UXVIicwtnehi0fNsTT1YXpu04yc9dJtSMJIVSW4Zb9wIEDOX36NLdv36ZRo0am59PS0l7pfvZxcXEYDAbTY51OR2pqKnq9Hq1WS/78+YEnW/EJCQnUqVOHc+fO0bdvXzp16sTly5f56KOP+P3331EUxbQr0snJidjYWLPzf3blIicLCwtTO0KOkBXHeXrtgny4PYEvtxwl9u4tWpXOrXakfywrjrM1knG2HEuNdYZlP2XKFKKjo5k4cSJjxoz57wR6Pfny5TP7wQaDgfj4eNNjo9GY7jK7RqOR6dOnExkZyZw5c9BoNJQsWRJ3d3fTz7lz5+bu3bvpjs/Hx8fj4uJidv5eXl7Y2dmZfZ81CwsLo1q1amrHsHpZeZx3eJSj3rxtTDx0kyoVPGjjVUztSG8sK4+zNZFxtpy3OdZJSUkv3cjNcDe+wWDAzc2NIkWKULRoUdN/BQsWZPTo0WZnXLVqVUJCQgAIDw/Hw8Mj3euBgYEkJSUxf/580+78tWvXMmXKFABu375NXFwcrq6uVKhQgYMHDwIQEhJC9erVzc5fCAEVCuVmy4cNcbDR0XVZCHsu3lY7khBCBRlu2Y8ePZqrV68SERHB+fPnTc+npqa+0m70xo0bs2/fPvz9/VEUhUmTJrF582YSEhLw8vJi7dq1VK9e3XT8v2fPnnTs2JGRI0fStWtXNBoNkyZNQq/XM3z4cMaOHcusWbMoVaoUTZs2fQuLLkTOUNPdlbUf1KP197to+8Mudn7ShCpuedWOJYSwoAzL/pNPPuH69etMnDiRgQMHmp7X6XSULl3a7AdrtVqCgoLSPffsdBldmGfmzJnPPVeyZEmWL19udp5CiBdr4lmEpV3r0H3FnzRbFMy8DjVoVq4oBjsbtaMJISwgw7J3c3PDzc2NX375hWvXrnHhwgX8/Py4ceOG6Xa3Qojso0uVEtyMTWDopjC6/PgnBjs9Ixt50bBsYaoUzYuNTq5dIYS1Mvs9+61bt7JgwQISExNZvXo1/v7+fPnll7Rp08YS+YQQb5GPu6vp57ikVEZvDQfCcbTVUau4K3VKFqBOSVdqubvibC9b/UJYC7Nlv3jxYn766Sd69OhBvnz52LBhA71795ayFyIbqlgoN+UKuHDmziNK53NmRCMvjly9z77IO+y8cIudF24BoNNqqFwkz9/lXwDfkgUo5OKgcnohxJsyW/ZarTbd9+ULFCggl6oVIpsy2NlwcHALTt6KpmKh3BjsbOhTswwADxOS2H/5Lvsi77Av8i6Hrtwj7NoD/vPnk/NrSudzxrfUky1/35IF8HB1kUvxCpFNmC37smXLsnz5clJTUzl9+jQrV658pcvlCiGyJoOdDTWf2Z3/VB5HO1pWcKNlBTcAHqekmbb690beYf/luyw9fJGlhy8C4Gqwo3aJAviVerL1L8f9hci6zJZ9YGAgCxYswM7OjlGjRlGrVi2GDx9uiWxCCBXZ2+jwLVUA31IFGA4YjQonb0ezN/IO+y7dYd/lu2yKuMqmiKsAONjoqOWe37TbX477C5F1mC17R0dHhg4dytChQy2RRwiRRWm1Gt4pnId3Cufhk9qeAFx5GP+k/P/+b/fF2+y68OTCPVqNhspFnz3u70phF0c1F0GIHMts2ZcrV+6543Kurq6mq+MJIXKu4nmc6JanJN2qlgSeHPcPjbrH3ku3Tcf9j157wJxnjvvXKfnkrH+/UnLcXwhLMVv2z178JiUlheDgYNPd6IQQ4ll5HO1oUb4oLcoXBZ4c9w+79t/j/vsi7/LjkUv8eOQSAPmd7Ey7/euUdKWqWz457i9EJjBb9s+ysbGhefPmfPvtt5mVRwhhRextdKbd+F/y5Lj/qdvR7I28a1oByOi4f52SBfCR4/5CvBVmy37jxo2mnxVF4fz58+nuXieEEK9Kq9XgVTgPXoXz0L/2k5tjXXkYbzrmvy/y7guP+79bPD92CdGUrZCMi4OtmosgRLZktrWf3m3uqTx58jB79uzMyiOEyGGK53GieJ6SdP37uH90YvIz3/e/w8Gouxy99gCAhSfW8L1/bfyrlJBj/UK8BrNlP3nyZFJSUoiMjCQtLY2yZcvKlr0QItPkdrBNd9z/z0u3qT9vOwBJaUZ6rNjL7JDTBDWrTBPPwlL6QrwCs60dERHBoEGDyJ07N0ajkXv37jFv3jwqVapkiXxCiByuStG8pkv8lsproFLRPGw4cZUWi3fgW7IAQc0rU690QbVjCpGlmS37r7/+mn//+9+mcg8PD2fChAmsXbs208MJIcTTS/yu232ADvVrYbCz4fiNhwT+Hs7mk9doOH8773kUJqhZpRdeGVAIAWa/45KQkJBuK75y5cokJSVlaighhHiWwc4Gr/yOGOyenJnvXSQPG/s0IPTz5jT2KEzwuZvU/s/vtPl+F8duPFA5rRBZj9myz5UrF8HBwabHwcHBcj97IUSWUKN4fn7/+D12fdoEv1IF2HLqGlVn/or/jyGcuR2jdjwhsgyzu/EnTJjAsGHDGD16NADFihVj2rRpmR5MCCFeVd3SBdn1aRP+OHeTwN/CWXMsinXHr9C9WkkCm3hTKp+z2hGFUJXZsi9RogRr1qwhISEBo9GY7na3QgiRVWg0Gpp4FqGxR2F+OXmNcb+Hs+zIJX46GkmfmmUY/d47uOV2UjumEKowW/bHjx/nhx9+4OHDhyiKYnr+xx9/zNRgQgjxJjQaDW28itGqghtrjkUxftsxFoWeZ+nhi/Sv7cHwhl4UdHZQO6YQFmW27IcPH06PHj0oU6aMfJ9VCJFtaLUaulQpQQfv4iwPi2TCH8f4JuQMiw+c5zPfcvyrQUXyOtqpHVMIizBb9vb29nTv3t0SWYQQ4q3T67R8UKM03aqW4PtDF5j0xwmm7jzJgv3n+KJeBT6vWw4Xe7kEr7BuGZ6Nf+PGDW7cuEH58uVZsmQJV69eNT1348YNS2YUQoh/zFav45Panpwb1ZaZrathp9cyftsxykzcyIxdJ0lITlU7ohCZJsMt+x49eph+PnDgQLpj9BqNhh07dmRuMiGEyAQONnoG16vAh7XKMufPM8zYfYrhW44ya88pRjV6h498ymKn16kdU4i3KsOy37lzpyVzCCGERRnsbBj53jt8UseTWbtP8c2fp/l842Fm7D7JmMbe9Hq3NDY6s5ciESJbyLDsR44c+dIJJ0+e/NbDCCGEpeV2sCWoeWU+8yvH9F0nmbf3LB+vOcD0XScJbOKNf5US6LRS+iJ7y7Dsa9SoYckcQgihKleDPdNaVWNIvfJMCo5g8YHz9Fy5jyk7IhjfrBLtvIqj1co3kkT2lGHZ+/r64urq+sYn4xmNRsaPH8/Zs2extbXl66+/xt3d3fT6li1bWLp0KTqdDg8PD8aPH09aWhqjRo3i+vXrJCcn88knn9CoUSNOnjxJ//79KVGiBABdu3alRYsWb5RLCCFeprCLI3Pa1+Bf9Svw9R8nWHrkIp2XhlClaF6Cmlemebki8jVkke1kWPZjxoxh4cKF9OjRA41Gk+6COq9ygl5wcDDJycmsXr2a8PBwpkyZwoIFCwB4/Pgxs2fPZvPmzTg4OPDFF1+wa9cuoqOjyZ07N9OnT+fhw4e0a9eORo0acerUKXr37k2fPn3e0mILIcTLuec1sLiLD8MaViRo2zFWhV+m1Xc78XF3Jah5JRqWLax2RCFeWYZlv3DhQuDNT9QLCwvDz88PeHKnvIiICNNrtra2rFq1CgeHJ1exSk1Nxc7OjmbNmtG0aVPT+3S6J2fERkREEBkZyY4dO3B3d2fUqFFy2V4hhEV4uLqwvIcfIxp5MX7bcTacuELjb4NpWKYQQc0r41NCbqsrsr5XulxuWFgY3bt3p3///pw6dYpp06ZRt27dl04XFxeXrpB1Oh2pqano9Xq0Wi358+cHYNmyZSQkJFCnTh3TrrG4uDgGDRrE4MGDAfD29qZTp054eXmxYMEC5s2bx/Dhw186/2dXLnKysLAwtSPkCDLOlqH2OI98x0DboiVZeOwuOy/cYuec36lTxEB/b1c881rPJXjVHuecxFJjbbbsv/76az777DO2bduGnZ0d69ev57PPPjNb9gaDgfj4eNNjo9GIXq9P93j69OlERkYyZ84cU9HfvHmTAQMG0K1bN1q1agVA48aNcXFxMf08YcIEswvm5eWFnV3OvhRmWFgY1apVUzuG1ZNxtoysMs7VgB6NYV/kHQJ/C2f3xdvsuxFHe+/ifNW0EhUK5VY74j+SVcY5J3ibY52UlPTSjVyz3ycxGo34+fmxe/dumjZtSpEiRUhLSzM746pVqxISEgJAeHg4Hh4e6V4PDAwkKSmJ+fPnm3bn37t3jz59+jBs2DA6duxoem/fvn05fvw4AKGhoVSsWNHs/IUQIjPVKVmA4E8as/3j96hZPD/rj1/Be8Zmeq7cy4V7j9SOJ0Q6ZrfsHRwc+OGHHzh48CCBgYH8+OOPODmZv01k48aN2bdvH/7+/iiKwqRJk9i8eTMJCQl4eXmxdu1aqlevTq9evQDo2bMnBw8e5NGjR8yfP5/58+cDsHjxYsaPH8+ECROwsbEhf/78r7RlL4QQmU2j0dDIozANyxbi19PXCfwtnBVhkaz66zLdq5ak7TvFaVS2EAY7G7WjihxOozx7mv0L3L59mzVr1lC7dm2qVq3K9OnTCQgIoFChQpbK+Fqe7sqQ3fiyO85SZJwtIzuMs9GosP7EFQJ/C+fs3Sdb9+55nDg+rFW2KfzsMM7WIjN242fUfWa37AsWLMjAgQNNj4cNG/ZWggkhhLXRajV0rORO0VwO+M7ZBkDUw3iOXL1P/TJZcwNJ5AxyDUghhHjL3imch3IFXEyPlx25pGIaIaTshRDirTPY2XBwcAt2D2iCV6HcLDl8kU0RV9WOJXIwKXshhMgEBjsb/EoVZGWAH3Z6Lf1+DuXWo0S1Y4kcSspeCCEyUcVCuZn6flXuxSfRZ/V+zJwTLUSmkLIXQohMNtC3HE08i7DtzA3m7zurdhyRA0nZCyFEJtNoNPzg70N+Jzu+3HyUU7ei1Y4kchgpeyGEsIDCLo4s6uzD49Q0eqzYS1Kq+SuRCvG2SNkLIYSFtPEqxoe1ynDsxkMCfwtXO47IQaTshRDCgma2rk6Z/M7M3HOKXRduqR1H5BBS9kIIYUEGOxuWdfdFq9Hwwcp9PExIUjuSyAGk7IUQwsJqFM/PuCbeXItJ4JO1B+XreCLTSdkLIYQKRjTyok4JV9Yci2JZmFxOV2QuKXshhFCBTqtlabc6ONvZMGj9YSLvx6odSVgxKXshhFBJyXzO/Kf9u8QmpdBr5T5S04xqRxJWSspeCCFUFFCtFJ0qubPv8l2m7oxQO46wUlL2QgihIo1Gw4KONXHL5chX249z6Mo9tSMJKyRlL4QQKsvjaMeSbnUwKgoBK/YSl5SidiRhZaTshRAiC2hQphBD61Xgwr1Yhv5yRO04wspI2QshRBYR1LwylYvk4bsDF9h44oracYQVkbIXQogswk6vY1l3X+z1Ovr9fICbjxLUjiSshJS9EEJkIRUK5WZaq6rcT0iiz6pQjEa5up7456TshRAii/m0jifNyhVh+9kbzN93Vu04wgpI2QshRBaj0Wj4vktt8jvZ8eWWME7eilY7ksjmpOyFECILKuTiwOLOPiSlGumxfC9JqWlqRxLZmJS9EEJkUa29ivFRrbIcv/mQMVvD1Y4jsjEpeyGEyMJmtq6Gh6sLs/acYse5m2rHEdlUppW90WgkMDCQLl26EBAQQFRUVLrXt2zZQqdOnfD39ycwMBCj0ZjhNFFRUXTt2pVu3boxbtw4jEa5WYQQImdwsrNhWXdf9FoNvVft50FCktqRRDaUaWUfHBxMcnIyq1evZujQoUyZMsX02uPHj5k9ezY//vgjq1atIi4ujl27dmU4zeTJkxk8eDArV65EURR27NiRWbGFECLLqV4sH+ObVuJ6TAKfrD2IosjX8cTrybSyDwsLw8/PD4DKlSsTEfHfuznZ2tqyatUqHBwcAEhNTcXOzi7DaU6ePEmNGjUAqFu3Lvv378+s2EIIkSV92bAiviULsPZYFD8euaR2HJHN6DPrg+Pi4jAYDKbHOp2O1NRU9Ho9Wq2W/PnzA7Bs2TISEhKoU6cOv/322wunURQFjUYDgJOTE7GxsWbn/+zKRU4WFhamdoQcQcbZMnL6OP/rHRf+unaPgWsPkCfhDkUNtpkyn5w+zpZkqbHOtLI3GAzEx8ebHhuNRvR6fbrH06dPJzIykjlz5qDRaDKcRqv97w6I+Ph4XFxczM7fy8sLOzu7t7Q02VNYWBjVqlVTO4bVk3G2DBlnqAbMNxSk18p9TDsWw+4BTdDr3u4OWhlny3mbY52UlPTSjdxM241ftWpVQkJCAAgPD8fDwyPd64GBgSQlJTF//nzT7vyMpqlQoQIHDx4EICQkhOrVq2dWbCGEyNK6Vy1Jl8olCI26y5SdsgdTvJpM27Jv3Lgx+/btw9/fH0VRmDRpEps3byYhIQEvLy/Wrl1L9erV6dWrFwA9e/Z84TQAw4cPZ+zYscyaNYtSpUrRtGnTzIothBBZmkajYV6HGuy/fIeg7cdp7FGYmu6uascSWVymlb1WqyUoKCjdc6VLlzb9fObMmRdO97/TAJQsWZLly5e/3YBCCJFN5XG0Y0nXOrz37R/0XLmPsC9aYrCzUTuWyMLkojpCCJEN1S9TiH/Vr8iFe7EM2XhE7Tgii5OyF0KIbCqoWSWqFM3LD4cusOHEFbXjiCxMyl4IIbIpW72OZd19sdfr6PdzKDdiEtSOJLIoKXshhMjGyhfMxYzW1XiQkEyfVfsxGuXqeuJ5UvZCCJHN9a/tQfPyRfnj3E3m7n3xyc8iZ5OyF0KIbE6j0fB9Fx9cDXaM+PUoJ24+VDuSyGKk7IUQwgoUdHbguy61SUo1ErBiL49T0tSOJLIQKXshhLAS71dw42MfD07cjGbMb3+pHUdkIVL2QghhRWa0roanqwv/3nOa4HM31Y4jsggpeyGEsCKOtnqWdfdFr9XQ+6d93I9PUjuSyAKk7IUQwspUK5aPr5pV4sajRPqvPYCiyNfxcjopeyGEsELDGlSkbqkCrD9+haWHL6kdR6hMyl4IIayQTqtlSdc65LK34fONh7h4L1btSEJFUvZCCGGl3PMamNuhJnFJqfRauY/UNKPakYRKpOyFEMKKdatakq5VShAadZdJwSfUjiNUImUvhBBWbm6HmhTP48TXwSc4EHVX7ThCBVL2Qghh5XI72LKkax2MikLPFfuIfZyidiRhYVL2QgiRA9QrXZAvG1Tk4v1Yhmw6rHYcYWFS9kIIkUOMb1qJqm55+b9DF1l//IracYQFSdkLIUQOYavXsaybLw42Oj5eE8r1mAS1IwkLkbIXQogcpFzBXExvXY0HCcn0/mkfRqNcXS8nkLIXQogcpr+PBy0rFGXH+VvM2XtG7TjCAqTshRAih9FoNHzX2YcCBntG/nqUEzcfqh1JZDIpeyGEyIEKODvwvX9tklKN9Fi+l8cpaWpHEplIyl4IIXKoFuWL8kltDyJuRTNq61G144hMJGUvhBA52LRW1ShXwIVvQs6w/ewNteOITCJlL4QQOZijrZ5l3X2x0Wnps2o/0UmpakcSmSDTyt5oNBIYGEiXLl0ICAggKirqufckJibi7+/PxYsXAVi/fj0BAQEEBATQuXNn3nnnHR49esTJkyfx8/MzvbZ169bMii2EEDlOVbd8BDWrxM1HiYwIucbFe7Eoinwlz5roM+uDg4ODSU5OZvXq1YSHhzNlyhQWLFhgev3EiROMGzeO27dvm55r37497du3B+Crr76iQ4cOuLi4cOrUKXr37k2fPn0yK64QQuRoH/uUZcL24xy9m4DH5I3Y6bSUyGugeB4nSuQ1UCKvE+55DLj//biQswNarUbt2OIVZVrZh4WF4efnB0DlypWJiIhI93pycjLz5s3jyy+/fG7aEydOcOHCBcaNGwdAREQEkZGR7NixA3d3d0aNGoXBYMis6EIIkeOcufOIhGfOyC+Zz8C9+CTO3n30wvfb6rS453F6ZmXgvysC7nmcKOzigE4rR4qzikwr+7i4uHSFrNPpSE1NRa9/Mstq1aplOO3ChQsZMGCA6bG3tzedOnXCy8uLBQsWMG/ePIYPH/7S+f/vykVOFRYWpnaEHEHG2TJknDNPcoqREi62XH6UTAkXW76tVwRHGy0JKUZuJaRwMy6Zm/Ep3IhP4VZ8Cjfjk7kZl8D5e7Ev/Dy9Fgo52lDYyZZCTjYUcbKhsMGGwk5PnnN10KOTPQMW+5vOtLI3GAzEx8ebHhuNRlPRv8yjR4+4dOkStWrVMj3XuHFjXFxcTD9PmDDB7Od4eXlhZ2f3BsmtR1hY2EtXqsTbIeNsGTLOme9Ylcqs232ADvVrYbCzeaVpEpJTiXoYT9TDOC4/iCfqQRyXH8Zx5WE8lx/Ec/h2/Aun02s1FMvtRIm8ThTPY6BEHifcn9lD4JbLEb3OuvcMvM2/6aSkpJdu5GZa2VetWpVdu3bRokULwsPD8fDweKXpDh8+TO3atdM917dvX8aOHYu3tzehoaFUrFgxMyILIUSOZrCzwSu/4ysXPTw5m798wVyUL5jrha8npqSaij/qYRxRD+O5/CCOqL8f77pwG7j93HQ6rQa3XI6m8nfPY8A979+HDPI44ZbbCZu/VwbiklI4eSuaioVyv1b2nCTTyr5x48bs27cPf39/FEVh0qRJbN68mYSEBLp06ZLhdJGRkbi5uaV7bvz48UyYMAEbGxvy58//Slv2Qggh1Odgo8ezQC48C7x4ZeBxShpXo5+sAFx+GM+VdHsI4gm5dJsXfTFAq9FQNJcDxXI7EnErhkePUyjobM/sttUpmz8Xbrkdye9kh0YjhwogE8teq9USFBSU7rnSpUs/975ly5ale/zhhx8+956KFSuyatWqtxtQCCGE6uxtdJR1daGsq8sLX09OTeNqdMLfKwNP9wg8PWwQR+jlezxdF7gd+5iuy/aaprXTaymayxG3XI4UzeVIsdxOuOV+8rNb7ieHCgoY7HPEtwoyreyFEEKIf8pWr6N0fmdK53d+4esPE5KoOXsrF+/HUcTFgf61PbgT95hrMQlcj07gWkwCf0beeeHeAQAbnZaiuRxwy+X090rA3ysHf/9vsdxOFHS2z/bfLJCyF0IIkW3lcbTj6ND3X3rMPiXNyM1HiVyLjudqdALXYxK4FhPPtac/Ryew//JdjBmsEei0Goq4/L1C8PdKgFvu/+4dcMvlSGEXhyx9QqGUvRBCiGzNYGdDTXfXDF+30Wkp/vc1ATKSmmbkdtxjrkanXwm4FhNv2kNw+Oo9QqNevEKg1Wgo5Gz/92ECJ4ql20Pw5PBBERcHbPU64MlJhRH3EvBMSrHISYVS9kIIIXI8ve7J8f2iuRzB/cXvSTManxwi+Lv8n64EXIuO//t/Ewi//pBDV+5nOJ+CzvYUcXHkwr1HxCalMi38AQcHt8j0wpeyF0IIIV6BTqulsIsjhV0ceTeD9xiNCvfiH5vK/3pMwpO9Bc+sHJy8FU1ymhF4cuXCk7eiX7pn4m2QshdCCCHeEq1WQwFnBwo4O1DVLd8L3xP7OJkas3/j3N1HlCvgQsVCuTM9l5S9EEIIYUHO9rYcHtLita9W+E9k3VMHhRBCCCv1Jlcr/Cek7IUQQggrJ2UvhBBCWDkpeyGEEMLKSdkLIYQQVk7KXgghhLByUvZCCCGElZOyF0IIIayclL0QQghh5aTshRBCCCtndZfLVf6+H3FycrLKSbKGpKQktSPkCDLOliHjbBkyzpbztsb6aec97cD/pVEyeiWbio2N5dy5c2rHEEIIISzOw8MDZ2fn5563urI3Go3Ex8djY2ODRqNRO44QQgiR6RRFISUlBScnJ7Ta54/QW13ZCyGEECI9OUFPCCGEsHJS9kIIIYSVk7IXQgghrJyUvRBCCGHlpOyt1P3796lXrx4XL15UO4pVW7hwIV26dKF9+/asWbNG7ThWKSUlhaFDh+Lv70+3bt3kbzoTHDt2jICAAACioqLo2rUr3bp1Y9y4cRiNRpXTWY9nx/n06dN069aNgIAA+vbty7179zJ13lL2ViglJYXAwEDs7e3VjmLVDh48yF9//cVPP/3EsmXLuHXrltqRrNKePXtITU1l1apVDBgwgNmzZ6sdyaosXryYMWPGmC7uMnnyZAYPHszKlStRFIUdO3aonNA6/O84T5w4kbFjx7Js2TIaN27M4sWLM3X+UvZWaOrUqfj7+1OgQAG1o1i1vXv34uHhwYABA+jfvz/169dXO5JVKlmyJGlpaRiNRuLi4tDrre7Cn6oqXrw4c+bMMT0+efIkNWrUAKBu3brs379frWhW5X/HedasWZQvXx6AtLQ07OzsMnX+8v8aK7N+/Xry5s2Ln58fixYtUjuOVXv48CE3btzg22+/5dq1a3zyySf8/vvvcjGnt8zR0ZHr16/TvHlzHj58yLfffqt2JKvStGlTrl27ZnqsKIrpb9jJyYnY2Fi1olmV/x3npxtjR48eZfny5axYsSJT5y9b9lZm3bp17N+/n4CAAE6fPs3w4cO5e/eu2rGsUu7cufH19cXW1pZSpUphZ2fHgwcP1I5ldZYsWYKvry/btm1j06ZNjBgxQq7dnomevfpafHw8Li4uKqaxblu3bmXcuHEsWrSIvHnzZuq8pOytzIoVK1i+fDnLli2jfPnyTJ06FVdXV7VjWaVq1arx559/oigKt2/fJjExkdy5c6sdy+q4uLiYrvWdK1cuUlNTSUtLUzmV9apQoQIHDx4EICQkhOrVq6ucyDpt2rTJ9G91sWLFMn1+shtfiDfUoEEDDh8+TMeOHVEUhcDAQHQ6ndqxrM4HH3zAqFGj6NatGykpKQwZMgRHR0e1Y1mt4cOHM3bsWGbNmkWpUqVo2rSp2pGsTlpaGhMnTqRw4cJ89tlnALz77rsMGjQo0+Yp18YXQgghrJzsxhdCCCGsnJS9EEIIYeWk7IUQQggrJ2UvhBBCWDkpeyGEEMLKSdkLkQVdu3YNT09P9u3bl+75hg0bprsK15t6W5/zMjdu3KBp06a0adOGuLg4s+/39PR8o/mMHDmS69evv9G0QuQUUvZCZFE2NjaMHTv2lYoyKzp06BBeXl5s2rQJg8GQafM5ePAg8g1iIV5Oyl6ILKpAgQLUrl2bqVOnPvfawYMHTbfKBBgxYgTr16/n2rVrtGnThiFDhtCqVSuGDx/OqlWr6NKlC82aNUt3e9i5c+fStm1bunTpwpkzZwC4d+8en376Ke3bt6dDhw6mm6DMmTOHvn370qJFC1auXJkuS2RkJAEBAbRq1YouXbpw/PhxTp8+zezZs/nzzz8JDAxM9/7o6GgGDBhA8+bNadOmDaGhoelenzNnTrobhjzdC3HmzBk6d+5M+/bt6dq1K5cvX2bRokXcuXOHfv368fDhQ44fP07Xrl1p164dffr04erVqwAEBAQwcOBAmjZtyvHjxxk2bBht27albdu2/Pzzz2/y6xEiW5GyFyILGzFiBHv37n1ud/7LnD17lo8++ohNmzZx9OhRrl+/zurVq3n//fdZvXq16X3u7u5s3LiRTz/9lBEjRgBPbrvZoUMH1q9fz4IFCwgMDDTtWUhOTmbr1q1069Yt3fyGDRtGQEAAmzdvZuTIkXz++eeULl2aQYMG0bBhQ4KCgtK9/5tvvqF48eL89ttvTJs27ZVvWbt06VJ69+7N+vXr6dy5M+Hh4fTr148CBQqwaNEinJycGDNmDDNnzmTDhg307t2bsWPHmqb39PRk27ZtPH78mJiYGDZu3MjChQs5cuTIK4+tENmVXC5XiCzMYDAwYcIExo4dyy+//PJK0+TPn58KFSoAUKhQIXx8fAAoUqRIuuP0nTp1AqBevXoMGzaMR48esX//fi5dusR//vMfAFJTU01bx97e3s/NKz4+nitXrtCkSRMAKleuTK5cubh06VKG+Q4fPsyMGTOAJwX87ArIy9SrV4+goCD+/PNPGjZsSIMGDdK9fvnyZa5evconn3xieu7ZQyBP85ctW5bIyEj69u1L3bp1+fLLL19p/kJkZ1L2QmRxvr6+z+3O12g06Y5Tp6SkmH62tbVNN31G1+t/9nlFUdDr9RiNRpYuXWq6oc+dO3fIly8fwcHB2NvbP/cZLzpWrijKS29Uo9fr090G+OLFi5QsWTLdshmNxueWrVmzZlSpUoVdu3axZMkSdu/ezddff216n9FoxM3NjU2bNgFPrj9+79490+tP8+fJk4dff/2Vffv2sWfPHtq1a8evv/4qd3cTVk124wuRDTzdnX/nzh3gSWFdvXqVpKQkoqOjCQsLe+3P3Lx5MwB//PEHpUuXxtHRkVq1apmOyV+4cIFWrVqRmJiY4WcYDAbc3NzYvn07AOHh4dy7d4+yZctmOE316tX59ddfgSdF/9FHH6Ur/zx58nDhwgUAjh8/brpF8+DBgzlx4gT+/v58/vnnnDp1Cniy0pKWlkapUqWIiYkx7ZZft24d//rXv56b/44dOxg2bBj169dnzJgxODo6cvPmzVcbNCGyKdmyFyIbeLo7v2/fvsCTXdH16tWjZcuWFC1alGrVqr32Z16+fJk2bdrg5OTElClTABgzZgyBgYG0atUKgGnTppk9k3769OmMHz+eOXPmYGNjw5w5c57bu/CsQYMGMWbMGFq3bo1er2fatGnpyr5FixZs27aNFi1aULFiRdMhif79+zN69GjmzZuHjY0N48ePB6B+/fr069eP7777jm+++YaJEyeSlJSEwWB44cmNdevWZfv27bRs2RI7Oztat279xl/7EyK7kLveCSGEEFZOduMLIYQQVk7KXgghhLByUvZCCCGElZOyF0IIIayclL0QQghh5aTshRBCCCsnZS+EEEJYOSl7IYQQwsr9P4s7BAKeqQGcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7JYy2aKelpXH58mVDxxBCCCH0zsPD46kz0xlt0X40oYeHh8cTs2wVNefOnZPZifRA+lk/pJ/1Q/pZfwqyr7Oysrh8+XKesw8abdF+NCRuZWX12LSERZX0gX5IP+uH9LN+SD/rT0H3dV6nheVCNCGEEMJESNEWQgghTIQUbSGEEMJESNEWQgghTIQUbSGEEMJESNEWQgghTIQUbSGEEMJESNEWQgghTIQUbSGEEEYhNTObkBsJpGZmGzqK0TLaGdGEEEIUHfEpGTT8age3UjKo6e5EyIRuOFg/fSrPokyOtIUQQhjc8LVB3ErJAOBi/H3CbyUZNpCRkqIthBDCoFafjOCvi3Gax1VdHahTytlwgYyYFG0hhBAGE5GYwjsbQ7C3smBauzoANC5fQobG8yBFWwghhEHk5KrxXxXI/QfZLO7TlNldG1K/THHWn4nkTNxdQ8czSlK0hRBCGMSne8MIvpHAgAaVGNq4CmZmKuZ1fwVFgQ93nDJ0PKMkRVsIIYTeHbl+m7n7wqhY3J4f+jXTrB/dqUZp2lUrxV8X4zhw9ZaBUxofKdpCCCH06l56Jv6rjgKw4jUvnG2tNK+pVCrm9XgFgGl/nkRRFINkNFZStIUQQuiNoii8/UcI0UnpzOhYl1aV3Z94T+PyrvRvUJET0YlsPBtlgJTGS4q2EEIIvfn1+DU2nLlBq0pufNihbp7vm9O1ARZmKqbvPEV2rlqPCY2bFG0hhBB6cSk+mXe3HKeYjSUrXvPCwjzvElSthBOjWnhw5U4KP4dc1WNK4yZFWwghhM5l5eQyZNVR0rNy+aFfMyq6OGjdZnrHuthbWTB7zxmZj/wfUrSFEELo3IxdpzkZc5dhTaoysGHlfG1T0tGWST61uZ3ygG8OX9BxQtMgRVsIIYRO7bt8ky8PnqdaCUe+8W3yXNu+510bNwdrvjhwnoTUBzpKaDp0usqXr68vjo6OAJQrV4633nqLqVOnolKpqF69OrNmzcLMTH5vEEKIwioh9QGvrwnEwkzFyte8cLR5vulJHW0smdGxHuM3n+CzfWF8/ZxFv7DRWcXMzMwEYMWKFaxYsYJ58+Yxb948JkyYwOrVq1EUhYCAAF01L4QQwsAUReGNdcHcvJ/BnK4NaFKhxAvt583m1ani6sCPQZeJSEwp4JSmRWdF++LFi2RkZDBixAiGDh3K6dOnCQ8Pp2nTpgC0adOGoKAgXTUvhBDCwJYEXebP8zG0q1aK933qvPB+rCzMmdO1Adm5amb+daYAE5oenQ2P29jYMHLkSF599VUiIyN58803URRFM1Wdvb09KSnaf2M6d+6criKalNDQUENHKBKkn/VD+lk/DNnP15IeMGl3BE5W5rzn6cipUydfan9VFYWaxW1YfTKCru5Qw8W2gJIWDH31tc6KduXKlalYsSIqlYrKlSvj7OxMeHi45vW0tDScnJy07sfT0xNra2tdxTQJoaGhNGrUyNAxCj3pZ/2QftYPQ/ZzRnYOIxbtIjNXYc3Q1nT1LF8g+/3GqSydf9rH79cf8FdHrwLZZ0EoyL7OzMx85sGqzobH//jjDz7//HMAbt++TWpqKq1atSIkJASAw4cP07hxY101L4QQwkCm/nmSc7eSeKulB70LqGADdPAoTQeP0uy9fJN9l28W2H5Nic6Kdr9+/UhJSWHQoEFMnDiRzz77jI8++ojFixczYMAAsrOz6dy5s66aF0IIYQA7zsfw3dFL1C5ZjC96FvyR/rzuDQH4cMdJ1Oqit5iIzobHrays+Oqrr554fuXKlbpqUgghhAHdvJ/OiLVBWFuYsWpIa+ysCr7EvFLOlYENK7H2VCQbztxgQMNKBd6GMZObpIUQQrw0tVph+Jog7qRlMr/HK9QrU1xnbc3p2gBLczNm7DpNVk6uztoxRlK0hRBCvLRvjlxg7+WbdKlZhrFeNXXaVhVXR0a3qM61xBT+71jRWkxEirYQQoiXcirmLtN2nKKkow2/DmypubVXlz7qUBcHawvm7D1LyoOis5iIFG0hhBAvLC0zm9dWHiE7V80vA1vi7qif+6fdHW1536cO8akP+PrQeb20aQykaAshhHhh7237m0sJ95nQphZdapbVa9sTvWvh7mDDV4fOczslQ69tG4oUbSGEEC9k49kb/N+xq9QvU5zP/rkVS58crC2Z2akeqZk5zN0bpvf2DUGKthBCiOcWfS+N0euPYWtpzqohrbG2MDdIjjeaV6daCUd+Cr7MtTuFfzERKdpCCCGeS65azbA1gdzLyGJh78bUKlnMYFkszc2Y07UBOWqFGbtOGyyHvkjRFkII8VwW7A/n0LXb+NYtz5vNqxs6Dv3qVaRxeVfWnY4kNDrR0HF0Soq2EEKIfAu5kcCs3WcoW8yOpa+20MvtXdqYmak005tO2/Fyq4kZOynaQggh8uX+gyyGrDqKWlH4bXArXO2NZwXGdtVL06lGGQKu3GLvpThDx9EZrUU7OTmZ6dOnM3ToUJKSkpg2bRrJycn6yCaEEMKIjNt0guuJqUxp50nbaqUMHecJ/zvaPlVoFxPRWrRnzJhB3bp1SUpKws7ODnd3dyZPnqyPbEIIIYzEqtDrrAy9TpPyrnzcub6h4zxVg7IuDH6lMqdi77LudKSh4+iE1qIdExPDgAEDMDMzw8rKiokTJ3Lr1i19ZBNCCGEEriem8M7G4zhYW7ByiBeW5sZ7ZnV2l/qFejERrT1vbm5OSkqK5mKDyMhIzMyM9w9MCCFEwcnOVeO/6igpmdks7tOUaiWcDB3pmSq7OvJ2Sw8i7qayNPiKoeMUOK3Vd/z48fj7+xMXF8eYMWMYPHgwEyZM0EM0IYQQhjZnz1mO3bjDwIaV8G9UxdBx8uXDDnVxtLZkzt6z3H+QZeg4BUpr0XZzc+OXX35h/vz59O3bl23btuHj46OHaEIIIQzp0LXbfBYQRiUXe37o28wobu/KDzcHGya3rc2dtEwWHrxg6DgFSmvRnjhxIi4uLvj4+NC+fXtKlCihj1xCCCEM6F56JkNXHcVMpWLla60pZmtl6EjPZUKbWpR0tGHhofPcul94FhPRWrSrVavGd999x5EjRzhx4oTmPyGEEIWToiiM3nCMmOR0ZnaqR4tKboaO9NzsrS2Z2ak+aVk5fLr3rKHjFBgLbW9ISkoiJCSEkJAQzXMqlYrff/9dp8GEEEIYxi/Hr7LxbBStq7gzrb2noeO8sJHNqrHo0HmWHbvCu21qUd3NuC+iyw+tRXvFihUApKamolarcXIy/Q8thBDi6S7FJzNhywmK2Vjy+2AvzE34biFLczM+7daQAb8fZsau06wd2sbQkV6a1j+N6Oho+vXrR/v27enQoQO+vr5ERkbqIZoQQgh9yszJ5bWVR0nPymXJq82pUNze0JFeWt96FWhawZUNZ25wIuqOoeO8NK1Fe+bMmbzxxhuEhIRw/PhxRo0axYwZM/SRTQghhB7N2HWaU7F3Gd60Kv0bVDJ0nAKhUqmY1/0V4OFiIopi2tObai3a9+7do0uXLprH3bp1IykpSZeZhBBC6NneS3F8dfA81Us4ssi3iaHjFCifaqXoUrMMB67eZs+lm4aO81K0Fm0rKyvCw8M1j8+dO4etra1OQwkhhNCfhNQHvL4mCEtzM1YNaY2DtaWhIxW4z7o3RKV6eLRtyouJaL0Q7cMPP2TcuHE4OzujKArJycl8/fXX+sgmhBBCxxRFYeS6IG6lZDC/xys0Ku9q6Eg6Ub+MC6+9UoWVoddZcyqC10xkdrf/0lq0GzRowO7du4mMjEStVlO2bFkcHBz0kU0IIYSO/Rh4mR3nY2lfvRTvedc2dBydmt2lPutPRzLzr9P0q18RawtzQ0d6blqHx3fu3EmfPn2oXr06tra2dO/enX379ukjmxBCCB06d/Me72//G1c7a5YPaoWZmWlMU/qiKro48I5XDSLvpvFT0GVDx3khWov2jz/+yK+//gpAhQoV2LRpE4sXL9Z5MCGEELqTkZ3DayuPkpmj5v8GtKBMMTtDR9KLae3r4mRjyad7w0jOML3FRLQW7ezs7MfmG3d1dTX5S+aFEKKom7L9JOduJfF2Sw96eZY3dBy9cbW3Zkq7OiSmZ/LVwfOGjvPctBbtRo0a8d5773HgwAEOHjzI5MmTadCggR6iCSGE0IU/z8fwfeAlapcsxhe9Ghk6jt6Nb12L0k62fH34PDfvpxs6znPRWrRnzZpFnTp1WLduHX/88Qeenp5Mnz5dH9mEEEIUsJv30xm5Nghri4e3d9laar0eudCxs7JgVuf6pGflMmdPmKHjPJd83afds2dPlixZwrBhw7CwsECtVusjmxBCiAKkViu8viaIO2mZLOjRiHplihs6ksEMb1KVGm5O/F/IFS4n3Dd0nHzL15H2okWLuHr1KpMnTyY8PFyOtIUQwgQtOnyBfZdv0rVWWd7xqmHoOAZlYW7G3O4NyVUrTN95ytBx8k1r0Q4LC2Pu3Lns2rWLvn378tlnnxEREaGPbEIIIQrIyZhEPtx5ipKONvwyoAUqVeG+vSs/fD3L07xiCTaejSLkRoKh4+SL1qKdm5uLWq0mICCANm3akJGRQUZGhj6yCSGEKABpmdm8tvIo2blqlg9qhbujTEUNjy8mMvVP01hMRGvR9vX1xcvLi7Jly1K/fn369u3LgAED9JFNCCFEAZi49W8uJ9znPe/adKpRxtBxjEqbqiXpXrssh6/Hs+tinKHjaKX1ssHhw4czbNgwzP5ZCH3lypW4uLjoPJgQQoiX98eZG/wccpWGZV34tFsDQ8cxSp91a8jOC7F8uOMknWuUxtxM6/GsweQrmdm/PoAUbCGEMA3R99IYveEYdlbmrBriZZJzbeuDZ+niDG1clbCbSaw6adzXbBnvrxNCCCFeWK5aYejqoyRlZPF17ybUcC9m6EhG7ePO9bG2MGPWX2d4kJ1r6Dh5kqIthBCFTGpmNgv+vsnh6/H0qVeBkc2qGTqS0atQ3J6xXjWJupfGj0GXDB0nT1qLdmxsLMOHD6dTp07Ex8czdOhQYmJi9JFNCCHEc0rNzMZzwTY2X03CwkzFV70aye1d+TS1vSfFbCz5bF8YSUa6mIjWoj1z5kxGjhyJvb09bm5u9OjRgylTpugjmxBCiOegKAozd50mOunhfNo5aoWb9+UW3fxysbNmantP7qZn8cWBcEPHeSqtRfvevXt4eXmhKAoqlYr+/fuTmpqqj2xCCCHy6UF2LiPWBvHNkYuY/7Mudk13J+qUcjZsMBMzrnVNyhaz45vDF4hNNr7FRLQWbRsbG27duqUZXvn777+xsrLK184TExPx9vbm2rVr3Lhxg0GDBjF48GBmzZol85cLIUQBiU1Op+0Pu/n97+s0Ke9K2OSe/NKpEiETuuFgbWnoeCbF1tKCWZ3rkZGdy+w9Zwwd5wlai/bUqVMZPXo0kZGR9O7dm/fffz9fc49nZ2czc+ZMbGxsAJg3bx4TJkxg9erVKIpCQEDAy6cXQogiLjgygaZf7+R4VCJDGlXh4DudqeFeDM8SdlKwX9CwxlWpVbIYv4Rc4+LtZEPHeYzWol2xYkX++OMP1q9fz/z589mzZw8lSpTQuuP58+czcOBA3N3dAQgPD6dp06YAtGnThqCgoJeMLoQQRduvx6/S7oc9xKc+4KtejVg+qCU2lnIv9suyMDdjbreGqBWFj3YZ12Iiec6IdvPmTRRFYdSoUSxbtgx7e3sAbt++zZtvvslff/2V5043bdqEi4sLrVu3ZunSpQCac+IA9vb2pKSk5CvguXPn8v1hCrPQ0FBDRygSpJ/1Q/r55eSoFRadvM36y3dxsjJjbpvyNHPI4OTJk4+9T/r5xZVVFOqVsGVLWDS//nWEem52z3y/vvo6z6L97bffEhISQnx8PK+99tr/NrCwwMfH55k73bhxIyqViuDgYC5cuMCUKVO4e/eu5vW0tDScnJzyFdDT0xNra+t8vbewCg0NpVGjRoaOUehJP+uH9PPLuZP6gIErDnPg6l3qlCrG5uFtqVrC8Yn3ST+/vMUuFfD+fje/XUvnQGevPG+dK8i+zszMfObBap5Fe968eQAsXbqUUaNGPfZaVtaz719btWqV5md/f38+/vhjvvjiC0JCQmjWrBmHDx+mefPm+foAQgghHjobdw+/Xw8QeTeN3p7l+W1QKxxt5Ly1rnhVcadnnXJsD49hx4VYetQuZ+hI2s9p//eCMbVaTd++fZ+7oSlTprB48WIGDBhAdnY2nTt3fu59CCFEUfXHmRu0WryLyLtpzOxUjz+GeUvB1oO53RpiplLx4Y6T5BrBXU95HmkPHTqU48ePA1CzZs3/bWBhQbt27fLdwIoVKzQ/r1y58kUyCiFEkaVWK3y8+wxz94Vhb2XBH69741e3gqFjFRl1SjkzrEkVfj1+jRV/R/B606oGzZNn0f79998B+PTTT/N1i5cQQoiCdf9BFkNXB7I9PIYqrg5sGu5D3dLFDR2ryJnVqT5rTkYy66/TDGhYEVtLrata64zW4fHp06ezfft2vv76azIyMtiyZYseYgkhRNF2JeE+Lb/9i+3hMbSvXoqQCd2kYBtI+eL2jGtdk5jkdH44atjFRLQW7S+//JJDhw6xZ88ecnJy2LhxI59//rk+sgkhRJG0+2Iczb/ZxYXbyUxoU4udb7bHxa5o30VjaFPa1cHZ1op5Aee4l55psBxai/bRo0f54osvsLa2xtHRkV9//ZXDhw/rI5sQQhQpiqLw1YFwevzffjKyc/hlYEu+6t0YC3NZRdnQittZM629J/cysliw33CLiWj9JpiZPXzLo/vTsrKyNM8JIYQoGBnZOQxdHcgHf56klKMNB9/pzLAmhr3oSTzuHa8alCtmx7dHLhKTlGaQDFqrb5cuXZgwYQLJycksX76cIUOG0KNHD31kE0KIIiH6Xhre3+1m9ckImlcswfGJ3WhaQft00UK/bC0t+LhLfR7k5PLJ7rMGyaD1ErhRo0Zx5MgRypQpw82bNxk3bhxt27bVRzYhhCj0jl6P59XfDhGf+oDhTavyfd9mWFvI/OHGamjjKnx96DzLT1xjonctaut56dN8jXOXKVOG9u3b06FDBxwcHDhx4oSucwkhRKG37NgVOizZS2J6Jt/6NWFZ/xZSsI2cudm/FhPZqf/FRLQeac+YMYPDhw9TocL/buZXqVSa+7iFEEI8n6ycXCZu/ZslQZdxtbNm7dDWtKte2tCxRD71qF0Or8rubAuPITAiHhs9tq21aAcHB7N3716srKz0kUcIIQq1+JQMBvx+mMPX46lXujibhntT2fXJBT+E8VKpVMzr3pDW3+3mg22hvFnDgRqZ2XpZv1zr8Hjp0qXJzDTcPWlCCFFYnIq5S7NvdnH4ejx961XgyLjOUrBNVMvK7nSvVZZjUXcYuTeSZot2kpqZrfN28zzSnjZtGgC5ubn07t2bxo0bY27+v3Mtj1YBE0IIod3aUxG8sS6YjOxc5nRtwLT2nnku9ShMw9AmVdhxIRaAi/H3Cb+VRLOKbjptM8+i3bRp08f+L4QQ4vnlqtXM2HWa+fvDcbS2ZPPw1vTyLG/oWKIAdKlZlpruTlyMv09Ndyfq6OFK8jyLtp+fHwBxcXE6DyGEEIVRckYWr606yq4LsVQr4ciWEW2pVbKYoWOJAuJgbUnIhG5sPHiMvj7N9XJOW+uFaEOGDEGlUqEoCjk5Ody5c4datWqxceNGnYcTQghTdfF2Mn6/HuRywn061SjD6iFeFJf5wwsdB2tLPEvY6aVgQz6K9v79+x97fPbsWVatWqWzQEIIYep2nI9hyKqj3H+Qzfs+tfmse0PMZfpnUQCe+1tUr149wsMNN1m6EEIYK0VR+DwgjN6/HCArR83vg1sxv2cjKdiiwGg90v7uu+8ee3zlyhVcXV11FkgIIUxRWmY2b6wPZv3pG5QrZsem4T40Ki//VoqCpbVo/1fTpk3p3r27LrIIIYRJunE3lT6/HuR03D1aVXJjw+velHS0NXQsUQhpLdpjx47l7t27nDlzhtzcXBo0aICzs7MeogkhhPE7dO02/X87xJ20TN5sXp1v/ZpgJfOHCx3ReqLlyJEj9O7dm02bNrF582Z69erFgQMH9JFNCCGMlqIo/Bh4iU5L9pKUkcX3fZux5NXmUrCFTmk90v76669ZvXo15cs/nAwgOjqasWPHyvKcQogiKysnl3Gbj/N/x67i5mDN+qHetKla0tCxRBGgtWjn5ORoCjZA+fLlUavVOg0lhBDG6nZKBq8uP0RgZAINy7qwabgPFYrbGzqWKCK0Do+XKVOG5cuXk5qaSmpqKsuXL6ds2bL6yCaEEEbl7+hEmn69k8DIBAY0qMThsZ2lYAu90lq0586dy+nTp+nQoQPt27fn1KlTzJ49Wx/ZhBDCaKwMvY73d7uJvZ/OvO4NWTXECzur574BR4iXovUb5+rqyqJFi/QQRQghjE9yRiZjNx1n9clIitlYsuF1b7rVktFGYRh5Fu127do9c9m4gIAAnQQSQghjkZyRScU5m0jJzMHK3Ix9b3fklXIyYYownDyL9ooVK/SZQwghjIparTBkVSApmTkAZOWqyc6Vi3CFYeVZtC9fvkzbtm3ZsmXLU1+Xi9GEEIWVoiiM23ycnRdisbEw50FOrt7WSxbiWfIs2mFhYbRt25aQkJCnvu7r66urTEIIYTCKojDlz5MsCbpMvdLF2TrSh5v3M6hTyllvyy8KkZc8i/b48eMBmDdvnua51NRUbt68SfXq1XWfTAjxTKmZ2YTfSpJiUsBm7znLVwfPU9Pdid2j2+PuaEuF4g6GjiUEkI+rxzds2EBoaCgffPABvr6+2Nvb07t3b9566y195BNCPEVqZjbVP9tMfGomVV0dOTmpuxTuAvDF/nBm7zlLFVcH9rzVEXdZ9EMYGa33aa9Zs4b33nuPP//8k/bt27N9+3b27Nmjj2xCiDycibtHfGomANcSU9h5IdbAiUzf90cvMnXHSco727H3rY6ULWZn6EhCPCFfK7O7u7tz6NAhfHx8sLCwIDMzU9e5hBDPcDf98b+D4zaFcDIm0UBpTN8vIVcZv/kEpRxt2ftWRyq5yHC4ME5ai3a1atUYPXo0MTExtGjRggkTJlCvXj19ZBNC5OGvi3EALO7TlK99G5OYnkW7H/Zy6NptAyczPWtORjBqQzCudtbseasD1d2cDB1JiDxpPaf92WefcerUKapXr46VlRW9evWiTZs2+sgmhHgKtVphS1g0rnbWjGpeHQtzM0o52jJ0dSBdl+5jrX8benmW174jweawKIatCcTJ2pK/RreXW7qE0dN6pG1hYUGTJk1wdnYGHs6UZmEh8+0KYSjHbiRwKyWDXp7lsDB/+Fe4f4NKbB3RFnMzFf1+O8SKv68bOKXx++tiLINWHMHGwpwdb7aXmc6EScjXOW0hhPHYci4aAN+6FR57vnPNMuwZ3RFHa0teXxPIt4cvGCKeSTh49RZ9fz2EuUrFtpFtaVHJzdCRhMgXKdpCmBBFUdgcFoWDtQUdqpd+4vUWldw4+E4nSjnaMnHr33z81xkURTFAUuMVHJlAr58PkKsobBzujU+1UoaOJES+aS3aUVFRbNu2DUVRmDFjBn379iUsLEwf2YQQ/3H25j2uJ6bSrVZZbCzNn/qeuqWLc2RcZ6q4OjBn71ne3XwCtVoKN8DJmES6LwvgQU4ua/1b06WmTMcsTIvWoj1t2jTUajUBAQFERkYybdo05s6dq49sQoj/2Hz24dC433+Gxv+riqsjh8d2xrOUM98HXmLYmsAiv9hF+K0kuvwUwP3MbH4b1OqJ0wtCmAKtRTszMxNfX18OHDhAz549ady4MVlZWfrIJoT4j81hUVhbmNE1H0eIpZ3sOPhOJ1pUdGP1yQj6/HqQjOwcPaQ0PlcS7tNxyV4S0zNZ+moLBr1S2dCRhHghWou2ubk5u3fv5uDBg/j4+LBv3z7MzORUuBD6diXhPuduJdHBozSONvmbsrS4nTW7R7enU40y7LwQS9elASRnFK1fuiPvptJxyV5upzzgW78mjGhWzdCRhHhhWqvv7NmzOXjwILNmzcLd3Z0dO3bw6aef6iObEOJftoTlb2j8v+ytLdk6wof+DSpy5Ho87X7Yw+2UDF1ENDqxyel0XLKX6KR0Pu/+Cu941TR0JCFeitaiXaNGDcaMGYOVlRW5ubm899571KwpX3wh9G1zWBTmZip61i733NtaWZiz8jUvRrWozum4e3h/t5sbd1N1kNJ4xKdk0GnJXq4npjKjYz0mt6tj6EhCvDStRXvnzp2MGTOGuXPnkpSUxMCBA9m6davWHefm5jJt2jQGDhzIa6+9RlRUFDdu3GDQoEEMHjyYWbNmoVYX7QtjhMiv2OR0QqLu4F2lJCUcbF5oH+ZmZvzQtxnT2nty5U4Krb/bzflbSQUb1EjcTc+ky9IALsbf5z3v2szqLFMvi8JBa9FetmwZa9aswd7eHldXVzZv3szSpUu17vjAgQMArF27lvHjxzNv3jzmzZvHhAkTWL16NYqiEBAQ8PKfQIgiYGvYowlVXm56UpVKxafdGvJFz0bEJqfj/f1ujkfdKYiIRuP+gyy6LQ3gTNw93mrpwYKer6BSqQwdS4gCobVom5mZ4eDwvxVv3N3d83UhWocOHZgzZw4AcXFxlChRgvDwcJo2bQpAmzZtCAoKetHcQhQpm8OiAOhdQHOKv+dTm2X9W5CUkU2HH/cScPlmgezX0NIys+n18wFORCcyrElVFvs1lYItChWtk4hXr16dlStXkpOTw4ULF1i9enW+z2lbWFgwZcoU9u7dy7fffsuBAwc0f4Hs7e1JSUnRuo9z587lq63CLjQ01NARigRj7OekBzkcunYLT1dbbl+7SEGt41XfAuZ5lWV6YCzdlwXwaauytC2vnxWudNHPmblqJh2K5vitNDpUcOKtqlacOnWywNsxJcb4fS6s9NXXWov2zJkz+fHHH7G2tubDDz+kefPmTJkyJd8NzJ8/n/fff5/+/fs/tg53WloaTk7a/4Hw9PTE2to63+0VRqGhoTRq1MjQMQo9Y+3nX49fJVeBIc1r0ahRwV5M1agRNKx9E79fDzLtaCw/vVpe57dE6aKfs3PV9Ft+iOO30uhZpxwbhnljaV60b0011u9zYVSQfZ2ZmfnMg1WtRdvOzo5JkyYxadKk52p4y5Yt3L59m9GjR2Nra4tKpcLT05OQkBCaNWvG4cOHad68+XPtU4ii6NHQ+Muez85Le4/S7Hu7I92XBfDm+mCSMrJ4z6e2TtrShZxcNUNWHeXP8zF08CjNWv82Rb5gi8JLa9GuWbPmE+eE3NzcOHz48DO369SpE9OmTeO1114jJyeHDz/8kKpVqzJjxgwWLlxIlSpV6Ny588ulF6KQS3mQzb7LN/Es5Ux1N90NXTetUIJD73Smy9IAJm8P5W56JnO6NjD688FqtcIb64P548wNWldxZ/NwnzznZBeiMNBatC9evKj5OTs7m3379nH69GmtO7azs+Obb7554vmVK1c+X0IhirBdF2PJzFE/94QqL6J2KWcOj+1M55/2MS/gHInpmXzXpynmRjoDoqIojN10nBV/X6dpBVe2jWyLnZXWf9KEMGnP9bfR0tKSrl27cuzYMV3lEUL8y6Ohcb96uhka/69KLg4cHtuZBmWKszT4CkNWHSUrJ1cvbT8PRVGYvD2Un4IvU79McXa+2R4nGytDxxJC57T+WrplyxbNz4qicOXKFSws5LdZIXTtQXYuOy/EUsXVgXqli+ut3ZKOtgSM6UTvnw+w/vQNkh9ks2FoG+yt8zffuT58vPsMXx+6QK2SxfhrVHuK2xXti1VF0aG1+oaEhDz2uHjx4ixatEhXeYQQ/9h35SapmTmMblFB7+eWnW2t2DWqPf1/P8yuC7F0WRrAtpFtjaI4zg84x6d7w6jq6sie0R1wd7Q1dCQh9EZr0Z43b54+cggh/mPLo6vGC2hCledlZ2XB5uE+DF8TyJpTkbT9YQ+7RrWntJOdQfIALD5ygQ93nqK8sx173+pAmWKGyyKEIeRZtNu1a/fM3+5lClIhdCcnV822czGUcrSleUU3g+WwNDfj98FeFLez5ofAS7T5bje7R3egiquj3rP837ErTNjyN6Ucbdn7Vkcqujho30iIQibPor1ixQp95hBC/MuRiHgS0zN5q6UHZmaGve3KzEzFt35NcLGz4tO9YbT5bje7RrWnrh7Ps68Kvc5bfxzD1c6aPW910Ontb0IYszyLdtmyZQHIysri0KFDpKWlAQ9X74qJieHdd9/VT0IhiqDNZ/+5alwPt3rlh0ql4pMuDXC1s2bi1r/x+X4Pf77RjhaVdD8KsDksiuFrg3CytmT36A7UKeWs8zaFMFZaz2m/9957JCcnExUVRePGjQkJCeGVV17RRzYhiiS1WmHLuWiK21rhXbWkoeM8ZnybWjjbWfHGumA6/bSXja/70KlGGZ21t+tCLINWHMHW0pydo9rTsJyLztoSwhRovU/70qVL/P7773Ts2JE33niDNWvWEBsbq49sQhRJf8ckEpucTo865YxyOs6hjavyxzBvctUKvX4+wIYzN3TSzoGrt+i3/BDmKhXbRrYz6Ll9IYyF1n8RXF1dUalUVK5cmUuXLlG+fHmys7P1kU2IIsnYhsafppdneXa+2R4bC3MGrTjMsmNXCnT/QRHx9P75ALmKwsbh3kY34iCEoWgt2tWrV2fOnDk0a9aM5cuXs3TpUhRF0Uc2IYocRVHYFBaFnZU5nWqUNnScZ/KpVoqAtzviamfNWxuOsWB/wSyjGxqdSPf/28+DnFzW+remS82yBbJfIQoDrUX7448/pmvXrlSrVo1x48YRHx/PV199pY9sQhQ54beSuHonha41y2JrafwzDzYq78qhdzpT3tmOaTtOMWV76Ev9Un/u5j26LN1HSmY2vw1qha8RjzYIYQhai/b48eNJTEwkKyuL9u3bM336dDw8PPSRTYgiZ3NYNGDcQ+P/VbNkMQ6P7UINNye+PHieUeuPkatWP/d+Lifcp9NP+7ibnsXSV1sw6JXKOkgrhGnTWrT79evH3r176dixI9OnT+f48eP6yCVEkbQlLApLczO61TKtIeEKxe05NLYzr5Rz4ZfjVxm44giZz7HQSOTdVDr+uJfbKQ9Y7NeUEc2q6TCtEKZLa9Fu27YtX375JXv27MHLy4vPP/+ctm3b6iObEEXK9cQUTsfdo331UhSzNb0Vq9wcbAh4uyPeVUuy6WwUPf9vP6mZ2i9ajU1Op8OPe4lJTufz7q8wxquGHtIKYZrydT/J1atXWbJkCd988w3Ozs4ysYoQOrDFBIfG/8vJxoodb7ajZ51yBFy5Racl+0hMy8zz/bdTMuj4414i7qYys1M9Jrero8e0QpgerVe69OzZE3Nzc3r16sVvv/2Gu7u7PnIJUeRsDovCTKWiV51yho7yUmwtLfhjmDdvrA9mxd/X8fl+N3+N7kDZ/yzucTc9k84/7eNSwn0m+dRmZqd6BkoshOnQWrS//PJLatSQ4SohdOnm/XSCbyTQpkrJQrHUpIW5Gb8MaElxWyu+PXKR1ov/Yvfo/80ZnpyRRdelAYTdTOLtlh7M7/GK3pcfFcIUaR0el4IthO5tPReDohhuGU5dMDNTsbB3Yz7pUp8b99Jo891uTsfeJSNHTc//28/f0YkMa1KVb/2aSsEWIp+M/0ZQIYqAzY/Wzjbh89lPo1KpmN6xHi621ozbfJy23+/Gzcaca8mZ9G9QkWX9mxt8FTMhTIkUbSEM7G56Jgev3qJxeVcqFLc3dBydGONVAxtLc95cH8z9zBwcrCz4oW8zzM2Mb251IYyZ1r8xsbGxDB8+nE6dOhEfH8/QoUOJiYnRRzYhioQ/z8eQo1bwq1t4hsafpk6pYpqfU7NyuJxw34BphDBNWov2zJkzGTlyJPb29ri5udGjRw+mTJmij2xCFAmF4Vav/KhTypma7g8vRKvp7iTrYgvxArQW7Xv37uHl5YWiKKhUKvr3709qaqo+sglR6KVlZrP7Yhy1Shajhnsx7RuYMAdrS0ImdOOXTpUImdANB2tLQ0cSwuRoLdo2NjbcunVLc3Xn33//jZWV6c3WJIQx+utSHA9ycgv90PgjDtaWeJawk4ItxAvSeiHa1KlTGT16NFFRUfTu3Zvk5GS++eYbfWQTotAzhbWzhRDGQ2vRLleuHH/88QeRkZHk5uZSpUoVOdIWogBk5eSy40IsFYvb07Csi6HjCCFMgNbhcT8/P8aNG8eVK1eoXLmyFGwhCsj+q7e4/yAbv7oVZHIRIUS+aC3aBw4cYOjQoRw9epSuXbsybdo0goOD9ZFNiELtfxOqFI3z2UKIl6e1aJuZmdGyZUs+++wz5s2bx6VLlxg7dqw+sglRaOWq1Ww9F427gw0tK7kZOo4QwkRoPad9/vx5tm/fzt69e6lcuTLDhw+nY8eO+sgmRKEVGJFAQmombzavLrOCCSHyTWvRnj59Or1792bt2rWUKFFCH5mEKPQeDY3LVeNCiOeRZ9FOSEjAzc2NxYsXo1KpyMrKIi4uTvN6mTJl9BJQiMJGURS2nIummI0lbauVNHQcIYQJybNoT58+nZ9++gl/f39UKhWKomheU6lUBAQE6CWgEIXNyZi7RN1LY/ArlbGyMDd0HCGECcmzaP/0008AbNq0CWdn58dekwVDhHhxMjQuhHhReV4Bc/PmTeLi4hgyZIjm57i4OKKjo3njjTf0mVGIQmVzWBS2luZ0rlHa0FGEECYmzyPtb7/9lpCQEOLj43nttdf+t4GFBT4+PvrIJkShc+F2Mhfj7+Nbtzz2Mv+2EOI55Vm0582bB8DSpUsZNWqU3gIJUZhteTShiqcMjQshnp/WW7769u3L8uXLSUtLQ1EU1Go1MTExLFiwQB/5hChUNodFYWGmokftsoaOIoQwQVpndZg4cSIXLlxg27ZtZGRksHv3bsxkMgghntuNu6mExtylbbVSFLezNnQcIYQJ0lp94+PjmT9/Pu3ataNTp06sXLmS8+fP6yObEIXKlnPRAPjVk6FxIcSL0Vq0ixUrBkDlypW5ePEixYsX13koIQqjzWFRqFTQu44sECKEeDFaz2k3b96c8ePHM2XKFEaMGEF4eDg2Njb6yCZEoXE7JYOjEfG0rOhGKSdbQ8cRQpgorUV74sSJREVFUbZsWRYuXMiJEyd455139JFNiEJjW3gMiiJD40KIl6O1aG/ZsgWAkydPAuDs7MypU6eoUqUKHh4eeW6XnZ3Nhx9+SGxsLFlZWbz99ttUq1aNqVOnolKpqF69OrNmzZKL2kSRoFk721OGxoUQL05r0Q4ICOD8+fN06NABgIMHD+Lu7k56ejo9e/bk9ddff+p227Ztw9nZmS+++IJ79+7h5+dHzZo1mTBhAs2aNWPmzJkEBATIMp+i0EvOyGL/lVs0LOtCZVdHQ8cRQpgwrYe5CQkJbN68mWnTpjFt2jQ2btyIoiisW7eOTZs25bldly5dePfddzWPzc3NCQ8Pp2nTpgC0adOGoKCgAvgIQhi3HRdiyc5V41dXjrKFEC9H65H2vXv3sLe31zy2trYmOTkZCwsLVCpVnts92iY1NZXx48czYcIE5s+fr9nG3t6elJQUrQHPnTun9T1FQWhoqKEjFAm66Odfjzy81au6WYr8Of5D+kE/pJ/1R199rbVod+rUiWHDhtG1a1fUajV79uyhffv2bNmyBTc3t2due/PmTd555x0GDx5Mz549+eKLLzSvpaWl4eTkpDWgp6cn1tZFeyKK0NBQGjVqZOgYhZ4u+jk9K4djf1zCw82JV9u1fOYvukWFfJ/1Q/pZfwqyrzMzM595sKq1aE+aNIkDBw4QGBiIubk5b7zxBt7e3pw+fZqvvvoqz+3u3LnDiBEjmDlzJi1atACgdu3ahISE0KxZMw4fPkzz5s1f4CMJYTr2XIojPSsXv7rlpWALIV6a1qIN4OTkRNWqVenbty9nzpwBoEGDBs/cZsmSJdy/f58ffviBH374AYCPPvqITz/9lIULF1KlShU6d+78cumFMHKbw/6ZBU3WzhZCFACtRfu3335j3759xMfH07VrV2bOnEm/fv0YOXLkM7ebPn0606dPf+L5lStXvnhaIUxIdq6aP8/HUK6YHY3Luxo6jhCiENB69fjmzZv5+eefsbW1xdnZmT/++IONGzfqI5sQJu3g1VskZWThK0PjQogCorVom5mZYWVlpXlsbW2Nubm5TkMJURjI0LgQoqBpHR5v2rQp8+fPJyMjg3379rFu3Tq5gEwILXLVaraci6KEvTVeld0NHUcIUUhoPdL+4IMPqFixIjVq1GDLli14e3szZcoUfWQTwmQdu3GH2ykP6FWnPBbmMlWvEKJgaD3SNjMzo0ePHnh7e6MoCvBwje0yZcroPJwQpmpLmKydLYQoeFqL9pIlS1i6dCnOzs6oVCoURUGlUhEQEKCPfEKYHEVR2BwWhaO1Je2rlzJ0HCFEIaK1aP/xxx/s27cPFxcXfeQRwuSdibtHxN1UBjSohLWFXLQphCg4Wk+2lS5dmmLFiukjixCFwqNlOGVoXAhR0LQeaVeqVInBgwfTrFmzx279Gjt2rE6DCWGqNodFYW1hRteact2HEKJgaS3aJUuWpGTJkvrIIoTJu5xwn/BbyfSsUw4Ha0tDxxFCFDJai7YcUQuRf1v+GRr39ZShcSFEwZMbSIUoQJvDojA3U9GzTjlDRxFCFEJ5Fu309HR95hDC5EXfS+N4VCI+VUvial+014AXQuhGnkX7tddeA+Djjz/WVxYhTNrWczLXuBBCt/I8p52RkcH777/PkSNHyMzMfOL1efPm6TSYEKZmy7mH57N7e5Y3cBIhRGGVZ9H+9ddfCQkJITQ0lKZNm+ozkxAm507qAw5di6d5xRKUKWZn6DhCiEIqz6JdunRpfH19qVmzJlWrViUiIoLc3FyqV6+OhYXWi86FKFK2hcegVhQZGhdC6JTW6pudnU3nzp1xdnZGrVZz584dvv/+e+rXr6+PfEKYhEezoPnWlaFxIYTuaC3ac+fO5euvv9YU6dOnTzNnzhz++OMPnYcTwhSkPMhm3+Wb1CtdnGolnAwdRwhRiGm9Tzs9Pf2xo+oGDRo89cI0IYqqnRdiycpV4ydH2UIIHdNatIsVK8a+ffs0j/ft24ezs7MuMwlhUv43NC7ns4UQuqV1eHzOnDlMnjyZjz76CIDy5cvzxRdf6DyYEKbgQXYuOy/EUtXVkbqlnQ0dRwhRyOVrla8NGzaQnp6OWq3GwcFBH7mEMAl7L8eRlpWDX93yqFQqQ8cRQhRy+b53y85O7j0V4r+2hP0zC5qsnS2E0ANZMESIF5STq2ZbeDRlnGxpWr6EoeMIIYoArUV77dq1+sghhMk5fP02d9Oz6O1ZHjMzGRoXQuie1qK9cuVKfeQQwuRsDpMFQoQQ+qX1nHapUqUYOnQo9evXx9r6f8sNjh07VqfBhDBmarXClrAoXOysaFO1pKHjCCGKCK1Fu0GDBnqIIYRpORF9h7j7GQxrUhVLc7k0RAihH1qL9tixY0lPTycqKgoPDw8ePHggV5KLIu/R0LivLMMphNAjrYcIwcHB9O7dmzFjxpCYmEjbtm05evSoPrIJYZQURWFzWBT2VhZ0rFHa0HGEEEWI1qK9cOFCVq9ejZOTE25ubqxatYoFCxboI5sQRuncrSSu3kmha62y2FrKMrVCCP3RWrTVajVubm6ax9WqVdNpICGM3eazD+calwVChBD6lq+rxw8cOIBKpeL+/fusWrWKMmXK6CObEEZpy7lorMzN6FarrKGjCCGKGK1H2rNnz2b79u3cvHmTDh06cOHCBWbPnq2PbEIYnWt3UjgTd4/2HqVxsrEydBwhRBGj9Ujb1dWVhQsXkpqairm5Oba2tvrIJYRR2hImQ+NCCMPRWrQvXbrE1KlTiYuLA6BKlSrMnz+fChVkFihR9GwOi8ZMpaJXHSnaQgj90zo8PmvWLCZMmEBISAghISGMGDGCDz/8UB/ZhDAqccnpBN9IoE0Vd9wcbAwdRwhRBGkt2pmZmXh7e2sed+zYkdTUVJ2GEsIYbQ2XucaFEIaVZ9GOi4sjLi6OmjVrsnTpUu7evUtycjIrV66kcePG+swohFF4dKtXb5kFTQhhIHme0x4yZAgqlQpFUQgJCXlsiU6VSsX06dP1ElAIY3A3PZOD127TpLwr5YvbGzqOEKKIyrNo79+/X585hDBq28NjyFUrMjQuhDAorVePX79+nfXr15OcnPzY8/PmzdNZKCGMjeZWr3pStIUQhpOvVb66detGjRo19JFHCKOTmpnNnks3qVOqGB5uToaOI4QowrQWbScnJ8aOHftCOz9z5gxffvklK1as4MaNG0ydOhWVSkX16tWZNWsWZmayDrEwfn9djONBTi6+nnKULYQwLK1V08/Pj6+//prg4GBOnDih+U+bZcuWMX36dDIzM4GHw+kTJkxg9erVKIpCQEDAy6cXQg82a2ZBk6IthDAsrUfap06d4uTJk5w8eVLznEql4vfff3/mdhUqVGDx4sV88MEHAISHh9O0aVMA2rRpQ2BgIB07dnyZ7ELoXGZOLjvOx1LJxZ4GZYsbOo4QoojTWrTDw8PZs2fPc++4c+fOxMTEaB4rioJKpQLA3t6elJSUfO3n3Llzz912YRQaGmroCEXCf/s5KC6FlMxselZ2fOwXV/Fy5PusH9LP+qOvvtZatKtXr87FixepWbPmSzX07/PXaWlpODnl74IeT09PrK2tX6ptUxcaGkqjRo0MHaPQe1o//3QtGIC3OjShUWV3Q8QqdOT7rB/Sz/pTkH2dmZn5zIPVfN3y5efnh5ubG5aWlpoj5uc9J127dm1CQkJo1qwZhw8fpnnz5s+1vRD6lqtWs/VcNCUdbWhR0c3QcYQQQnvR/v777wukoSlTpjBjxgwWLlxIlSpV6Ny5c4HsVwhdORqRwJ20TEa1qI6ZmcrQcYQQQnvRzutK8bJly2rdebly5Vi/fj0AlStXZuXKlc8ZTwjDkavGhRDGRmvRDgkJ0fycnZ1NaGgojRs3xtfXV5e5hDAoRVHYEhaFs60VPlVLGjqOEEIA+Sja/52uNCkpiYkTJ+oskBDGIDTmLtFJ6QxpVAUrC3NDxxFCCCAfk6v8l52dHbGxsbrIIoTReDQ07ltXluEUQhgPrUfa/v7+mvurFUUhJiYGb29vnQcTwlAURWHT2ShsLc3pXKOMoeMIIYSG1qI9btw4zc8qlYrixYtTrVo1nYYSpu1GYirHY+7QpUYZHG2sDB3nuV24nczlhPv41a2AnZXWvyJCCKE3ef6LFBcXBzy8Avxpr5UpI0cg4kmrQq8zbHUgCmCuUtGpRmm8q5aiZWU3Gpd3xdoEzg9vORcNgJ8MjQshjEyeRXvIkCGoVCoURdE8p1KpSEhIIDs7mwsXLugloDANarXCJ3vO8OneMM1zuYrCrotx7Lr48BdAawszGpdzpWVld1pVdqNlJXdc7Y1vtrvNYVFYmpvRvfaTv7AKIYQh5Vm09+/f/9jjtLQ05s+fz9GjR5kzZ47OgwnTcf9BFkNXB7I9PIaKxe0xU6mIuJtKTXcnNg334XTsPYIi4wmKTCD4xh0CIxP44sDDbWu6O9GqsjstKz0s5NVKOGquoTCEyLupnIy5S6caZXC2Nb2hfSFE4ZavE3bBwcFMnz6dVq1asW3bNhwcHHSdS5iIq3fu4/fLQc7fTqZ99VKs8W+DtYUZ4beSqFPKGQdrS2q4F2NAw0oApDzIJiTqDkER8RyNiCck6g4/h1zl55CrALg72NCyshut/iniDcu66PWWqy2aCVVkaFwIYXyeWbTT09P5/PPPNUfXrVq10lcuYQL2XIpj0IojJGVk8W6bmizo0QgL84d3ETbLY65uRxtLOniUpoNHaQByctWE3UwiMCKewMh4AiMS2BIWzZawh+eVbSzMaVrB9eHReGV3WlQsQXE73Q2pbw6LRqWC3p5StIUQxifPov3vo+vt27djb2+vz1zCiCmKwteHLjDlz5NYmKn4eUBLXm9a9YX2ZWFuRsNyLjQs58LY1jVRFIWoe2kERiYQGBFPUEQCRyLiOXw9HgCVCuqUdKblP+fEvSq7UcnFoUCG1BMzcgiMjMersjslHW1fen9CCFHQ8izaw4cPx8LCgqNHjxIYGKh5/kVX+RKFQ0Z2DqM3HGNVaASlnWzZ+Lp3nkfVL0KlUlHRxYGKLg4MfqUyAMkZWQTfSCAoIoGgyHiO3bjDuVtJLA2+AkBpJ1taVnLD65+j8QZlimuO+J/H4dgUFEXmGhdCGK88i7YUZfFfMUlp9F1+iL+jE2lWoQR/vO5NmWJ2Om+3mK0VXWqWpUvNh4vUZOeqOR17958h9YfFfOPZKDaefXg+2t7KgmYVSjw8N17ZneYVS+CUj/vFD0TfB2RoXAhhvPIs2vlZxUsUHYER8bz62yFupzxgWJOq/NC3GTaWhrnn2tLcjCYVStCkQgkmeD8c/bmemEpg5MPh9MCIePZfvcX+q7cAMFOpqFva+Z+r1B8W8grFHz/dk5SRxYlbabxSzoVKLnKhpRDCOMl0T0KrZceuMG7TcdSKwiLfxoz1qmnQ27L+S6VSUbWEI1VLODK08cNz63fTMwl+dF48MoHjUXc4E3ePHwIvAVDe2U5zm1mryu6E3LhDrgLdaskvq0II4yVFW+QpO1fNe1v/5ofAS7jYWbFuaBvaVS9t6Fj54mJnTffa5TQTpGTm5HIy5q7mVrOgyATWnY5k3enIx7ZbFRrB5LZ1cLC2NEBqIYR4Nina4qkSUh8w4PfDHLp2m7qlndk03Icqro6GjvXCrC3MaVHJjRaV3JjUtg6KonDlTgpHr8ez9VwUf55/uHJdxN1Uwm8lFejFdUIIUVCkaIsnnI69i9+vB4m6l0afehX4dWDLQnfkqVKp8HBzwsPNif4NKtJs0U4uxt+nprsTdUo5GzqeEEI8lRRt8Zh1pyIZuS6IjOxcPulSnw/b18XMzHjOX+uCg7UlIRO6sfHgMfr6NC90v6AIIQoPKdoCgFy1mpl/neHzgHM4WFuwabhPkbr1ycHaEs8SdlKwhRBGTYq2IDkjiyGrjrLzQizVSjiyabiPDBELIYQRkqJdxF2KT8bvl4NcSrhPR4/SrPFvrdO5vYUQQrw4KdpF2K4Lsby28gjJD7KZ5FObz7o1fKHpP4UQQuiHFO0iSFEUvjgQzoc7T2FlbsZvg1sxpFEVQ8cSQgihhRTtIiY9K4c31gWz7nQk5YrZsXG4D43Luxo6lhBCiHyQol2ERN1Lo8+vBzkVe5eWldzYMMybUk6yBKUQQpgKKdpFxOFrt+n/+yESUjMZ2awai/s0xdrCMAt+CCGEeDFStIuAJUGXeXfzcQC+69OUt1p6GNWCH0IIIfJHinYhlpWTy7tbTrA0+Aol7K1ZP8wb76olDR1LCCHEC5KiXUjdTsmg/2+HORoRT4Myxdk03IeKsk60EEKYtCJVtO+kPuBifDINyroU6ukqQ6MT6fPrQWKS03m1fkV+HtAC+0L8eYUQoqgoMkX7XnomZT7+g1xFwc7SnDGtPPCpVpoWldxwtrUydLwCs/pkBG+uCyYzN5e53RowpZ2nnL8WQohCosgU7csJ98lVFADSs3P58uAFvjx4AZUKPEs507KSO60qu9GqsjsVi9ubXKHLVav5cMcpvjx4HicbS9YPa0P32uUMHUsIIUQBKjJFu04pZ2q6O3Ex/j7V3Rz5rFtDTsXeJSgigZCoO4TdTOKn4MsAlHGypVXlh0W8ZSV36pcpbtTTe95Lz+S1VUfZfTEODzcnNg/3oWbJYoaOJYQQooAVmaL9aM3k8FtJ1CnljIO1JX3qVQQgO1f9TwGP52hEAkGR8Ww4c4MNZ24AYG9lQfOKJTRH480ruuFoYxzniC/cTsb3lwNcvZNC11plWfmaV6Ea7hdCCPE/RaZow8PC3ayi2xPPW5qb0bRCCZpWKMEE74dzc19LTCHwnwIeFJFAwJVbBFy5BYCZSkX9MsVpWcmNlpXd8KrsTjlne31/HLaHR+O/KpCUzGymtKvDnK4NMDcz3hEBIYQQL6dIFe38UqlUVCvhRLUSTgxrUhWAxLRMgm8kEBQRT2BEAiei73Aq9i7fB14CoEJxe1pWeljAW1Z2w7OUs84KqKIozAs4x8y/TmNjYc6qIV4MbFhZJ20JIYQwHlK088nV3poetcvR45+LuzJzcgmNTiQoMoHAfwr52lORrD0VCYCTjSXNK7r9c17cjWYVShTIbVepmdmMWBvExrNRVChuz6bXfWhYzuWl9yuEEML4SdF+QdYW5rSs7E7Lyu6837YOiqJwOeE+gREPi3hQZAJ7LsWx51IcAOZmKhqWdaFlJTfNRW6lneyeq82IxBT6/HqIszfv0bqKO+uHtsHdURb8EEKIokKKdgFRqVTUcC9GDfdijGhWDYD4lAyCIhMe/heRwN8xifwdnci3Ry4CUNnFgVb/DKd7VXanlnsxzMyefqvZgau3GPDbYRLTM3mrpQdf926MlSz4IYQQRYoUbR1yd7TFt24FfOtWACAjO4e/oxMJikjgaEQ8wZEJrAy9zsrQ6wA421rRopIbXv/catakgis5uWoWnbzF2kvnMTcz48d+zRjVwsOQH0sIIYSBSNHWI1tLC1pXKUnrKiWZAqjVChfjkzn6z3B6YEQ8uy7EsutCLAAWZirMVCqyctWYq1RsG9GWTjXLGPZDCCGEMBgp2gZkZqaidilnapdy1hw937yfringey7GcSH+PgC5ikIxW+O4N1wIIYRhyE29Rqa0kx1961VkYe8mHJvQjRruTgDUdHeiTilnw4YTQghhUHo90lar1Xz88cdcunQJKysrPv30UypWrKjPCCbFwdqS4xO6sfHgMfr6NC/UK5MJIYTQTq9H2vv27SMrK4t169YxadIkPv/8c302b5IcrC3xLGEnBVsIIYR+i3ZoaCitW7cGoEGDBpw7d06fzQshhBAmTa/D46mpqTg4OGgem5ubk5OTg4VF3jGksD8UGhpq6AhFgvSzfkg/64f0s/7oq6/1WrQdHBxIS0vTPFar1c8s2ACenp5YW1vrOppRCw0NpVGjRoaOUehJP+uH9LN+SD/rT0H2dWZm5jMPVvU6PP7KK69w+PBhAE6fPo2Hh0wSIoQQQuSXXo+0O3bsSGBgIAMHDkRRFD777DN9Ni+EEEKYNL0WbTMzM2bPnq3PJoUQQohCQyZXEUIIIUyEFG0hhBDCREjRFkIIIUyE0S4YoigKAFlZWQZOYhwyMzMNHaFIkH7WD+ln/ZB+1p+C6utHNe9RDfwvlZLXKwaWkpLC5cuXDR1DCCGE0DsPDw8cHR2feN5oi7ZarSYtLQ1LS0tUKpWh4wghhBA6pygK2dnZ2NvbY2b25Blsoy3aQgghhHicXIgmhBBCmAgp2kIIIYSJkKIthBBCmAgp2kIIIYSJkKJt5BITE/H29ubatWuGjlKo/fTTTwwYMIA+ffqwYcMGQ8cplLKzs5k0aRIDBw5k8ODB8p3WgTNnzuDv7w/AjRs3GDRoEIMHD2bWrFmo1WoDpys8/t3PFy5cYPDgwfj7+zNy5Eju3Lmj07alaBux7OxsZs6ciY2NjaGjFGohISGcOnWKNWvWsGLFCm7dumXoSIXSoUOHyMnJYe3atbzzzjssWrTI0JEKlWXLljF9+nTNJB/z5s1jwoQJrF69GkVRCAgIMHDCwuG//Tx37lxmzJjBihUr6NixI8uWLdNp+1K0jdj8+fMZOHAg7u7uho5SqB09ehQPDw/eeecd3nrrLXx8fAwdqVCqXLkyubm5qNVqUlNTsbAw2gkZTVKFChVYvHix5nF4eDhNmzYFoE2bNgQFBRkqWqHy335euHAhtWrVAiA3Nxdra2udti9/a4zUpk2bcHFxoXXr1ixdutTQcQq1e/fuERcXx5IlS4iJieHtt9/mr7/+kkl9CpidnR2xsbF07dqVe/fusWTJEkNHKlQ6d+5MTEyM5rGiKJrvsL29PSkpKYaKVqj8t58fHVSdPHmSlStXsmrVKp22L0faRmrjxo0EBQXh7+/PhQsXmDJlCgkJCYaOVSg5Ozvj5eWFlZUVVapUwdramrt37xo6VqGzfPlyvLy82L17N1u3bmXq1KkyN7YO/Xs2rbS0NJycnAyYpnDbuXMns2bNYunSpbi4uOi0LSnaRmrVqlWsXLmSFStWUKtWLebPn4+bm5uhYxVKjRo14siRIyiKwu3bt8nIyMDZ2dnQsQodJycnzVzKxYoVIycnh9zcXAOnKrxq165NSEgIAIcPH6Zx48YGTlQ4bd26VfNvdfny5XXengyPiyKvbdu2nDhxgn79+qEoCjNnzsTc3NzQsQqd119/nQ8//JDBgweTnZ3NxIkTsbOzM3SsQmvKlCnMmDGDhQsXUqVKFTp37mzoSIVObm4uc+fOpXTp0owbNw6AJk2aMH78eJ21KXOPCyGEECZChseFEEIIEyFFWwghhDARUrSFEEIIEyFFWwghhDARUrSFEEIIEyFFWwgdiomJoUaNGgQGBj72fLt27R6bVelFFdR+niUuLo7OnTvTu3dvUlNTtb6/Ro0aL9TOtGnTiI2NfaFthSgqpGgLoWOWlpbMmDEjXwXPGB0/fhxPT0+2bt2Kg4ODztoJCQlB7kAV4tmkaAuhY+7u7rRs2ZL58+c/8VpISIhmiT+AqVOnsmnTJmJiYujduzcTJ06kZ8+eTJkyhbVr1zJgwAC6dOny2LKW3333Hb6+vgwYMICLFy8CcOfOHcaMGUOfPn3o27evZrGIxYsXM3LkSLp168bq1asfyxIREYG/vz89e/ZkwIABnD17lgsXLrBo0SKOHDnCzJkzH3t/UlIS77zzDl27dqV3794EBwc/9vrixYsfW1jh0ajAxYsX6d+/P3369GHQoEFERkaydOlS4uPjGTVqFPfu3ePs2bMMGjQIPz8/RowYQXR0NAD+/v6MHTuWzp07c/bsWSZPnoyvry++vr6sX7/+Rf54hDApUrSF0IOpU6dy9OjRJ4bJn+XSpUu8+eabbN26lZMnTxIbG8u6devo0aMH69at07yvYsWKbNmyhTFjxjB16lTg4XKBffv2ZdOmTfz444/MnDlTc6SflZXFzp07GTx48GPtTZ48GX9/f7Zv3860adN49913qVq1KuPHj6ddu3bMnj37sfd/8803VKhQgV27drFgwYJ8L7X522+/MXz4cDZt2kT//v05ffo0o0aNwt3dnaVLl2Jvb8/06dP56quv2Lx5M8OHD2fGjBma7WvUqMHu3bt58OABycnJbNmyhZ9++om///47330rhKmSaUyF0AMHBwfmzJnDjBkz2LZtW762KVGiBLVr1wagVKlStGjRAoAyZco8dh771VdfBcDb25vJkydz//59goKCuH79Ot9++y0AOTk5mqPVevXqPdFWWloaUVFRdOrUCYAGDRpQrFgxrl+/nme+EydO8OWXXwIPC+m/f5F4Fm9vb2bPns2RI0do164dbdu2fez1yMhIoqOjefvttzXP/fvUwqP81atXJyIigpEjR9KmTRs++OCDfLUvhCmToi2Ennh5eT0xTK5SqR47j5udna352crK6rHt85oP/d/PK4qChYUFarWa3377TbPwSXx8PK6uruzbtw8bG5sn9vG0c8mKojxzQQ8LC4vHli+9du0alStXfuyzqdXqJz5bly5daNiwIQcOHGD58uUcPHiQTz/9VPM+tVpNuXLl2Lp1K/Bwfuc7d+5oXn+Uv3jx4uzYsYPAwEAOHTqEn58fO3bskNWsRKEmw+NC6NGjYfL4+HjgYeGJjo4mMzOTpKQkQkNDn3uf27dvB2Dv3r1UrVoVOzs7mjdvrjlnffXqVXr27ElGRkae+3BwcKBcuXLs2bMHgNOnT3Pnzh2qV6+e5zaNGzdmx44dwMOC/eabbz5WxIsXL87Vq1cBOHv2rGZp2QkTJhAWFsbAgQN59913OX/+PPDwl4/c3FyqVKlCcnKyZrh748aNvP/++0+0HxAQwOTJk/Hx8WH69OnY2dlx8+bN/HWaECZKjrSF0KNHw+QjR44EHg7xent70717d8qWLUujRo2ee5+RkZH07t0be3t7Pv/8cwCmT5/OzJkz6dmzJwALFizQeuX3F198wccff8zixYuxtLRk8eLFTxzt/9v48eOZPn06vXr1wsLCggULFjxWtLt168bu3bvp1q0bderU0Qz1v/XWW3z00Ud8//33WFpa8vHHHwPg4+PDqFGj+L//+z+++eYb5s6dS2ZmJg4ODk+9iK9Nmzbs2bOH7t27Y21tTa9evV74djMhTIWs8iWEEEKYCBkeF0IIIUyEFG0hhBDCREjRFkIIIUyEFG0hhBDCREjRFkIIIUyEFG0hhBDCREjRFkIIIUyEFG0hhBDCRPw/ZOhRPn8p3WAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loop for different number of clusters\n",
    "\n",
    "from sklearn.metrics import silhouette_score\n",
    "from sklearn.metrics import silhouette_samples\n",
    "\n",
    "# define our scaler\n",
    "scaler = MinMaxScaler()\n",
    "# scale down our data\n",
    "df_scaled = scaler.fit_transform(df)\n",
    "kmeans.fit(df_scaled)\n",
    "\n",
    "# re-do our loop, try to find values with no negative scores, or one with the least!!\n",
    "negative_values = [0 for i in range(3,13)]\n",
    "\n",
    "silhouette_score_average_list = []\n",
    "\n",
    "# remember, anything past 15 looked really good based on the inertia\n",
    "number_of_clusters = [i for i in range(3,13)]\n",
    "\n",
    "# we start with 1, as we can not have 0 clusters in k means\n",
    "# iterate through each of our values\n",
    "for i in number_of_clusters:\n",
    "    \n",
    "    # iterate through, taking each value from \n",
    "    kmeans = KMeans(n_clusters=i, init='k-means++',random_state=32)\n",
    "    \n",
    "    # fit it\n",
    "    kmeans.fit(df_scaled)\n",
    "    \n",
    "    # find each silhouette score\n",
    "    silhouette_score_individual = silhouette_samples(df_scaled, kmeans.predict(df_scaled),metric='euclidean')\n",
    "    \n",
    "    # find each average silhouette score\n",
    "    silhouette_score_average = silhouette_score(df_scaled, kmeans.predict(df_scaled),metric='euclidean')\n",
    "    silhouette_score_average_list.append(silhouette_score_average)\n",
    "    \n",
    "    # iterate through to find any negative values\n",
    "    for k in silhouette_score_individual:\n",
    "        \n",
    "        # if we find a negative, lets start counting them\n",
    "        if k < 0:\n",
    "            negative_values[i-3] += 1\n",
    "\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.set_title('K-means clustering : silhouette score average')\n",
    "ax1.set_xlabel('Number of clusters')\n",
    "ax1.set_ylabel('silhouette score average')\n",
    "ax1.plot(number_of_clusters, silhouette_score_average_list, marker=\".\")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_title('K-means clustering : Number of negative values')\n",
    "ax.set_xlabel('Number of clusters')\n",
    "ax.set_ylabel(\"Number of negative values silouhette score\")\n",
    "ax.plot(number_of_clusters, negative_values, marker=\".\")\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f62c3216",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v_measure_score 2 :0.08667890113657652\n",
      "v_measure_score 3 :0.5278984286066358\n",
      "v_measure_score 4 :0.45645023459410133\n",
      "v_measure_score 5 :0.39989570556938114\n",
      "v_measure_score 6 :0.43041133055285646\n"
     ]
    }
   ],
   "source": [
    "# score of the clustering\n",
    "\n",
    "from sklearn.metrics.cluster import homogeneity_score\n",
    "from sklearn.metrics.cluster import v_measure_score\n",
    "from sklearn.metrics.cluster import completeness_score\n",
    "for i in range(2,7):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 0)\n",
    "    y_pred1 = kmeans.fit_predict(X)\n",
    "\n",
    "    print('v_measure_score {} :{}'.format(i,v_measure_score(y,y_pred1,beta =1.0)))\n",
    "    \n",
    "# print('homogeneity_score :',homogeneity_score(y,y_pred1))\n",
    "# print('completeness_score :',completeness_score(y,y_pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "51c7c006",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time for running the algorithm = 4.89477992799948 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# score of the clustering\n",
    "\n",
    "from sklearn.metrics.cluster import homogeneity_score\n",
    "from sklearn.metrics.cluster import v_measure_score\n",
    "from sklearn.metrics.cluster import completeness_score\n",
    "\n",
    "v_measure_score_list = []\n",
    "homogeneity_score_list = []\n",
    "completeness_score_list = []\n",
    "\n",
    "time1 = time.perf_counter()\n",
    "\n",
    "for i in range(1,30):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 0)\n",
    "    y_pred1 = kmeans.fit_predict(X_scaled)\n",
    "\n",
    "    v_measure_score_list.append(v_measure_score(y,y_pred1,beta =1.0))\n",
    "    homogeneity_score_list.append(homogeneity_score(y,y_pred1))\n",
    "    completeness_score_list.append(completeness_score(y,y_pred1))\n",
    "    \n",
    "time2 = time.perf_counter()\n",
    "timeA = time2 - time1\n",
    "print('time for running the algorithm =',timeA,'s')\n",
    "    \n",
    "number_of_clusters = [i for i in range(1,30)]\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_title('K-means clustering')\n",
    "ax.set_xlabel('Number of clusters')\n",
    "ax.set_ylabel('Score')\n",
    "ax.plot(number_of_clusters, v_measure_score_list, marker=\".\", label=\"v_measure_score\")\n",
    "ax.plot(number_of_clusters, homogeneity_score_list, marker=\".\", label=\"homogeneity_score\")\n",
    "ax.plot(number_of_clusters, completeness_score_list, marker=\".\", label=\"completeness_score\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "92774e91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Number of Clusters: 3 | silhouette_score_average : 0.36642006818694955\n",
      " Number of Clusters: 4 | silhouette_score_average : 0.3531365155397055\n",
      " Number of Clusters: 5 | silhouette_score_average : 0.29261412961090827\n",
      " Number of Clusters: 6 | silhouette_score_average : 0.3043238648810394\n",
      " Number of Clusters: 7 | silhouette_score_average : 0.2534016206029578\n",
      " Number of Clusters: 8 | silhouette_score_average : 0.24511142439474376\n",
      " Number of Clusters: 9 | silhouette_score_average : 0.22225970380286328\n",
      " Number of Clusters: 3 | Number of Negative Values: 19\n",
      " Number of Clusters: 4 | Number of Negative Values: 4\n",
      " Number of Clusters: 5 | Number of Negative Values: 11\n",
      " Number of Clusters: 6 | Number of Negative Values: 8\n",
      " Number of Clusters: 7 | Number of Negative Values: 42\n",
      " Number of Clusters: 8 | Number of Negative Values: 27\n",
      " Number of Clusters: 9 | Number of Negative Values: 53\n"
     ]
    }
   ],
   "source": [
    "# Manhattan distance\n",
    "\n",
    "# re-do our loop, try to find values with no negative scores, or one with the least!!\n",
    "bad_k_values = {}\n",
    "possible_K_values = [i for i in range(3,10)]\n",
    "\n",
    "# we start with 1, as we can not have 0 clusters in k means\n",
    "# iterate through each of our values\n",
    "for each_value in possible_K_values:\n",
    "    \n",
    "    # iterate through, taking each value from \n",
    "    kmeans = KMeans(n_clusters=each_value, init='k-means++',random_state=32)\n",
    "    \n",
    "    # fit it\n",
    "    kmeans.fit(df_scaled)\n",
    "    \n",
    "    # find each silhouette score\n",
    "    silhouette_score_individual = silhouette_samples(df_scaled, kmeans.predict(df_scaled),metric='manhattan')\n",
    "    \n",
    "    # find each average silhouette score\n",
    "    silhouette_score_average = silhouette_score(df_scaled, kmeans.predict(df_scaled),metric='manhattan')\n",
    "    print(f' Number of Clusters: {each_value} | silhouette_score_average : {silhouette_score_average}')\n",
    "\n",
    "    # iterate through to find any negative values\n",
    "    for each_silhouette in silhouette_score_individual:\n",
    "        \n",
    "        # if we find a negative, lets start counting them\n",
    "        if each_silhouette < 0:\n",
    "            \n",
    "            if each_value not in bad_k_values:\n",
    "                bad_k_values[each_value] = 1\n",
    "            \n",
    "            else:\n",
    "                bad_k_values[each_value] += 1\n",
    "            \n",
    "for key, val in bad_k_values.items():\n",
    "    print(f' Number of Clusters: {key} | Number of Negative Values: {val}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a1c88614",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Number of Clusters: 3 | silhouette_score_average : 0.3621207131832771\n",
      " Number of Clusters: 4 | silhouette_score_average : 0.3202770136975127\n",
      " Number of Clusters: 5 | silhouette_score_average : 0.267735951464051\n",
      " Number of Clusters: 6 | silhouette_score_average : 0.280692700297834\n",
      " Number of Clusters: 7 | silhouette_score_average : 0.24578320422428512\n",
      " Number of Clusters: 8 | silhouette_score_average : 0.238237715805331\n",
      " Number of Clusters: 9 | silhouette_score_average : 0.2216546861491961\n",
      " Number of Clusters: 3 | Number of Negative Values: 3\n",
      " Number of Clusters: 4 | Number of Negative Values: 1\n",
      " Number of Clusters: 5 | Number of Negative Values: 8\n",
      " Number of Clusters: 6 | Number of Negative Values: 6\n",
      " Number of Clusters: 7 | Number of Negative Values: 33\n",
      " Number of Clusters: 8 | Number of Negative Values: 25\n",
      " Number of Clusters: 9 | Number of Negative Values: 34\n"
     ]
    }
   ],
   "source": [
    "# Minkowski distance\n",
    "\n",
    "# re-do our loop, try to find values with no negative scores, or one with the least!!\n",
    "bad_k_values = {}\n",
    "\n",
    "# remember, anything past 15 looked really good based on the inertia\n",
    "possible_K_values = [i for i in range(3,10)]\n",
    "\n",
    "# we start with 1, as we can not have 0 clusters in k means\n",
    "# iterate through each of our values\n",
    "for each_value in possible_K_values:\n",
    "    \n",
    "    # iterate through, taking each value from \n",
    "    kmeans = KMeans(n_clusters=each_value, init='k-means++',random_state=32)\n",
    "    \n",
    "    # fit it\n",
    "    kmeans.fit(df_scaled)\n",
    "    \n",
    "    # find each silhouette score\n",
    "    silhouette_score_individual = silhouette_samples(df_scaled, kmeans.predict(df_scaled),metric='minkowski')\n",
    "    \n",
    "    # find each average silhouette score\n",
    "    silhouette_score_average = silhouette_score(df_scaled, kmeans.predict(df_scaled),metric='minkowski')\n",
    "    print(f' Number of Clusters: {each_value} | silhouette_score_average : {silhouette_score_average}')\n",
    "\n",
    "    # iterate through to find any negative values\n",
    "    for each_silhouette in silhouette_score_individual:\n",
    "        \n",
    "        # if we find a negative, lets start counting them\n",
    "        if each_silhouette < 0:\n",
    "            \n",
    "            if each_value not in bad_k_values:\n",
    "                bad_k_values[each_value] = 1\n",
    "            \n",
    "            else:\n",
    "                bad_k_values[each_value] += 1\n",
    "            \n",
    "for key, val in bad_k_values.items():\n",
    "    print(f' Number of Clusters: {key} | Number of Negative Values: {val}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "311bddb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Number of Clusters: 3 | silhouette_score_average : 0.0745580304368759\n",
      " Number of Clusters: 4 | silhouette_score_average : 0.09283889650920589\n",
      " Number of Clusters: 5 | silhouette_score_average : 0.06208629381847224\n",
      " Number of Clusters: 6 | silhouette_score_average : 0.06415178185681346\n",
      " Number of Clusters: 7 | silhouette_score_average : 0.0351194091724919\n",
      " Number of Clusters: 8 | silhouette_score_average : 0.029642732949957794\n",
      " Number of Clusters: 9 | silhouette_score_average : 0.01914676555787435\n",
      " Number of Clusters: 3 | Number of Negative Values: 2\n",
      " Number of Clusters: 4 | Number of Negative Values: 6\n",
      " Number of Clusters: 5 | Number of Negative Values: 40\n",
      " Number of Clusters: 6 | Number of Negative Values: 43\n",
      " Number of Clusters: 7 | Number of Negative Values: 129\n",
      " Number of Clusters: 8 | Number of Negative Values: 146\n",
      " Number of Clusters: 9 | Number of Negative Values: 187\n"
     ]
    }
   ],
   "source": [
    "# Hamming distance\n",
    "\n",
    "# re-do our loop, try to find values with no negative scores, or one with the least!!\n",
    "bad_k_values = {}\n",
    "\n",
    "# remember, anything past 15 looked really good based on the inertia\n",
    "possible_K_values = [i for i in range(3,10)]\n",
    "\n",
    "# we start with 1, as we can not have 0 clusters in k means\n",
    "# iterate through each of our values\n",
    "for each_value in possible_K_values:\n",
    "    \n",
    "    # iterate through, taking each value from \n",
    "    kmeans = KMeans(n_clusters=each_value, init='k-means++',random_state=32)\n",
    "    \n",
    "    # fit it\n",
    "    kmeans.fit(df_scaled)\n",
    "    \n",
    "    # find each silhouette score\n",
    "    silhouette_score_individual = silhouette_samples(df_scaled, kmeans.predict(df_scaled),metric='hamming')\n",
    "    \n",
    "    # find each average silhouette score\n",
    "    silhouette_score_average = silhouette_score(df_scaled, kmeans.predict(df_scaled),metric='hamming')\n",
    "    print(f' Number of Clusters: {each_value} | silhouette_score_average : {silhouette_score_average}')\n",
    "\n",
    "    # iterate through to find any negative values\n",
    "    for each_silhouette in silhouette_score_individual:\n",
    "        \n",
    "        # if we find a negative, lets start counting them\n",
    "        if each_silhouette < 0:\n",
    "            \n",
    "            if each_value not in bad_k_values:\n",
    "                bad_k_values[each_value] = 1\n",
    "            \n",
    "            else:\n",
    "                bad_k_values[each_value] += 1\n",
    "            \n",
    "for key, val in bad_k_values.items():\n",
    "    print(f' Number of Clusters: {key} | Number of Negative Values: {val}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "794830e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Number of Clusters: 3 | silhouette_score_average : 0.46505520711054\n",
      " Number of Clusters: 4 | silhouette_score_average : 0.49664203421102976\n",
      " Number of Clusters: 5 | silhouette_score_average : 0.42097971707552145\n",
      " Number of Clusters: 6 | silhouette_score_average : 0.44011135947035385\n",
      " Number of Clusters: 7 | silhouette_score_average : 0.39735318282197707\n",
      " Number of Clusters: 8 | silhouette_score_average : 0.38206017448327884\n",
      " Number of Clusters: 9 | silhouette_score_average : 0.3607283553941373\n",
      " Number of Clusters: 3 | Number of Negative Values: 56\n",
      " Number of Clusters: 4 | Number of Negative Values: 6\n",
      " Number of Clusters: 5 | Number of Negative Values: 10\n",
      " Number of Clusters: 6 | Number of Negative Values: 11\n",
      " Number of Clusters: 7 | Number of Negative Values: 59\n",
      " Number of Clusters: 8 | Number of Negative Values: 46\n",
      " Number of Clusters: 9 | Number of Negative Values: 55\n"
     ]
    }
   ],
   "source": [
    "# Cosine distance\n",
    "\n",
    "# re-do our loop, try to find values with no negative scores, or one with the least!!\n",
    "bad_k_values = {}\n",
    "\n",
    "# remember, anything past 15 looked really good based on the inertia\n",
    "possible_K_values = [i for i in range(3,10)]\n",
    "\n",
    "# we start with 1, as we can not have 0 clusters in k means\n",
    "# iterate through each of our values\n",
    "for each_value in possible_K_values:\n",
    "    \n",
    "    # iterate through, taking each value from \n",
    "    kmeans = KMeans(n_clusters=each_value, init='k-means++',random_state=32)\n",
    "    \n",
    "    # fit it\n",
    "    kmeans.fit(df_scaled)\n",
    "    \n",
    "    # find each silhouette score\n",
    "    silhouette_score_individual = silhouette_samples(df_scaled, kmeans.predict(df_scaled),metric='cosine')\n",
    "    \n",
    "    # find each average silhouette score\n",
    "    silhouette_score_average = silhouette_score(df_scaled, kmeans.predict(df_scaled),metric='cosine')\n",
    "    print(f' Number of Clusters: {each_value} | silhouette_score_average : {silhouette_score_average}')\n",
    "\n",
    "    # iterate through to find any negative values\n",
    "    for each_silhouette in silhouette_score_individual:\n",
    "        \n",
    "        # if we find a negative, lets start counting them\n",
    "        if each_silhouette < 0:\n",
    "            \n",
    "            if each_value not in bad_k_values:\n",
    "                bad_k_values[each_value] = 1\n",
    "            \n",
    "            else:\n",
    "                bad_k_values[each_value] += 1\n",
    "            \n",
    "for key, val in bad_k_values.items():\n",
    "    print(f' Number of Clusters: {key} | Number of Negative Values: {val}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "78f47ad5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Silhouette Plot of KMeans Clustering for 649 Samples in 3 Centers'}, xlabel='silhouette coefficient values', ylabel='cluster label'>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from yellowbrick.cluster import SilhouetteVisualizer\n",
    "from yellowbrick.datasets import load_nfl\n",
    "\n",
    "\n",
    "# Instantiate the clustering model and visualizer\n",
    "model = KMeans(3, random_state=32)\n",
    "visualizer = SilhouetteVisualizer(model, colors='yellowbrick')\n",
    "\n",
    "visualizer.fit(X)        # Fit the data to the visualizer\n",
    "visualizer.show()        # Finalize and render the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "dcbd93a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits import mplot3d\n",
    "\n",
    "fig = plt.figure(figsize = (12,8))\n",
    "ax = plt.axes(projection ='3d')\n",
    "\n",
    "sctt = ax.scatter(pd.DataFrame(df, columns =['fixed acidity']),\n",
    "                    pd.DataFrame(df, columns =['volatile acidity']),\n",
    "                    pd.DataFrame(df, columns =['citric acid']),\n",
    "                    s=80)\n",
    "\n",
    "plt.title(\"3D Scatterplot : Clustering representation with 3 features\",pad = 1)\n",
    "ax.set_xlabel('fixed acidity')\n",
    "ax.set_ylabel('volatile acidity')\n",
    "ax.set_zlabel('citric acid')\n",
    "plt.savefig('3d_scatterplot.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "e7d6ee7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    type  fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
      "0      1             22                27           35             172   \n",
      "10     1             33                27           40              10   \n",
      "20     1             14                80           47               6   \n",
      "30     1             37                22           38             105   \n",
      "40     1             19                20           38              24   \n",
      "\n",
      "    chlorides  free sulfur dioxide  total sulfur dioxide  density  pH  \\\n",
      "0          29                   44                   151      365  17   \n",
      "10         17                    8                    49       53  16   \n",
      "20         13                   27                    59        8  50   \n",
      "30         28                   18                   123      295  37   \n",
      "40        101                   64                   139      140  28   \n",
      "\n",
      "    sulphates  alcohol  quality  cluster  \n",
      "0          19        4        3        1  \n",
      "10         30       45        2        0  \n",
      "20         13       55        5        0  \n",
      "30         27       18        3        1  \n",
      "40         10       10        3        0  \n"
     ]
    }
   ],
   "source": [
    "# Adding clustering column to the dataset\n",
    "\n",
    "b = io.open('wine-quality-white-and-red.csv', encoding='latin-1')\n",
    "df_cluster = pd.read_csv(b)\n",
    "df_cluster = df_cluster.take([10*i for i in range(649)], axis = 0)\n",
    "df_cluster = df_cluster.apply(LabelEncoder().fit_transform)\n",
    "df_cluster.loc[:,\"cluster\"] = kmeans.labels_\n",
    "print(df_cluster.head())\n",
    "\n",
    "# save the data\n",
    "df_cluster.to_csv('wine_quality_Kmeans.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50d7925e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}