ML0101EN-Clus-K-Means-Customer-Seg-py-v1.ipynb

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   "source": [
    "<a href=\"https://www.bigdatauniversity.com\"><img src = \"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width = 400, align = \"center\"></a>\n",
    "\n",
    "# <center>K-Means Clustering</center>"
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    "## Introduction\n",
    "\n",
    "There are many models for **clustering** out there. In this notebook, we will be presenting the model that is considered the one of the simplest model among them. Despite its simplicity, the **K-means** is vastly used for clustering in many data science applications, especially useful if you need to quickly discover insights from **unlabeled data**. In this notebook, you learn how to use k-Means for customer segmentation.\n",
    "\n",
    "Some real-world applications of k-means:\n",
    "- Customer segmentation\n",
    "- Understand what the visitors of a website are trying to accomplish\n",
    "- Pattern recognition\n",
    "- Machine learning\n",
    "- Data compression\n",
    "\n",
    "\n",
    "In this notebook we practice k-means clustering with 2 examples:\n",
    "- k-means on a random generated dataset\n",
    "- Using k-means for customer segmentation"
   ]
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   "source": [
    "### Import libraries\n",
    "Lets first import the required libraries.\n",
    "Also run <b> %matplotlib inline </b> since we will be plotting in this section."
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     "text": [
      "C:\\Users\\damaro\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\deprecation.py:144: FutureWarning: The sklearn.datasets.samples_generator module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.datasets. Anything that cannot be imported from sklearn.datasets is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
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   "source": [
    "import random \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.cluster import KMeans \n",
    "from sklearn.datasets.samples_generator import make_blobs \n",
    "%matplotlib inline"
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   "source": [
    "# k-Means on a randomly generated dataset\n",
    "Lets create our own dataset for this lab!\n"
   ]
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   "source": [
    "First we need to set up a random seed. Use <b>numpy's random.seed()</b> function, where the seed will be set to <b>0</b>"
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   "source": [
    "np.random.seed(0)"
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    "Next we will be making <i> random clusters </i> of points by using the <b> make_blobs </b> class. The <b> make_blobs </b> class can take in many inputs, but we will be using these specific ones. <br> <br>\n",
    "<b> <u> Input </u> </b>\n",
    "<ul>\n",
    "    <li> <b>n_samples</b>: The total number of points equally divided among clusters. </li>\n",
    "    <ul> <li> Value will be: 5000 </li> </ul>\n",
    "    <li> <b>centers</b>: The number of centers to generate, or the fixed center locations. </li>\n",
    "    <ul> <li> Value will be: [[4, 4], [-2, -1], [2, -3],[1,1]] </li> </ul>\n",
    "    <li> <b>cluster_std</b>: The standard deviation of the clusters. </li>\n",
    "    <ul> <li> Value will be: 0.9 </li> </ul>\n",
    "</ul>\n",
    "<br>\n",
    "<b> <u> Output </u> </b>\n",
    "<ul>\n",
    "    <li> <b>X</b>: Array of shape [n_samples, n_features]. (Feature Matrix)</li>\n",
    "    <ul> <li> The generated samples. </li> </ul> \n",
    "    <li> <b>y</b>: Array of shape [n_samples]. (Response Vector)</li>\n",
    "    <ul> <li> The integer labels for cluster membership of each sample. </li> </ul>\n",
    "</ul>\n"
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   "source": [
    "X, y = make_blobs(n_samples=5000, centers=[[4,4], [-2, -1], [2, -3], [1, 1]], cluster_std=0.9)"
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   "source": [
    "Display the scatter plot of the randomly generated data."
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], marker='.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Setting up K-Means\n",
    "Now that we have our random data, let's set up our K-Means Clustering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The KMeans class has many parameters that can be used, but we will be using these three:\n",
    "<ul>\n",
    "    <li> <b>init</b>: Initialization method of the centroids. </li>\n",
    "    <ul>\n",
    "        <li> Value will be: \"k-means++\" </li>\n",
    "        <li> k-means++: Selects initial cluster centers for k-mean clustering in a smart way to speed up convergence.</li>\n",
    "    </ul>\n",
    "    <li> <b>n\\_clusters</b>: The number of clusters to form as well as the number of centroids to generate. </li>\n",
    "    <ul> <li> Value will be: 4 (since we have 4 centers)</li> </ul>\n",
    "    <li> <b>n\\_init</b>: Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n\\_init consecutive runs in terms of inertia. </li>\n",
    "    <ul> <li> Value will be: 12 </li> </ul>\n",
    "</ul>\n",
    "\n",
    "Initialize KMeans with these parameters, where the output parameter is called <b>k_means</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "k_means = KMeans(init = \"k-means++\", n_clusters = 4, n_init = 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Now let's fit the KMeans model with the feature matrix we created above, <b> X </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=4, n_init=12, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means.fit(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Now let's grab the labels for each point in the model using KMeans' <b> .labels\\_ </b> attribute and save it as <b> k_means_labels </b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 2, ..., 3, 0, 0])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means_labels = k_means.labels_\n",
    "k_means_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We will also get the coordinates of the cluster centers using KMeans' <b> .cluster&#95;centers&#95; </b> and save it as <b> k_means_cluster_centers </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.03743147, -0.99782524],\n",
       "       [ 0.96900523,  0.98370298],\n",
       "       [ 1.99741008, -3.01666822],\n",
       "       [ 3.97334234,  3.98758687]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means_cluster_centers = k_means.cluster_centers_\n",
    "k_means_cluster_centers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Creating the Visual Plot\n",
    "So now that we have the random data generated and the KMeans model initialized, let's plot them and see what it looks like!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Please read through the code and comments to understand how to plot the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot with the specified dimensions.\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "\n",
    "# Colors uses a color map, which will produce an array of colors based on\n",
    "# the number of labels there are. We use set(k_means_labels) to get the\n",
    "# unique labels.\n",
    "colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means_labels))))\n",
    "\n",
    "# Create a plot\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# For loop that plots the data points and centroids.\n",
    "# k will range from 0-3, which will match the possible clusters that each\n",
    "# data point is in.\n",
    "for k, col in zip(range(len([[4,4], [-2, -1], [2, -3], [1, 1]])), colors):\n",
    "\n",
    "    # Create a list of all data points, where the data poitns that are \n",
    "    # in the cluster (ex. cluster 0) are labeled as true, else they are\n",
    "    # labeled as false.\n",
    "    my_members = (k_means_labels == k)\n",
    "    \n",
    "    # Define the centroid, or cluster center.\n",
    "    cluster_center = k_means_cluster_centers[k]\n",
    "    \n",
    "    # Plots the datapoints with color col.\n",
    "    ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.')\n",
    "    \n",
    "    # Plots the centroids with specified color, but with a darker outline\n",
    "    ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,  markeredgecolor='k', markersize=6)\n",
    "\n",
    "# Title of the plot\n",
    "ax.set_title('KMeans')\n",
    "\n",
    "# Remove x-axis ticks\n",
    "ax.set_xticks(())\n",
    "\n",
    "# Remove y-axis ticks\n",
    "ax.set_yticks(())\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practice\n",
    "Try to cluster the above dataset into 3 clusters.  \n",
    "Notice: do not generate data again, use the same dataset as above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ErLHiqGojq2RXk3Xg9FlGePKrvp/IEijOAJ4JmjoOY9cvBhLDzEnVniXOCaB7Nyti4JrvzNnALZG5DLT3gax5YPqdYJ/eA8Cme4CSB2PrR0Guw/pBTPyYVZptfowVOsyeYok1f8oGcuPSg4FU3+7go/6x9zM3AxG7bsAvvd4KAx5zWvQ/wKLh/p9QLr8hvY81lg2agDUagoaOwqnUiFyuZuu/P2ohwMI1YMMAgBagvReYeIvJE0Yz61qWPgO6eQRGa3dQgMDPOfAgaPodGJnLfjTok2JrN0tszbwLY+AhIDMs+YE/CFCJkXrfB9jxitOsKY9/DPR9AEZ7D4jrybyAY+ZkoN0K3+4BFiX3HgR6DjLi9y11yF4Dhh6F0dTCSHn8zWA23NCHyu+H35inFl233tJyjaVDE7BGY7DUHgoSyqrR0mcZcWav+e6DXwINPe4TU4j05y74hRMJFkVOvCXaLRrdO0HNHcw/3HMAmDwK6hgE0CTOSZ4Lo70P6Nrul/t+EEZxlhF5xxBzGEweDXzEmx9n5Jv2vcPkgEaPsCKK9h7moOi7GzAzfnSeYEm8+SvBOYwmFnUjAWz6QKBB7/qMn7B7V5AouUUY+Sl2bbOngP4HpMThOCg1oD4A++5mD4CIYoy4h6aeK7dy0D7gdQhnoYizf/4inIVi4w7awCbhCqRG5+jazkiv/wHAuhX4gv1pFTT6AyZJ7P/1YB2DHwq1WzQYYeYnmfd34sdAcRYA99EeAbq2A0j6STQDKEyz6NlogdG907/WX2JWspmTgJNjka/RxAjf9yqjVGLEN/5DoGUDmK3BZf/u2g6giZFz+izoxH9g+5hzwG17/V7BDzPZofcgjOYN7Nq69wYVcr0HgVuXWI/h7DXQ7GmlFSe5Nrue3gMASsyJwSdwAPHtO1e4af56ho6AlxmrVXVWCbwQAzAaZkdbttdX3uicJ4c2P+a/6t8ddPHquwsY/5GaYCu5jJCHPgRMHA2i89vvA5papKY/TewPoOrAvQeB5Cb2AOjZy4hs4CEQJdn2Q4+zCLRnn7+/XzEnR6RE0jFLQN+9gbcYYD0frrwM457fYha0gQfZsbLX2PHJYQ+egYeYVAFDdXYYTez++B3WDBhA1x2K24NmTsLou0cUhbCWk/6E5gbPu9ORc/3QBLzMWA6yWypqLcRYzMOj0b+ERlMSNPgojJZu9jqdaAJ17QCN/SOMzY+zLl5kKMk55CeCooMNA0wzbelkibREMzDzjkLWwk4mt2VMtAi3QuABLrHzNbUCqduBji1+05t7me+47x7AM1kRR34cSA1IE0D80mN5nTDYvkaSFZLwY23+sH8NF305BaJgg6ZOBO6NiR+x4/Td7ZPwwVBPYn+/5q6gTabUXyLuobnoh2kDZaj1Ak3Ay4y1WHVWayHGoh4ey/BLaCRbQb0HfOcA+9vY/CEABiNDwwCQZGSUPgfKXGaOh5Yu383gdx4jD7j5ZlCN1nuQJdN67vRPlAA6t7O/C1NMb37w3wXJtoGHgmh3nOm8xuYPs+MYCRaxTh1nkeOGzayKbeZdPwGYBOavAM0dwNAj7KFhJBjxDv9/MHb+AiPfgYeAzCUmUbQ/4uvQj/gFGyeY37lrB5C9HiTfOreJyjzq3uOvN1SEkc+AbtvHmgqN/mB5olTtoqgbmoCXGatddbYULOrh0YBfwsgoeuJNloiy5wPi6d4NtHWzRjeTR30P710wevYB6fMsocXLhDu3hXzAH2baLX9lT/tWs9bbACSA9j6mw7Z2ATBYwUfCr6wTkojhR5S+j1cq/8Xmx33LGLFoefYU0Hc36OSXYGy6h0XSndtYYm+hn5Hx0GOsAq/rDgAldg1DjwX9i/s+AKP3YNBkaP9nfX3XYP/ddxA0/F9hdO9VG8G7FjB3FsbgB8WYpLgHZPje1/NGo10U9UMTsEYsFvPwaMgvYVQUPfiw7/s9CNGMPJH0X9Uv+a/qBNz+E5I/97PBA0EZWfRBRtrTbzMCzV5j7gIQI8qBhxhZO3kY6XOsGi51OyvCKEypOi8A5MZB81dhbPkwi7p7DwC5MdDEWzB2/7JfxXY3gCYYB38DaGpjhGveAkoW04JP/WcYm+4LqudGebXgp9kkDXKEp1hpZQm/0q53PzD+oyDaDt9PPniz7x4/wffB2u592UikxUtMWiMuhybgBmAtJtpWAmppbzK2NWRNx5B/KcVYnwdZb9tkC5MW8hMwmjtEz1vcPMxIsL1PWMbArWMwgpaSmz/ESnVTA6ykGAabxTbydzA2DAJdO2DkJwDygj4RQ4/BaO4EUv1sTeNHWELrnt/yI9PHmakBLmBlYGz5CJMYeg8C1hyQGoRxx6fYNl07GOHOnmREKSSMhwFsBOAx+UEmxcGH2QNh8GHWiW3mXZFEY3qwVPWWG1XGFJWVdw8+yrzLk0eZjtzWCyN9nlW/he9/+A0m/O+lSExaIy6DJuAGYCmJtvc0ecsZ/s4di5uqIBrBbPQJwWa2MJ6U2vwRVkDQexerEOva4e/3I/HLbGz9iO8LdsCaPDRJ0WERuPkWs2pN/Mjv0/Bplpjb/+uszwLvOAbA2PMrAem297DvBh9mJNbSBSABtHYzPTmRZFVyt3GXhP+Kn0jCaL0NmDrGztvcztwM/Q+wngut3YFUMfQYi5CHHgHmrwV6dPqcH4GfZRa3wUdZR7TQ1A1ybdZNLTSmSIY6eJNg9H0AaN4Q+hkimOqhDO0MvdEoD0envt4TWiMugybgBmAxWqmzUMTcqWHMHru45lwSNUMeQLnYeWKDj/jtHvez5NDAQ6DxIywptePngK7tQdIolghcAG50IxneYrKlSyKAR2A0tQD9P8Ea7QiHwMMAEn4T9UfVdpdDj/lugzNBK8qeg75N7gjkajTDs3xik1tDSm0uqeR3bvsgSyz6xSUYejxYd+9BNqyzZx8je5T8fhGhfg+5MdDUMRhDjykWNXUEUkJyYzys3h/JlUG3LjHCr1AxZyRbQZ3bmH1u5y+ARn+ovL1Ukhm0RlwOTcANwGK00kvPfAdXn/9H/NR3/hRrzSVRCWUR+xLHmItGMFNvsyTb3AUY/Q+ywoeNu6SEF3wtlBPto0DvQRYl7/91NnzSJ0GSJRHRKvIg6/8rJ7UAv9PYKaH3iui0pVOVAUTVnZz8SrCiDjmyy4+zCJqfl2vPLV2MwI0E+9O9h0kGO38hGD9Enl9F9zArNBl6DCiwXhZRk5DFEFNJ9yXXZslF4TdmyUrKXoXRE0zFCIjyQ8wrTS4Mc6626JS37Zw8Wi4pLFJmWK/6sCbgVQKPmptaWt5TkW8jfM2Rv2x8PDtvrgMwHy5PeA08xBJgokcDgM0fZrPPmjsZsfLG6X4/YPISoncEzFnWvEZqIclOAj/aXAgcBbt+iRF29hoo0cwi2sxI4K3lDddd25cheKnwWV+nvsKi2c0fZj0h2jYFFXGDDwPWApDqZ5OQEy3liTeAPWAK04zMqRQtMUjJNUPIPx6QvaL2qYABozAJtHb6bxMPi8kcRuc2tpsgeFR9qBrJ1og5dj4q9fGoRLDrVB/WBLxKeK/a0xria476ZZt4q7wVYu9Bn0AfYwmurjuA9l4/Kn2E9dAd+pBfMfZjv1nNddbcfP+vs+GXN98IEnIzJ5mPmL+aG0lGVht3gtp6YFgLQG6URcPWLWDjbhhdd7AEXu9BGPv/BSP2kseiTDvHEmS9B9k5+u6B0bENaN3IIvVxLgFQoD8DzH7W2sXIdfxIMHFZkkiAEpC5yEYeGUl2PD6lg0+s4Mm1uQusxDg3DlgZUH4SRvsmUP8DMJwieztwcqz5jySFGLs+I9p2BgQfJALV3svXmTd54i1BolGSQqzMUI1g16k+rAl4nSAq2beYBGBDHhxRv2yhz0QyrrWbFSr0Pxgkn7Z+DJS9Dnr76aAvrjKt4rN+c/G3WLlxopVJGWEfcf8DLIE1expG6nZGiq0HYKT6mR+YD+fk3dVu28e8wCUbmH5HODBQmPQjXFauzBqln1P9yv0PsP8eeIjJJjt/UfEl0+DDgNHMEooe6xdBM6dgDDwcOE3IZQ8UewHY+pEgucavqzDNGtGnbmfOEN5BbfBR38K3X9xjoympjiXyEkD3XgDSG4KYHkKgDQMwJqTOa/VGqVUIdr3qw5qA1wmipIOlygn1EHjZK2hM5ESuLV6TyS3C6NoOw5oXySc5KjP2f1by9yJ43RbNxf9OJMYw8BCMuQtBzwRe1Za9CvTdA3ILMLJX2UBLj01UDgjb76429JhfcCEVdPQ/wEYA3XwD3/jyn+ELz53B+Ss3sX/vLvzJ7/4P+LXHfMLJXvc7pxGrnuvYzEYMcW2YSuwh4fuS0f8Acz0InZU5TYw9v8o6s8F/SMnXVUwzrZqvn7setn4sSKwJ+UT9eRhNSaBzSP15Sf0yjOw1n0SjnRbVsF4Jtho0Ab9PUI0Mo6SDff/6l9A+2Iutn1zca19dBF6rxjfxIxZ1DT4Co2s700F77w5G9UjHUVwRWz8qun/R4KNqxDU/wo65+zNAyQ31XWCNe4ziLLBxd1A+fPdvqoQ9+Ah7/b/5ht+/F0JLRe9BfOMH1/C/ffXH+MoffgyP3T2EI6fH8cT//TTQMYh//mv/kl3LTdZYB713Admr7Dy7fpFpw7zAomUja+yeGWaRc8eg5DTxo9PCJKhji9/X+A0YO3+R6eadm9mDR8yHQ7QW6+SA2dOVCyzC95nb42og0fWaUFsMNAGvESzVD1yNDMPSQSP8x3XpwbVqfIOPwOjew/675LDEW3sfKHOdRZqVjsO9wS2dwKZ7WeTrywhGcwfrmNbWA6N7NysHHngIKHksarTm/LHyvl2LdxqbH/EbwDeDeKLQaGI2tcKMLwlk8YX/6//EV/7wY/jYfVsBAB+7byu+8m8+jN/70z/Fr336p/3koeSZ3rgTRqJZsboZe36FRbc3Dwcad3ufXwbdzKSYVD94+0n03g2jezfQJP38+P0xmth+4Y6z5LGoX+oXwe6d1AlOHgy6GE12nSbUFgPdD7iBqLfPrrz99I/Pojidweh3F9eDde/nPoWHnvod7P7sT8PNW1W354R96Znvlq3pyrdeg2c5keuUwUm9FgI3kq0wtn6kpn4CRudmlviaOwdq72GksXCdWaVCx+F9gMm1GWnwJjs3DwMlG3TzDVbCu2GAkU7JZUmtvnuZ1S3RxF7z85OMwE9/mSXJPIuddyNrlE6eK4ojkL3GJIPMZfaKPvgwzg9fw2N3DynX8tjdQzh/aQRo28jWtuvTjFg7twEwWFJr8OFgmvOGQamf8Fusoc/cReGEMLp3MxmE956APyIJHhuBNPoDduLb71V7DMuYPAo6/3Ugfc5/g0iAPIc9eHb9oq9hl/+8gvtc/f+tZesN/T6EJuAGIo7Uqm0/+t23cPsHD6CtbyO2fjKmRr8KmjvbMfjxe/HKp/4tLn7lv1XdnhN2OHod/e6bGPzYfTj/n/5OEG6911Uvgl9uk/mBXZtFu713wTCafR3079nfhRmVBETz8DcZWWy6NyCxyaMs2TXwIJMeCpOsdzA8FhmjBFhZRoIbhhhxbLqXJeYyl9j35DLynz0F9Oxl23Xvls5xDDCS2L9zM46cHleu68jpcezf1gNMHgeMBJMNeKEHuayvMQxg6HEYyTYYiURA1AMPsgdA712AvcDW2bPf39cDzZxk156fYE4RcvwG8z9ijdl79gX+Yhmc8HsPsH97BWB+GAD5neBiXorraNJe68NWAwAR1fzn/vvvJ4142NkCnXn6RbKzhbq2d02bzjz9An3V+DidefrFRZ73BbKzeRp75Ti5pl33MThc06bTX3xeWUul6+LndnJmhXXF34+S61Dpxmvkvf577O+xI1S6NcK+u/E6eSeeppJToNKNH1DJKZB34TkqZW8G+zum/53F/nvyBJWcIpVuvM7+NufJu/YKlW6NUMlzqZQdY8d9/ffYNrdG2H6eR6XcNHmZq1SaPB58nx1j53SKVEqfZ3+Lc/yASg677q//zTN0x+ZeOvTUL5P5yufp0FO/THds7qOv/8W/Y9s6DjuXOPdr5I28zM7hOsH1+Gv0xo5QafIYW69TDO3L1y2t6cbrVLLz/j14m0qOLd2f18U62bGG2efyfUpfUNZR9nMS97n856xRHQCOUwSnagJeI7CzBRp57rVFkadM3pzM48hy+ug5ck2Lhr9xiOxsPnYttT5Izjz9Ar38yOfJnMuWnbeWh0pp8gSVrGxABIIULOWXvpSf9j8rSGRjsWO4TkCsJ54OCMsxqZSblkioyLYVJFRk20+eULdxzIDg+DEmTzByvvE6I2Qzw87tWIKQv/4X/44O3rmLEokEHdy7g77+pX/PSHbyBJVc21+LRHqcyHPjwbFkQuXrnTxB3tjh4NqdIiPpa6+w+yCI+QfBfXWsEGmz78S/I76L/PmI+2JV/X9BIx6agN8DqCVijN/vRZ/EX40lvZHnXiUrs0Cnv/g8FabmaPgbh2o6b6Xv7WyeJo+cLiNbJ2f6pFyZyEXE5kdfkWSSHWNEmb0pff8qI1FOUDxSvjUcihSHqWTn2fd+tFlGgq5NpVsjPhkflyLKBRYZeq5PlNPS/q+JyNEbO6KeMzvGrssnx5Kdo1J2lJHo5HFG5IU58opzVJq/KpFcgby5y+qx+LlvvMa2K86V3TflQeVY4gHlXXuFSq7lR8RmsJ/YlhN6fFRbRuChiFqjNmgCXkXUSqw8mpw8cnrRx6wUvbqmTWeeYkR5+ovPk2uxaLsSaVf7Pn1ymGbfuUzFmQyd/uLz4rz1XIuM8Kuu+IW38+w1XnndzgckxP92HSVKFsTLI9r8tEpw1oJ6TNdWv3dtn/zy0VKA56lk6xQY0aYvsO9clx1j8gRbl2Oy7/g5lfO9xgjXzrMHjJ1n6wsRsnqvGBkqbwGv/15wPv59+kJsFFty7FhSLft51BA1a5RDE/AqolZ9N4gauZ57LJa0+TFPf/F5cu147S4MO5unM0+x4zs5U5xr5LnXYshc/d4pWIrcwP8dlk/CDwIenQfRZ20RVEAob/tR33EW1eWmRaRJxCNCFs1686NUsgsiOi45BVVn5aRi5xlZyqTledJ+7JjslX04WgqYPMGOc+E5XyYxg+h4/lpENO2xP0rE7e9jZamUm1Ilkul3JXIvBhGu65Rpwt6Jp4NtXVP53hs7Ih2n/OHGHlTVSTVKC64UFeuImSGOgLUPuE4sxj9bq182uaEVF7/ysvDzbnp4Py49891IX+/ez30KVCLs+Vc/A7JdnPmP/xV3PvEpNHemKp5j9LtvoTiTwcLVCUy89k5F73CUt3jqh6cx89Z5HPuj/yI+T7a3YOd/9zFl37DvePS7b2Lu5BXseDQB1OMR5X7Uvrv9to9/J1o3Kl3NxPj6IzAGHwVQCua2JVtZhZ15C2jZGFTdeS6M9j6g6w4YvQeAlm7WeYyPNypMALftYa6Bjm1Ahwdj4k0ACdZoZ+Yd0Pm/ZeW//Q+yHhCzZ1gviex1UOp2GLwijU/KmHnXL2++wZq7U4l5kP0+D+jZB8Dwp2YkYTS1MU/v4CPMGldyQIUpGLOn1W5tMFhjIqOZFZfkxiSv7wdZlVy4e5nk/S1r8h6DyIq2Sr5f7QmujChWjvujI+Dao9nFwpzL0uknv0XmXJbmL49WlC1c06bc+KxwLZz+4vNEVFnycAomDT97iKz5nIhSrfk8ORHJvyg5wylY5BTMyIg5CmGHRtg9UA9Ux0OgwxL57gHuXrg1LDkruHYpJ+9MNcoVUXmBSpPHVP01fYHJBOGI07WZLMFf8XlSTNaHc1OBnOCyyJU5H16TJJB5dqz5q4GM4NiqTp2bkj7zj+3YTH8eedl/K3Cke/R6oAf7jgf2nZ+Y48nLOqPZ6j+X6hLGegW0BNEY1Gs1U/d7gcy5bKRli8PJmTR55HTFbcqPnff1V+ZqUOQJiVhd0/YTcTlyCpb/WWVHRNT6KunZ8vee4wrNudEPrEA2OC6RRjGQKiSZQNmeJ++4wyGsCftEGVizbJYYE7rta8p3wm3B3RJcCrjxGnnTJylwVBR8gj8hHafgSwqvsUQdfyi4NpVuvCrWRERMtrjxGjv2rWH2mWOVE6ik0ZZuDYeSefFJN7FOrfEuCzQB14Fak2b1uBYEKT75rZoSU9WIrtq6uNNh5LnXYj8jIhE9c0dEGK5p+1p0XjxAXn7k88ox+PHl7888/SItXJ9SNOeoddZzjYHlS0p6hZNmhbSIJL2xIyL5VOaVFVptKAJ2bJb4Sl/w7XE+4boOi7BFxGkH+vCN1wPtVhzHDDRi11aSYCIydR2JsF/3HRu5kF3Nj14LM2XJvqikmupykB5Miv+5nFyFhc/O64h1GaAJuA5EyQxRZFGPHMEI7MWqETBHlPOAJ7I4GVaUJyyH0ieHxblio2LLpsylUXIKZpmXl5O2vVCg008FxRnsuEWxfbiQZOKNU2QvFMicW2DR9UKBRr93tMzjPHnklCDrSlCSRWNHpEhwRPr+B0EyiUfBN34QSAcnnqZScS5IsknFH6WsnLQ7zgjSc1WpoDinyCclx1aTY55b5lZQ3A1OkRE7/1wUmPCHQpEl8sQxJbLm5yjOhSL5kar/HxFRYNMLJeHK7/EPFFJfiQTaeknSaQKuA1EyQzwpV5cj7GyBhr9xiJyCSY4vA1T33PKqNoumj54jz3Fp5LlX6diffKWsUq3ycQrSv0N6ruS6sOZzNPzsIXFc/jmPjvkDhO8/eeQUnX7yW/RV4+NCDz7z9Itk3Vogp2CSa6mWN3uhoETNiiRTsNRoLX2h/LXaJ9wgOiyP0qJeyYlIsXWx7SSN1Sc/4TDgr+qhijrvwnNUmnw7OCZ3ROSn2fFcW7WP8eMKPVZyTpS97r8uomH5M3HtfJuYQpRqiCLX4PN48lsJOWK9SB6agJeIxWq/RBJ5P/UCTR45FUuechmwIDX/9X34m4fIyRUpc3mMnFyx6lo4WYelAhkyiZ55+kVyCqZ/3DyNfu+oIF2mGau/pE7OJGs+V6ZXu5ZDI8+9Ro5p+/a1V/2HyHlmW8uZNHnklLgnI8+9Rp7rkSdHi65FJXNeHJMRXCA/1IIgcvSj1Vsj/nH8KJe/6nsuK1ceO8Kq0DwvkBYmj1GpMMte37M3SVTncT148nhQ3jx5jB2Tl/m6TpD0UiLu1xlpKwUWxWDdrs2OxbVr2daWGyee8GNJQFu63kUkzySLX/lx1ErE5cJ6SdJpAl5FyOTNtN1o8hx57lUy0/M08cYpci1HiUCn3jxL1q0Fn5BrcR+oiblgHcH+Ts5kxwytZ+S5V+nlRz5P6ZPD5LmuImWUX1eFhJxpkzWfU7aRS5fZQyZPI998NSAjK8uI1k+UEUkkZC9QKTdd8bqreVtL+Wmf5I6rGq/c58G1mYfXtYSWK/bnTgZlfzOIml2JFEWFXlEt1Ihal2MpEoGsJ7MKureD8wvphPXECBO8ci8mT8T2eAj6RvCiEL+qcORlhZQ1lg5NwBIWW/K73OcICJE1t+H9IexsniYOn1JkkMkjp2K1ZNe0Fc2WI0pG4RGpfKwguo0/R9zx5OufeONU2Tb8AeTkTPIcV6xz7PvHfGJ8W61gK6TV13Be0RYTDZdvG2WxKpQ3AUqfKydlLkOEI83Jt0WFnKL55rldLCL5xSUUqWpPWfetkcD5ENl4J6oy0P9M9NMIrTt9nj20qkTG4lwhp4dG46AJWMJyeXll0q1W3hsHHn1ypwS3cgUNb5g+e/iJJyMb4PBj8F4PlarTojqfcch+5MrXW17t5jkuTR45Ja3xRSFHhCPtwtScUsJMFIpiLzxHUaXFQScxNbEX9roq38kJrGJa0pIlW5lEquwcEZHqrRFSyqIl0lPP8zp5+RllOyIK7GZyQ6HICDjGW3trhN0XWROX3BbRicDXY3+OZQnLBmqy6yXJVg2agCUsRc+thPKuZPW2pmR6b/Danqe5s1fJTGfpzFNBy0fuFY6MaAtWSMON9h67pk3D3zgkCNDK5JRtZI22Voy9coycvElWZkFJsBFVcnW8Rq4V94psBcSQHWMaqxzl2QtUmjwe2kcixVCPAyUCjng9LyfV4+UJPdmuNXkiaB8pyQolx2Lasdwi03WoZM6HpJZjNTsZ1GuUNHG/xFm9fu66yJUlIKsfu7Ga7HpJslWDJuAVQC3EHidNKPLC4VMiaccLJVwziOhc2yHHtCL128kjp2j42UM0+g9Hy7zHGamybuS5V6k4k2EJs4JJh594ssylwOWPOCnFDTk67Gw+Unaodm945BzXilN4cuWKL2EZk4jWc1nxgV8IUSqka/S98ld6v4cu9+3yv+XveJ+GSCuZFWwjd0vj5wn1A2Yas8WuxXd+VLKKibXLvSOke8LvlZc+J0i50hvBSmC9JNmqQRPwCiFMSmHEyR8BQfEIOHht5wkxjvTJ4dhuY1y/TZ8cVrzH1nyO0qeviPPa2QKlTw5T+uQwmel5ZpPLFQPtt2BRPlTmLEeqrmnT8DcPkZnO0tgrx8W1yVJJrdG/kCKeej70BsD2D3TXmEg5kgx990Pca7xrseKJiKSW2EZ2Q8g2Mv7aH9Zqs2Mh/7Df3Yxvp3hxpd7EPJF3azjQgYWXmUsSVhBxKyQe0pJr1HC1NLCy0AS8RFTuiRtEcLnRKXr3C89S9uqE9H3gSHAKVkVyCmvAcmKOSwnc4sVkiWKM5/cFciyHPMelhetT5FpsbWEpQsgAtkPzl1lBxvA3DpF1a4Fc2yErk6PTX3yezPS87+VlhSCnn/Kr6r75KrmWrfT/dXLlv/yV7p+iRT/1Ak28cTJ4A7Bs8myzYhTHXvnlZF1ExKvYq2SZQq1ei9yH93GIOHYQYfpeYd4AXurBEBwrVOggR+y8pFpU+IV6T+SnmQSjOCoqJSErv/JHWdA0lg+agJeISok7NZmUJydXVCxg4WY5cQhaPUqJMssJChqkMmbu8+XRsLy28ikVQQKsUokzX3e4XNm1mORx+ovPK4UgrKrOoszlMXHOiTdOiiZC8nn4esOJQX7dVianvAGMPPca2bkiZS6Nknf9tYqkInTWSokrTjjhsUBjRxgJjh2OjCiDc/AiD9WBoTgeuEdXKcCwBPFHrluO2M1bjJQ9j30vF3b4ZF268apSBRh9zOqv/GUWNGWtOipuNFaVgFfC9rXcqNboPCigeJWyVyfo5Uc+L3UnK/fk8mPKpcXmXLYseuZabbiMma+HJd3K3Qhyko4TJ7evhZNr4T4Op7/4fFnhheyscHJFmjx8iqz5HCsxthyxBnMuS67tlJUZy/eASxYcUXIKj7KdXJFKdnQP4bKKsgrRnPKqr/TzfTtIuFnZUEJLJkhf3xX9H/yEmxRxllwrYiLHSKSkEN5XnE8qRQ5H0iL5VmV+Wz1Q1s/Xus4TZsuBVSXg5W7huBbACwpU54H6Kh6OPiePnKJzX3pJqX4L6592tkBjrxwnz3HLzsm/5/KHazs08tyrZSQdbqguJ9fCfRwmj5wmK5MjO1dQNF8uVThWUFxh3VogJ1ek6aPnFGkjqvObIOSnXiiLgMXDxPc+Tx45pVTy5cZnI50S5bpqhcSVKDA4zqLdUAQZJMLk1pRyRVw62C5uxE9Zz4WC4vuNJFxJluAVdbwQQgwJ9YtPlGi7jqrASlCb96il3GUatBTJRz2cNOKxBiLgxtu+1goYSR4jz/UqPmzkkmTXtBQSO/P0i0r1W7ViibJj+k1wZPnAyZk0cfgU2bkiuZaaHJRtZvz135rPk2s7lH53mMxbC5EWNNeXIvg5Z9+9rJwz/D2/P7W04uTrOvzEk0rEPPq9o5F+ZIWQypJRoWhTEI06AVjuC0wkR4SvSaOEwn7fmN4J8ut8fkqyzZWTddxa1Z6/gb6sEvhrMR7lxibWyqLjSDdH7U2B1jO0BtxAhCUVmczUqPNVlszyozfeXcxMZ2n4G4dqLoCQPwtHgqIJTiZHh5940k9cOcq6uKTAJQHXdkRFGi/qSL87TK7jklMwWelx0RIeXg7Xcmjkm68q3mEuWfCodvR7R8leKPgNfNT7c+bpFxUrXNy9VTzOT70g5JMwSo5NJTMT+TpeNSEn9+W1skGprxLZFUIz2V4TDdUjz8mj7PT5uooaovVpnvzzNWAx3Znr0G+XE3mV89VL0HJErExZLrPvLU8E/H5yamgCbhCiqseiIny58GDi8Cml+mzslePCIhauEItqyC4fK3N5THzOo0PXsil9cphNucgVxIw4Tvg8Qp144ySZc1nKjc/Swo0p8lyXHNOi2XeGhSRhzefKBmwKV0WuSE7BpNHvHWWd3UTLy6KyJm4n4+APECuTo6vffqMmKSpKPgkjqsggKKCI6ZjGS5CVyRQRdi4hNYQ6tU2eYJGtpBUH5w4khXo6l6n78b4XebXfcA2ab7UEXKQEokT45dJDpbUuN95PRRyagCOwmOSg7Hjg1WNOwSqTCOSkVKWet+mTw2VRJIdrWkJX5n+zcUBM8x393lG6+u03gjFDtxbIlvr0OjlTdCezs3mafWdYkKg5tyA6rc2+e1lEnJNHTpdNsQi7KpxckexcoawUOm4ChlMwxbqjOqstFuEyW8WDO3kieh/eN6ECSUfux8t/s2NU8hyq5nQo3Xjd15qHpYbrIzGkxqUGXggS1qOLVHmqsVl+/ggSLRvjJMqtX4tNLK4m3k9FHJqAI7CY5CCPYJ2CSe9+4VlKnxym/PhsJMlmLo+SU7Ro9t3LZa/zRKo2aqbnFXcA10C513b0H47S2CvHqTibISdvSoUTzA/Mq9qyVydEi0k+ej59cpgcyybPYV5f13aUXr3nvvSS5OE1yyJ6cy5LE2+cVEh68rDayjIoQFEje9dk5w/eGgL9e6k5gfAvaLiLmNoYR56eHNEMp4oFi+3nk7bidJDdA0EkHUzvCPoOx00ejkomlrJjQb8H/t8xLTmjCDPys1CnMxZlHyt7EL2fiG+tQBNwBOpNDsoRM2+1KDc0D5OsOmUiLxqbh3vinnn6RSrOZoQMIHcJ417bQLYICJv7bwUJWjY5RUsUT3DbGPfunvvSS4yMlcGaLLrmUSlzSbxKTtEi13HJc1xy8iblx2f9ZCEfaV8UpG3N52jslWN07E++IhJmXDYZe+U4u1d+2bR1a4FGvvlqWTIyqllPvRCEx0f7ZG/WrotK0zRij88j7NDoHqXHsDR3rZSbZmPli3OBtKC02GRRa8llPYlVS5sj6a+2f1xVr1avIW64ZiGIrMseWGrk3Wi8nzTcpUIT8BIRpf2qDc0De5XQTE2LJt44KSSGY3/yFdGU/fATTyrFB3JBhSBbXyKYvzJOROpcuYk3TpHnuBFTlPOUG51Shm1yop0+ep481xP72AsFsvNFcixHECCP7K1MTjRT58RrZdi0C8d2yJrPUWEmQ+mTw/TuF55Vq/1MW9jnxOcLBfJcl1xhY3uRzLkFGv3eUVGgISfb5ArAWgeGls9tc+rQRV/3p1tULkwokwtiRwb9QHRXEz0hxo6QUsIctqxVWGul8uP4+2H5jeJru3/Kvg0gz7UiZawFaAJeIqJaJ/LOY5x4po+eJ6JAM1VLiIuCCHlkzCPN0e8dLWs3ycuIx145znTmnCkImx/PsRwxlcJeKDAXxNMv0rkvvUQjz71G5q0F8lyPXMdhckiBRbJhTde6tUBjrxwLtO35vPj3maekAo0nv0XZKxPkuZ5oHm/N5yh7dSJSzoma8HH6i89T0SfuwlRaeVAwwmb3Nlzh5zluTbJFvf1sw35WudAiqjBBiSxlp8Pk22WTh5VjySOHeJP2cNFGqBdF+TrrkwXCGnk9aAR5aikjgCbgJUL0TAglyuSCAdcKIuCwlerqt9+gycOn6Oq33ygjFz4KyMmZlLk8ymxh0jQKlkh7lcxbC0xCMC2ycwURfTpm4IIQjXhyBUX24NE0b1OpSiAvkGs7SoQf/nf26oT/EPGTgT5xFmcySgEKLyXmxRSc1OWCD97YJ+zSkOFaTll0H93ESK0yVKvQ+Ph5u2I0p/TXDb/6cx9umRf3VX9MkVzS+1ogfbhOZIFIQEqO1IyHR8CNdRbUQoBxBRWaPBsLTcAxWEqZtGurJCF3LAuO/aIiA4w895qS6LIyOXIdRySxHL9oQui9fnLONYMhl8PPHqLs1QlyHdajQV6DY9o0+84wTb15NujdIOnJ/GHhua7Uda1AruMo/3YsR5FIuKeZV7PNnb5CTpEVXbz8yOcF6dq5Ag0/e0hE2GOvHCPPcaiYnhcFJ1YmxxKBon/EqchKN9mSJ0/S4D8zefIHL/sWxCGSXpXbUop9Ysnydb/v8DG/z4NUHBF2PUgSgaiKq1CkIGxgNVvMYtbXEJlAF1QsJzQBx2ApZdLWfM6PJk+LJJzcWH3slWOsoMEst6mFq9t4BZhjWiKalQsniMp79FrzOcpcHlWuYWF0ipxcUfwdTDT2E4HPHiJzLkv5qTSd+9JL/pBPk/VdKJiKJFBMz5N5KxskzfxexTyydgomu/7QuCTXshlxFpl+zKUY69YCOaYteg8HycD6km9q4UsQ3cuQk17htpRlzojsWJkViyhMqlakq0Jsa+dJqXpzHTHIMw41F2oo45MqOx1qgXBoCI28PALWaCw0AcdAdkJU6hQWBR7ByZFvVAOc2XcvR85nO/zEk6Lxjms55BSCxJOVyVHmItNtOQGHz3Hm6RcjG7Pzh4CVyZFrOZQbmybPccl1HOZmyLN5bLzROz9e9uqE0unMc13Kjc8Ka5l1a4E8xy2bbuE6DjlFS5Q1O7miIEXZgsa7uclR7cQbp8hMz9c1eSNqtFI9JB5u3lNy7CpygVm+b1RbSt4JLaK4IXIdNb7mqzPfqqyvahGFL3eIMmlNuisBTcA1oN5o2Lq1wJJWtxbEZ7LswKNNz3FEVMkhEnh+1RjXbTm5KZGudPzgHOzzd7/wLOXGZ2OvI3NpVDRaV9pT+iXFPPF15ukXhRsi3FYz3FSIE745lyXXcYJ1+kUWo99nzgbeK1jeXn4LiCqxdnKm0L1rRb0/N1W3rUBWEfJATa4Kpbjh9Zqvo+p6a5EqeJIvQk4I6+O1RM+OZ9ONhTPkeMtjVVsv0ARcA+J8wXE6sVOwWHLKt13x6jP+HYv0imWTIzjkqjFeJuyYFp1+8lt+Yi6eWFzHUXzGnCTVnsJ58lyXJg+fook3TiqFE7wLm0x0nuPSwo0pmnrzLBWmM7F9jD3HpYnDrLtZfnxW6VfMJ3EEljQWYbk2L1sunzvHGxE5OTNUYVebN3ixzZ4q9otY9Ot9qM/EIixgS0F4uGfZdzGdz+JwY+EMvX7zr+nGwllyPdcnY0cTc53QBFwHwlJEebMdPjY+0B/VyNVvEWk7oulOmOzkCRJyubJrOYy4I/r8EkkPg1yRzFsLNPyNQ4qljDfXmTx8ihzTIsefVmHnCkHXs0zQM8K1HaZVSyXMTKtVLXa8yIRfm5MrsnWGtuPJta8aH6fhbxwSTYW491nu+cv7CvPrmDxyWqnkO/N0fCOeRqCi77ZWeSBOExYOivpf8SOr9+ppohN6eCyFLNm+Z8nxbJrMj9DrN/+abpmTCjFrVMe6JODFOhzCkScnQtlKpfRM8DuQTR45LSxjx/7kKzT2ynG/oIHZ19g4oqAH78uPfJ6VKXN3wDxzRBAxgh575RjrBxFqIxmedhG2tU28cUr4aucvj5LnuvS1Z75KO/uGKGEYdEd3P/3Nl58RhG3Ns34QcoRMRDR/eZSyV8aFv5j7fQ8/8WTQt9i3pnHJwTFt3+P8miKxTB89p0gqXIKRW3I6BUuMW4pqRSmkl/lcqFdxdMP7lcByFBuUNc2JOEclUg03zOHEuVSyDMjYIcdz6JY5SY7XmMbw73esSwJerMOh0iut2jxcrf4K5AiVEOSk2NSbZ2n66HnFA8sbrsuv6Dz6UwoZcgVyLWYzK39IBM6Iq99+g0Xgvp3tz/7736WB9o30R7iPvoyP0h/hPupv7qDfxAGRGHMtu2wcUlCNFrS75JHz1I/PUubiKM2dvRok6ebZm8DhJ54kp2AqFrHMxVElGZe9OqH0o5ATmbK3mvmPTfH5uS+9xMj/qeDBWuvIp0oIa761Rp1RhR/1RKzx9rLKvRl4BDqZvxJ7bJmkJ/NXBFk3Qj5Qjz2ipYgqWJcEvByN4GX72PTRczT6D0epMJUWpCu7CngFl1zQwG1cRDx7zyQLaz6n9AEWTX9yRb9/g9pXwl4IOQEcR0TLfAoyb/C+GRvoj3AffdX4uPjzR7iP7rhtQGzrFKxInVbo3AWTpo+eJ9e0aH7kpvrwsR1yLZvGvn9MdD7jEa9cJSi7KYIIOKjsq/Zz45JPuMNcvRFwJOnJToOYXr5x+8kNboiqOCXC+y8ygpZJNY5M42SCRsgHwTHO0Mj8cS1FVMH7hoBXe76cUvnmT3/gJPvyI58XvXWD+WfHlJluQetJVRuM0plla5xTMNXuY5bNdF7fuhaubOPlyHa2QAkY9GV8VCHgL+OjlDAMKkxnBJmlTw6Lhj0clq/P8p4Qp7/4PDlFNbo157I0++5lUZDBI/vpo+fJTLOy6uzVCXr3C8+GNGCT0u+yh4ucoBQPuUL0PeKjk6pN14hDJLnKXlsxv626BS0ozIivIitr+BMm9ZDDoZ4ItRKZyvpt7Z/Xdl5VG76iI+AqeN8Q8FIKJ+JQjdRVQiwq5PryI59n49ylJJa9UKBzX3rJjxhtmr88Koor5NJb3t9ArowLD7RMnxwuGx1kZ/OUuTQqqsg4+ZtzWZEUnD56XhDU3m13xETA/SRX5/Fr4G0kiVhPBjM9r0SevPQ4SNAxrzLvMyF3cXMs2183t6kFRMUTl+EHUrnOLfuby/3OdbsfYhJsVS1mNfhuo+xrqv2rvCdwmPjqiVBtz6QbC2fI9pZeMqwTa8uH9w0B12sVqwVxpM4HRKqR1ynFOsUJy3PdMuKUK9DshYJfUuwXLNxaUPobpE+ykUByy0lrnhU18FJkIlawoQzFLEg67XyOzn3pJRax+vrvyHOv0te++tfU39alasAtnfSbOOCvnTXsmX1nmGbfuUzWfE6ZkiG/8vOhnZM/PEOe65KZYdVtXCoRMoNEtiop25LLhPmRWUWfOulj4o1TsT+Xaj+3WhDMgpMnXizCsVBDwoxNMr5IpZiEVZj4eHRpe2bVJFcjk2FxkfF7AWvdFve+IeA4LOWXMa4abuS5V5UuZbzRjPwAEF3BQp3PeMcw7pLgZb+yzhqey+a5QR9gvo9aSswa8MhSxMKNKXItmwqTcyI6L0zN0ew7w0oznL/5yjO0d9sdlDAM2nX7ZvraX31VeiC8SlY2z3TcV45FEuDkkdOBdm07ZS0zuYbNGwLJ5ca8aTyP/OWHmOe44t7xiH3slWMs2eY/WOKkhvD8vSi7ntw7Qv4+eg5b/U4GMb3C9dtTei6VireoVCrVfIwo4rtlTtKJ6e80PBpdDaJaiXOu9ej9fU/AjUq4KcUBpq0QACcGWSN1CqyclpMiS4IF436iWi0S+UUPo1MseebPVhNWLeEt9nVmiZRlt4J1a8Fvhn6cRaNzC+TaDrNupeeZbiwlADO+VMI7qlnzeUUSYWXBrPdDWXtM0y4bQxT0iHhBIdX0yWFW3eY3Kxp+lmnIY98/Jt4G5ArCOGtd4D2unGQLl0bzz479yVfKLHocslUrbNuKAicROSpdTmIJH5u7DVzPrbhdtePUQ1SNur6VIMe1Hr2/7wl4MeBRLo8wo8bxqNuXE4Fj2mQvFARZhQlz9B+OltmpFL3Y98hyDTZ3Y4o81yPHcag4m1H69JrpLGuq7rgi6cWnSgQ9Ifwm7pdHhSWOk3X69BVBTPJDQ52a/IJILvKpG7zFZbiajrfR5GXL/DxcrvBcVyG/udNXRHnyxBunyMkVhY1Nad/pPyy47FLNZhb1M5ObCvE3jWoPZ5lwHM+JIa4zdMucLPssTIy1Io7kJvMjNDJ/XNjMslaabLdYtm01couTN5aa4KsHqn94bUsFywVNwBEIe22ryRfl0XG05hxuQ2ndWqBzX3pJaLky+XECnXrrLDkFk7JXJsRoeD5pgk+m4PvL0oKZZkUPuZuzFNaq7Wyexr5/jFzLJtcKZrZNvXVW6dHAG+gwycBk44gsW/QOFjp2rqBE3+9+4dlg2KbvP5YlmtzoFFnzORr+Juucxid2DH/zkLj29OkrZC8UFP2Yyzhs7p1ZNQKOuv9xfYYrQSacW+ZkVV1WJpN0Mb6ZugzHc0TiTK4u4+dUvboBaUVVnwXHiibU8PfMr+uQ67mKdzeOFJcjqlzrUsFyQRNwBNT2jtUjJDnSinrt5RANZWxe1VYUAzK5Zixe79PzlLk4yqJeyWEx70+wGHvlGOvNMJVmSTk/qh35ZkDKdrYQ9Cb2bWHc7sYLF+SiD7G/ZZPnukzqsFn/X+5Fzk+myfM8kVgc+eartHB9korpeZEsDEet4SY63Ckia9jF9Dy5li2iUx5tf9X4OKVPDovzyd3kiJhkIz8MKvWICCf9yn+GwYMzIB9HIRwWAVcmnyBKLZcG4hAmUtZfgRF7nOY7mR+hsdx5cjxLWdONhTN0Yvo7IiIPQ/4+a6Ujy4gLTnZFSXGtSwXLBU3ADUYlqSI8vogn8+SJFa7D+jDIuip/fT/9xedp6s2zYvqyLC9YtxYoNzpFo/9wVIxA4uDELw/PnL80qpCaazvK+uQCiHBPXyFpLBQUbXr0e0fJyRcpNzqlPLyiemiEq/m4JY1Hs8paQv2Umd2NHSt7dYKGnw0iZ5mcw6Ra6WcT1oOjyMfxnJpIdTFk4nhWZNRaifDlh4Truf4DIvo4MlikeyX0QAkeNLZn0oW5H5LjWQ1xUqxXeaEWaAIOoV7bWrjbWSWEB3i6JmtQPvLcq6woQyqjVQd7qn0c2JQK5kgIF4BENSEnIvIch6aPnhMeXLnfgr1QEE3XFe/w03zScaCVym4Lpe/F0ywROPzNQ2Xnl9fI7pk0EsnXjMP9JsSoJ9sR8oRrWjR/ZbxsyCmXdOS/45JslX+OUrl1BInWGxGG9eJq22attCD3uOQa/y4qYuWfVYp+ayFD9qCpXE1XD8ISznIT8XuJ8DUBh1CvbS2uOCAOYuTQrQXRYMe1nDK9We6KZmfzvq/WEpONTz/1POvBoJT1nhNENy8lq+xsgc489QIVZzI0+v2johRZPAhcl0XUPjGHJ1nwNard0Zi/OHhwMFLlI++55MDbY/LkGlFAyKPfO8oawSutMsvvn1xdFy7MiPpZldsEG1MdGSblar/o1Qjb8RwqOFlyPIfmzWmFdCfzI2S5hcjj31g440eoanJQjX6tyMg1bk1RibBGJ9sqySmNxHtJT9YEHEK9tjUeFdZD3OmTw2TK/uCYFpNhjDz3KmUujgqiKxv540ewvNF6uCWm8Nf6Tdi5rpu7OUOTR07T/MhNJh/kCqypvP99cSYjRiGlTw5T+uQwjb/+jkiI8cbynCTtXJHshYIyvj7cz2LkudfKupfF32PJFvfNV5V7H198E+8TbhQq/aIzScCqWI0mE57neSLxNpm/InrsRkWzPAIOOyI4slaaLsz9MDIKjpNHTCcneYzPlK2vEahFP2/Med47erIm4AZAjVarE7c8SJN3HeOf86Y5nDz4azZ3AHB7FncxBGOHAqINSwP8FZ+3d+SRLI9Keclw5vIYOabNJhMvFETj+JFvHiJrPucnANnk5Yk3TrLZb7mi4oKws3nKjU4JDTfcJIdH3+IhUaPc45qWKHUO38t6Z8c1CpV+0R3P8qNZJ1Yzjno1TxdvSjKE6oyQERB8VO+G+P0qX4vqZ44jeHn72vtDVJM94q1+72doAm4AuAwhN5SpBh6lyWQrsvT+BAlZM1aI2nIoNz4r2jBmLo+Rk5ceAgt5RZrgTdLlpuhjrxwnp2jSzImLkdMmnIJVNufNtR2pFaWvD5uW70F2WEGHXyQhuxhUDfsYe0gcPlXR5lerFh/15rEcjZnqjQar6Z6qFcyiE9PfidRdK0XZppOj6cK1SIKs9TU8zsoWXHP0A6bW4/NIvtq2lax+72doAm4AKpW+ctQSqcmkHHYlyNOVT3/xeTJvLSgky0e+Tx5ho9yHv3FI6MGFmQxNvXmWcqNTogKNR71sGOYpZdrE6D8cFV5dO1eQpIrZsm1Zb4nAXzv8jUPkuS6NvXKMzDRrAsR7UYy9cty/rtfIyRVF/4twqXC4z0b5ParsbFgOUq6u55ZXqKm6p1qUccucFDIBt5y5nltWalxJd57Mj8T2hahVr+YyRzXnRHh/2UlRCZW07Lj1rpRUsRagCbiBiPMAh5usE5Hi7Y0ClzVEEUTeVCLYcE8GufpsYXRaVKA5uSLJEyhYqXHw6p/zx9TLEkL6zBWF3OcvjwqrnJ3NK8M0udYr9F+/uRB3MfA+wNxnzDTiQlnzdKLAJRJOLoYnhlTT2msl5XpQTVeMI2g5oca1XVkbdjyH0sUxYSMrL2tm7gjeXGex/XYrz3A7G0PijvSACBq91ys/8OsOH1dPzYgn4CQ0YuEsFHHpme9g7+c+hebOdvH51k8+jAef/G3c+cSnxHZzp4Yxe+wijv3hf0b7YA+2/fwjOPvnL2L3Z38G1158AwBw1x/8Stk5Ln7lZVx9/h/xwJ/9Ji7/9ffgFUwM/dT92Pu5TwEw0PcTe9F7724ABvZ+7pNItrfi4O//E7h5C9df/Efs/dynsPXnPwgCcPEr38GxP/zPMAwD+//nz+DiX/49jv7BXwIwsP/zn8bVF34AO5PH4MfvRXFqDhv3bMG1b78Bc3YeN/7bm9jxTz8CALj2wj9i688/jJbuDvQ9uBevfOrf4o5f/Sj2/c4v4ZOHb0fvvbsx9eZZ9D9yF4zmJqSG+vAPn/hD3PGrH8We3/hZXHrmu7j24hvY/suP48Z/PYKj/+YvAcNA34N70XPPbkz/+Cx2/YufxPzFMYx+9y2Ys/MY/e6PsWFLH47+wV+i+647sPdznwSVCHs/93OxP5/mznYc/P1/onzG79vez32y5p+zW3IwUbiEwdReJBPN2NpxIHI7r+RhMLUHgOH/Xb5/1p5Bb9sWFNx5ZOwJjGSPAwC6WwZxau4Q7u39WSw4s/7nhjjXROEinJKJ5kQbZorXcXfvJwAAg6m9SJtj6G3bIs6VNkfR27ZVWbNX8mC6WQym9vr77cGseV2cZyh1J7pbBmDAUI7T17YdAGHBmUVH823+/gb62rZivHCxbJ1xSCaa0Z+6Q/mM4InjuiUXE4WLYr0aPqJYOe7PeouA4yqqxGgcqRG6bIcKR3BcF5Wh9qEIypbDo9vjILe+9ByXhr9xSFjFrHk/mr21oDS9kSNG12a2MTkCzd2cEd7aIDkYFGCECy2IiDUQWij4ft+CmHTB23Ty/a1MLkjQ+e4I13KUNXEfMpdxVE9x4MOWp5I0ArVolyJqdU3KWumq+3O7GNunKEWTVqRrgkeQcgQeldTjCbO4SHXenCbXf72X5YZ0cUxIBOX2N0uRQ7hsUk2ukO9LNf14Pem9UYCWIFREaYVRumNUTwGmX54uI16ZEKrZzfi+6ZPDrMw2Vygr5a22du6YYM18mNtg8sgpX0c2FQcEB3NBjIr+xUGC7AXyXK+suo5PyZi/PBqywtkKwYYfMHxbZl9zfXmhPtufvD0/Hu+0Jtvdloqo1+cwKjXeqSRbyBqq67mULt6kcDEGh6wXq2sbUbzDzEvMZQNHWRvfjmvMt8xJytnzZfJCOGkW14eiGqpP5JAdD+tD742CJuAQorTCqM/kuW1h8OhRJrhaEVjaXvAboLPuZuGRRNXWzidmyB5lPiYpljClZu6yJszbRYrpH74ma2XYtGZOfGzS86uRjYzKI/vGOBQ4IYeLS2q613Xao+K/jy98qAROhnLDnqgKOJn4ZNfCWO58WTe0wMIVFFYw/XnE15m5/hqsNzphV28pdbgs2lHIWr6P6eJYrL1tvUETcAhREWq9xn4eHdbTcUtGuHuZOZcl17KDdpYxBMNewdlMNB5Z8sSZvH45Ah575ZhCmKef/BZlLo2S6zjkeR6NfFMlVDb7zaLDTzzpNxJygvP6Lo2oRkb8mkQLzlB/h3BznsWg3iIa7gAISwfhbaq9Isc1Sa+FvHkUPJkfIc/zFEkgyvvL18wli7CFjDfX4RG5K5UVV1oTi8LHIqN4HmkHxF6eRFPdFKobJK7R0HqNemVoAq4DtViZVBmgNsLm0aHa4+BFpYBh/vKoKGyQeylEnZsVXLAGPIefeFI0ipejUK4Bu5ajECaPgNnwS5Os+VzwvV/OPPsOa9xupudrfsiEr0npmPbUC2w8fZUHTKOh6LE12rmijxO9jSwBcHKNOs5kfkREsZUkAFmflclYnv/mSq/0k/mRsgdMtBvCIjemeCNwW7D9slY6ct5cnJ+4VpvbeoUm4DpQi790MXYn7vdlU4aD/0HFOB+fUNmEC49Gv380shG5LDVkLo+JsT6c3MPRbtwa47TU8Ny5+cujopS6VvCEmmhyL3VSk9tKrhRkkuSWq6WU4Mo6bpRnN+q1W9VhR4hXuIV7J8iadPDwOKtY1fLOPFlu0d/eEp/znr/hPsBcD7acoqIFB9cjR8Bnyx4Q4euQq/PYn+Df4X7DS0G9P6Ol/EyXE5qA60At/tJ6X4OJmJ7M+zvwVpVEVJYsk1/ZoyJgeZYcHzGktsDMBzPbqiQC5QGjcd8VZzN0+Ikna25EFAXPcUUjdtaL2KHpo+f9xvHLX14sF0xwJ0M9zVzKCzDUiFru8SAn6ioVbsge4EpJvLBkEC5BDvRgi8Zy50U0yhNw6vZMEzadXEWyKjoL5LjVy5xNJyf6Fd8yJ8X5G+V4qPdYa7VBjybgJWIxhBuFcKtKImblWrg+RQujU0REZa/svKhDJub0u8P08iOfp+FvHKKpN8+SEyoJrvWaRr75Ktm5ohhjr9jHQiRfKeqPrlyTGp8XLL8X8ItUnMn4bwFMY+YFHY0uK45C4Eqw6kpCyZMriKJHFEUdLywF8CRZnBYrQ02uXaFb5iR5nldWqCFbvPL2vBRBO3Rh7oehhw17OEQ9gHg0L5O16zlUcHKx61OdGeVWuqWi3mM18tyNhCbgNYSynrTSjDg5weVaNmuME+ohwccY8aY1ovotNPusFr+svL/cAzj97rDiNZbXHbbbRZUUy8k4uf/w5JHTNPvOsLD2sV4Y9T88lgJu96qlEbma1b8Zimwrj453PVfRUXP2vNTXN+cnzspf1ct11jPkeg6dmP6OiGhl+YJH445nk+2YoXVbklOCERMny/JxRRZ5IenB87zY6wsq/wI3Byfxeirg1qps0EhoAl7D4H0XuKdYdgqEO6aJUufvH2NWs6fio9Ja/LJKo3fLCebEzeekDmxRbSBfEMk+vm5GtmqC0TVZ83ZZdpFHEvE1hGWZSlhqtMwJolIXMI7FlOaqTgJLsYNZblHRjXl0Gz4nd0BwnTk83UK1mdnkeo54QKj67ZmyaDvcAjNwVJz1tWQ7RMxcPglcEcyG5pT1gKgm7UTdw7UqGzQSmoDXMJwccyFkr04IQoxqY0kk+5JZQks08cmw10SZnHgyjpMrEZFr2czdUDBF1Zrcq4JHzbwpUBTKh5O+GLKbhQk7LyQHoqjJGQWFvMNoRAI0CrX7gysnpWR4nqeQn+MyAh7PXRbnCxdPhGWIoGAjiGJdn/Q45P3HFi749jZXaNGcFFlCcETpJyG7MfiDIdytjZOrfL3haN5xrdC1nJWi/tq7q8UVptSLtRxJawJeBBYbaYXtZvWdS02gmXNZRoYRMgJzPBwXjXCIwuRoCXmB9RcOrHN2rkiZy6PKiKDs1YmayC1OD6+1XWe9VXGNSIBGod7IK65CjP/iczlCJoKCkxPRYrkDgu3DI1fVhxu4F1RN1lXcClwaCUenXBZgljgWectJP7a2rNie/zc/dlg+sf2EnCxN3Fg4I6ScMOHGOSGiNFq5cdHiikIaO9ljOaAJeBFYbKRV7yt13Lny47OiQk7uMSGsXZHRZkBOPALm0yzkPsQTb5xijd79xJiVyQmnA5+sXC/k6r5KlYP12s/qIdx6oqDFJmzif/HPCOeB5yemXr/515S3M1LUeVZxTvBIdzI/QmMLFxRpgLetjCrAYJVxgaNBJsuis0Ce59G8OU3ThauR96TcU8wInrsj2IPDEv7kKKKNeyCp96R6n2K+jnCRS7WG8+FzrNUEHJEm4EWh0i9+peg4yulQDVF6bXgChzIJo6ZINSC8IAJmUbWZnmfRs0jUFUX/XrkZT72otC65ym65UK/+WCsC+aBciggn5CynSNfmT4pXcR5Npotj5Hmev486DYLrxcG8N0aI8vF5+S/3/HKCniuOh2SA4NiWWyT5ARF1n2QNeLpwTayZe5GDaNap2eNb672OK0YhopjKunJL33vBI6wJuMGoRoD1vibL5cVxkF0ItRy/zDXx3KusC5ppi2btwXW8QNkr43T4iSfrmvgRvoZKI5sWGwHXtYYKv5T1JNTiotywzYoTE1H4VZpFp3k7EyqoCOSFsAbM9dy4h0i51MGPJxdrlB87LImUrzM4Lq+Ai7oPtergRJXHHEXfZ4um8+rUj6iCkqVIDKspUcQRsO4HvEhU6zsb1au2IhIGAKq4SfiYccd3FooY/e6bbG0E7PmNn8XEGycxd/IKAAPd+7dh+2ceAxIJ6Tp+DkSE2w7sQM89u2KPG9UfmYP3Nn7wP/wWmjvby7Zv7kxF9kRuJCr18w163W7DeOGC0it3vHBB6VWbNkeVXrhDqX1oSaTQ27YVANDZ3AuAcDN/AXbJRF/bVrglBx45mCleB2BgU9s2tDSlMGteR1/bdowXLov+wEOpfdjVVUR/+07M21PoaRvye/+OordtJwADA6ldcEsukgn2azpRuCT272vbhlnzhvh3wmhCZ3Mvkolm9LZtxa6uB9DfvhOz5g30p+7A5tR+lOBh1rzGehkTobO5D0ACAPtvA01oSbTBQJO4Z8E5DXS3DGCgfZd/L+P6IU+jq6UfvW1bYRdMcb8q/7wOwi25MEt55bjsupPi59nTthm7uh7AQCr6/08Z4R7P8s9fPseqI4qV4/7oCLjxkBNjsud2KZA9uJ7j0vTR80r0efiJJxd1nnqj/qW6FZazOCMcxUa95rJot/IY91vmJNmeSdfmT5ZFqHLfXe695ceZN6fJE0kytcNYzp5XJhfHeY/VhN0VxZMsF5y4nktFZ6FMdnA8mwr2PHmeR7ZjRr4VyPdJ9fjavv6cVySOdHEspOPWW0ocP6izngkbay0hBx0BNwbVosB6cemZ74ipFQ/+h9+KjT7rAY9qt37yYSSSTdj04D6c/fMX2bSMRAJ7PvszNZ0nfK1ytHzlW69h6ycfUe5BOEJfzHQKGfK9qettAtERkAw5UmYR44NlUyT496O5s8png6k7wSOpZKIZbsnFxtZ+ES3ySHnBmUVP62bMWTexqW0HSnD9qJOwofk2eOShu2UQNwvnMVO8js7mPjQn2mF7eXS29GFT+w4MpnaDUELGmoXl5ZFAE0w3jw3JbmTsCSSQhOUV0d3Sj9PpV7GpfQe2dhxAU6IJqeRGgAjjRRbdyxGggQQsL4eWpg24WTjvvwVcFBH11o6D4j5tTu1DCa7/lrAHV7InsKl9Ozqb++CUitIUDjYRRI4wc86ceCPobO5FR3OP8vMI/5wmKkzhSCaS6G7tr+nnvyaj3ShEsXLcHx0BN86DytEoS9VSzxM17SLuWuNm4q30miuhlghIjjzVz1R9N64wIfp4Z8s0Vj6yPk6DDqrSgn/zwgluWZNdCLz6jWu/3HLGq+Q4ws1/eAKQrTVY31jufMh3rF57lBODn1+uDoy/vywKvzD3w7Kfx3vJybAUQCfhGoN6SKHaK3QtfuHFtL2sB5UkkLhrrZcYK1vmll9iiEMlkk4Xx8q+i0sCyUTOCwrk3sE5O6NUmIWLFwr2fJmswRJYQSIra6XJ84/Lp2bwRjjyaKHyPr+W4i9mtrOoVpgOuS5LFvLr5MQrt5kMl15Xkhjkij45MVnvz2ktF1jUCk3Aq4Bq0XIt0bTs3Q27ExpBYOHpGo0gw1or1xr9NlEv4vrd8l92HhWGt5edA9xZMJY7L7qCqZ3SbFGd5voaZjBvLSAlOdvPI0s1Sg63mox2JnB9lhdgyJ3aLsz9kApOliynqGjEzCuskmRwXkt5uHBdmqPSQ4xH5UvVYytVDb5XEEfAWgNeRlTTQPd+7lNoH+zF9k8/VvUYuz/702hqaVG+q1cjDWu6btFWpg8bySSS7S1Vj1MN4XXF3YelasRLhYEmkf3nkJ0GQ6l9aEqw77ySBwOGv31C0UuHUneiv30ngARsr4CJkNvhZuE8BlK7kXduoaO5BwvNszDQhO6WQRhownjhAmaK17Gz634YSGDenhTasHwc082h6GaVacubU/uFzjmQ2iV04NtaB5E2b6CndQg3iyMYTN2pTFKeNa/7DgUDzYl2Zc3dLYPoa2Xfpc1RDKX2gUBoT3ZiU/t2DKb2CndGoLXulfTcOwGUMFG47GvPwfoW7Dm0Jzvrmow8mLoT3S2DaE92CcfI+wZRrBz3R0fAjcdSoth6pYBwxDl55BQNP3tIVMtNHjldMQquda0rpWvXi7BLodI24SY5k/kRclxTRJi2HxnybmC8qY3jylFjlCeXnZe7InLWnNiOt3Xk/YqD1/NARy53P4zQ177+Ndp3YC8lEgnave8O+trX/6asKU+4uxrvDcGlkiACZzKGWnp8xu9FYZPp5Mn1HMrZ82X3LvpcZ4UkE56+3Mif21oHtASxNrGSr+HlbTCDwgk2jaMoRhNVWysvi15rJFsJ4ZJhoujhmHLnMQ5u9eIkGCTJrgipIVy04DhWLKHzUmFuSVPlBkuQHis5DuQKLieki2M0mR+hJ7/8f9Dm7QP01PN/TK9c+yt66vk/ps3bB+gvnvmzMrIO29Ycz5KO7ZDlFBSrl9xTotocO3aP5H3L+0MsdjJy+OcWrphrBJZbZ9YEvEZR64j1xUTKte6TuTxK5770Eg0/e4j1iKjYlUwti14t/bYeyO6FcCIp3E6xFvDkGJ9yzAjKLYv6eCcyubsZXw8/HxsZrybleIN1TvhyQi9vZxSXwr4De+mp5/+YXr/51+LPU8//Me24c7OfAGR9KRzP8r3HHjmOLfr88nPw65DbZ/LP+JglTsh8/lyYtMK6eD0/n+pViYH7YzFRdDUst284joATq6yAvCfgLBRx9s9fhLNQbPixm1pasOnhA2X6bhhcV730zHdrXl+lfWSk+ntRshxs+bmH0PcTe9HUGq3PcZ9vc2c7tn7yETz01O8o+q2btzD1w9Nw81bF8600JgqXcD5zGGlzDFs7DqA50Sqqy/ratmOqeAUj2eOYKFyu6Xi9bVuxIXkbNrb0wyt5sLwcZszr6G/fiV1dD2AwtQcFdx6ThWGczxzGeOEC2pOdsLyiWA873yW0NaVAIAym9vpe5N3oaO7Frq4H0Ns6BIKHBWcWd/d+AoOpPRjNnUXaHENXyyYYMHDpwjDufkj1ut790B7cuDyBwdRutCZaMWNeh4EmpJq7cbNwHmSUkHczSCaS6E/dIXRu08uDQFhwZkEgeCUP44ULuK11EHPmTeScNPratuPu3k/AI0e6jstl19WaqN0jnzZHMVO8jpwzF/k992w3J1oBJHx/dGP9vcH9X1nfsE7C1YClFATIiCriSG5oRf+H7qq6751P/ALiElZx66s1yVV32XTMPp5tY+at8+g+sAPJDa1l+zS6iKVW8ERRb9uWsu/m7UkMpHb720WX14YTRk7JRm/bFr/EeJtfeNGOtDmGodSdMN0FtCe70JbcAJagYsfnyb7B1F60JNrR17YNplcAALQ1pdDdMoCR+eMY2rAPm1P7kXczUsKNlQLv3Hg/DBg4lT6EzRv2Y8++3Th99DLu+9B+sb7TRy9j777dSCCJjDOB3ratKLjzyNgTmClex0BqNzL2BNqTnQAMTBQuYjB1J9qaNigl2t0tA0qikQCUyMVkcRgzxeu4u/cTkIsd5IKMGfMaetu2lt07NVFH4h73tm3BROFyWaFGGMlEMrbUfCmoVMK+rIgKi+P+rFcJolFJpdpKeVXJoBYZYa0kvRphu1tpyNqujChrF7dnlU+CsKQ5bzeFhc3zPyv6PYH5+dTyY9XSJn/Ot+cJKP7qzZJrTEv92te/VkEDDtZsu0UhL8jXZjp5ISmwQg6u3aqN2ufMcVI9xeVtKNPFMdF6U+6+Fk4aRiXq5Ll21X5eapHK8nmDG6kLQ2vAKlZqCGT5OWubUlzps7WKate3Wg+KxfwiybojJ75Amx0p023DyFppMalCJneeQItzJ8hEFAzxtAUZ8oSf6zd5dzyHvvb1v6bd++6gRCJB+w7spb945s/E8WzXVHpK8J4PvDUmc2EE3l9Zc57MXyHTyYvkmlzJx+5ReUtNXhnHSTLsUw4eHnKFoaU4RyqB69FRic1Go5G6sCbgENYisUUR1FqJbtca6iHVuBLg2s4TZO7liDBo8xhEi3xdYVcDT9bx4/F+vsG+4Wg4IFpOnnwashwFuyFivLFwxrfBqREnT6CxdVnC+iavT364yAUqARHnpDFG0U2J5Ijb8dh8uFqSnKxt57xSKh2HqHl3y4VGlkXHEbDBvqsNDzzwAB0/fny51JAVBdMjv4u9n/vkkvTI1dI11zt4g5xdXQ/GanduyUXRzaI92YWJwiXRdpLvx5vDsIKDG0qhQiUd0it5fiHDNtheHhcyP8TmDfvRn7oDc+ZNbGy5HeN+QmpX1wOioMMreSjBxWRhGIOpPUigCYAB8tuQEggGgAUn7Te82SHWmEATPDiYLAxjILUbBhkoejm0JztAIEwWRpjWTISCm0VHcy9mzGs4nzmM/d0fRndLPxJGk2gMxIs8dnU94LeqLIlrt708WpvYcZk+vBe8KKOvbZtI2rklB2lzDH1tWzE8fxSp5o3obO7Du+l/wK6uB9CW6MBtbUP+ftvhlmw0JZqRNm+gr227cpyJwiV2XWgSCdI4uCXbL/LYg2Ri6YVDKwHDME4Q0QPhz9dtEm4xiacoVErQaXJePtTS7Yrg+cmmDkHSfD+5aqyzuRd59xbsQrGsG1gYjHRG0du2FVl7ChtbBrCj8150tWzCaO4shlL7MOs7Ivj5ONHwRJic5Goykkg1dwFgD5WCM49dGx/wK852I5logVty/aTWNlG5Z5UKaE92ASjBdPMYSu3FWO4cNrb2o7O5DzknjZ62zdjf/bif4GIE19e2HV7JURJmBBLJLbfkwi6ZaGnagKw9BadkIm2Ooj+1E/2pO8Q94Ik01vUtjV0bObcYwk3glWykzVHk3VuAaaC1KYWOxG3oad2s9F+Wqw87m/tq6HiWkHoZv7exbgm4UajkNGiUe0KjHLVkrScLwxGtDRnxGWiS7EwGLK8gXBKcnMLwSh7S5ihuax0U0SIvjR3NncVM8TqGfFLiJbycfAGgPdmF9mQHuDPCQEK04C+VShhI7caceRMGmtDbugWcYCYKFzFTvI7ulgGkmjYCKGHOGvUbqyeQatoIAmGo405MFobR0XwbNjT3IO/MKdEubxKfNFjU2Ne2DQB7MPDyYG5729Dcja6WfuTdjGiq7pYc5Jw5LDizGMkeR3fLINqSG8T2tmehtSkliDxjT/mN2YvoaRtSHnhqe0/2MxhI7VLKwuN/9rW3pVzr0AS8RFSKpFe718FqYa1E/lFRMu83u7/7wwqB8+guLvIFWETNpmlcVHo08HN1twzCLdnIu7dE1MePCzDi8EoeNrVtA/x+EryvApdKetu2YrxwAf3tO8Wr+mBqLwZSu33pYq/wzXY296GreRNKKMGAgZyTEb13t3YcQKp5I4peTrKJsVd8p1TETPG6kGL62rag6C4glexSHlq9rVuUh8hE4ZJiP0slNyq2tQSakEy0oD91B9LmDV/+eNzvJ0wiqgcSIc9tAt0tA0ggqTywwqjW4/m9CE3Ay4hGyRzvNayVyD8qSq7kCa6GicJlIRFwYueEkUw0oz3ZKQiCe4U53JIL7nsdSO1GWowTMjCU2ouMPYFUsktox8wnvB3z9hS6WwZwUxoNNJS6Ez1tm0W0a6AJBA+Wl8cHen9aRNVpcww9rZsBQLyyz9uT6GreJEkQe5C1p9HZ0gcClM/DJDeU2oeNzbejCc0hSYdtX3AySDV3YzR3FoOpvdjf/Th6WoeQd+eRSm4Uawh7eZOJJFLJjaJBUBy5yuORVtKzu5zErwlYo+FYzci/nkkY9RwzbY5iMLXXr/pKYCh1JyOzlttFkkkmiM2pfcjYE+L7TW3bcTMUOe/qMv0pHDfEJA1WpJFCb9sWoZMmEomyaD4vol3g9rY7kLEn0dO6GQtOGh3NvQDgJ75MFJwskolWFN0sTs0dwr29PwvLywt9l0XsGXQ296Gzuc+XJgzlXsqJypuF8+L+JhPNQvfm0guPzvkUEF70EScb8Iq7aknVarr/chHlchK/JmCNhmM1I/+l/rJE/RLzY8qyxWjuLJySibybgVMykXK7RUXdQGqXX8U2Cadk4bbWQcyY15XE14KTxpCvIfORSL1tW5BMNPtkHei2zAnRi6HUnWhKNMEt2aI82YABIIG+tm0oSfotkASB0JRohmEk/IQdsKvrQXT4x2t1NqCjuRcpt9vXphNoSjShPdEJAMhYU4LkN6f2wSVbSZhxuUYmz6HUnWJgaKm5B2lzLHQd5ZVuatIyPqka9fCUf17hQaqNwnKON9IErAFg7ei2S8Vif1nCCSb5l3gwdSc6m/tEZMk/424Irt8CJdEvuD3ZhQ3JHjGBeaZ4Hb1tW/zXcANNRlJoyfu7P4zb23YAMAAAeTcjpg8Hbo3t/jZtUu9ew9dXS3BKJmZC8+yKblYkyTqab0My0SKuyS05/uc9/hUlFPsX/56TfAkldLcMobUpFXEvgp7A5NvZmIY9hr627djVVVRcJ2FyZJHzWFnSshZMhKQZ/lbRSCxnmbImYA0Aa0e3XSrCvyxuyRVe1kqvpeEEk/pLTIKs5OP1p+6AW3LEK7aBJjQZ7BxOiTkCxnJnsbljHzqb+wRx7up6EP3tO0VE3Nu2BSPzx7CpfQe6WjYhY09gMLVHNELnTdANJJRocSC1GzPmdbQ2pZB3bynJNte1RZKM93/g2qxq/WKRKcGDTAdypJtAEiV4vrvBgeXl/QdJcM+HUnfCLVnI2JNlDe352PlN7TswkNolmrnL+8vJynogP3DZuVahn8MSoAlYA0DjdNu1EknL5v5KU3k5+C+ygfJf4jBZyVFm+DtehDCU2ofmRAsWnDSABLpb+0XRB09wTRWu+DrsGPpTu9DVvEmZgtHVsgleycHm1H4kEsySJkeL8/Ykets2AzBQIheKPzZBIJQwmNqDzuY+zJk3pXPdIcifR6abN+xHb9tWEDzMmTcVYsvaM+hq2QQAwoYn+3gBgFDCtHnVT759GL1tW5VoNplIorO5NzYKXixWrYlOg6AJWAPAyhSmrCRkYtzZdT82NHfjyvwJIIXIZFClX2RV1jAUC1Xcd4x8mkLZfvUcvW1bAdNAX9tWZJ0ZWGUj3qfR1XI7iu48NrTcJo7BIm8XCSMJIAEDBjYku5E2b6CtqRNFNwsA8MjBVGEEWzruQkdzD+xCUTgzePKMUMKm9h3o8wsquATD7GN3YibkTOhr2y5Zz1iXNNZRTdXe3ZKjRNwA0NHcU7GVpFzk0ohk2nvBtqYJWKOhWCve57Cdyi252Lnxft+6VbnlYRhh4qxEqqr8Ud2RwdwOF0USqbdtG/pat6JUKmFjy4AoeQ7Kb5kUwavwCCWMFy76VW7bMG9PY0PzbeCWt50b70cCScWvywsluM+4t3ULCEBHc6+fwDPQ27YVM+Z1nM8cVpwJTYkmcW/723cK37N8v4HoZGi1aHWicAlOyZQqEg1RPr4YEo2b77eWoAlYo6FYTgdEJUILf5c2R5VX7mQiidHcRWGRWolKqrA8saG5u6zXAdeeO5v7fJfBRQyl9mG8cAEDqd24mb+A1qYNkm1sDKnkRnQ096DgzqPguzDmzHGkkhuxsYVdV1AsYqCndYtCkFOFK+hp24yb+QuwS6ZCigt2Fgk0IWvP+O6MB8oi1sB6xmxp/EFRLnHUlwxlssqUL3cYvkXv+qKdDfKDYta8sWideTnx3i+m1lg3kCcueCUv5js2naG3bSuaE+1KMcRg6k7c3fsJLDizcEtOTefkr9Jx2wffu2Xb8ikLA6lduJI9gcnCMHJOWuw3VbiirIkl5vaA4GGmeB2ThRHs7LofHc29/vW0obdtC0wvi6niFaSSG5XP25s6YZcKmDWvY6B9l0+euzGWOwOAROLMdHPIOxns2fhwGUG2JztRgoeuFubmGPLdHvL1uyVHNKNPJpLIO3Pobdsi1gHI0a5R8f7JxxwvXEBXyyZl3562zYueVMEfFLesyUUV3qwEdASs8Z5BpYgmHHFFve7yKLieiCpK25Qjbf59Z3NfmYWNr4E5ALaLNQJAzpnDzfwFpJLdypj521qHkHcyPvEGUgnv/sZ7WdzWugVcZpD12On8VWzuOABuiQMS2Npxl+TAeABbOg7GVp0lE83obhkAiLDgMvtaOFIOfNGs1DrVfBvS5o3I13zex6LaW0eUXMD33dl1/6I13KZE05qMfDk0AWu8Z6C++m4p+64WQo16Na4kbcgeV6/klREydxl0NPcoLgcZafOG8nquemybxGigwdQekYQy/KKIqHUnE81YsOfglIriXOyhNIqNrf0wYGBcItyh1L7Qdtfhko2cM4fO5r4y0kwlN6IEVxR17Nn4MNqSG5B3smhtasdg6k5RrTeaOyukkrCcwdZ9p+hjUUl7j3q48msOvMqLx1pNyGkJYpmxnAM91yN4RLPYX6Lwq7FMqlFDOfn2yURSeHBVXdTAgjMb2lZdW1gO4eebLIwgmUgimWhGn1/Gez5zGBOFSwBIeXUPH7s92YWultuRtWcwlNqHnDOHvrZtaE60S0M+H8Bgai9mzeu4mbuAzal9MN0c+tq2o799pz98U5Vy+D3mTXkmCyNoSbTBgIGMPY6JwiUkkEB/6g7lOnZ23S/uiSzFJBNJcaxKQ0+j5IK4+7kYVPoZryZ0BLzMWCu2LA0VvCsa779QS8IoqmKrttJnloTj8oNcsszBCfre3p9FR3OP6A8Rd1wW5bJruKfnJ9HVcrvvFmDXcDN3Hps79iNrT6O3dTOoDbjpuwnCfYk3p/aVDdGU70nRXUB7ogtJowW97dtgegtIJTaWReXBvQ2/JdR2f+PkgmrRay3R7XKWEy8FmoCXGWvFlqWhQu6KVqt8EVWxVcsvttwCsz91Bww0oa9tGww0haYEG6JsuLdtG+7v+wWkkhsjjylXxG1sGVC8uZ3NfT7JG+hq2YSim1N05oTRpEyCnjGv4bbWQSGR8Gvl96SzhUkAvLlO2hpDS1Mq9r4Fx95ddqzFoNpDrpaH4Fot2NAEvMxYry0p1zrkX8il6IO1/GLLmulU4Qp627Zi1ryBwdReWF4RCaMJRTerkORQah/akhsiPbBuyUVf23bknTlRJSdP+jDQhLR5Az1tmzGeu4gtHQd8nZkXeMzAQAKbU/vF2CKmFd9ZViYsI5FI4GbuclWyM9CE7pZBAAlkrCm/bwQp97iee17tIbdWo9taoDVgjXWP5dIHuRYKkNBMOfHw8zmlIm5vvwPtyS4Mpvb4lqu9cEumpJ2GbXcljBcuINXcjRnzmq+1sgdBc6JVNEOfLIygp20LDCQUX3RP2xCSiWYR+XJNe3j+qK8/x12Pi4HU7qq2sGQiic6WHjFdAyiJTmX8HvN7kDbHxH1ySpbfNzl8vMpacCO14pWGjoA11j1kp0MjUa6F3gmg5A/hZGOJTqUPYVP7djHdgnU8AzL2FHhv4L62rSCUMJq7gKHUPqkbGvzKMdV9wNtb8uthY4W2wS6YintE1rQJhFRzdySxyp3iZorXsWfjw+BJzEoRrTxdI9ypjN9z3i2uvplw7x9oAtZY9wj8uuX9C5aCcm9yUsyO29l1P/hcOi4bJBIJJNEqCOmenp8Spcq8qVA4aVhwMuho7i3r+sYJ2SlZYjDp5tQ+0dSHX7esacdJCtVGEYWbEkVNywh3KpPXGJZP1hM0AWto+Gj05INKI5FYzwWgu2UA08WrSBqtSCU3oj3ZJRJoG1v6FaLbf9vjaE60KyN9Us3dYkJFwZlXEmmAGoV2twyIhJqManosJ1I+LDToB2GURbRyBB0uB6/nPq0XaALW0PCxHMmcMLklE82iLSNrAbmFjRNqH8J04Roy9oSIkMNEx4s0ZMgVZDs670WT3+ayr217aH82DDMKUVMuZGTtaZ9IWfMeoJw0o0iUTUQ2l1wGvJxFFKtdoKGTcBoaPhaTzKnWK0LuX8HB2zLKFjheETeY2otN7TtE9Rf/Xi3SCCAXXLQmU0q/Xp6c624ZwFTxCtLmaOQaB1N34t7en/U16vJr62q5HRuSPWU9fqtBvp/h+1TtvkXfw8UnSePOt9oFGjoC1tBYAqJkCzmq4qQmJ/ji+lR0tWxC2hxVBn7yh0HQmUxNFGbtaWxIdiNrz6CnbSjUr5etqT3ZBY8cZYyQCgqNJ4q/tsUifKx6jt2IN5O48622hU0TsIbGEhD1Cxx+pY96rY8C329XV9Ef+Dkf6qVbfpyultv9PsHs/Ex2CM+wUwnWK3kgeKK/cNwwy0aSU5xmHDWiKIxGaMTy+eMSlqsBTcAaGktAfKINZdFqNah671hN2mk0gaiEy4mdF4OEh4/GDbNsJDlFacbLMaKolvPLneVWO/mnCVhDo8GIi1Zr248RwlJaKMb1YpAnNMvDR1drmGW1EUXLhdWWHWRoAtbQqBOrnTmPgtxTolJv5ErDR1cajX79r/XnstqygwztgtDQqBOrnTnnkDP7QanvpYruA7m95kqtbaWwmJ/LaqxTho6ANTRiEBdRLVfpcr2QpYY4HbfRxSWLWdtKnXcx0sJq3R8OTcAaGogm27hfzkaULtcrY0RtLxNOnI67Wnrnapx3MdLCauvBWoLQ0ED06ysfqhn3y7kUKaLefaO2r2Xw5VI6hcmv527JretVvZ7zrqYMsNqd1HQErKGB6EioWkS1lOip3n0rbZ8OTc9oVJJQfgPobO5dtlf11ZYBVhOagDU0sLjX16Vk0+vdN2p7mWj3d3+4bOZcXHVeraSskr5R9jagTvOgRZP+assAqwlNwBoaK4jFEGHcPvWU1y4mygyTfng/fszO5j6lsGMlH2TvdWgC1tBYQSyGCOVKNrlAIy5yrNQGcylRZvhBEIyN7/Wr7tZnFLsUaALW0FhBLIYI+WSM3rYtiuuinsixEVFm+OFRLUKWsRaLV9YCtAtCY11hpTLuceeplnWP2o9ProhzTsSdaynXGrVvNVdIJayV4pW1Bh0Ba6wrrFTGPefMYaZ4ve7zVFpfXPQct89SrjVq36VE0es50VYJmoA11hVWggjckoMFZxZ3936i7hlnldYXR4DB8M5tNR9rKetYDNZzoq0StAShsa4QJQE0Wpbg0eNkYSS258JiJYooJBPN/vDOC2XSxWKvdbULFNYLNAFrrHs0Wp+sRStt9DlrPd5SzrvajWvej9AShMa6x3JYtBoxZqce50Ct17CUa13PFWvLBU3AGusey2HRasQ56zlmrdegE2lrC5qANTQagDA5NcL3utYITyfSGg9NwBoaDUCYnBrxuq4J7/0PTcAaGsuAtRa9aqxNaBeEhsYyYK3YuLRzYW1DE7CGxvsYy1kCrMl96dAShIbGMmCtNJ9ZTilE29KWDk3AGhrLgLVCTsuZyNM699KhCVhDYxmwHshJuzSWDk3AGhrLAE1OGrVAJ+E0NDQ0VgmagDU0NDRWCZqANTQ0NFYJmoA1NDQ0VgmagDU0NDRWCZqANTQ0NFYJmoA1NDQ0VgkGEdW+sWHMALi+fMvR0NDQeF9iOxFtCn9YFwFraGhoaDQOWoLQ0NDQWCVoAtbQ0NBYJWgC1tDQ0FglaALW0NDQWCVoAtbQ0NBYJWgC1tDQ0FglaALW0NDQWCVoAtbQ0NBYJWgC1tDQ0Fgl/P8leUBA4rPG7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# write your code here\n",
    "k_means_3 = KMeans(init = \"k-means++\", n_clusters = 3, n_init = 12)\n",
    "\n",
    "# Initialize the plot with the specified dimensions.\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "\n",
    "# Colors uses a color map, which will produce an array of colors based on\n",
    "# the number of labels there are. We use set(k_means_labels) to get the\n",
    "# unique labels.\n",
    "colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means_labels))))\n",
    "\n",
    "# Create a plot\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# For loop that plots the data points and centroids.\n",
    "# k will range from 0-3, which will match the possible clusters that each\n",
    "# data point is in.\n",
    "for k, col in zip(range(len([[4,4], [-2, -1], [2, -3]])), colors):\n",
    "\n",
    "    # Create a list of all data points, where the data poitns that are \n",
    "    # in the cluster (ex. cluster 0) are labeled as true, else they are\n",
    "    # labeled as false.\n",
    "    my_members = (k_means_labels == k)\n",
    "    \n",
    "    # Define the centroid, or cluster center.\n",
    "    cluster_center = k_means_cluster_centers[k]\n",
    "    \n",
    "    # Plots the datapoints with color col.\n",
    "    ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.')\n",
    "    \n",
    "    # Plots the centroids with specified color, but with a darker outline\n",
    "    ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,  markeredgecolor='k', markersize=6)\n",
    "\n",
    "# Title of the plot\n",
    "ax.set_title('KMeans')\n",
    "\n",
    "# Remove x-axis ticks\n",
    "ax.set_xticks(())\n",
    "\n",
    "# Remove y-axis ticks\n",
    "ax.set_yticks(())\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double-click __here__ for the solution.\n",
    "\n",
    "<!-- Your answer is below:\n",
    "\n",
    "k_means3 = KMeans(init = \"k-means++\", n_clusters = 3, n_init = 12)\n",
    "k_means3.fit(X)\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means3.labels_))))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "for k, col in zip(range(len(k_means3.cluster_centers_)), colors):\n",
    "    my_members = (k_means3.labels_ == k)\n",
    "    cluster_center = k_means3.cluster_centers_[k]\n",
    "    ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.')\n",
    "    ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,  markeredgecolor='k', markersize=6)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Customer Segmentation with K-Means\n",
    "Imagine that you have a customer dataset, and you need to apply customer segmentation on this historical data.\n",
    "Customer segmentation is the practice of partitioning a customer base into groups of individuals that have similar characteristics. It is a significant strategy as a business can target these specific groups of customers and effectively allocate marketing resources. For example, one group might contain customers who are high-profit and low-risk, that is, more likely to purchase products, or subscribe for a service. A business task is to retaining those customers. Another group might include customers from non-profit organizations. And so on.\n",
    "\n",
    "Lets download the dataset. To download the data, we will use **`!wget`**. To download the data, we will use `!wget` to download it from IBM Object Storage.  \n",
    "__Did you know?__ When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "!wget -O Cust_Segmentation.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/Cust_Segmentation.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Load Data From CSV File  \n",
    "Before you can work with the data, you must use the URL to get the Cust_Segmentation.csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Edu</th>\n",
       "      <th>Years Employed</th>\n",
       "      <th>Income</th>\n",
       "      <th>Card Debt</th>\n",
       "      <th>Other Debt</th>\n",
       "      <th>Defaulted</th>\n",
       "      <th>Address</th>\n",
       "      <th>DebtIncomeRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>0.124</td>\n",
       "      <td>1.073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NBA001</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>100</td>\n",
       "      <td>4.582</td>\n",
       "      <td>8.218</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NBA021</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>57</td>\n",
       "      <td>6.111</td>\n",
       "      <td>5.802</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NBA013</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NBA009</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>253</td>\n",
       "      <td>9.308</td>\n",
       "      <td>8.908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NBA008</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer Id  Age  Edu  Years Employed  Income  Card Debt  Other Debt  \\\n",
       "0            1   41    2               6      19      0.124       1.073   \n",
       "1            2   47    1              26     100      4.582       8.218   \n",
       "2            3   33    2              10      57      6.111       5.802   \n",
       "3            4   29    2               4      19      0.681       0.516   \n",
       "4            5   47    1              31     253      9.308       8.908   \n",
       "\n",
       "   Defaulted Address  DebtIncomeRatio  \n",
       "0        0.0  NBA001              6.3  \n",
       "1        0.0  NBA021             12.8  \n",
       "2        1.0  NBA013             20.9  \n",
       "3        0.0  NBA009              6.3  \n",
       "4        0.0  NBA008              7.2  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "cust_df = pd.read_csv(\"Cust_Segmentation.csv\")\n",
    "cust_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "As you can see, __Address__ in this dataset is a categorical variable. k-means algorithm isn't directly applicable to categorical variables because Euclidean distance function isn't really meaningful for discrete variables. So, lets drop this feature and run clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Edu</th>\n",
       "      <th>Years Employed</th>\n",
       "      <th>Income</th>\n",
       "      <th>Card Debt</th>\n",
       "      <th>Other Debt</th>\n",
       "      <th>Defaulted</th>\n",
       "      <th>DebtIncomeRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>19</td>\n",
       "      <td>0.124</td>\n",
       "      <td>1.073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.3</td>\n",
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       "    <tr>\n",
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       "      <td>4.582</td>\n",
       "      <td>8.218</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.8</td>\n",
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       "      <th>2</th>\n",
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       "      <td>6.111</td>\n",
       "      <td>5.802</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>253</td>\n",
       "      <td>9.308</td>\n",
       "      <td>8.908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer Id  Age  Edu  Years Employed  Income  Card Debt  Other Debt  \\\n",
       "0            1   41    2               6      19      0.124       1.073   \n",
       "1            2   47    1              26     100      4.582       8.218   \n",
       "2            3   33    2              10      57      6.111       5.802   \n",
       "3            4   29    2               4      19      0.681       0.516   \n",
       "4            5   47    1              31     253      9.308       8.908   \n",
       "\n",
       "   Defaulted  DebtIncomeRatio  \n",
       "0        0.0              6.3  \n",
       "1        0.0             12.8  \n",
       "2        1.0             20.9  \n",
       "3        0.0              6.3  \n",
       "4        0.0              7.2  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = cust_df.drop('Address', axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Normalizing over the standard deviation\n",
    "Now let's normalize the dataset. But why do we need normalization in the first place? Normalization is a statistical method that helps mathematical-based algorithms to interpret features with different magnitudes and distributions equally. We use __tandardScaler()__ to normalize our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.74291541,  0.31212243, -0.37878978, ..., -0.59048916,\n",
       "        -0.52379654, -0.57652509],\n",
       "       [ 1.48949049, -0.76634938,  2.5737211 , ...,  1.51296181,\n",
       "        -0.52379654,  0.39138677],\n",
       "       [-0.25251804,  0.31212243,  0.2117124 , ...,  0.80170393,\n",
       "         1.90913822,  1.59755385],\n",
       "       ...,\n",
       "       [-1.24795149,  2.46906604, -1.26454304, ...,  0.03863257,\n",
       "         1.90913822,  3.45892281],\n",
       "       [-0.37694723, -0.76634938,  0.50696349, ..., -0.70147601,\n",
       "        -0.52379654, -1.08281745],\n",
       "       [ 2.1116364 , -0.76634938,  1.09746566, ...,  0.16463355,\n",
       "        -0.52379654, -0.2340332 ]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "X = df.values[:,1:]\n",
    "X = np.nan_to_num(X)\n",
    "Clus_dataSet = StandardScaler().fit_transform(X)\n",
    "Clus_dataSet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "In our example (if we didn't have access to the k-means algorithm), it would be the same as guessing that each customer group would have certain age, income, education, etc, with multiple tests and experiments. However, using the K-means clustering we can do all this process much easier.\n",
    "\n",
    "Lets apply k-means on our dataset, and take look at cluster labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " 0 0 0 0 2 0 0 2 0 2 0 0 2 1 0 2 0 0 0 0 0 0 1 2 0 0 0 0 2 0 0 2 2 0 2 0 2\n",
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      " 0 0 0 2 2 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 0 0 0 0 0 2 0 2 2 0\n",
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      " 0 2 0 0 2 0 0 0 0 2 0 0 0 0 2 0 0 2 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 1\n",
      " 0 0 0 0 0 0 2 0 0 0 1 0 0 0 0 2 0 1 0 0 0 0 2 0 2 2 2 0 0 2 2 0 0 0 0 0 0\n",
      " 0 2 0 0 0 0 2 0 0 0 2 0 2 0 0 0 2 0 0 0 0 2 2 0 0 0 0 2 0 0 0 0 2 0 0 0 0\n",
      " 0 2 2 0 0 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 2 0 0 0 0 2 0 0 2 0 0 1 0 1 0\n",
      " 0 1 0 0 0 0 0 0 0 0 0 2 0 2 0 0 1 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 2 0 2\n",
      " 0 0 0 0 0 0 2 0 0 0 0 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 2\n",
      " 2 0 0 2 0 2 0 0 2 0 2 0 0 1 0 2 0 2 0 0 0 0 0 2 2 0 0 0 0 2 0 0 0 2 2 0 0\n",
      " 2 0 0 0 2 0 1 0 0 2 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 2 0 0 2 0 0 0 0 0 0\n",
      " 0 0 2 0 0 2 0 2 0 2 2 0 0 0 2 0 2 0 0 0 0 0 2 0 0 0 0 2 2 0 0 2 2 0 0 0 0\n",
      " 0 2 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 2 0 2 2 0 2 0 2 2 0 0 2 0 0 0 0 0 2 2\n",
      " 0 0 0 0 0 0 0 2 0 0 0 0 0 0 1 2 2 0 0 0 0 0 0 0 2 0 0 0 0 0 0 2 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 2]\n"
     ]
    }
   ],
   "source": [
    "clusterNum = 3\n",
    "k_means = KMeans(init = \"k-means++\", n_clusters = clusterNum, n_init = 12)\n",
    "k_means.fit(X)\n",
    "labels = k_means.labels_\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Insights\n",
    "We assign the labels to each row in dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Customer Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Edu</th>\n",
       "      <th>Years Employed</th>\n",
       "      <th>Income</th>\n",
       "      <th>Card Debt</th>\n",
       "      <th>Other Debt</th>\n",
       "      <th>Defaulted</th>\n",
       "      <th>DebtIncomeRatio</th>\n",
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       "      <th>4</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer Id  Age  Edu  Years Employed  Income  Card Debt  Other Debt  \\\n",
       "0            1   41    2               6      19      0.124       1.073   \n",
       "1            2   47    1              26     100      4.582       8.218   \n",
       "2            3   33    2              10      57      6.111       5.802   \n",
       "3            4   29    2               4      19      0.681       0.516   \n",
       "4            5   47    1              31     253      9.308       8.908   \n",
       "\n",
       "   Defaulted  DebtIncomeRatio  Clus_km  \n",
       "0        0.0              6.3        0  \n",
       "1        0.0             12.8        2  \n",
       "2        1.0             20.9        0  \n",
       "3        0.0              6.3        0  \n",
       "4        0.0              7.2        1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Clus_km\"] = labels\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can easily check the centroid values by averaging the features in each cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Edu</th>\n",
       "      <th>Years Employed</th>\n",
       "      <th>Income</th>\n",
       "      <th>Card Debt</th>\n",
       "      <th>Other Debt</th>\n",
       "      <th>Defaulted</th>\n",
       "      <th>DebtIncomeRatio</th>\n",
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       "    <tr>\n",
       "      <th>Clus_km</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>432.468413</td>\n",
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       "      <td>31.164869</td>\n",
       "      <td>1.032541</td>\n",
       "      <td>2.104133</td>\n",
       "      <td>0.285185</td>\n",
       "      <td>10.094761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>410.166667</td>\n",
       "      <td>45.388889</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>19.555556</td>\n",
       "      <td>227.166667</td>\n",
       "      <td>5.678444</td>\n",
       "      <td>10.907167</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>7.322222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>402.295082</td>\n",
       "      <td>41.333333</td>\n",
       "      <td>1.956284</td>\n",
       "      <td>15.256831</td>\n",
       "      <td>83.928962</td>\n",
       "      <td>3.103639</td>\n",
       "      <td>5.765279</td>\n",
       "      <td>0.171233</td>\n",
       "      <td>10.724590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Customer Id        Age       Edu  Years Employed      Income  \\\n",
       "Clus_km                                                                 \n",
       "0         432.468413  32.964561  1.614792        6.374422   31.164869   \n",
       "1         410.166667  45.388889  2.666667       19.555556  227.166667   \n",
       "2         402.295082  41.333333  1.956284       15.256831   83.928962   \n",
       "\n",
       "         Card Debt  Other Debt  Defaulted  DebtIncomeRatio  \n",
       "Clus_km                                                     \n",
       "0         1.032541    2.104133   0.285185        10.094761  \n",
       "1         5.678444   10.907167   0.285714         7.322222  \n",
       "2         3.103639    5.765279   0.171233        10.724590  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Clus_km').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets look at the distribution of customers based on their age and income:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oNCOQQTmzhRDfsHMp+tv3P0KIrx3j/DFCiNeFEDuEENuEEF+2txcLIV4VQuyxfxf1OudbQoi9QohdQoirByOvRqPRaN4/g416+iSw+Sj7Ntr7ByINfFVKOQNluvq8EGIm8E1guZRyCrDc/h973x3ALOAa4Od2gp9Go9FoThGDVRRjgT1H2bcfGDCGTUpZL6Vcb/8dAnYAo4CbgAftwx4Ebrb/vgmVAZ6QUtYAe4Ezq9C7RqPRjHAG66OIogb2/hiNin46LoQQ44EFwCqgQkpZD0qZCCHK7cNGASt7nXZ4gNfXaDRHwZKS/e1trDxcS1ssSoHXy9JRY5hSXILT0EWkNQMzWEXxNvA1IcQTUsqMUhBCeICv2vuPiZ2H8STwt1LKrgEqLfa3IyeeVwjxaeDTAGPHjj0eETSas4akafKHLZvY0tSI1+nE63TRHImypbGRycUl3DVvAb4zrCy2ZmgZ7FTie8AUYLcQ4gdCiM8JIX4A7La3f/dYFxBCuFBK4mEp5VP25kYhRJW9vwposrcfBnpnFY0G6vpeU0p5n5RysZRycVnZ2d0MXqPpy5/27mZLUyOj8vIp8fnxOBwU+XyMystnX3sbT+/cNtwiakY4g+1wt0kIcSnwn8A3UIrGAlYAH5RSbhrofKGWDr8Bdkgpf9Jr13PAXcCP7N/P9tr+iBDiJ0A1ShmtHozMGs3ZTCSZ5N3aQ1QEghzs7OBARzspy8JpGIwtKGBsQSEbGxq4ZnL0jC1brnn/nEjC3WrgIiGEDygC2qWUseM8/Xzg48AWIcRGe9u3UQriMSHEPcAh4Hb7tbYJIR5DFRxMA5/vrlqr0WiOTW1XJ5aU7Glrpbark4DLjdfpwpQW+9raCCWSlPr9HOrs1IpCc1ROODfeVg7HqyC6z1lB/34HgMuPcs4PgB8MTjqNRgOqFEY8neZIqJN8jwdhf/0cwiDf46EpEsbncmJKa5gl1YxkBq0ohBATgQ+hQmW9fXZLKeU9QyGYRnM6EE4m2dRQz5q6I8TTKUbnF3Du6DFMLCoeEe0wywNBQskEUsqMkuhB/d+VSFAZyDv1wmlOGwbbCvUm4HGUb6KJ3HDYwVUY1GhOYxrDYX61fg2hRJx8jxeXYbCjpZkNDfWcP2YsN0+fiTHMyqLE72dCYREHOzrI76f1dMI0qQgEqM7TikJzdAa7ovg+8AbwMSll89CLo9GcHqQti99uXEfatBiVV5DZ7nW6sKRkxaGDVOXlce7oUxeu3RAOsbGhnpRpMbuigvEFhQgh+MS8hbxXW0tnIkbA5cFpGJiWRTSVwiEEdy9YNCJWP5qRy2AVxURUCQ6tJDRnNXtaW2iNRhmdX5CzzxCCEp+f12v2s7R6NI5TkNC2q6WZ325cB7aB6Y2DNdw8fQYXjh3P2MJCPrtkKY9u2UxHIo5EGZ3yPR4+MHU6s8oqTrp8mtObwSqKneh2pxoNu1pbB+yTHHC7qQt10RGPU+I/udFEUkqe2bWDoMtDnkfZl1KmyYu7d7OoahR+l4sPTJ3OuIJCXqvZT30kRJkvwKUTJrKwqlqvJjTHZLCK4uvAvUKIVVLK/SdDII3m9KB7Xn7so042SdOkJRplVF5+ZpvL4UBKSXs8ht/lQgjBvMoq5lVWnQKJNGcag1UU30OtKHYIIfagmhb1RkopLx4KwTSakcyEwiLeOXTwqPtjqRQBt5tCb9/AwKHH7XBQ6vcTSiSyVhRCCIq8vpP++pozn8EaT01gF/Au0Gz/3/tHB2Nrzgqml5YR9HjoSuTWwZRS0hyNcMm4Caek4J4QgpumzSCcStAQDtEYDtMQCXPdlCn4T9MaTpFkkud27WDl4VoG2655KElbFs3RCPF0athkGAkMtoTHJSdJDo3mtMLjdPLJ+Qu5b90a6kNJin0+nIZBKJmkKxFnfmUV548dsOr+kDK9tIy/O+d81tfXkzJN5lRUMKGw6NgnjlDW1h/h1f17cTscTCkuOel+nv4IJ5P8et0a6sMhvE4n9yxYzLjCwlMux0jgmIrCTrA7brTvQnO2MLagkK+cez6rDh9mdd1hEsk01cF8bp0xk1ll5ack2qk3lcE8rptyZuRDjCsoJM/toSIYzJjTTjWrDtdSF+5iVF4BHfEYz+zazpeXnTcssgw3x7Oi2Mvx+eSEfZzuQKc5ayj2+bl2ylSunTJ1uEU5oxhfWMT3Lrkcp2EMW9JiylJl5aSUOAyDlHn2WtaPR1Ecq72pRqM5yTRFwry4Zxe7W1spDwS4dtIUppeVH/vE05iBwo9PBQsqq3ho8ybW1dURcLv59gVnb5zOMRWFlPLBYx2j0WhOHtFUil+sXU0inabU5yeUSHL/hnV8fuk5p7UfYqSzt62NYp+XCYWFhFNJtjU3cu6Ys7Mxmu6BqNGMcHa1NBNOJigPBHEYquqr1+kcMDxX8/7piMfxu9yU+AOU+YM0R6LDLdKwoRWFRjPCiafTOU5Cl+EgkkoOizxnC3MrKpHA4a5OWqIRzjtLVxPwPvpRaDSaU8PEomIEgqRp4rYzrjsTca6ZPGW4RTsjkFL2W8ZkTEEBX152Lgc62in2+ZlWUjoM0o0MtKLQaEY4FcEgt06fybO7diABy5IsGTWKRdWjhlu0YUOaTcjoH8BqBfe5CO/VCDE4A4mUKWTkAbDqIXAPwpH7PKvz8qnuVRrlbEUrCo3mNODcMWOZXV5BYyRMgcdLWSCQc4y0osjkWiCFcC9AGMWnXtDjRMokyBiIvEEP8AAy+ghYbSAKILEcnGPBNWtwF7G6IL0bSCFT+/pVFBqFVhQazWlCnsdz1OQzKU1k9HeQPgA4kMmVEPwywgieUhmVLBKsFhBOhJEblSXNI8jIb8GKgHMc+O9CGIPMvLaaQZSAcAIG0uo8jhKNfTCKwXs9WI0I94LBnn1WoRWFRnMmIDvBPAzGaBBCmVOsBjAmn1oxzGZ7tt8A0kK6piN8H0IYPSsgGX0apAVGFaRrkMnVCO8lg3sh9zmQeANwgvAgnIO/TyHE4F/3LEUrCo3mNEFa7WAeAaMo10wiAoBHKQzpAilBnFrbupQWMvp7sDpBVKpaDaldSPECwv/hXgfGAbdSaMIgt6PysRHe68A5Dml2IVxTEI4zO/lwuNGKQqM5DZBmIzL8c5AJQCL9H8ZwL+x1hAtcCyH2MMgkeK8Do/DUCmk1gdkMjl49L4wKSG5E+m5FCLuSrfcqiD4CZicIP8K1aNAvJYQDXHMRw1wcV8o4MvI7sEKIwD0Ix5nZ100rCo3mNEAmNygF4KhWtv34cuilKGTiLUi+Bc75IByQ3o2MPYHwf/QUSukAIe3VTLfHQNqrhh4PguGei3SUq5WHoxph5BYylDKOTLwHRimGe86pEf9EMJsgvRekCWYtaEWh0WiGDeED0moQJq5MOzZSmspeb1SCcKuNxihIbkZ6r+3XoXxSMErBMQFSO5RSQyh5vJcjRM9QI81mZGoPyAhYIXDNyPJhACp6K/YsCA/S+c1Tdw+DxTEKPJeBDIPzzC0MqRWFRnOSkVYYmdoC6X1g5CNc88ExZlC9qoVnGTK9C9L7wchD+G/ptddUdn/RazDtnsXL+FDdRgZpdanQVKM0K6pKCIH0fkApLbNFbXROVgMpdt5C7BlIrlWy4QCRRsadSO9NGJ6lPddyVCKFX83QxanvRXG8COFA+K4fbjFOOlpRaDTvEyktFXFEEowyhFHQs89sQEZ+rcxFwgckkYkV4L0cPFdllIVM70Um3lbHuecj3EsR3asDQAgvBP4aZBSEV9noM/vcSOcUMGtA2E5dKwRGEIyyob3X9EFk5DcgU+p+gp9BOCp6ZJERpGM8uJagFFUTggTgR8aeh+QaMKptRdZ90STEHscSvh4zk2Mi+G9Xz1MMTz8KTQ9aUWg07wOZPoSMPgqyDTUwSqR7KcJ3A+BCRv+o7NeO6l4nmcrH4JwKzglYye0QfcCeObsg9iwyfRD8H81adQgh7OimXITvZmT0N2DW2e4AL8L/iSyTz5Dcb+IvgKEc1mYjMvE2wn9bzwFGkfJPyCiQUvKKANJqg+SqXCUByjwliiD+J6RrFkIYyOR6iP1BnZv39WHJB9H0oBWFRnOCSKsDGbkfcKkBEJQSSK5E4kB4loJZr/IFeiMcgBuZXItwToDEKyrDuNupK4OQ2gzWFdBrtj4QwlECwa+AeVDJ4Bx3kmbiBmA38BG5fcqEoxTpvwvif1YrH98NCOHGSu5C+SyOkoVtBNWzsprAUZlJpFPXH57GRZoetKLQaE4QmVynTDCOXsXihEMphtRKpHOCGhj780UIL1gd6m+rVWUZZ/bZA6oMAcenKNRpTnBOOrq8UiJTW8HcD8ZoVeZjkOUzhPcapRzNejAKEN7cZj6Gaxq4pvV58RjHHvCFHf4LwjUPgkXKH2P0v4o6FUiZVu+TEVTmv5P1OlYXAMIYmXWltKLQaE6U9P7+TUHCYTcPFoClZviiT7c2GQHnePW3czqkd/b4F7ojhozKrFMsqwtSu8FRheHsvy6RNFuBJMJRlbsvtRGiDwM+4G0kMYTnguO9W/vWqiDvayq01SjK8qMMiFFCZiXSr+CW2m/7d4QQqrzHMCKtsPLHmA0gPBC4G+Ec2lLj0goho49Bagsgka55CP/t/YYMDydaUWhGLFKaYB5Q/zjGZzlwRwRGPqQP5W6XKpdAOEqRriW5tnmrS9VBci8GQHivRUZqlX8BCRjguzXLLm9FHofob1CKx8LyXAh5/4Bh9Ao7tdqQ4f9Ws/LgpxF9VxepbSCCqsaRFVKD0yAVBdiOdcfgZtfCNR0pPHZ0Vj/nylY7TLZQRUcl10FqI4ggwnO+MtGdYmRyjXpPHKPA6kDGnkPkfWFoXyP2hDIzpg9gzy6QMYEIjKwO1FpRaEYkqhzEI/ZMC3DNtZ27I6fXlnAvts1PfVYMskPZ2Y1KhO8DSExIrketMKQy2fg/lckNEI4SyPuKSpqz2sB9CYazx+RkpfZA5D5VksPwgmVC4k1wTIXgR3q9blqZwkhnTDhZOEZBagNIny3j3BO+96P1cDgaQniQvg9C9CHljxF5ttPbAtkCuBHe65R5LPYYJDeq46hHprYg/Z/EcE/vdat7kfHlyizkmoXwXHISHN7JXmZDp/p/CJFWGFK7QBQD+9VGUaLKnljhEeXA14pCMzKxGtUM2LBNLKktPY7OkYJjIngugsRbqNpFbjt81Y/w32EPpG6E/0NI7xUqi1d47RyKvqaoOMRfUyYpx3jopShIvIMyy9gzccMBlh+SrwE9ikI4yiH4GXUtZx8fASA8FyBlRCXEuc9FeK8a9C0rU8mjYO5HOmcqM8lx2u4N9zws4YX4n1TRQqmixHDNRnivUSswsxGSm1VxQ0yUQzusHP62opDpvcjwfWp1JDyQWIFM10Dwc0O66hTuJWoiYNUDAjy3HPOcE3uhfHCMUa8h8oCR13JVKwrNCMWFWoqbvbaNrI+rEEKVqXbNUuGcMgTOSQjXgpzZoDCKlcnnKEik6o0gu5T9P+tkD7mOYKtfE47o9nv0K69Lhe36bjjGnR0dGf+TnfRXAaktyETFoBSO4ZqGdE5VDnwSIPKyHbgyZDvyOyC1XvmAnAtU2fKMDK+p7d21rBw+VSzRPDCgM3+wCKMYgl9WlXBF4ZDXcRJGEOmaCcmVkLb7nwvAfc6IWk2A7pmtGaEIR6kqHieb1I/3arVthCGEUDkFRhBwg1F+Ql9ygQWOsWqV0rc3g+cypSysdrAkWFFlWvKdpBnuQFgtdm6EoRLuzNZBX0IIgXCUIhyjcqN8jEpAQKoe0o2QOqJWl73LY1gdR/FzRAYtyzFlNQII56STVuxP+G4F1xwQafXjmoMYjvf1GIysKZpG0wvDeznSvQzgpM6wpNmsynMblSf0OjL6BKQ2qYS51CZk8PODjo4RRjEy8FdgNSPc2dVUDWcFVsG/QOh/7DLjQfD/DYb3skHL+r5xL4Poo2CG7P8HX/l1IIQRRHqug/g/AWEgClYXwnttz0GuOapMSHepdWn7DhxjhlSWU4EwgojgZ7G8HwTAcI7MculaUWhGNCd7CW4lt6qS10KqmXLwc4NvIZrebkc1OcA8gjQPn1AYpTFAlzXDvQhKHsSyooAXwxgeY4DhXog0CpBmk1oRDHG4KIBwFCFdEyDtVKsrIz+rZLrwXIRM71PVWrtNcr6bB1040EruUBV3rQi4FyA85w1buZCRqiC6OaWKQgjxW+AGoElKOdveVgz8ERgPHAA+JKVst/d9C7gHZaj+kpTylVMpr+bkoUJfa2wzQj44Jw55uYnjIv6KHTIaBKsOmVyH8F45uGs4JqqCfyIAWCfUREfKNKS3I60YwjUN0aeXhJVugPCPIbUVjHKs4JcwPEM7mz9ehHNSbujtIJAypZ6X7FLJiY7RuRFURjF4ZitzjGyjO3QUlDmI4GftLPSIOn/QSmIzRH9vO49dEH8JaR5UbVkHEc11tnCqv5kPAP8L/F+vbd8ElkspfySE+Kb9/zeEEDOBO4BZQDXwFyHEVCmliea0QFpRwMpZFUizBRl9UEUBdc8IjQIIfKLfRLGTinCpvAbpVGGnJ/CVEP47kPGXlf3ede0JteWUsSdVwTwMZCIfgl/K2O+ljEHXDyC1HUShem6hHyAd9+bM6KWMgTRPrqkuXYtMbUK4l2QVBDyuc62o6pdt1pIZ/N3ng+/GngHaOUVVnU3tVKu03k2PbIRwYJkhMPciHCeQY5F4FUSR7VsCpF9Fg1mNIyuyboRwStevUsq3gLY+m28CHrT/fhC4udf2R6WUCSllDbAXWIrmmEgrrAZjmRo2GazEKmTXvyK7vo8Vex4p7WQimUZGH1CDs2OUKpbnqAaZQkZ+g+wv/v9k4j5XRdckXlWDhGvwTXKEEcTw344R/OwJzfKlTEBygwoJdYxWyXDpmp4DUrvtKCEJsk6Vw5ApZPLd7OtYXcjQj5GhHyHThwctx3HLG/0DxP+CjD19lPuRqueE1Z67L7kKzEP2ez9arSiS79iKQyGEG9wXAmmVcJfVyU9hWREIfQfC/4uMPJiz/5h0O8RlXJmewI62Cg/+WmcBI8FHUSGlrAeQUtYL0V3HgFHAyl7HHba35SCE+DTwaYCxY4feZnq6IK02ZOw5NRNDgPAhvZch3Oed0kQ1aYUh/oxdtsEBibfBNQ+cY9UAaDb3OCK7MQqVfT+1E+Ged8pkJbkK3AsAv8oOTm0FxyWn7vUBcIIRsAcpPyopr2dFIGVMVWU1a1W0k0iDmJYbRivDYDaCTCKtVgSjT5K4k8FqVr/7IM1mlShpNYC0kK7pCN+Heuo1pXfZiXQ2dt6DNI9kr45kG2CovBQZs0OE+9KdM3EC/VCdUyD+koogE0JdwzG6p7ijJouRoCiORn+GQtnPNqSU9wH3ASxevLjfY850pBVBhn+lbLZGpT07SkDsGaRMIk5phIypMm6FE/U2CsBe3cjcWWYPDpVUdyqRccCnnKZWB3CKVzQoMwr+u5DRh5Wy8l6l/B7d+53jkUYQnPNUuQdjNDiC4JqfOUZKiYyvgsRqwITY63bJ7qH/igvfLUrGPnWuVDb975UCE5X2274LKV5A+D+sDjLK7dVD71pGIquHB9jJbtKymyMV5shgGAGs/B+BeXDQ9aoUPrWq7VY23aG1w92Ee4QyEvIoGoUQVQD27+6R4jDQO95tNFB3imU7bZCpjXaVy/KemkLCo5b2idfUrPRUIfLBvRSsOvXjnKhyBEA5jo96nkVWl7ZTgfcqFRpr1gN+hGu4HMRjEXnfhLx/xvBekd2HwlEJvg8AjWC4gCYVpZPVM3sFRH5GJkEx8RQy8siQy6maNNWoIoZmbcakCCglbzbbK0mpfowKSG7MmEGF5zzAUMdaYRXu66jOWZ0I4cbwXojhnnFUWQz3NAzfVYi+eSfHQ3obeC5Wfcedc+xcFa/dgErTl5GwongOuAv4kf372V7bHxFC/ATlzJ4CrB4WCU8H0jv7H4SFSzlpzcaeaqUnGSGESgZzLwRMcIztcUY6J9sRRl0q7LEbKwK4Ea6jDwwnA8M9F+koV7NgR/WwVe20Uvsh9ghYESzPxQjv1VnKwvBcgEUBxB4G1zkI/0093fGkBYmX6endYAIe1RfDunnQpau7JxVC+PpsTyIjD0N6B2qOKVUehe8227TpUGHGZgjS61RJE+cCMm1ZsZVe8PN2N78mcC5FuM89/iq0Q0X3atfom0g3+BIgUiZApoe1HPrJ5lSHx/4BuAQoFUIcBv4JpSAeE0LcAxwCbgeQUm4TQjwGbAfSwOd1xNNAuMkud9GLfhrMvB+kTCFT21UpB0cZwjU/t2SFMKCfip9CuJWZJfI7NZvEiXJausH/V8PyZROOyvcd6SKtiLKlG0WDrjckpQmx34N0qRVhYjm4JvfjA+hEVY9t7xPCmYZ0u206iwJSFReUcdvvcfyKQpotyPDP1D/BL2RlJMvkFlV/SxpAh4rASqxW/ifXNDBKwRhvO6ebUX6yoMpx6GUCE45KhP/245apXzllEpl4F6w2hHs+wjnx2Cf1xn0BxF+0M8GdIJvBUZbrOzuWHFYIGf4f9ayDf4MY5PmnC6dUUUgpP3KUXZcf5fgfAD84eRKdOQj3ImRqM0i7FWU3Vlg5Dx1D46ST0rRnldsAL5BSs8Pg53LszEeV1TkW8r6mlI3VBEYxwjV7xNW3OR6kTCPjL0HyPWVtMfLB/5EBay7lYiqnqlFlz75tJ27OYXZfbvNAVvVWKZ1AWDm8pVddTxSB1YAUeRlnn7QiyORa1bjIMVaFt/ZZbUirze6/IJRDvHfpivQutRK06tRKVR4BoxqZrlG5H0IgXRNVboo01b2YUTiR8NVjoMKJ1wNeZHI1BD+PcB5/ZrYqkBiH5AqQaXBORvhvzVLyUsZVDa/0HlWaxbMsNxlTdikFLdMqMk0rCs2IxjlNleJObVaKQbjVhxgLfJ8cuqqa6f12JvLoHoVk1iMT7yJ81w58bi+E4Ud4Fg+NTO8DaUWR8RfV6sY1H+G5aFARYjK5SkV1GdV2VdcutVrK+8Zx286FcCPdS1VxOIQa8PsZXIX3GqRRinBO7dNLO4U0insieKSlookcYxAyBOSpQS/yS2WCFEHlZE6uVqsG29wmpVRhujKhlF5yA9I5pee1HGVgHQYrrlapEpTfpCjzLIm/rhzxwo+K5iqB2EtI18yhTWRLbemTDV87OEUhnOCajDQPq8mUa3qW6VbKtHof0zX29l7Pq7fyNKrBdwcQA2eu2VSa9cjEKlWB1ihFuJedlGz2k41WFGcIKnLmI8jkTHt2G7YHvguUaWWIkFZz9wv2evGgypIdoXQ7XPsbqGT8Wbv3QSHEX0AaBYg+pTSkTNkZ0YW5DXSSq0EUZ8I8MfJVsxvzABgzj1tG4bsZXDOUCcM5JcdX0p2bgNWCNIPKn5Ix57hUb20RhNRawFThn4ZLBRYAMrlVJeo5ukNmC8HszkS/RG2yGlVOiWseINXf1sU9Zjlp2kUAIyDt3hpmCrod2rJdfQ6skB3OGlfHy05U1Nvx+yEGfOYAjnHqGcs8QCKM3IKR0mpTDYFEnlox9Hr/reR2iD5gKzQXxJ5Fpg/aPU+EWkWka1SZ++7zrAZk8h2E78bMdYQQiKPkzljJjaoulnAAAft5r0H6bsLwnH/cz2IkoBXFGYQQTvWhHSDpS0qpolXSB1U/Ytf0QfUCFkaZmkhK2fMFkmFw5CZFjQSs+Jt2qQYfMvC53Cia9EE16xVeMMN2l7k+iiK5FmJ/VINK3jf6mNgMIKXMNTKZaeXZf3T30RHCAa7+FYu0wio3IbFc2f6FG+m+EAJ/hXCOQQgD6b0eIr+zX9ehVpOeD/aY86xGcr7uwmtHe/W+F3oif4TIbJNWm+ojYRSpCrbdA79RCPGnkO65SOlQ+RWU9ChOaYFsQ0pHz8dFxiC9B2lFEI4ycEzIWfEO/MxR2fCxF9TruS9TirH3+WYrMvy/dtirBO9V2aVZEq+olXe3QpZBtRq3rgBHRU+yYNbkImB/PrKxrDhgYfRaQUorArHHVSmSzPcrCDIFseeRrhmDqikmpVTfMxEclhIjWlGcZcj4KxBfDsIEKZCOMuWE6ydWvV+cE9USO70d1Xs5CUY+wnPu0MtqtatluwDhPuf4Zew+36xTHdXSR9SGyK+Rrh9mK0bXbAg/rGbDznH9JpGpBDG3yhvoG53jmgOhf1fmGCzACa6J6jkNAVJKlQltHrBzE6J2gTyp+jnn/b3KDHfPw+KvIXy/cqoHPo5wn9dzIcc44I0+Cj6aLadRBq6pEP69+j/4V2obKH+S2aJWCq7JyiYvnHaIazOk9yAcZUjHGLVyMaMopRWwVz8m4LC79f1ODbgygTTyVQnx4N3Z9ZoGeuaAMAoQgY8d/bmld9FdBwqZUs2leisKqxVELxOSEMqnIkNABcJR2c+EqAucy3q9xgFbgb8B0sJyzYHApxCumZDeq56R0WcSZkf/ydQOxCBWFTL2lDJNeq5E+AbfcOr9ohXFWYSVboDoY8oUINPqe2wdRsZeQgQ+elzXEMIBgTuRyY2qtLZjrKq6OcSOaCkTdgJhB0iQyc2Q9+XBVfeUUVRES5RMIyQZI6uXgfsqkP9uRwiNwnDldoYz3DORjr8H4c8JGcVss231dsKWtL9SVicMojiglBKZeB2sRoT3+h4ns1WvBh1RBaJBrViMIjWAWg3I1GaE5zxVVDD6CKRWARZELKRzLsKhZszCNR3pmG47b8MgfOBegnDPz8gghEA6zwXjMbXBdW7P7FXGUKsIC9K1KIe5U5m2ZAo1YSi1S327VF0qDHCNUa1KhRtpdUDk15DaY59vqFWc7ECGXZD3xczrDfjMjwMhAki7vzgy3GulZ+OcboeU2++RTKL8Q7aZzTFehXcn16MiBi1wlCPc56i3JbFKfZfSW4EidS+pHRD5GdJ7Tc91+peu/2CFgUjvUzKaNcc+9iSgFcXZRPwVsA6qmZThVV8iKwSxp5D+W477CylT2yD+HCoscy+SBHivH9oyIWazmvVZ3aXBLFV0r09UibTCqnaQc0Ku/I5RPaUwhKlm1SJ7wDAcbizXImV28Fx6VHGOWhHW3GcPmJNsxRuxZW0YnKIwWyB8HxBBko/wX2/fdps9021Rg5JwQ9pW9I4yO8QYZGK13TI1hfIv7IT4sxC4U8kvnEjXdEitBAqVM9o5NUfxCocH6Rin/jbcvbaPQYp8ZZaSEgw3WAkQXeAaC0aFUga+D0P01yrTXTjBUYzwq0Y8MrkJ0nUoJWGvRkVQrUrSO9S9OEf3es33UXrbNQvc50Byrd2jPHsiJLzXIsN7VCCCTKqIs+A9mQmPEAb4PgSuRUiz1o7Mm6F6f5tNEHvajjJzq9WoSAM+EKXqmt7r7De214okg0Q4BufQFoGPqUmBa3gCQLSiOMOQMmWXX46rL6/DNh3IOKQ2oMxFdvKbMDJ/y+RmhGdZv9fMur7ZCtE/2rNar3JwJt5EGqMQnl6ZwjKtWnuSVs7Zwc4KjUJ1D93F4kQgZ5AHVMmI1G5wL84xRQjhQwa/CPwXEFTmjf6UWcGPgSjGiayKHJX2CiUGeFDLC7dyjg8G84iaLcqEWqlhKwoR6FHoOO0B1qWUqFEARrGyXydeV05badgDuQ9S65HWLT25KWaNXc+oSF3P3J8jhpTOjHNaSmePp8U5WflQ0nuAqH1MWr2ma36maZDhnoll3QbyCfVc/Hf2VAQ2G9RzEm7bN5K0zWgudd+yAwZRn8pKblDvve/6nPdOCCfCfxvSdzPg6L+MuQiAoxSkC4QD4azucw0DXFMQrj7+j+Qa9d0x8sFRqFZPUoIjDxxFYDkgtUs9s/S+XiV1LJBNqiPiIMu0C8eoYc3R0IriDEKm99kF2bpbQkqkey7Cd5v6YgqvsjlbLahBLa0Oc0y0Sxoch6JI70UlfbnVlxs7sia9HmxFYVlp6Pq+mr1KC5xTkIU/xDjOPAtAdToLfha6/kNtCH62f/OWFVG5GDLe/3Uc1UjPjSACCNF/1rVqAjSwkpAyDjhzaicJz2XI1BZIH1KzfGeJKl/Sq9uaCiA4gkztUVE87plZM3kpLQj/FBVJ5IbEG1jJmzHcM1XpE6MErBiqhlZC/RiVgES45gLJnkz3dAMg1QCOU5kZsRWFY5yd45BSzmZXf+bGqMqTQKCUn32fwgnBe5CkIf6ykkUEwP9BhP/OnpwOswniz6vEQVIQfRSZ/y2VaOkoB3zKD2AHTSHtlxKeTITW8WCZXdDxFRUSnN4FBd/t97ij17pKqNWaY7qa8Vt1ygdzPINxei+ZWlWuBeo+ZQrcS1DZ6YVq5Z73j2rlndxknyjBNQPh++Dw9F55H5xe0mqOijRbVdw3/p7kOikhuRmJy7atCnDPVUrDaga84Bxj22eP76MghAtphSC9Vjn3cCl7b+8QxvgLkHwbVe/fULbg8G8g/yuDuifDNRNZ9D/qdY9aWiOtBpl+SqpLKZGh/7Gjngxk/r8ifNf1OcZUSVVWEzinYbj6qYia3o8M/0aZeoKfyyo3IZxjIO/vVZawbAfnTIR7UdYMVkZ+q5zMdAFOpGMmsujnGE47/0BGlLMat23CSChTjHtmj08ofL+ytVtdakA1ilTpDEeZUkSOAkitI1Naw4rYq8rizH2SWGnbulPKLJR4D+m7Mrt8hkRFGmX+6UEY+Yj8r2H5b1MmKMdEDGefgdXqVAOvEVCfP6teOZWFG+Geh3RWQNrOFheWklf4VNTSYFqZxl+1M9GTkNxkd9zLNlVJaSlFKXz9RPZ5VEmbdI393B1qpn9c2D4LUM+xd9CAeggo7ecFx2QQe1T+iqNErTJOwOdyLFSm+pvKp+M+b8gjo7SiOEOQqbWAlVWeWn1hqyC5Hum5XH15rbgyP3TH00sJsi2nT/NRX8eotpfaXWSintJbwPEx+3ImJN5TyseyW1UKA8zdSKstKyRQyrhtgy/MSU5Tzt3lEP+L+t97BcJzee4XwDlerZD6S7Yyj9jOSEOZZOIvIT2XZL2WjL0MsUftwTMPK/+bOcpCpo+owc5M247gPmGNRqF6tqYamHsPvFaqBsI/R83OLfVjbobQf0DRD+2jfOoaVo29qnDYUUoK4ai2ldFbkPiLUka+mzKJX0IIpO8WiL6E6jMt1fvqvrpngEzvUis8o1KZpawEpLfYJsdedm+Z7ClfbvXfz8RwTui3PAugBlsRUDN0aar3x14pCKMY6b8HIvdBap+9MioG11xE4Pg7y0mrC2KPoQZjF1iNyOhLEOy5hqpL9aAdCOBG+u/Kel+FEOD/uHqmMqSi6o7XJ+KaY6+q8tQ9Jt9T9+I5TykBqw3cc1T9rcQbyifomqYCAmLPqCQ/34eHOAFxuyqbjlN9dpzHb8I7HkZC9VjNUJA+iOpl0AdhgBAI2QXeDyo7sNliDwhhlWnrnKkyu48DYdUpOywmqvZQnOz4chOsQyA9apYm7Bljug5p9ZTwthIrkV3fR4Z/igx9Hyv+ep9KpC1q1mhUqJ/4q8ou31ce3+2I/G8jvNfl7IO4Wl25z7Ub4fiBZJ9DnrEd5ik1q4+/3s9NBwGHXUCun6S96BMq5Di9CyK/RaYP9br+n+zXzBTSUH+nVmQOMQwn5P+rysYWReD/RI7iFoZfrQKlBHNvZqWQ2e8Yo0JbRTmIMhX26p7dI2P6AMq8llLmQDusWfkcemHuVzkXVlO/PgxAlQJJ7eq3yZQwguC/Syk8UQS+j2flSAhnpW3br7SrChepldpxhj5LGUNG7lODLn4yPrfUCmTynZ7jkpuVj8yoVsfEHuvnakn1fbDaVd7DcSLcC5SfxQqr1Xl3/+70ftscmwTnLDtjf5SaRID6Hhij1eTFDkIYMhwVyodnlGUy5YcSrSjOFIyi/u30UqqBQfgw3DMQwS+oAUVGVeST71ZE4M7jtplKmQLhJm26icf9pFJeZfrJ9HEQanbuCKgQQ8c427Gb7rlG+jDEnlKz2tReNROPv2TbfrsxSSUl+zZH2bc5SjpF1jWULHG6jvwfrXv/g3Djo7kd/RxjbDNcCoiBZ36uQ1wm1Woj0/wm+xmqXIwHlCM4tQ4Z/l22QgO7pElVphKp7F2qWrjUM8Gix5QjyVnMG5W2P6FL+TH6mW0e3FvNXx6Ls+m93IgZKdPs2JDHwz9dwIM/XsC6t8uxzF5K0SgFZ5kaUIgpWY0K+3/7GlZErVhwohpO/aXfAVRGf48M/1LVW+oHYTWrz5dsRciO7HMT7wIpNeN2z1EDanq/GmyPA5ncrCLi3OeoAVhgK5xpEP9zL+WVRq1mhXoPZPYEQUqpVhwpO2M9+qB6r48DYeQjAnejPvMppQCEG9VPpQ38H0KQRPmKHH1PVmbQvgr6/WJUgO9W9X0+CYU1tenpNCIWjvHOM2vYvnI3/jwfy65fyOzzp6syAu4lKhqjOxGqG9mqBkxDDQjCOQbhvJNkPInT7bQducePcE4mGnWya40PwzBIpyQTZkuKCnuZLxzjOLSjlj89FCOZhItvcjDn/EmZmaVM76DpiEXzod04jA4gRH75PKqnbcpEmJiylGd+7aNmi1IeE+eN49avlWZ9YBt2PUT9jqfpaHVSXL6ZUXPclE34YI+swoP0XkyqfRPCcON0X5o7AHuvgNgT6gtulKr+zb0xm5UpRsaVYjMOoVYIvcJKHRMINb5EZ3OEyonjcQV7mTA810P455hm3PbfShUw485Ommqs2Uzb7nYQEst4jdlX39rLjCJ55YHX+eO/PYtlCmAri67+DX/z47twe5SC2/D6If78Gzf5ha0YhuAvj/lp6zzI1Z9Qz1O4ZiMTb1B/QNJYW0pJVZoxUwuyVy5WOwg/0jnLfn4+OxJpkAOPc4KK6jHycqvymrVAkI6mTuLRBPklefj90u7I1+fQtIkwRPZnNL1VmbaERw3OokiZ0gyPKhdi1oNzPMI1C5l8yzaBSVX2PouEisIyqpQyMSN258XjK54pnOOVOTC1ERxTgDS4pyFcixGOEpVZPsjs/PdFeidEHwRcyLyvvL/Q4n7QiuI0QUrJ0z99mSO76ymuLCSVTPPir14FKZlz4Uw1c/deo0oTSAcq4iVhx5DfkTVA7l6/jyd/8gLjZo7mjm/eMihlIRwl7Nx6GYKH8PhMXB7J+hXTuGL6XLVfuIhby3jm/jbcPjfeoOCVR5NUTR1HWZEK1Q21p3n0x61g5FNYJAmF8khEm7nreynK7EThtrp2Vi+HpoNqIGxpklxyZwflY3pq+uxe9RYdjWGiIQfxUJpw6O0sRRELx3jhF+vZtLwTw+Hk4o9s5OIPl+F09frY+26zM9UtZSvvU+cJRyU4SkglQhhODw7nFPrWLErIq/nJl56nvdnDFR8LcOOXe2zhhquKxo6bKPT/gUiXwBuQJOIFFFR8KXOMZVk8ce9WXLKM/OI4+3e7KJxQx5hpylHcdKiFVx98E4QgEUviC3rZ/Po2tr69g4VXzMWyLN59ZjUlY+fi8cQBE29JHlve2s15N51DXlEQYfipqbmBJ378kApGEH5u+Ny1zDqv1wrLUUJzXZqGfWEQksqJpZTPyC0zIfx3Kmf2USrkCqMIkfelfvfhnEDLwQ3sWhdRExyHYN5FhQR6KdeuthCvP7KC3ev34/F7WHbdQpZcM9/+nDpRM3WXUkZWW6/BvafkiDDyIPAFu9JtsJ96Zx7l10ofspWOMehS88LIQ3guBM+FuTu7izpKM3tVIS2QEtGn5Mj7RnhQEYhuTkaXPq0oThMaDzZzZHc95WNLEULg8rgQQrDyhXXMudCuzOm5FFzT1fJchlQNHfesrByGxoPNPPz9J9m9dj87Vu+lenIlF9127qCURcPhsTz/81kUlCaIdLkYP3s6V3yqRxElrEtJpVdRmNeFQIARJJq6NqOsdm8K0Fxn0nxYYDhLMNMmpdVptq0OcomtKHat3cfutfsy53Q072PXmr1ZimLt65K5S1KkkwYud5q1b6S5wE6lkFLy/C9fZcPrDRzaVYgQDoyntuDyFnLRbT3lRoTwIT3nKbOXJzdaRDgq2Lv3Bta/eB9OTwE3fOkjeHoXl7Ms3nn2AGtf95NOWSRTsOzmZirGlWWOqTtyA6+/vY3dGyIUlgvyys7hkz/s8SelEil2rTtESamXlnontfvDNB5sziiKun0NOJwG7Q0dxKMJOltDTFk4gd1r97HwirmkUyaxcBwE1O5qw0yZVIwzkFISC8fJK1IBDtvercWbN4HC8gLCHRE2vXmIWef1rAS7WlM88csqZi9WA9s7r1bwse+kKCjNjtIRRhCM6Uf9fDQfbuXtp1bi9Xu46LZzCRb2rEiE+zzq9v+B0so4pmUgrQRHaqYwddSEzPN86t4Xaa1rp2x0Cemkyet/WIHT5WDRlfNUOGpqK8hCu/yI/YGRUWWK6rUiOLijle3vHaCoopBFVxbj9vaKVhNC9UVJvAFWCOE5F9HLDPd+EY4SpOfCHme2EVB+FatFNXsa4pwI4ZwEeX+rostOgo9CK4pTiJk2aTjQhMPpoGJc2aCiHhKxJMIQWee4PE662sKZ/4UQqq6Or//lcyKW4PEfP0c6mQYkbq+T955dQ3FlEXMv6ilIV1/TyMbXthINxZi2ZDIzzpmCw6EGj1B7mB0r9+ANFtHREsfldhLtjLF/80EmzRsPQH5JCWNnX0jNlhqEkBRWlFAxoSfBKBZ2EeosxelpxeszSSYNutoLiIS8mee08sV1+IM+wu3q/oLFeax6cT3n3bQkI0tb83QeuTeKaVk4DCczL+wZvDqaOjmw9RCWZdHRGMdwGDjdTta9upnzb1mauUbjwWa2vjOFzqYiqqcEmXVeiPzi7FDcx+7dxDtPgcMRYsKSwyy8vGeWveXtHax6cR0l1aXEI3HySgp5/MfP89f/9jE8PmWeGjNjAn/4t1k01dTBDgcf/YfcHs/JmMmqV10YhhOPP531PvvzfBRVFlJQnk/8QDPegJeqCZUUlKpoIofTIBaKsfntHUTaw0gJtbuOUFhegD+/Z5AvKMun/kATu9ftI68wyIQ52b6ORDRBNBzgwL4lAERCLSSig+shHmoP86uv/R87V+3BcBgc3HGET//bnTic6nkLI5+9e2/g4KaXCQSihMPjuPyuGzOJkI0Hmmk+3IqUkr889Bb+fB9zLpzBmj9tYNGV8xCuGUjnVCJtmziww6S13mTaIicllT6cBXdnfG11+xp4/D+fw+11EYskaDnSxgf+JtvcJ4wgwnfDoO5vMAjvdUijDDO6nFj7brx5ZTiDN9od/YbeLDWUVaL7ohXFKcJMmzz1Py9Rs/kgAEuumc8lHz7/uD8wlePLcLqdxCJxfAE1oLbVdzD34uMvZV27q45Qe4RIZ5RkPAUdUYTTwYblWzKK4sC2Wh77j2dJxJIYhmDHqr3U7jrCtXer3lIHttZimRYzz5lCV2sYX54Xy7TY9Ma2jKIwDINbvnQdu9bsI5VMMXXRpIzMABPmjMXtzae9OU5bYwqXx01xZR7TFiuTTaQzirQkpaNLCHeqmjilo4qxLItIR5T8EjWQf+jrN/ODjxwg1hUjrzSPD/5tz5c+nTJpOtRM/f4m0ikTkTbZs6GGMVOrkZYEB6z+0wbeePQdnC4nbq+Lmi2rWfXCOm7/+xsZNVnF1O9YtZstb+1AIBAC/u+fH2P+pbMzK7ANy7cQLApQUl1EqC1MYXkB0VCU2l11TJ6vZsml1cV86eef5s3H3mHU5KqsFQ2Ay+Ni9NRKarYcwkRQWOahckKPKWbCnLGUVBcz+/zpLLh0FhKBlTJZeKUy921/bzeRzijRUIxIl0qSk1JSXFnIyufXcsWdF6vrzB3HoW21hNojeAMe7v5/2X3ESqqLGTO1moM7lDN+3MzRlFTnmp7CHREO765j3MzR+ILZq43WunY6GjoJt0cwHIL6fY2EOyIZpQZw+ceu5LuPrae9sZO5F09jzoU90VmpZJp4JMHWFTvoag3TWteOw+lgxrKp9hFO3n55Kke27WLXmlriUYs9GwJ4Cidy7V9XUmZHhdbvbwQJRRWF5JsWe9b3H8F1MhHCQHiW8dIDHWx928OEuRP58Nf7MVOdBmhFMQgG6mtwLI7sbaBm80EqxpUhLcnaVzax5JoFWctygKbaFur2NjB18ST8eT1fQo/Pw42fu5rnfvYnQq0hJFA5vpzzb1madX5bQzsv3b+c9oYOFlw+h/NuWpIZ1NIpk0Pba0lElZ3b4XSwe+1e/MGeQfyVB15j+8rdRDoiWJbE4/cQag1xzvWLKKooREpJPJpg9SsbCbeFcXvdTJo3jqpJ2ct2t9fNnAv77389emo1H/jsVfz3535NpCOCJ+DhI9+6mYnzVO6AL8+Lw2EQbg/j9ip7a7g9hMPhwJfXI+u8i2fx9Qc/z+oX13PJh89n4tzxmX3eoIfO1jCRriiWqZKjYl0xErEEDqeD5sOtvPHoO5SOKs74LPJL8oh0Rnn2Z3/iM//xVzicDt55ZjVdrSEATNNi/6aDNB1qpnK8ul9pSWq2HCIeSeDP93NwWy1VEyv65qoxenIVH/r7m3C6nP1+fjpbQpm/Y9GEUuS9nuUd37yZtx57j3V/2cSYGaO55hOXZsxbb/zxXVobOiitKiKdSCEtSdmoYiKdUd58fCUXf+g8XG4XW9/ewcT5E2hvaKeospCNr21jysKelZ7D6eDWv7uBfRtV4blJ8ydkVgK9efqnL3Fw62FmXTCNW76YHZZcUJaPP9+H2+fCMAyKKrJXNQChtgjzLpnFkT0NTF00kXBHhMIy5SupnFCuyiMZAsNh4HAahFpDzFimJhG1u+pY+cIWiqsXUHfIIh5N4CsaT0m6kBfve5W7/lnlJ5RUF2NJi0hnlEhnlLEzck09ZtrkmZ++ROOhFm77uxsoH1uWc8xQ0N7YRWtDmIKKzpNy/VOBDo89DpKJFK8/uoJ7P/Mr7v2b+3j90RUkE/0nIx0Nh9MgmUhxeHc9dfsbsCwLw5H9+E3T5FdffZAHvvsoz/7sTznXmDhnHB/++k1MXTKJRVfO5UNfv5FAfnbuxNP//RK71+ylvamTPz/wBvs2Hsjsc7octDd10lLXSsvhNppqW2iv7yAZS2Xu863HV9Le2Ek0FCPaFSXaGWXn6j3sWK2ij8bPGkPrkVbq9zXS1Rqi5Ugru9btUw7140QIwbhZY0inTSxLYqZMJs4dn1FoLreL6edMpaWunUQsSSKWpKWug5nnTsXlznbUrXxuHTtX72XNnzZmbQ+3RXA4DGKhnnDXUEcE07RIJlLssE0jTpcTy7JIJdNIKQkU+Il0Rjmyt4FYJM57z60llUiRiqdIxlIk4yme/+WfM9ecf9ls2hs6sKQk2hUlGU9imRajp2Wb/7a9u5NvX/dDfvW1/8OyrKx9ka5Y1vsU7Yqx5e0dWcfkF+dRV9PIrjX72LZip1JGNvs3HcDldmI4BKZpYaZNLCnxB3001DQSj6hn4HQ5yC8JMuv86RSWF+B0584T4+E4b/zxXd7447uZ83oTag+z/Z1d7Fqzl61v7yDW55ii8gIu/egF+IJe8ooCXPfXl2e9Z/X7G/njvz9DPJxg9NRq9m86xKM/eoZETJm43B4Xt37pWvx5PvJLggSLAoydPpoL7VXYlre34/G5adjXiGVZOJwOmmqbcXldNNe20nJEFZEcN3M0195zOcGiADPOmcL1n74i51662sLs2VBDW30HB3f0n9fQ1RpixdMrObynvt/9x+LAtlqO7K3H43PTVtfBztVDHBZ7itCK4jhY/vBbrHl5IwVl+RSU5rH65Y289sjbg7pGYXk+odYwu9buZeeqvRgOA28gu3LnyufXcnhPPW0NHaxfvjnnw9nZ0sXjP36BXav3sebljbx8/2s5+997fi37Nh1k+7u72LuxhhVPrcrsb6tvJxlPkYimMNMm6WSaZCpN0xGVyLZj5S7CHRHikTjJWIp00iQWjpFKpHjnqZVIKckvyWPMjNGAxEybSAnegJepiwbXf2HLW9uxTBMEWFKyfvmWrP3jZ47G4bQjWFCz3fGzs7OvLctiw+tbqNl8iA2vbc7Kb6jZcpDOli4Ml4FwCAyngWEIOhu7OLKnnnB7GKfbyaGdh3ny3hf4478/w8u/WU5ncxdCCBLRBDtW7ibSEUXInhWA4TBY86cNmf/nXjyT2RdOp35fI40Hm0nEktz21Q/g9We/t0/e+yLbV+7mzw+8QVtjn5mltEgnzZ77Mi1ioRh9ObitllBbiLp9DVn3mk6ZICTNta2kE2nMtElbfTvptIlpWiq/Dhg/Zyzr/7KZl3+znJXPr2X8nNx8jNb6djqaOulo6qS1rj1n/1uPv0dRZSFLr1uAy+tizUvrs/aHOyLUbD4ECCxLsnvt3qxJ1bpXN+FyuwgU+EnGk5RUF9HVGuLA1trMMTPOncr8y2YTjySQaYsPfvWGzPMMd0Rwe13U7WsgvziPkqoizJSl3mtDZHwqQgjmXjSTj3/3dq7/9JUECnJDfAvL8rnotnOZe/FMpi/tpw8J8NYT7/HmY+/x5E+ez82fOQ62vLUdX9DH1EWTKCwvyPmcny5oRXEMYpE4297ZRfnYUpwuJ06Xk4qxpWxdsTNnNjUQzbVtlI4q5oKbl3HhB5cBgnB7djLT1hU7WXDpbM67cQmV48ozJoBuDmyrpX5/Ix3NXUS6omx8bSuJeE8i0bvPrqGzNURrfTudLV20N3WwZcUOWurULKurNUwqmcblddi5SAKHw6CzqQOAFU+uUuUges14LVMihODAtlo6W7oAuODmpfjzfAjDwOF0cMHNS3PMKWba5Llf/IlHfvgksXDuoJdfmofb7VJ10lxOCsuyC8IVlRdQWFaAIZQDv7A8n4KSbCfz2lc20tHUhWma1B9oYft7uzKvvfbPm/Hl+TCTJlJKLNMinTIJFgVY8/IGRk2ppPFAE+88u4Z4OIG0JK117bz+x3dIRBIUVxVhWZJwZyRrgEglUkQ6e953h8PBZ++9m5KqQpxuJ1fddQnzLp6VJWd9TSPb3tlJMpYk3BHh1f/Lzv7e8tYOgkU9A5k34OHwnnpM08w6bu5FMxk1pZrpS6dkPe+x00epe8gayASRzijlY0rx+FW0z8v3L8flcTF+1lgCBQFe6LUy6mb01CrmXDSTeRfPZPTU3NpHna0hUok0tbvqMFNmVjCFlJLnfv4n2hs7OPfGJSy7biHb39vL20+uzBwTbo/g8jjZ9u4uNizfwuHd9UiJitqyObjtMId3qTDwQFGA1x5+O/MsxkytJhqK4fV7SNpmNiklDqcDCRRV9IT7WpbF4d11hDv6z7oWQnDejUu48bNXZyLD+lI1qRKny8mY6aNOyOScX5qnFJ6UxMKxnM/58bJn/X7++O/PcGjnEGd0HydaURyDtG2SEEbPh0QYAimVyaQ3bQ3t7N1YkxlQe+N0O2ipa2PHqj1se3c3kc4IDle2/bd0dCnhzigut5NUIkVhec+Hvqs1xJ9+s5y9G2vYuWoPW1fsZO/GGl575O2MKWPtKxtJxVL48314A148Pg8N+xs5Yq9MXB4nDoeDRDSlygFZklQ8jTegbMi1u+vIKwlkZqDd+PJ8RDqjmS/chHnjiEfjmCmTRCzBlCW5q4mdq/fw4D89xuP/+RyvP/puzv6l1y2kqLIQt89FXnEwx8FbPq6Mcz6wCKfbgdPj5JwbFlExPjuJ6OXfLCfSESGVTNPZ1MlrD6tVXiwcJ9QeRloy4+9wOh34831EuqLU1zQxdfFkDu08QjqRne0d7lCKoaSqiKKKAgxnn6+IhPySnkG9pa6Np37yPNFQDMu02PzmdlY8szprkN/0+jaSiRSGIXC4DN55anXWJTe8vpVpy3pmtIECP2bapPFAc2abZVnkleZRUJbHqClVxHtFI13y4fMoH1cGQmBZFpYlsSyLkqoizrtpcSb6KtIZxeVy4nI7cXudRLuym+dIKXn5/td49n9f5umfvszL97+WM4tedMVc9m8+QP3+Rg7vrmNuL6XY3tjBkT0NFFbk03K4hbbGDkpHFbPp9a2YafU8pi2dTFdbmHgkkVk5CQGjpvQopVQiRXFlIZPmT2DGsimAyPiZZl8wA6fbSfXkSiJdUWr31FFUUYCZNJl3ycyslcP6v2zmoX99gof+9XHSqez3+XhZePkcvvDTu7npC9ec0PlLr1vIpPnjaDrUQvWkipzP+fHyl9+/xf5NB3nrifdO6Pz3i1YUxyBYGKBqYgXtvcwF7Y2djJpcSaCgxz9Qs/UQv/vHR3nmpy/z23/4Aw0HmjL7ulpDvPTr5cTCcWp3HaZuXwPRrhjP/+LPWcvyyz92AfklQer2NzBtyWRmnd9Tf+mZ/32ZXWv3kYwliHRGiIViJBNJXvzVq+xepyI6Ip1RMCARTii7eiKlbOedakZfPbkS0zQxDKHsOQKkZWVyE7x+D053brKO0+XA4VQDDMAjP3xKmUoMZYp58B//mHV8PJpg6zu7iIXjxCMJtq/cTVdbKOsYj8/NF3/2Ka655zK++L+fynJSq9d0ct6NS8grySOvOMj5Ny3Jcqyue3UT617dgpm2QIKZMnnryZXsWLUbt8+tkrkMNWu0TKlCaB0G0pIUlOThC3gzIbK9EQLyStTsMtYVwx/M9gEJQ5nBzLRJpDPCH3/0DI2HWnB7XHgDHpxuB28/+R7vPLMmc45pmhhC4HCq52ilszWxmbYQvQdkCYboGRwBVr+0nof/5Ql2r93Hn3/3Og/9yxOZffMumcXs86djpk08PjcenwdpSaomVXDJh3syza/71BVIKWmpayMZT3H9p3u1BgVa69pY8dRKmmtbaa5tZcVTK2mta8s6ZtqSyXzwKzcwce44PvLtDzJ2eo+T2DItpLTY8JctrPvzJla/uJ5da/Zi2bN+gNkXTGf60skUlufjC/rwBj1c+pELKBvd05Z07MzRFJYXEOmI0NHUpcxc9ucyvySPD3/tJqVMU+p+04k08y+fnXWvAKlEGsuSpOxVZV/MtMnTP32JX/39gzQfzq0jBurzEygI9PtZOR58AS+3/d0H+OpvPstHv/3BnOCV42XhlXPwF/iZd8msYx98EtCK4hgIIbj+01eSXxKk6VALTYdayC8Ncu2nrshaiq56cR0ev4fyMaUI1EDWzVtPvEfz4VYS0SSjJlVRPqaUcEeE/VsOsvnNbYBy8j3yw6fobAnhC3jZu2E/T/z4BSJdUULtYVY+v1Y5YuPqw2+aFvFQwjZlvAnAmOmjVGknQ31phRB4A17KxqovoZm2yC8OYpmSnl4AgrxiNTBOXzaFRCQ3br6rPUz5+NJMWOqR3fXKLm6p12npNZjEInEe/dHT7N90gGBRAG+el1goxu+/9zhtDcrm3dnSxQPfeZQf3/MLXr7/Nf77c/fxq6/9Pus6AIf31NHZ3EVnc4jDe3v8NR3NnSx/5G36hhZZpuTl+5djpk2WXrdAlRhJppV5wpSkUya+PB9Lr1PZ16VjckM/pZSZ2e2Kp1eTiCUIFvlxeZ14Am78+X46m7vYvnI3297dReOhZvZvOkikK0a4M6pMg01drHxhbY/JTQhikQSpRJpENIEn4CHSazY//5JZdLb2MuFg4cv3ZYXIrn9tK7FIHDOlnPGb3tia2ef2urnw1mX48rwUlBWQXxrEX+Bn0VXzs3JC5l0yi3966mt84p8/zD/+8StccEt2/xFhGHjzfHh8btw+Nz7bvNiXxVfN5+pPXpoT1VZcVYTb56G9qROn24XT7aRuXwOT5o/LRJa53C5u+vw1FJblEyz0M/uCGSy5en72+5g2SafUhMYwjJxcjqqJFXzmx3/FrX97PYuvns/nfno3V3/i0pxAhyXXzOdDf38jH/uHW3P2gZrA7Vq9l6baVg5sPZSzH9TnIZlI5QQgDJbBlsrpy7LrFvHln/81cy7oP5LwZKMVxXFQVF7AJ/71Dm79u+u59e+u5xP/cgdFvcxCoD4IDQcaObi9lqbDrTjt2W8ilmDn6r0qOsmShDujGbNNsCDAhuVbiHRFeew/nwMJJdVF5JXkUTq6hCO763nhV3+mubaFaFcMw1DZtlJKpO1Mdns9HNimPuRjZ4zGTJukYilSiTTJRAqHYVA6Sg2IHq8rZ0ZjOAUlVYUATF08KctW3E0qmaJ8dCm+oI9YOKaS4HrXt5NweLcqqLZh+RYaD7UQ6YzS2dRFpCNKQ00jyXiSNx9/D9M0efLeF1n76iYaDzaRiqdoOdzGxte28PiPn8ussLpaQ6x4ajVmysRMp1nx5KrMqmTvhhrq9zbmfPkdToP6miYObqvl3A8sJq8oSCLW48OJheKMmzmK6cumEOmK9vvllRZ0NIfoaO5k42tbKB1VTCqRJhVPk4gm8fjcOFxO/vL7t9j+3m5qd9VxZF894Y4IyWiS5tpWDu44zJHdDSqnoLmT7e/uonpyBU63E2/AQ3F1Eatf7nECL7x6Hod312U6ZoZaI0yaNz6r1EheYYBoV5RYOE60K4on4MmaJY+aUkUwP0BrXSttde0gYdZ5U+mL2+uiraEDj9eds89Mm7g8TqLhOLFwHJfXlTEZ9ebhHzzJIz94kqfufSFru2EYXPPJS/EGvCp4wOEgvzSfSz6UPdMXQhCNxDm060iWP6yb2l11xEIxxswYxegZ1Wx6Y3tOlKHD4eC6T13OXd/7EBPnjMu5BqhV6eQFEyiu7D9TubC8gPNvWcrs86czrY8zW0rJtnd38ptvPcx//819/O8Xf8uKp1eRjCf7vdaZjlYUx0GkM8ITP3meh7//JA//65M88V8vEOnscZAd2FbLrvX7qdl8kE1vbOfQzsNsems79TWNpBJpkODP9xGNxGg40ER9TRMIlS8QCyfYuWoPyVgSKSWrX1rP6hfXs+mNbRSU53Fw+xFlUhLKTm2ZytSCpQrMpZJJfAEvLUda2bl6D0bvsjKmJFAc5PU/qJLW+aV5tBzJXmKnYml8dpz7wW21pPoL+5VqxROPJqjZWptJ6urGMk022NEcm97YRmdLF4f31GeUWntTJ/U1Texeu499mw5St7ee+n2NWb6Qhpommg62ZBISo6EYHU0dmKaJmTZpb+zIhLo2HmimrbEjO3dDQMX4MpoONtPR1MnG17YiLSsTOQXKR9PW0Mn+TQfZuWoPiWgChyvbQRks8lOz+SB71tcQiyRwuZ1Y3QOyVD4rb8BD7c4jtNW3E+2MkOqV8yAlxEMxWhvaSMSSNNQ0YabTFFcWESzyUz66FK/Pzd71B3pe1LRwe9w4PS6cLge+vOy8g0hnhPp9DT1FAgGHw+DAtp5IoWgohhR2sWApcTgFXS3Z5j6A+7/xEE//z4v89Iv3Z21vPtzKIz94knTKxB/04A96SSfSPPKDJ3M+M401TbQ3ddFQ00RfRk+tpriqkHQyTTqdZtzM0eSXZgchWJbFxte20tnUxVtPrMy5hjfgpas1xNpXNrH+z5tweZ04+/jzzLTJw99/kvu+/hBb39mZc43jQQjBhbeewy1fui4nG3/Vi+t44Zevkk6ZVIwrw5/v451n1vD0T1/OCTIYClrr22mtz40yGyloRXEcPPU/L7H84RXUbDlIzdaDvPbQ2zz905cB9QV9/MfPUbvjiGpV7DSwTIuazQd5/D+fw+V14svzsu2dnRiGCtF0OFROxfZ3dzFmejWNB5vx+Nxse2cXTYdaaW/u4tDOOo7sbcAQ4HQ7CRYFaavvUFnFNtKUtDV0MvvCGWx+aweGYRAPZ894wm0h9m44QHtTB3/8j2dzIq0Anvnpy9TXNLJlxc5+E6zcHjdNh1roag2RjCWJ91l1JBMponY4Z+PBZpoONisXiB2xhJS0N3bQdKiF9oZ2OltCWH1mqpZp2XkZtvlJCELtEVVDzYJwezRTjDOdSmOZJg37ew1UEhoPtWBZklg4waoX1xFqj2TlqhgOg47GTla/vIFGW0ZfnzwUX54PyzRJJ1MIoQIU0vEeR2i4M0qoLUywKIAv6CWVSKughIwukSCUycTlceINetny1k52r92PNKG9qZN3n11DIt5jTgkWBpl9wfTu2yCvKMCCy3qylWu21tJ8uC0T/CAMQbhDRb118/qj79DR2InT6cDhchILJXj518tzbPO+PB8ScjKq3312jcpkLi1AOFRIcWF5AVLCu8+tzTr2yr+6hBnLJnP5nblZxi1H2rL8K7FQjEhHttPcMAzyi4MIQ1A2piTnGuNmjmbCvLF0tYaIdsW55pOX5az+0qk0u9bsZe/6/Rw6Sg7E8dBS18aBbbVZpqVoSFVpLhtTmkl6dXtcVI4v4+C2Wg5tP3y0y/XLG4+9w99d/B1euC83ygyg6VAzD3z3UR747qM01bac8L2cTLSiOAbhzgjvPrOGVDxJU20rTbWtJOJJ3n1mNeHOCHs31FC3r5HWI23EInGSsQTxcJKmw60c2VvP4V31+PK8tDa0k4gmMglRyXiKliOtFJUXUFCaTzKRoqm2hXgkrn5CMZoPNSMl+PP9FFUU4uiToJdOmwTyfRRXFNJU24Iv6M0ZGLrzB2p31fHOU6v7JgyDgJbDbbz95CrCHRECfbJoAZweB5ZpkYwlGDN9lPJP9MJKS2aeq8wc3T6BtoYO5dy0JJ0tYSTK2Z5XHERaUjmhe2GmLWWOs8NEVzy9ivYmdQ3LtGhras9ECxVXFRGPJnPCHrtaQqRTafz5XiJdUdIpM8txnE6miUeTNB1qoaA0n2BRUE3Be9+r00GgwE/JqBIQgnBHLPuZSgi1hqgYX8b4OWMJFAVwOHtMRA7DQAionlSJP99PW0M70VAMw6GKjAuHwLQkdX1yZO7+fx9j8oIJjJ81ho986xYqxvX4J8y0iWVZmTIiAhXC3B3Jk06l2f7ebpKxJKlUmnQqTSKWoP5gI20NHVmvs+y6BZSNLsn4aUA52/es309hRQEIiZmyMFOqf0ZReQG71+7LmkVf9tEL+PqDX+Sc6xfTl2goyqGdR4iF40Q6IhzeVZcVwg1qAvGjP3+Hbz38Jf72l5/JuYYQgoWXz2HMjFFMWTSBMdNzs6otS0UiOt1OpZxPgEhXlIf+9Qke+eGTbH9vd2Z73b4GLNPKWcUIIXB7XOxZnx22fnDHYXav23dUP8YLv/wzTYdaVLXnfkgm0pjJNGbK7H9FPwLQisKm22nVd6DtrqGTiCdIRNVPMp4k0hUj2hWjqzVEe2M78WicaEeUSFeMSGeYRCRBe2Mnnc1dtDd0MnbGaFob2jMZvq1HWpmyaBL7txxkxjlT7NeVtj08RTqVJplIU1iejz/PS7DIT1FlQVaJe8MwmDhvHEf21lNaXUQ8ksiKxweomlCBZUm6mkMkE6ncCvl2uYRdq/dQNaEiJ2QXQEgoKMvDn++npKqIQEG2MpFSMvNcFaEVLApiOIysD3w6lSadTFM+ppTqiRWU9aoA25vi6iImL5hAIp5k+e/fzPJBuNwuXn3oTRLxJG6viuxxerIziz1+t1p9FQbx5/mUqa7X+ykticulfDYzzplCsCRItDN7dRQNxSgfW0rLkTYqxpWRVxTIeuYOp0HZ2FI6m7sYP2sMk+aNJ9gr+s20LKomVDDnwhmUVBXRdLCFQJ6PeDhOZ0uIrpYQ/qCXzpZw1uuWVhex8LLZjJlezbxL52TtGzdzNKXVxRRVFOD0qHDiospCZtuOTRXZZapMc6cDh8PA4XJiJnMHrpfuX07zoRb+9Lt+OvlJ5RgvqSqipLoot9qqTWtdGxuWb6GjOTtx0EybvPn4e0xfNoWS6iIqxpUxfvYY3no817xUPqaUBZfPzSpT0xu3x01RmVLmffMXpJTsXref2p11aiX85vacqDpQq4V1r25i19r+B3EzbbJ3fQ07V++jvbEje+dRUiYkZIXKtxxp5bF/f5YnfvIC+zcd7PecK++6hMKyAi7/+MX97h81uZIPf+NmPvz1m6iedPIK+70ftKJAzQh++49/4N7P/Ir7vv579mzoKSCWXxzEn+elrb6juzkVbfUd+PO85BUHKa4qIhlLk4ylsCwVTWSZkkQ0aduz1SzfSluZfuwAZsoiUOAj3B6luLKIa+6+LCvCRErlw7jpC9fidDlpOtiiEv56lV0oHV1Mw/4m4pEEcy+aiZk2M45pQL27EsbPHkNRVSHegLvfMMFAgQ+X28n5Ny/pO8EGVJjh1EUTM3kdvcOCARwuI2OyGju9mtFTq7KcoJZpUT62lNIxJeQV5/Gxf7iVcbOze/qOmlLJR751C4F8Py2HW4l2xckv6UmCyi8JEu2K0VrXRiqRoqi8gLLR2VFLJVXFlFQWYZkWS65ZQEFpPtLsuSHTNCkZVczSaxdQXFnE1AUTcwaQWDjOuTcuYf2rm5g0fzwLr5yrkriEGiAmzxvPkqvn4/a6aa1r59pPXZ6JGgPlZJ2ycCLX/bWKihs/eyyxqIp4AvV+dLZ05WSZtzd28t5za9m5ai+b39iWta+wrIAPfuUGCisKKSjNw5fn5epPXMq0JapOk8PpYOk1C3DbzmczbWEYgqlLJlJcWZh1rdkXTCdQFMysALtlnrZ4Eu2NHQQLA8y9eCZzL5pJsDBAW0MH05dOzjL9PPvzV3j1wTf48wNvZF27tb6dSHuUsdNHMWPZVGYsm8qoqdXs33SAVDJ7pvzbf/wD37rm+6x4ZhX9ESwKkE6ZeH3unFIj+zYe4KX7XqWgPF8VjmyP8Nh/PJeVK9Fa385D//I4yx9+m6f++0XefDw3lyceSRANRUknkoR6JQ6OmlypJjvJvh0VJal4iikLe/KG3F4VIdYdHt0fV991KT9b/SNu+9v+K9UKIRg3cwzjZo45KVVlh4KzXlG0N3bw5E9eIBFJZAr2PfM/L2fyIHxBH7MvnIGZtpCm6kNipk3mXjwTX8DLpHnjcLj6f4wev4fJiyYoW3Yyd0kZ6YxlHLITZo/NSQpyOB2UjirOJLy53K6sSJhAgZ90Mo3D6aB8bBnX3H0pxdU9ER4OQzBh7jiuvedyxk4fRWFZYY5tuvse5182hzkXzsAX9OZ8KoJFARZeNT8zWHiD2V8Ip6tn5r/s+kVIC4zevg4BSMnSa+bjcDoYPbWaf/vzd7Ou8Z+vfY8pC9QXsKiyEKfHmVUor7NFldwoLC/A4/cwemoVk+aOx+VxgVAyTVkwgapJKrronA8sZt4ls/D0KqURLAxw0W3nMnWxGmCTiZSKArO/m8IQlI8pJdIZJR5J4PV5qBhbxpUfv5g5F8zg3A8sYtHV8/Hn+fEGvDQebGbZtQsZO2MUo6ZVESwOMG3JRM67ZWkmKm7qoomZvhKZxyEEH/raTVnbLNPCNFVTm/7MD1MXTeJbD32JD3z2ar72u89z8e3nZQ3eC66ch8vjyhhhDKeDxVfPzxl47vjGLfzLM1/nU//vY1nbz7t5KYbToLWuHV+eD1+ej9Y65Rc598YlWcdWjlOVjMvHZRfR8/jURKS1rp0D22rZt+kAXS0h3F5Xlq/IsixWPLmKhppm3nz0Hfqj6VALhsNBV1s4p5zJ6pfXk1+Sx6Ir5rLo8jlMXTKJ9oYODu/uMecd3FZLMp6icnw5FWPL2Pjatr4vgdvronRUCUVVhVnVbX1BHxfddi7NtS2ZBMzuwIRJC8YzZnpPHa/8kjw++f07uPuHH2H01OPrjnc6ctYrit1r92GZFsHCAEII/Hk+nC4H23pFUiy7flHWTFxKybLrVQtJj8/DtEWTclrjGg7BvItm4Xa7OOeGRVm1fLox0ybLrlsIwIbXtpCKZdtyWw63cnh3PaG2MNWTK5BSku6lcLpaVVnr7oFuzoUz+dtffgbDjuSZe+ls7vrnDxEsDJBXFOSKj1+EP79PYpvbwYQ5Yzn3xsX4gj4WXjGHvKJeKwYBE+eOY2qvWZTX36eRTa97nzh3HNd+6jKcvez2QgjOu2UpS67psYsHCwLc8rfX4s/zculHL8hUDwXwB30su35BplghQDKW5NwPLMIf9DF5/ngMh0H15CpGT6uisLyA8bPGUDa2FJfbxbiZo3F7XNz5nduYd9kslYnsc3PFX13M1Z/saYdaVFbApHnjySsO4s/3UVRRyNgZo9UKQcrMaqO1voMje+qp2VpLylbmZiqdqT9UPrYMLCiuKEQIIyt02jAMvvXwF/Hm9SisD/7t9Yybkb2iKh9Tyhf/9x7+6p8/zOV3XkR/VE2o4KPfupW5/RRgfO/Z1RiGIL84SF5RAF/Aw9tPrspZQTqcDirHl+eEFpdUFXHnd25n6uKJtNW301rXxrSlk/n4d2+jpCo7vPSaey7jb35yFxffnp1lXFCaz5RFE4l0RXG4HLi8LsLtYZZetzArYa125xE8AY8K5U6Z/ZbYmH3BDC7+0Lnc8JkrswZxUEUTnW6nXTMqoJpjCbJCoYNFAUy7bEuoLZRV2qObwrICPvEvH+Yj37yVRVfNy9q3+Kp53PLl6wkU+Gk81EwynuSSO87nxs9dnZN8l1+cd9QQ3DOFs77MeCqpmsQ0H2nl4LZaRk2pwuPz9JgKUJEbLpczsxR1uZ3EeoWILr1+EZve3EZHU0/pjqKKQhZepWzNCy6fw/aVu9m5ak8mGsTtdXHNJy/NZLYe2nEEh9sJvQZHIQxa69qYsnAi/qCfSfPHc2jnYUApHbfHqcxKvQamYGEAp9NJMp3C6/dkfaivvfsyWmpbefy/nse0FdfoqdV86v99LPNlPP/mZbz+x3cBFani8jgZM606k4vRLXtvPH1s2bPPn87E+ePY/o6qvVQ9oYLFV87PiajKKwyy5JoFOTkpAFd+/GJe+e0bhDrUqiKQ7+fyj12YebYXfnAZbz2+kpKqIixTUlCaT3t9O9fcc1nG7u0LePn2Q19m69s78PjdzDp/etYsfOGVc9m+cjf5ZXlIS2IYBpUTypmycCJ7Fk7g4LbDFFUU0tbQTjyaIJlIEQ/HcRe7SMSSzDhHmW9u+vzV7N9UQ8OBZs6/ZRmTF0zIupeKseX802Nf5bf/+CiT54/nE9/P7gPRzbxLZve7/VikU2n2bqhh9LRR1O9vQFqS4uoiol0x2ho6cgb6o1FSVcT1n76S6/5aVVo9mhnE4XBkKfbeXPepyymqKGDj61txe9wsuXY+C6+Ym3XMa394h7kXzsAXVKuWzW9t57w+qxav38OFt57T72vMPHcqbz+1Cu94jyrgGEtiGAajp/TY9ycvmMC5H1jEhuVbKa4s5IY+TYt6rjWt3+1CCKYtnsS0xZNUIIEQI9YsdCo46xXF5AUTeO/ZNRzZ3aBKbOw8wpjpKimrm2lLJxMoDGRyJwIFgazaPOd+YBFP//eLhDsimGkLp8tJ9aRK5l6k0u0Nw+Aj37yFda9uYtuKXSDg6rsv45wbeqJGqiZW4A/6MuU2ECr3orC8gGBhgEnzx7Hhta0UVRTS3tiB0+XE5XUT7YplfRETkQSGw4HLLe1Odj24vW7u/uFHMZwGz//yz5SNLuGHL32bkqoeJTD7guksuWo+K55eRSqRYsLssdz4+WuyviQzz5vKulc3k0qmMAyDSfOzB0aAMVOqCOT7ScSSlI4qyokuAlh89XzC7VGWXb8wZ19eUZAZ50yhZsshtaqZM468XrHuy65fRNWkSja+toWWunYqx5Wx4PI5Oc7AQL4/s/rrS8W4Mj767Vt574W1tB5pZ+LcsSy9biFuj4tl1y9iz/oa4pEEE2aNobO5i7wiVfa6pa6NivFljJupVgVlo0v54Uv/qGqCHWUwWXz1AhZdlWsKGjoEUxZNoGpCOZZlkV+aR3t9x4ld6X3I6Pa6ufj287j49vOO+RqZqKJBVmVdfPV86vc3sn/zIbswpeD6T1+RVefJMAwu+dD5Ocl+J8L7zao+ExAnUjp3JLN48WK5du3aYx/Yi42vb+XZn/2J+v2NlI0u4YbPXMnS6xZmfWE2vbmN//vnxxCG4OPfuT2nQuhfHnqLx/7zObpauiiqLOST/3oHS6/NHgD3bT7Ir/7+Qbx+D1+9/7NZS+rOli5++oX7WfPKJlLxJMHiINd/6gru/O5tOBwOQu1hHvjuH1n7pw0qkc2SBAoD3P7VD3DZRy/MyGpZFr/8ygPsXL2Xz977SWYsHXwT967WEG889i6RjihLrp2f6dSW2d8W4kcf/yl7N9RQVF7A3//2s1kNcABWPLOa955dA0imLZnM9Z+5clD1cqSU/OX3b7LxdZUrMP+yOVxx50WndFa3b9MBnv/FK6QSaVyeHkdx1cRybvnSdSdct+dk8MoDr7PpjW2Znupt9e2Uji7mzu/cPuJmwge31/LUf7+ImbYoKMvnjm/cfNTqrUdDSknLkbZMlFrvDoqaE0MIsU5KmRvzjFYUGeLRBF0tXeQVB/t1+B4L0zRZv3wLdXsbmDB7LHMunNHvF7TlSCsujyvH7gpqAF73ykbq9jcy45ypzL5gBm5Pj5knmUjx3nNrWPnCOoKFfq65+3Imzh03LANBMp6kraGDvKJAv7X+pZ1kl06ZlI4qPqFZmXKMqgS8kuriYbnPeDTB3g01NNQ04fI4mTRvPNWTK0fcLDMRS/Dq/73JzlV7kUjGTKvmuk9dkanPNdKIdEYItUcorizMCsPVDB9aUWg0ZwmxcAwzbREo8I+4lYRmZDOQohhZ06KjIIS4RgixSwixVwjxzeGWR6MZqfiCvkwEn0YzVIx4RSGEcAA/A64FZgIfEUIcf4NmjUaj0bwvRryiAJYCe6WU+6WUSeBR4KZjnKPRaDSaIeJ0UBSjgNpe/x+2t2UQQnxaCLFWCLG2ubkZjUaj0Qwdp4Oi6M/YmuWBl1LeJ6VcLKVcXFZW1s/hGo1GozlRTgdFcRjoXUFtNFA3TLJoNBrNWcfpoCjWAFOEEBOEEG7gDuC5YZZJo9FozhpOizwKIcR1wL2AA/itlPIHAxzbDPRfGP74KAVGZpupbLScQ8vpIiecPrJqOYeWky3nOCllv7b700JRnEqEEGuPlnQyktByDi2ni5xw+siq5RxahlPO08H0pNFoNJphRCsKjUaj0QyIVhS53DfcAhwnWs6h5XSRE04fWbWcQ8uwyal9FBqNRqMZEL2i0Gg0Gs2AaEWh0Wg0mgE5axWFEGKMEOJ1IcQOIcQ2IcSX7e3FQohXhRB77N/D2jV9ADm/J4Q4IoTYaP9cN8xyeoUQq4UQm2w5/9nePqKe5zFkHVHPtBshhEMIsUEI8YL9/4h7ptCvnCP1eR4QQmyxZVprbxtxz/Qocg7LMz1rfRRCiCqgSkq5XgiRB6wDbgY+AbRJKX9k974oklJ+YwTK+SEgLKX8z+GSrTdCNUAISCnDQggXsAL4MnArI+h5HkPWaxhBz7QbIcRXgMVAvpTyBiHEvzPCnin0K+f3GJnP8wCwWErZ0mvbiHumR5HzewzDMz1rVxRSynop5Xr77xCwA1WV9ibgQfuwB1GD8rAxgJwjCqkI2/+67B/JCHueMKCsIw4hxGjgeuD+XptH3DM9ipynEyPumY4kzlpF0RshxHhgAbAKqJBS1oMapIHyYRQtiz5yAnxBCLFZCPHbEbJUdgghNgJNwKtSyhH7PI8iK4ywZ4oqXfN1wOq1bSQ+03vJlRNG3vMENSn4sxBinRDi0/a2kfhM+5MThuGZnvWKQggRBJ4E/lZK2TXc8hyNfuT8BTAJmA/UAz8ePukUUkpTSjkfVeF3qRBi9jCLdFSOIuuIeqZCiBuAJinluuGU41gMIOeIep69OF9KuRDVNfPzQoiLhlugo9CfnMPyTM9qRWHbp58EHpZSPmVvbrT9At3+gabhkq+b/uSUUjbag50F/BrVCXBEIKXsAN5A2fxH3PPsTW9ZR+AzPR+40bZVPwpcJoR4iJH3TPuVcwQ+TwCklHX27ybgaZRcI+2Z9ivncD3Ts1ZR2A7N3wA7pJQ/6bXrOeAu+++7gGdPtWy9OZqc3R9qm1uAradatt4IIcqEEIX23z7gCmAnI+x5wtFlHWnPVEr5LSnlaCnleFR5/deklHcywp7p0eQcac8TQAgRsINCEEIEgKtQco2oZ3o0OYfrmTpPxYuMUM4HPg5ssW3VAN8GfgQ8JoS4BzgE3D484mU4mpwfEULMR9kxDwCfGQ7helEFPCiEcKAmII9JKV8QQrzHyHqecHRZfz/CnunRGGmf0aPx7yPweVYAT6v5F07gESnln4QQaxhZz/Rocg7LZ/SsDY/VaDQazfFx1pqeNBqNRnN8aEWh0Wg0mgHRikKj0Wg0A6IVhUaj0WgGRCsKjUaj0QyIVhQajUajGRCtKDSaE0AIUSSEiAshpBDizuGWR6M5mWhFodGcGB8D3EANcM8wy6LRnFR0wp1GcwIIITYAbahSD/cCU6SU+4ZVKI3mJKFXFBrNIBFCLERV73wQeBhIAZ/s5ziHEOI7QoiDtplqsxDiw3aXMmmXje99fJUQ4hdCiENCiKQQok4IcZ8QYiSUvNacxegVhUYzSIQQP0MVjquQUkaEEE8BS4BxdlXP7uN+AfwN8Dqq+mcZ8HmUuWoRMEFKecA+dizwHsqc9RtgHzAZ+CzQiOp01nlKblCj6YNWFBrNIBBCeIE64Dkp5SfsbTcBzwDXSSlftrfNQlX2fMXebtnb5wAbUav53oriWeBcYKGU8nCv11sMrAS+L6X83km/QY2mH7TpSaMZHLcCRfS0zQR4EdW/4O5e226wf/9371WGlHILSnlkEEIU2Mc/B8SFEKXdP6gKoXtRZaY1mmHhbC4zrtGcCPcAzcBhIcTkXttfBW4XQpRKKVuACfb2Xf1cYxeqa1k301CTtns4egTV/vcltUbzPtCKQqM5ToQQE4BLAQHsPsphd6KioMRgLm3/fojslUpvYoO4nkYzpGhFodEcP59EDep/DXT0s//7qBXBvSiHNajVQt/VwLQ+/+9FNaJxSyn/MkSyajRDhnZmazTHgRDCQPkLOqSUc49yzD8B30P1MY4yOGf2C8DVwIVSypV9riuAUill81Dfl0ZzPGhntkZzfFwFjAGeHOCY7n33SCm3AfehBv+/CCG+KIT4F+ANYIN9XO9Z2mdR0VRvCSHuF0J83j7nv1Chsp8fulvRaAaHXlFoNMeBEOJx4DZgrh25dLTjdqH6HVcBSeAfUeaoCpQT+/uoFcdXUXkYTb3OLQW+AdwEjAXiQC3wGvArKeX2ob8zjebYaEWh0ZxihBDPA5cB+VJKc7jl0WiOhTY9aTQnCSGEr59tc1Ghsa9pJaE5XdArCo3mJCGE+Bvgr1AJec3AdODTqAna+VLKDQOcrtGMGLSi0GhOEkKIpcC/ogoIFgMhYAXwz1LKdcMomkYzKLSi0Gg0Gs2AaB+FRqPRaAZEKwqNRqPRDIhWFBqNRqMZEK0oNBqNRjMgWlFoNBqNZkD+Pz8gOLu3atCIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "area = np.pi * ( X[:, 1])**2  \n",
    "plt.scatter(X[:, 0], X[:, 3], s=area, c=labels.astype(np.float), alpha=0.5)\n",
    "plt.xlabel('Age', fontsize=18)\n",
    "plt.ylabel('Income', fontsize=16)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D \n",
    "fig = plt.figure(1, figsize=(8, 6))\n",
    "plt.clf()\n",
    "ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n",
    "\n",
    "plt.cla()\n",
    "# plt.ylabel('Age', fontsize=18)\n",
    "# plt.xlabel('Income', fontsize=16)\n",
    "# plt.zlabel('Education', fontsize=16)\n",
    "ax.set_xlabel('Education')\n",
    "ax.set_ylabel('Age')\n",
    "ax.set_zlabel('Income')\n",
    "\n",
    "ax.scatter(X[:, 1], X[:, 0], X[:, 3], c= labels.astype(np.float))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "k-means will partition your customers into mutually exclusive groups, for example, into 3 clusters. The customers in each cluster are similar to each other demographically.\n",
    "Now we can create a profile for each group, considering the common characteristics of each cluster. \n",
    "For example, the 3 clusters can be:\n",
    "\n",
    "- AFFLUENT, EDUCATED AND OLD AGED\n",
    "- MIDDLE AGED AND MIDDLE INCOME\n",
    "- YOUNG AND LOW INCOME"
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    "## Want to learn more?\n",
    "\n",
    "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: [SPSS Modeler](http://cocl.us/ML0101EN-SPSSModeler).\n",
    "\n",
    "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at [Watson Studio](https://cocl.us/ML0101EN_DSX)\n",
    "\n",
    "### Thanks for completing this lesson!\n",
    "\n",
    "Notebook created by: <a href = \"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>\n",
    "\n",
    "<hr>\n",
    "Copyright &copy; 2018 [Cognitive Class](https://cocl.us/DX0108EN_CC). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/).​"
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