Fuzzy_c_means_algorithm_implementation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5dQCo_BHL1ky"
   },
   "source": [
    "# **Importing required packages**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "dpMJcpsXFbNn"
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'pandas'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-b92b1ec33cd6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m \u001b[0;31m# reading all required header files\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0moperator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pandas'"
     ]
    }
   ],
   "source": [
    "import pandas as pd # reading all required header files\n",
    "import numpy as np\n",
    "import random\n",
    "import operator\n",
    "import math\n",
    "import matplotlib.pyplot as plt \n",
    "from scipy.stats import multivariate_normal \n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wgK_wglI-BYk"
   },
   "source": [
    "# **Load Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "UKxMoTGvnmPb"
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "from google.colab import files\n",
    "uploaded = files.upload()\n",
    "df_full = pd.read_csv('Iris.csv')\n",
    "df = df_full.drop(columns=['Id'])\n",
    "df.shape\n",
    "df.head()\n",
    "df = df.drop(columns=['Species'])\n",
    "df.head()\n",
    "\"\"\"\n",
    "\n",
    "iris = load_iris()\n",
    "df = pd.DataFrame(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "UhKJbYvmnxFZ",
    "outputId": "08c45cd6-f9ef-42b4-87a8-60f8396c0bc5"
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3\n",
       "0  5.1  3.5  1.4  0.2\n",
       "1  4.9  3.0  1.4  0.2\n",
       "2  4.7  3.2  1.3  0.2\n",
       "3  4.6  3.1  1.5  0.2\n",
       "4  5.0  3.6  1.4  0.2"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7MsuuAC4tX3u"
   },
   "source": [
    "# **Defining parameters**\n",
    "\n",
    "Data = { x<sub>1</sub>, x<sub>2</sub>, ... , x<sub>n</sub> }\n",
    "<br>\n",
    "Cluster = { C<sub>1</sub>, C<sub>2</sub>, ... , C<sub>k</sub> }\n",
    "\n",
    "n = number of data<br>\n",
    "k = number of clusters<br>\n",
    "d = dimension of data<br>\n",
    "m = fuzzy parameter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "fWiSJnFJoczP"
   },
   "outputs": [],
   "source": [
    "#number of data\n",
    "n = len(df)\n",
    "\n",
    "#number of clusters\n",
    "k = 3\n",
    "\n",
    "#dimension of cluster\n",
    "d = 4\n",
    "\n",
    "# m parameter\n",
    "m = 2\n",
    "\n",
    "#number of iterations\n",
    "MAX_ITERS = 12\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ngyZiwoq-NU2"
   },
   "source": [
    "# **Visualizing Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 711
    },
    "id": "BzA-5UfRQ_Xs",
    "outputId": "8b20adf0-4e09-48d6-bad1-18e4ce4d00f1"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(0,figsize=(5,5))                #scatter plot of sepal length vs sepal width                              \n",
    "plt.scatter(list(df.iloc[:,0]), list(df.iloc[:,1]), marker='o')       \n",
    "plt.axis('equal')                                                                 \n",
    "plt.xlabel('Sepal Length', fontsize=16)                                                 \n",
    "plt.ylabel('Sepal Width', fontsize=16)                                                 \n",
    "plt.title('Sepal Plot', fontsize=25,color='b')                                            \n",
    "plt.grid()                                                    \n",
    "plt.show()\n",
    "\n",
    "plt.figure(1,figsize=(5,5))                #scatter plot of sepal length vs sepal width                              \n",
    "plt.scatter(list(df.iloc[:,2]), list(df.iloc[:,3]), marker='o')       \n",
    "plt.axis('equal')                                                                 \n",
    "plt.xlabel('petal Length', fontsize=16)                                                 \n",
    "plt.ylabel('petal Width', fontsize=16)                                                 \n",
    "plt.title('Petal Plot', fontsize=25,color='b')                                            \n",
    "plt.grid()                                                                 \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uBWvWxxKVLNa"
   },
   "source": [
    "# **Initializing Membership Matrix**\n",
    "\n",
    "### <b>w<sub>ij</sub></b> values are assigned randomly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "UIP8HmGVbKIv"
   },
   "outputs": [],
   "source": [
    "def initializeMembershipWeights():\n",
    "  \"\"\"\n",
    "  membership_mat = []\n",
    "  for i in range(n):\n",
    "    wts = []\n",
    "    sum=0;\n",
    "    for j in range(k):\n",
    "      weight = np.random.random_integers(1,10)\n",
    "      wts.append(weight)\n",
    "      sum = sum + weight\n",
    "    weights = [w/sum for w in wts]\n",
    "    membership_mat.append(weights)\n",
    "    print(membership_mat)\n",
    "\n",
    "    \"\"\"\n",
    "  weight = np.random.dirichlet(np.ones(k),n)\n",
    "  weight_arr = np.array(weight)\n",
    "  return weight_arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8hEKQCJM-uMP"
   },
   "source": [
    "# **Calculating Cluster Center**\n",
    "\n",
    "### To calculate centroids for each cluster we apply the following formula:\n",
    "\n",
    "## C<sub>j</sub> = $ \\frac{\\sum_{i=1}^{n} W_{ij}^m x_{i}}{\\sum_{i=1}^{n} W_{ij}^m} $\n",
    "\n",
    "### m(fuzzy-ness) ranges from 1 to $\\infty$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "hj0GFJDSMN2S"
   },
   "outputs": [],
   "source": [
    "def computeCentroids(weight_arr):\n",
    "  C = []\n",
    "  for i in range(k):\n",
    "    weight_sum = np.power(weight_arr[:,i],m).sum()\n",
    "    Cj = []\n",
    "    for x in range(d):\n",
    "      numerator = ( df.iloc[:,x].values * np.power(weight_arr[:,i],m)).sum()\n",
    "      c_val = numerator/weight_sum;\n",
    "      Cj.append(c_val)\n",
    "    C.append(Cj)\n",
    "  return C "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GDQ4HXcQ-y5o"
   },
   "source": [
    "# **Updating Membership Value**\n",
    "\n",
    "### Calculate the fuzzy-pseudo partition with the above formula\n",
    "## $$w_{ij} = \\frac{(\\frac{1}{dist(x_i, c_j)})^{\\frac{1}{m-1}}}{\\sum_{s=1}^{k}(\\frac{1}{dist(x_i,c_s)})^{\\frac{1}{m-1}}}w \n",
    "ij$$\n",
    "\n",
    "\n",
    "### where $dist (x_i, c_j)$ is the Euclidean distance between $x_{i}$ and $c_{j}$ cluster center."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "25HVFjJRNegs"
   },
   "outputs": [],
   "source": [
    "def updateWeights(weight_arr,C):\n",
    "  denom = np.zeros(n)\n",
    "  for i in range(k):\n",
    "    dist = (df.iloc[:,:].values - C[i])**2\n",
    "    dist = np.sum(dist, axis=1)\n",
    "    dist = np.sqrt(dist)\n",
    "    denom  = denom + np.power(1/dist,1/(m-1))\n",
    "\n",
    "  for i in range(k):\n",
    "    dist = (df.iloc[:,:].values - C[i])**2\n",
    "    dist = np.sum(dist, axis=1)\n",
    "    dist = np.sqrt(dist)\n",
    "    weight_arr[:,i] = np.divide(np.power(1/dist,1/(m-1)),denom)\n",
    "  return weight_arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "33aHhdO6-68y"
   },
   "source": [
    "# **Plot data for visualizing algorithm**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "SG1F7W3oOnkT"
   },
   "outputs": [],
   "source": [
    "def plotData(z,C):  \n",
    "  plt.subplot(4,3,z+1)              #scatter plot of sepal length vs sepal width                              \n",
    "  plt.scatter(list(df.iloc[:,2]), list(df.iloc[:,3]), marker='o')    \n",
    "  for center in C:\n",
    "    plt.scatter(center[2],center[3], marker='o',color='r')        \n",
    "  plt.axis('equal')                                                                 \n",
    "  plt.xlabel('Sepal Length', fontsize=16)                                                 \n",
    "  plt.ylabel('Sepal Width', fontsize=16)                                                                                      \n",
    "  plt.grid()                                                  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rKQ0nkl7LTCP"
   },
   "source": [
    "# **Fuzzy algorithm**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "xVczx5dVVQP7"
   },
   "outputs": [],
   "source": [
    "def FuzzyMeansAlgorithm():\n",
    "  weight_arr = initializeMembershipWeights()\n",
    "  plt.figure(figsize=(50,50)) \n",
    "  for z in range(MAX_ITERS):\n",
    "    C = computeCentroids(weight_arr)\n",
    "    updateWeights(weight_arr,C)\n",
    "    plotData(z,C)\n",
    "  plt.show()\n",
    "  return (weight_arr,C)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PueVJoRfLa1f"
   },
   "source": [
    "# **Running algorithm**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "8rfS9w_hZ-Sz",
    "outputId": "d2343af0-4cc1-4055-f4c0-57d3924873e3"
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 3600x3600 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_weights,Centers = FuzzyMeansAlgorithm()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V_iNLjIcLioE"
   },
   "source": [
    "# **Visualizing Cluster final centers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "id": "-jsT6LREoS4t"
   },
   "outputs": [],
   "source": [
    "df_sepal = df.iloc[:,0:2]\n",
    "df_petal = df.iloc[:,2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 711
    },
    "id": "Bzq-URbNiXlG",
    "outputId": "387687f3-342c-49af-94bf-ff9afcc790f2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(0,figsize=(5,5))                #scatter plot of sepal length vs sepal width                              \n",
    "plt.scatter(list(df_sepal.iloc[:,0]), list(df_sepal.iloc[:,1]), marker='o')       \n",
    "plt.axis('equal')                                                                 \n",
    "plt.xlabel('Sepal Length', fontsize=16)                                                 \n",
    "plt.ylabel('Sepal Width', fontsize=16)                                                 \n",
    "plt.title('Sepal Plot', fontsize=25,color='b')                                            \n",
    "plt.grid() \n",
    "for center in Centers:\n",
    "  plt.scatter(center[0],center[1], marker='o',color='r')                                                                \n",
    "plt.show()\n",
    "\n",
    "plt.figure(1,figsize=(5,5))                #scatter plot of sepal length vs sepal width                              \n",
    "plt.scatter(list(df_petal.iloc[:,0]), list(df_petal.iloc[:,1]), marker='o')       \n",
    "plt.axis('equal')                                                                 \n",
    "plt.xlabel('petal Length', fontsize=16)                                                 \n",
    "plt.ylabel('petal Width', fontsize=16)                                                 \n",
    "plt.title('Petal Plot', fontsize=25,color='b')                                            \n",
    "plt.grid()                     \n",
    "for center in Centers:\n",
    "  plt.scatter(center[2],center[3], marker='o',color='r')                                                       \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WoIYt4sTLr_E"
   },
   "source": [
    "# **Visualizing clusters** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 527
    },
    "id": "ghNnX97eicCi",
    "outputId": "d7ef5c1d-5751-4c64-b705-b4f70349c0a9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CpqdPEIYVUqls3dtTqSxhWGF6+kSbK1NKdTpt7EqtAM87im3nm46x7Tyed7RNFSmluoU2dqVWQBh6iDhNx4g4hKHuildKRaONXakVYFkuxvhNxxjjY1l6ZLxSKhpt7EqtANfdSqVSaDqmUingulvbVJFSqltoY1dqBWQyG7EsmyCYqnt7EExhWTaZzMY2V6aU6nTa2JVaAamUSz4/gjHT+P4pwnAGYwxhOIPvn8KYafL5EQ2pUUpFpgE1Sq2QdHqQwcE9c8lzQVDAslxyue2aPKeUumja2JVaQamUSza7jWx220qXopTqErorXimllOoi2tiVUkqpLqKNXSmllOoi2tiVUkqpLqKNXSmllOoi2tiVUkqpLqKNXSmllOoi2tiVUkqpLqKNXSmllOoi2tiVUkqpLqKNXSmllOoimhWvVAuCwJs7WUulEnDmzO247lY9WYtSKnG0sSu1iHJ5nEJhlDCsYNt5RHxEHIrFw5RKR8jnR0inB1e6TKWUAnRXvFJNBYFHoTCKSAbHGcayegDBsnpwnGFEMhQKowSBt9KlKqUUoI1dqaamp08QhhVSqWzd21OpLGFYYXr6RJsrU0qp+rSxK9WE5x3FtvNNx9h2Hs872qaKlFKqOW3sSjURhh4iTtMxIg5hqLvilVLJoI1dqSYsy8UYv+kYY3wsS4+MV0olgzZ2pZpw3a1UKoWmYyqVAq67tU0VKaVUc9rYlWoik9mIZdkEwVTd24NgCsuyyWQ2trkypZSqTxu7Uk2kUi75/AjGTOP7pwjDGcAQhjP4/imMmSafH9GQGqVUYmhAjVKLSKcHGRzcM5c8Z0yAMYZcbrsmzymlEkcbu0q0+VGuYehhWe6KRLmmUi7Z7Day2W3Y9gGGhna37b6VUioKbewqsRZGudp2H8b4GuWqlFJN6HvsKpHqRbmKaJSrUkotRhu7SiSNclVKqYujjV0lkka5KqXUxdHGrhJJo1yVUuriaGNXiaRRrkopdXG0satE0ihXpZS6ONrYVSJplKtSSl0cbewqkepFuRqjUa5KKbUYDahRibUwyjUICliWq1GuSinVhDZ2lWjzo1xVciJ2lVLJtSK74kUkJSJfFpE76tzWIyKfFpEnRORLIrKp/RUqlTzl8jjj43dRLB5GpAfbXodID8XiYcbH76JcHl/pEpVSCbBS77H/PPC1Brf9JDBhjLkS+APgt9tWlVIJpRG7SqlWtb2xi8jzgZuATzYY8hrgL2vffwa4QUSkHbUplVQasauUatVKvGL/MPCrQNjg9g3A0wDGmArwLLC2PaUplUwasauUapUYY9p3ZyI3A3uMMW8Vkd3A240xNy8Y81XgRmPM12uXnwReZow5s2Dcm4E3AwwPD19/2223tWMJS1YsFsnlcitdRmy6fX2wMmusVCYQsYFmO68MxlSw7YEl3Ve3P4bdvj7o/jV2+/qgtTWOjIw8aIzZsfD6dh8Vvwt4tYjsATJAv4j8tTHmh+eNOQlcBnxdqv+TrQHOLpzIGPMJ4BMAO3bsMLt374679mVx4MABOqXWi9Ht64OVWeOZM7cj4mBZPQ3HzH7Wf2ho95Luq9sfw25fH3T/Grt9fbC0NbZ1V7wxZp8x5vnGmE3ADwD/saCpA9wO/Fjt+9fVxrRvt4JSCaQRu0qpViUieU5E3icir65d/FNgrYg8AfwS8I6Vq0ypZNCIXaVUq1YsoMYYcwA4UPv+PfOunwZevzJVKZVMsxG7hcIovl/EtvOIOBjjU6kUsCxbI3aVUoAmzynVMTRiVynVCm3salXy/XEmJw8xOXmQICiSSuXo799Ff/9OHGdwpctrSCN2lVKL0cauVp1S6UnGxvYThj7p9DocZy1h6DE+fg+Fwijr1++lt/eKlS5TKaUuSiIOnlOqXXx/nLGx/VhWL667GdvOYlkWtp3FdTdjWb2Mje3H9zV3XSnVmbSxq1VlcvJQ7ZV6/RCXdHqAMPSZnDzU5sqUUmp5aGNXq8rk5EHS6XVNx6TT65icPNimipRSanlpY1erShAUsazmR49blksQFNtUkVJKLS9t7GpVSaVyhGHzU5uGoUcq1d051Eqp7qWNXa0q/f27KJdPNx1TLp+mv39XmypSSqnlpY1drSr9/TuxLIdyeaLu7eXyBJbl0N+/s82VKaXU8tDGrlYVxxlk/fq9hGEJzztOpTJFGIZUKlN43nHCsMT69XsTHVKjlFLNaECNWnV6e6/gBS/YN5c85/unSaVyDA6+KvHJc0optRht7GpVcpxBhob2MDS0Z6VLUUqpZaWNXbWd553k7Nk7KBTuIwjOkUr1kc/fwNq1N+O6G1a6vLYKAm/upC5h6GFZLq67VU/qolQbeWWPE8+e4OjZo3hlDzftsnXtVjau2YibvvjnYVzzLkYbu2qryckHeeqpWwlDH8cZJpVaizElzpz5LOPjd7Jp0y3091+/0mW2Rbk8TqEwShhWsO08tt2HMT7F4mFKpSPk8yOk0/q2gFJxGvfGGT0+SiWskM/k6XP68AOfw988zJHTRxjZPMKgG/15GNe8rdCD51TbeN5JnnrqViwrS2/vldh2H6lUCtvuo7f3Siwry1NP3YrnnVzpUmMXBB6FwigiGRxnGMvqQUSwrB4cZxiRDIXCKEHQ/DP3SqmL55U9Ro+PkrEzDOeG6bGrz8Meu4fh3DAZO8Po8VG8crTnYVzztkobu2qbs2fvqL1SX1v39upZ1nzOnr2jzZW13/T0CcKwQiqVrXt7KpUlDCtMT59oc2VKrR4nnj1BJayQdeo/D7NOlkpY4cSz0Z6Hcc3bKm3sqm0KhftwnOGmYxxnmELhvjZVtHI87yi2nW86xrbzeN7RNlWk1Opz9OxR8pnmz8N8Js/Rs9Geh3HN2ypt7KptguAcIr1Nx4j0EgTn2lTRyglDDxGn6RgRZ9H4W6XUxfPKHk6q+fPQSTkXtSs+jnlbpY1dtU0q1YcxpaZjjCmRSvW1qaKVY1kuxvhNxxjjL3rCGqXUxXPTLn7Q/HnoB37kI9jjmrdV2thV2+TzN+D7p5qO8f1T5PM3tKmileO6W6lUCk3HVCoFXHdrmypSavXZunYrhenmz8PCdIGta6M9D+Oat1Xa2FXbrF17M5bl4Ptn697u+2exLIe1a29uc2Xtl8lsxLJsgmCq7u1BMIVl2WQyG9tcmVKrx8Y1G7Etmym//vNwyp/Ctmw2ron2PIxr3lZpY1dt47ob2LTpFsJwilLpCSqVcwRBQKVyjlLpCcJwik2bblkVITWplEs+P4Ix0/j+KcJwBmMMYTiD75/CmGny+RENqVEqRm7aZWTzCNOVaU4VTzFTqT4PZyoznCqeYroyzcjmkYvaFR/HvK3SgBrVVv3917Nly8fnJc+dJpXqY2jotasueS6dHmRwcM9c8lwQFLAsl1xuuybPKdUmg+4ge7bsmUuIK0wXcNMu2y/ZvqSEuLjmbYU2dtV2jjPIwMC3k8lcel6M6lJPvhIlnjUpUa6plEs2u41sdlvb7lMpdT437bJtaBvbhpb3eRjXvIvRxq7aKq4Y1SjzapSrUqqb6Xvsqm3iilGNMq9GuSqlup02dtU2ccWoRplXo1yVUt1OG7tqm7hiVKPMq1GuSqlup41dtU1cMapR5tUoV6VUt9PGrtomrhjVKPNqlKtSqttpY1dtE1eMapR5NcpVKdXttLGrtokrRjXKvBrlqpTqdtrYVdvEFaMaZV6NclVKdTsNqFFtFVeMapR5NcpVKdXNtLEnTFKiTi82nrVSCThz5vamNccVoxplXo1yVao1Xtmbyzv3yh5u2mXr2q2x552ri6eNPUGSEnW6lHhWER8RR+NZleoC4944o8dHqYQV8pk8fU4ffuBz+JuHOXL6CCObRxh09fmdNPoee0IkJep0qfGsoPGsSnUDr+wxenyUjJ1hODdMj139v6DH7mE4N0zGzjB6fBSvrM/vpNHGnhBJiTrVeFalFMCJZ09QCStknfrP76yTpRJWOPGsPr+TJnJjF5F1IvKChV9xFLeaJCXqVONZlVIAR88eJZ9p/vzOZ/IcPavP76Rp6T12EekH/hB4A9DTYFhquYpajcLQw7b7mo4RcQiC5uEq7a4jCTUrpZafV/boc5o/v52UQ2Fan99J0+rBcx8Fvg/4U+ArwExsFa1Ss1GnIo3+bmpP1GnUOpJQs1Jq+blpFz/w6bEbP7/9wNcj4xOo1cZ+I/ArxpiPxlnMaua6WykWD+M4ww3HVCoFcrntiaojCTUrpZbf1rVbOfzNwwznGj+/C9MFtl+iz++kifIe+2OxVaESE3Wq8axKKYCNazZiWzZTfv3n95Q/hW3ZbFyjz++kabWx3wZ8T5yFrHZJiTpdajwraDyrUt3ATbuMbB5hujLNqeIpZirV/wtmKjOcKp5iujLNyOYR3RWfQA13xYvId8y7+O/Ah0WkD7gLGF843hjzH8tf3uqSlKjTpcSzGhNgjNF4VqW6wKA7yJ4te+aS5wrTBdy0y/ZLtmvyXII1e4/9c4ABZN6/m4Efnzdm/u16VPwySErU6cXGs9r2AYaGdsdfoFKqLdy0y7ahbWwb0vjlTtGssY+0rQrV0Xx/nMnJQ0xOHmRmZhvHjr2L/v5d9PfvxHEujJucPz4IiqRSuYbjk5id30oevlJKrZSGjd0Y85/LfWcikgH+i+pn4W3gM8aYWxaM+XHgd4GTtav+yBjzyeWuRS2PUulJxsb2E4Y+6fQ6RBxEehkfv4dCYZT16/fS23tFw/GOs5Yw9OqOT2p2vubhK6WSrKWD50TkmIjU/UyDiLxIRI61eH8zwHcYY7YD1wI3isjOOuM+bYy5tvalTT2hfH+csbH9WFYvrrsZ284iIth2FtfdjGX1Mja2H98fbzjesqy645Ocna95+EqpJGv1qPhNNE6cywAtfd7BVBVrF9O1L9NiDSphJicP1V55D9S9PZ0eIAx9JicPRR6flBz6pNShlFKtivI59kYNeAfQcqagiKRE5GHgNHCvMeZLdYZ9n4g8IiKfEZHLItSo2mhy8iDp9LqmY9LpdUxOHow8Pik59EmpQymlWiXG1O/XIvKLwC/WLm4AngH8BcNcYBC4zRjzQ5HuWCQPfBb4OWPMV+ddvxYoGmNmROQtwBuMMd9R5+ffDLwZYHh4+Prbbrstyt2vmGKxSC6XW+kylsXMzNO199Rl7rrp6TSZTHnusjEGY3x6ei6rO36h2fGpVA4Rm+qHLhqOxpgKtl1/D8ByqFQmLqijVAro7Z3/IZD462inbvodrafb1wfdv8ZuXx+0tsaRkZEHjTE7Fl7frLG/Bvje2sUfo/r59WcWDJsB/i/wSWNMKWLdiMh7gJIx5kMNbk8B48aYNc3m2bFjh3nggQei3v2KOHDgALt3717pMpbFsWPvQqQX235uN/Wjj65n27axucuVyhTGlLj88g/UHb/Q7Pj+/pch0lN7T7u+anjODENDr16eBdVx5sztF9Tx0ENFXvzi555w7aijnbrpd7Sebl8fdP8au3190NoaRaRuY292VPy/AP9S+2GA9xljji+lUBF5HlA2xhRExAVeCfz2gjGXGmO+Ubv4auBrS7lPFZ/+/l2Mj9+DbW9uOKZcPs3g4Ksij3fdyxORQ5+UDH+llGpVS++xG2PetNSmXnMpMCoijwD3U32P/Q4ReZ+IzL7ceZuIHBGRw8DbOD8QRyVIf/9OLMuhXJ6oe3u5PIFlOfT374w8Pik59EmpQymlWtUsUvY9EeYxxpjfaGHQI8B1da5/z7zv9wH7Ity3WiGOM8j69XsZG9uP5xVIp9dhjKFSmaJcPo1lOaxfv3cudKbeeMtyCUOv7vh8foRCYRTfL9Y+P+5gjE+lUsCy7LZm58+vYzYPv511KKVUq5olz713weXZ+NiFZt+kX7Sxq+7T23sFL3jBvrkkOWN8jCkxOPiquklyC8f7/mlSqVzd8UnNztc8fKVUkjV7j31uN72I/C/gduATVM/0dgoYBn4Q+Gng5njLXD3ijFCNEuUap1TKxXUvByrz1nh5Wxtk1G3R7Xn4gRcwfWIa76hHJahw5vYzuFtdMhszpNylnQbCK3tzJxHxyh5u2mXr2q11TyISZaxSqr5mr9jn+yOqR77/zrzr/gf4bRGxgI8CN5V8d80AACAASURBVCx3catNnBGqUaJclzLvYpGyUdYY1/aIa1t0qvJ4mcJogbASYudtxBfEEYqHi5SOlMiP5EkPpi9q7nFvnNHjo1TCCvlMnj6nDz/wOfzNwxw5fYSRzSMMuoORxyqlGms1oOZlQKPPk90P1IuFVRHEGaEaJcp1qfM2jJQ9dizSGuPaHnFti04VeAGF0QKSEZxhB6vHAgGrx8IZdpCMUBgtEHhB5Lm9ssfo8VEydobh3DA9dvUx7LF7GM4Nk7EzjB4fxSt7kcYqpZprtbE/S/WjafV8V+12tQRxRpdGjX5d9nn/5BfhiiuY/sg7W15jXNsjrm3RqaZPTBNWQlLZ+rvbU9kUYSVk+sR05LlPPHuCSlgh69R/DLNOlkpY4cSzJyKNVUo112pj/zPg7SLyURHZLSIvrP37MeCXAD1RyxLFGV0aNfp1Wef94v9l8pG/AcC7/WPY/3h30/Gza4xre8S1LTqVd9TDzjd/R87O23hHo79SPnr2KPlM88cwn8lz9OzRSGOVUs21+h77e6ge/f4LwN7adQJMAR/kwiPoVURh6GHbfU3HiDgEQcux/HOCoIjjrG06xrJcfP/08s77uXux7h3FH6juxg3xsT/1t2BseGP9BOL5a4xje8S1LTpV6IXYfc3/GxBHCAoXtyu+z2n+GDoph8J09TGMMlYp1VhLjd0YEwK/LiK/B1xDNWjmG8AjxhjdDb8MLMvFGB+RxhGqxvhYVvQjg1OpXO3o88ZRrmHokUpFy15uOu/n7oV77yUkIFV7sWfNgAl85K/+qnpFneY+f41xbI+4tkWnslwL4xukp0l+v2+w3Cjni6py0y5+4NNjN34M/cCfO9o9ylilVGORnq3GmIIx5vPGmL+v/atNfZm47lYqleavRiqVAq67NfLc/f27KJebvwItl0/T379reeY9ewbuvAv8MuUB6P9K9Wr3aajkgOkZ+JNPwjfGLvjR2TXGtT3i2hadyt3qUilUmo6pFCq4W6M31K1rty76CrswXWDr2q2RxiqlmmvY2EXk20QkN+/7pl/tK7k7xRldGjX6dcnzrh2Cm/ZQzqewKtD/aPXqzGmwAgjWOPDTPwWXrj/vx+avMa7tEde26FSZjRks2yKYqr+rPZgKsGyLzMZM5Lk3rtmIbdlM+fUfwyl/Ctuy2bhmY6SxSqnmmr1iPwD8r3nfjzb4mr1NLcFsdKkx0/j+qdoZw6rRpb5/CmOmLzq6dDbKNQxLeN5xKpUpwjCkUpnC844ThqXzolyXMu9spKy360rC3TtZf28Gp1hbow/5x1zMD34f/uu+s+ka49oecW2LTpVyU+RH8phpg3/KJ5wJwUA4E+Kf8jHThvxI/qJCaty0y8jmEaYr05wqnmKmUn0MZyoznCqeYroyzcjmEdy0G2msUqq5Zu+xj/DcmdVG2lDLqhdnhGqUKNelzHtepOz334Lz9B/D+98PpRL09pL+hXcz+NO/0NIa49oecW2LTpUeTDO4Z3Auec4EphqZuz235OS5QXeQPVv2zKXJFaYLuGmX7ZdsvyBNLspYpVRjzSJl/7Pe9ype86NLl5vjDDI0tIehoT3LOu/8iNhUKqC//2XPRcTuq53P553vhHe/u3o5QqhMXNsjrm3RqVJuiuy2LNltWewDNkO7h5Ztbjftsm1oG9uGFn8MAy/AO+Yx+dgkpekS5UwZ7yqPYFsAFxd+F7vx0jiHTh7i4NMHKc4UyfXk2HXZLnZu2Mlg7+r6I1ElQ7Ozu50C/gv4PPCfxpjDbatKdYyFsa8iPiLO+bGv+/bBG94Al18ea2yu6mynxk5x5913Ug7KDPQN0JftY8af4b8P/zdf/uqXuenGmxheP7zSZZ7nyfEn2X//fvzQZ112HWsza/EqHvc8cQ+jx0bZ+5K9XDG4euKJVTI0e4/9v4CXAx8GHhKRcRH5VxF5u4i8VESWdmYI1fHqxb5Cg9jXyy+PNTZXdbbiZJE7774T13FZ/7z1uBkXy7JwM7XLjsudd99JcbK40qXOGS+Ns//+/fQ6vWwe2EzWqcYTZ50smwc20+v0sv/+/YyXVkc8sUqOho3dGPN6Y8ylwDaqoTR3UP0M++8AXwQKInKPiLxLRF7RlmpVokSNfY0zNld1tscffZxyUCaXq58fkMvlKAdlHn/08TZX1tihk4fwQ58Bt3488YA7gB/6HDq5OuKJVXIs+jl2Y8xRY8yfGGN+1BizCdgE/Bjwd8BG4H3oUfGrUtTY1zhjc1VnO/LYEQb66jfIWQN9Axx57EibKlrcwacPsi7bPJ54XXYdB59eHfHEKjmix0lBAIRUI2YN1WjZ6GeIUB0vDD1EnKZjRBzC0Luo8Wr1KE2X6HEap84B9Dg9lKZLbapoccWZIq7d/Eh913YpziTn7QO1Oiza2EXkShH5CRH5CxE5RvU87H8IrKN68pedQPOXYaorzcbgNjM/9jXqeLV69GZ6mfFnmo6Z8WfozfS2qaLF5XpyeJXmf4R6FY9cz+qIJ1bJ0Sx57jYROQk8Bvx6bexvAi8yxjzPGPNaY8zvGWP+2xjTPJNSdaWosa9xxuaqznb1VVczca5+GuCsiXMTXH3V1W2qaHG7LtvF6anm8cSnp06z67LVEU+skqPZK/bvB/qpvjp/PfDjtffav9bkZ9QqEjX2Nc7YXNXZtmzbQjqVplisv9u6WCySTqXZsm1LmytrbOeGnTiWw4RX/w+SCW8Cx3LYuWF1xBOr5GjW2H8A+EvgO4EvUT0K/t9F5Ndr52KPHh6tukq92FdoHPsaZ2yu6my5/hw33XgTnu8x9swY3rRHGIZ407XLvsdNN95Erj85u7UHewfZ+5K9lPwSxyeOM+VX44mn/CmOTxyn5JfY+5K9GlKj2q5Z8tzfA38PICIDwLcBrwC+h+r52QMReYhqgM3njTF3xF+uSpqFsa/GBNU40gaxr3HG5qrONrx+mO9/3ffz+KOPc+SxIxTOFejN9PLS7S9ly7YtiWrqs64YvIJ9r9g3lzx3euo0uZ4cr7ryVZo8p1ZMq+djnwD+pfaFiGSphtf8DPB24JdbnatbBIE315yq5/d2cd2tS25OUef1/fG5zPMgKJJK5ejv35XozPM4Y3PVcwIvmMt/D70Qy7Vwt7pLzn+PU64/x3UvvY7rXnrdomOLk8W5PwJK0yV6M71cfdXVdf8I8MreXAZ9MB1w+2O3s3Xt1mXJoB/srWbc79myeDzx/Dq8soebdpvWEWV8XGuMWrNaeS03YxER4Dqqr9y/jWpjnw2UPrP8pSVXXLGoUectlZ5kbGw/YeiTTq/DcdYShh7j4/dQKIyyfv1eenvjjbNsKVJWI2LbrjxepjBaIKyE2Hkbu8/G+Ibi4SKlIyXyI3nSgwkNX29BlPjZcW+c0eOjVMIK+Uwe3/JxUg6Hv3mYI6ePMLJ5hEE3/t/RhXX0OX34gd+wjijj41pj1JpVMjQ7Kt4WkW8VkXeIyF3ABHA/8PvAS4B7qb5iv9oYk6wA5xjFFYsadV7fH2dsbD+W1Yvrbsa2q3GWtp3FdTdjWb2Mje3H9+OLs4wUKavaJvACCqMFJCM4ww5Wj1X9XeqxcIYdJCMURgsEXv1zsCddlPhZr+wxenyUjJ1hODdMj92DIPTYPQznhsnYGUaPj+KV4/0drVuHNK4jyvi41hi1ZpUczQ6em6T6/vkHgauAzwI/CVxpjLnMGPNDxpg/Xm1HyccVixp13snJQ7VX6vXTutLpAcLQZ3IyvjhLjYhNpukT04SVkFS2/u72VDZFWAmZPtGZuVJR4mdPPHuCSlgh69T/Hc06WSphhRPPxvs7GrWOKOPjWmNStp2Krllj/yvgR4DLjDFXGGPeZIz5C2PMsTbVlkhxxaJGnXdy8iDpdPM4y3R6HZOT8cVZakRsMnlHPex883fZ7LyNd7QzX2lFiZ89evYo+Uzz39F8Js/Rs/H+jkatI8r4uNaYlG2nomt2Epi3GGP+1hhzsp0FJV1csahR5w2C4qIJbZblEgTxxVlqRGwyhV6IONJ0jDhC6IVtqmh5RYmf9coeTqr576iTctqyKz5KHVHGx7XGpGw7Fd3FZMWvanHFokadN5XKLdoww9AjlYrvI0IaEZtMlmthfNN0jPENltuZT/8o8bNu2sUPmv+O+oEf+9HdUeuIMj6uNSZl26noOvOZvYLiikWNOm9//y7K5eZxluXyafr744uz1IjYZHK3ulQKzVOeK4UK7tbO/A85Svzs1rVbKUw3/x0tTBfYujbe39GodUQZH9cak7LtVHTa2COKKxY16rz9/TuxLIdyuf5/cOXyBJbl0N8fX5ylRsQmU2ZjBsu2CKbqH/UeTAVYtkVmY2eGR0aJn924ZiO2ZTPl1/8dnfKnsC2bjWvi/R2NWkeU8XGtMSnbTkWnjT2iuGJRo87rOIOsX7+XMCzhecepVKpxlpXKFJ53nDAssX793lhDaqJGyqr2SLkp8iN5zLTBP+UTzoTV36WZEP+Uj5k25EfyiQ2pWUyU+Fk37TKyeYTpyjSniqeYqcxgMMxUZjhVPMV0ZZqRzSNt2RV/QR2mcR1Rxse1xqg1q+RYVWlxyyWuWNSo8/b2XsELXrBvLnnO90+TSuUYHHxV25LnokbKqvZID6YZ3DM4lzwXFAIs1yK3PZfo5LlWRYmfHXSryXBzqWxhgAkM2y/Z3tb0tIV1FKYLuGm3YR1Rxse1xqg1q2Ro2NhF5AVRJjLG/M/Sy+kcccWiRp3XcQYZGtrD0NDicZZxmV+zbR9gaGj3itWinpNyU2S3Zcluq/855E4XJX7WTbtsG9rGtqFtHPjGAXZftTv+AhepY7nHx7XGqDWrldfsFftTQPNDa8/X2S8BEiKuDPqoc3diBr1aXZKSYf7M+DN8/suf5wuPf4FzM+fo6+nj5VteziuuewXPG3zekuY++exJ7nj8Du47ft/c3DdsvoGbt9zMhjUblmkFqts0a+w/QbTGrpYorgz6qHMnIYNeqWaSkmH+6LFH+chdH8EPfNZl1zHgDuCVPe766l187muf42173sa2yy/ule6DYw9y64Fb8UOf4ewwazNrKVVKfPbRz3Ln0Tu5ZfctXL/++mVekeoGzU7b+hdtrGPVOz93/bldpyLV3PUgmKJQGGVwcE/kV+5R5g4Cby6DvqfnuVcElpXFtjdTLk8wNrafF7xgn75yVytifob5/LjT2QzzKX+K0eOj7NmyJ9ZX7s+MP8NH7voIuXSOfP65hLZcKkcuk6MwVeAjd32EW994a+RX7iefPcmtB24l62R5Qe9z74r2pfro6+njbOkstx64lY/f9HF95a4uoEfFJ0ScuetR5k5CBr1SzSQlw/zzX/48fuCTz9aPXc1n8/iBz+e//PnIc9/x+B34oc/a3rV1b1/buxY/9Lnj8Tsiz626X8uNXUTWicjPi8jHROTPFnz9aZxFrgZx5q5HmTsJGfRKNZOUDPMvPP4F1mWbP1fWZdfxhce/EHnu+47fx3C2+Ukzh7PD3Hf8vshzq+7X0sfdROQq4Iu18Vmq518fpHrA3ATwbFwFrhZh6GHbfU3HiDgEQfMkqKXOHQRFHKf+q4RZluXi+81T75SKi1f26HOa/z47KWfR1LSlOjdzjgG3+clo3LTLM1PPXNTcazPNn4e9di+np/R5qC7U6iv236V6LvZhQIDvBlzgp4AS8NpYqltF4sxdjzJ3EjLolWomKRnmfT19i54AxSt79PU0/yOk0dylSqnpmFKldFFzq+7XamN/CfAxYPbMC5YxpmKM+TPgj4APx1HcahJn7nqUuZOQQa9UM0nJMH/5lpcv+or59NRpXr7l5ZHnvmHzDZyaOtV0zKmpU9yw+YbIc6vu12pjzwHjxpiQ6m73oXm33U+18asliDN3PcrcScigV6qZpGSYv+K6V1R3+U/V/yOjMFXASTm84rpXRJ775i0341gOZ0tn695+tnQWx3K4ecvNkedW3a/Vxv4UcEnt+8eA18+77WYg3jezVoG4Muijzp2EDHqlmklKhvnzBp/H2/a8jWK5yLGzxyhOFwmCgOJ07XK5yNv2vO2iQmo2rNnALbtvYcqf4onxJzg3c44gCDg3c44nxp9gyp/ilt236EfdVF2tZsXfC7wS+Afg94HbROTlQAXYBnwgnvJWl7gy6KPOnYQMeqWaSUqG+bbLt3HrG2+dS557ZuoZ+nr62POiPUtOnrt+/fV8/KaPzyXPnZ46TV9PH6/d9lpNnlNNtdrY9wE9AMaYvxcRD3gD0Av8IfAn8ZTXPlGjXOOKfo0rgx6qNXveMSYnvzQXEws26fS6C2pOpVxc93KgMm99ly+6LSqVgDNnbl+2GNy4BF4wd4KU0AuxXAt3q7ssJ0jxx30mD00yeXCSoBiQyqXo39VP/85+nEGnbXUUJ4tzJ0kpTZfozfRy9VVXX3CSlIV1VIIKZ24/07COKPPGKUqG+fxo1lemXsnH/uFjTaNZozwuqUyKzAsyuOISzAS4PS6ZyzKkMhc+flFjcDes2cBbdryFt+x4S4Qts7godSQlujeKqDV34hqbEWPalxorIhngv6j+kWADnzHG3LJgTA/wKeB64CzwBmPMU83m3bFjh3nggQcuuq6FcasiDsb4VCoFLMu+IMo16vj5Dhw4wO7duy+61ou1MCbWslzC0KNcPo1lOefFxEZZ38KxDz/sc+21TkvbYqWUx8sURguElRA7byOOYHxDpVDBsi3yI3nSg+mGP9/sMSw9WWJs/xihH5Jel8ZyLUIvpHy6jOVYrN+7nt4repeljmZOjZ3izrvvpByUGegboMfpYcafYeLcBOlUmptuvInh9cN163jYf5hrnWvr1hFl3qRYGM16g3UD94X3cWrqFI7lXBDNGuVxeXL8Sfbfvx8/rEbKuraLV/E4PXUax3LY+5K9XDFYfV4tjMF1Ug5+4FOYLmBb9rLG4Db7HY1SRztrjmK51ncx49ullV4hIg8aY3YsvD5S8pyI9IvIt4rI62v/Rv2sxQzwHcaY7cC1wI0isvAorJ8EJowxVwJ/APx2xPuI5Py41WEsqwcRwbKqcasiGQqFUYLAu6jxSeD743Mxsa67GdvOYlkWtp3FdTdjWb2Mje3H98cjra/eWEj2tgi8gMJoAckIzrCD1WNV19dj4Qw7SEYojBYIvCDy3P64z9j+MaxeC3ezi521q9s5a+NudrF6Lcb2j+GP+7HWUZwscufdd+I6Luuftx4342JZFm6mdtlxufPuOylOFuvWgVC3jijzJsX8aNYrB6+kr6cPEaGvp48rB68k62S59cCtnHz2JBDt92O8NM7++/fT6/SyeWAzWaf6vMo6WTYPbKbX6WX//fsZL42fF4M7nBumx64+r2ZjcDN2htHjo4t+fG6potSRlJqjiFpzJ66xFVGS594DPA18Hvg08AXg6yLy7lbnMFWzz/p07WvhLoPXAH9Z+/4zwA0iIq3eR1RRo1zjjH6NS5SY2Cjr68RtMX1imrASksrW382dyqYIKyHTJ6Yjzz15aLL6Sn2g/qvs9ECa0A+ZPDQZax2PP/o45aBMLld/t3gul6MclHn80ccj1RFl3qSoG81aqcx9uzCaNcr2OHTyEH7oNwypGXAH8EOfQycPJSYGN0odSak5iqg1d+IaW9FSYxeRW4H3Um3orwSuAb4T+HvgVhF5b6t3KCIpEXkYOA3ca4z50oIhG6j+AYExpkL143XNI5iWIGqUa5zRr3GJEhMbZX2duC28ox52vvmhJXbexjsa/S/0yYOTpNc133WeXpdm8uBkrHUceewIA33NE9EG+gY48tiRSHVEmTcpLohmfeABGB+v/lszP5o1yvY4+PTBliJlDz59MDExuFHqSErNUUStuRPX2IqW3mMXkTHgb4wxv1Lntg8BbzTGrI90xyJ54LPAzxljvjrv+q8CNxpjvl67/CTwMmPMmQU//2bgzQDDw8PX33bbbVHufk6lMoGITTVQrxGDMRVseyDy+IWKxWLDVzxxmZl5GhGHZjs+jDEY45NK5VpeH3DB2FIpoLc3dcHYettiJVQmKogtiy0PUzHYA/X/g2/0GM48PYM4svh29g2pXGrJdTRy5swZ0vbi782XK2XyqfwFdZSCEr2p3gvqKASFlucdGhpadFw7PD7+OD2p6u5VpqZgaops/vlMFb4O2Sxks9WPygUzbBncEun34xupb+CkHKTJYIPBD3xyTg7bshcdWwkrDGSW/lxp9Ds6MT3Rch1AW2uOYjnWN5AZiDy+nVrpFSMjI3XfY2/1f4w1wD0Nbrsb+JkW55ljjCmIyChwI/DVeTedBC6jupvfrt33BSkNxphPAJ+A6sFzF3tA2pkztyPi1N4frm/2c99DQ7sjj19oJQ6eO3bsXYj0Ytv1dzcBVCpTGFOiv/9lLa8PuGDsQw8VefGLcxeMrbctVsKZ288gTvU900bCmbBa8+76zanRY3jsXceQXsHONn5aVaYqmJKh/2X9S66jkb/+q78mTIe4mcZH83rTHn7Z59o1115Qx0PFh3hx7sUX1HF38e6W512JA0Tr+dg/fAxXXPq+8lj1VXoQ8LLXfogvffbtkErBjh2cu+YqvMDjp3f/dKTfj9HcKL12b8PduFANyylVSrxsw8twUg49duPn1UxlBhMYdl+1eylLBhr/jt7+2O0t1wG0teYolmN9u6/aHXl8Oy2lV7T6HvuXaJwu95La7YsSkefVXqkjIi7V3fqPLhh2O/Bjte9fB/yHifHQ/ahRrnFGv8YlSkxslPV14rZwt7pUCpWmYyqFCu7W6B9x6d/VT/l0uemY8uky/bv6Y63j6quuZuJc/eTAWRPnJrj6qqsj1RFl3qS4YfMNnHrki3NN/TxBAA88wKlHvjgXzRple+y6bFdLkbK7LtuVmBjcKHUkpeYootbciWtsRauN/W3AT4jIr4jIJhFxa//+KvATwM+KiDX71WSeS4FREXmEahTtvcaYO0TkfSLy6tqYPwXWisgTwC8B77i4pbUmapRrnNGvcYkSExtlfZ24LTIbM1i2RTBV/2jzYCrAsi0yGzOR5+7f2Y/lWJQn6jf38kT1I2/9O/tjrWPLti2kU2mKxfpHpxeLRdKpNFu2bYlUR5R5k+LmnmtwvvY4Z1P113c2FeB87XFu7rkGiPb7sXPDThzLYcKr/7ya8CZwLIedG3YmJgY3Sh1JqTmKqDV34hpb0WpjfwS4Avgt4EmgWPv3N2vXfwUo174annbJGPOIMeY6Y8z/Nsa8yBjzvtr17zHG3F77ftoY83pjzJXGmJcaY45d7OJaETXKNc7o17hEiYmNsr56YyHZ2yLlpsiP5DHTBv+UP7dbNZwJ8U/5mGlDfiR/UeEwzqDD+r3rCUsh3nGPylSlup2nKnjHPcJSyPq963EGnVjryPXnuOnGm/B8j7FnxvCmPcIwxJuuXfY9brrxJnL9ubp1YKhbR5R5k2LDNd/KLS96K1O9whNr4Jxd/RjOORueWANTvcItL3orG675ViDa78dg7yB7X7KXkl/i+MRxpvzq82rKn+L4xHFKfom9L9nLYO9gYmJwo9SRlJqjiFpzJ66xFa0ePPdeLvxYWkPGmFuXUFNkSw2ogfYlz61UQA1UP88+GxM7mzzX37+rbkxslPXNH/vwwwHXXpvq6uS5xR7DTk+eezh4mGtT1yY+eS6Kk+9/B3f864e477KAV970Ie698+3c8HSKm7/n7Wx4929dMD7K4zJeGufQyUMcfPogxZkiuZ4cuy7bxc4NOxnsPf951a6Es8V+Rzs9eW4513cx49thKQE1LR08Z4x570VV1kHijHJNCscZZGhoD0NDexYdG2V7zB9r2wcSc6BcMyk3RXZbluy2xgc+LWVu93IXKjzXFC53l9yoo8r157jupddx3UuvW9Z53bTL1v6tXLbmMkKntr5+l0y6/tsGUZpklP9go8y74d2/xVtSa3jL+9/PgW+Bn/63Xnj3u2HfviVvj8Heam79ni2LP6+ixODGKUodUWtOQpOMWnNSHpflEu1zNICI5Kh+rnzMGNP8SKEutTBG1bb7MManWDxMqXQkkTGqqj0WxpHafTbGNxQPFykdKZ0XRxplbDtrFl8QR5al5ijjF0Z79jl9+IHP4W8e5sjpI+dFe17UtpvfxJs09aQ8Lp0oymOo4hMlee5mEXmIamDMMaohNYjIJ0XkjTHVlzidGCmr2iNKHGmckbJLrblRpGzUmqOMjxLtuaRtt28fXHNNw6aelMelE3VrPGsnajV57nuBfwHOAL/G+fENx3nu42ldrxNjVFV7RIkjjTNSNoo4a44yPkq055K3nePUvz5izep83RrP2olafcV+C/DnxpjvAj684LavAi9a1qoSrBNjVFV7RIkjjTNSNoo4a44yPkq0Z5zbLimPSyfq1njWTtRqY38h1Zx4uPDo+AlizHJPmjD0EGn8Fz9U09jCUJ/4q03ohYjT/HxF4gihF0YaG6c4a44y3it7OKnmzysn5eCVvVi3XVIel04U5TFU8Wq1sU8CjXItNwHPLEs1HcCyXIxp+FF9AIzxsaxkfsxLxcdyLYzf/FOhxjdYrhVpbJzirDnKeDft4gfNn1d+4OOm3Vi3XVIel04U5TFU8Wr1t/NeYN9sHGyNEZEe4GeBf1v2yhKqE2NUVXtEiSONM1I2ioZ1jH3jgjqi1hxlfJRozzi3XVIel07UrfGsnajVxv4u4BLgMeCTVHfHvwN4GHg+1VO6rgqdGKOq2iNKHGmckbJR1K3j7Dj80Bvhb/52STVHGR8l2jPObZeUx6UTdWs8aydqqbEbY54CXgzcQfXELQHwbcAhqqdUHYurwKTpxEhZ1R5R4kjjjJRdUs1/+Xdw9iwhafxP/Svmbz5z0TVHGR8l2jPObZeUx6UTdWs8aydqOaCmdn70n4yxlo6RTg8yOLhnLkY1CApYlksutz3RMaoqfunBNIN7BucS0YJCgOVa5LbnEskYsAAAIABJREFULkhEizK2LTW/8yN4f3s75oM/iCFNzj9C5p+/ROqaibnPfUetOcr4Qbea4DabWlaYLuCmXbZfsv2C1LI4t11SHpdOFOUxVPGJnDwHICJrgC3AN2sNf9VZDRG0Kn6BF+Ad85j80nO58tiQXpeu20DiiutMffh3yH7i/WT9EjY3M8QXa3cIvP/91e9rzT1qzXF51jzLodQhDuYOUkzXMtpTu9hpdjLIgnMfzIufrQQVztx+pmkmf1yRw0mIW41blHjWpGyPpNSxXBo2dhF5FTBijHnHguvfBbxn9mdF5NPAjxpjmh9xotQqECWOtPRkibH9Y4R+SHpdGmetQ+iFjN8zTmG0wPq96+m9ondu7tjiOo8dg3e+s/HtpVL19je8gZK5JFLNcUXKPjn+JPvv348f+qzLrmNtZi1exeOeJ+5h9Ngoe1+ylysGr6hbQ7PI3Dhp3Or5krI9klLHcmr2Hvte4LzDF0XklcBvAI8CvwD8MfAG4OfjKlCpThEljtQf9xnbP4bVa+FudrGzNpZlYWdt3M0uVq/F2P4x/PHqx4dijeu8/HL44Aeht7f+7b298MEP4uefH6nmuCJlx0vj7L9/P71OL5sHNpN1sliWRdbJsnlgM71OL/vv3894aTxSZG6cNG71fEnZHkmpY7k1a+zXAXcuuO5NwDTwKmPM/2+MeSvV5r5qsuKVaiRKHOnkocnqq96B+q8U0wNpQj9k8tAk0Ia4zn37qidGWdjce587C1rUmuOKlD108hB+6DPgDtQdO+AO4Ic+h04eSkxErMatni8p2yMpdSy3Zo19HfDkguteCXzBGPPNedfdyYJX9kqtRlHiSCcPTpJe13z3b3pdmsmD1SbZlrjO2eZu1f5b6D3/1KZRa44rUvbg0wdZl13XdOy67DoOPn0wMRGxGrd6vqRsj6TUsdyaNfZzwNyfMSKyhWp07KEF4yYBPUxUrXpR4kiDYrBoepnlWgTF6i7itsV17tsHl15a/X7BqU2j1hxXpGxxpohrNz+gybVdijPFxETEatzq+ZKyPZJSx3Jr9ix9FHjNvMuvoRpM8+8Lxm0GTi1zXUp1nChxpKlcatFmEnph9Yhz2hzXeckl8OSTF5zaNGrNcUXK5npyeJXm/9F6FY9cTy4xEbEat3q+pGyPpNSx3Jr9Nv8B8FMi8hkR+ShwK/AV4OCCcXuAwzHVp1THiBJH2r+rn/LpctOx5dNl+nf1AysQ13n55RdcFbXmuCJld122i9NTp5uOPT11ml2X7UpMRKzGrZ4vKdsjKXUst4aN3Rjzz1SPfH8J8KNUd8G/3hgz9+eviFwCfCdwV8x1KpV4UeJI+3f2YzkW5Yn6jbI8UcZyLPp3VptkEuI6o9YcV6Tszg07cSyHCW+i7tgJbwLHcti5YWdiImKT8PglSVK2R1LqWG5N9z8ZYz5ijNlojOkzxtxgjHl8we3fNMYM/b/27j9Ksrq88/j7qa6u6eop2pm2h8ZG+SEZmAOyCE7IuBC3W6PgYFA3LEt2VwNJlozHRPQkuythg8Y1e+LJbhIMOXJQN2g0jgR/nIkgmJiZVdgdXEQQRn7qAEIDQzPTNDVdPdVV9ewft2boqamu7jtdP2596/M6p8503frOredbt7qfqntvfcrdb2xtmSLJFyeONDOcYWzLGJXZCoXdBUr7S1QqFUr7SxR2F6jMVhjbMkZmODr+l4S4zrg1typSdnhwmC2/uIXZ4iy79+1mf3E/lUqF/cX97N63m9niLFt+cQvDg8N1a8Bpe0RsErZfkiTl8UhKHc12VMlzIlJfnDjSwVMGOeHqE5jZOcPMXTMU9xTpy/UxfMEwQ5uGDjXIg5IQ1xm35lZFyp4yfApX//LV7HxmJ3f9/C727N9DblWOC37hAjYdv4nhweFFa/Cy4+5tj4hNwvZLkqQ8Hkmpo5nU2EWaLE4caWY4w8jmEUY2jyxr3XHiOlslbs1xHo848xsejP4gb16/OVYN6R1pRsaXV3uzJWH7JUlSHo+k1NEsauySaAszviuFSnQGdYOM7yRoZc3PTT7H9u3bufPhO8kX8+QyOc7fcD4TExMcN3bcUa93YVZ2ea7Mtke2JT6zO7R8b5FmUWOXxIqTM54Uraz5gfse4Lqbr6NYKbIuu461A2splAp8+4Fv891d3+WqS6/izDeeGXu9tVnZxVSRTF8m0ZndIeZ7izRLaz+8KXKU4uSMJ0Ura35u8jmuu/k6BvujfPTcQI6+dB+5gVyUj94/yHU3X8dzk88tvbIF6mZlk+zM7lDzvUWaRY1dEikpGd9xtLLm7du3R/nog4vkow9G+ejbt2+Ptd6kZGXHqSMpNYsklRq7JFJSMr7jaGXNdz58J+uy6xqOWZddx50P3xlrvUnJyo5TR1JqFkkqNXZJpKRkfMfRyprzxWXmoxfzsdablKzsOHUkpWaRpFJjl0RKSsZ3HK2sOZdZZj56JhdrvUnJyo5TR1JqFkmq5PxVFFkgKRnfcbSy5vM3nM8LhRcajnmh8ALnbzg/1nqTkpUdp46k1CySVGrskkhJyfiOo5U1T0xMRPnos4vko89G+egTExOx1puUrOw4dSSlZpGkUmOXRIqTM54Uraz5uLHjuOrSq5idj/LR83N5yqUy+bl8lI8+P8tVl14VO6SmblY2yc7sDjXfW6RZFFAjiRUnZzwpWlnzmW88k08e+8lDyXNTs1PkMjneeeY7V5Q8V5uVXa6U8bInOrM7xHxvkWZRY5dEi5Mz3koLY2JL5RJT26YWjYmNU3Pc+Nl1a9dx8Zsu5u3HvP3w8WtXdkhiYVb2jmd3MH7a+KJjWxnlGiezO7R8b+mc0OKJ1dhFllAbE2tFwzK24pjYuPGzSYjYVZSrhCbE57SOsYs0UC8mFmPFMbFx42eTELGrKFcJTajPaTV2kQZaFRMbd71JiNhVlKuEJtTntBq7SAOtiomNu94kROwqylVCE+pzWo1dpIFWxcTGXW8SInYV5SqhCfU5rcYu0kCrYmLjrjcJEbuKcpXQhPqcVmMXaaBVMbFx15uEiF1FuUpoQn1Oq7GLNNCqmNi4601CxK6iXCU0oT6n1dhFGqgXE4uz4pjYuPGzSYjYVZSrhCbU57QCakSWUBsT62XH3VccExs3fjYJEbuKcpXQhPicVmOXuuJGnSZl3UnQyvklIWK3lVGu+Zk8jz38GLse2cXs3CyDA4OccdoZrN+wntxQvO+ab5fQ4kh7UWjxxGrscoRWRpcmIRY1rjiRsnHm142PRSs9P/k8t95+K/PledYes5ZjVh/DgeIBfnD/D/jRgz/iogsvYnRstNNlHibEOFLpfm09xm5mrzOz7Wb2EzPbZWZX1RkzbmYvmdl91cu17ayx17UyujQJsahxxYmUjTO/bnwsWik/k+fW228lm8kytm6M7ECWVCpFdqB6PZPl1ttvJT+T73Sph4QaRyrdr90nz5WA33f304FNwAfN7PQ6477v7m+sXj7R3hJ7WyujS5MQixpXnJpbNbYXPPbwY8yX58nl6u9uz+VyzJfneezhx9pc2eJCjSOV7tfWxu7uz7r7vdWfXwYeAo5vZw3SWCujS5MQixpXnJpbNbYX7HpkF2uPWdtwzNpj1rLrkV1tqmhpocaRSvcz98ZpVi27Y7OTgO8Bb3D3mQXLx4GvAU8Dk8AfuPsRv81mdiVwJcDo6Oibtm7d2vqimyCfzy/6riQJSvtKWNqgUXqpg5ec9NojG1Oj+a103Z1Qr+bZ8iyDfYOvLKjWDCx7fnHGtvux6MRzdGpqiv700ucTzJfmGRkZWdF9NWt+++b2kU6lsQYb0XFKlRJrBxq/aGm2pP+dWanQ5wfLm+PExMQP3X1j7fKO/PU0sxxR8/7wwqZedS9worvnzWwz8E1gfe063P1G4EaAjRs3+vj4eGuLbpIdO3aQ5Fqntk1hmehY72IOfoZ6ZPzIP7CN5rfSdXdCvZrvzd/LOblzDl0/WDOw7PnFGdvux6ITz9Ev/e2XqPRXyA4sfhZ5Ya5Acb644tqaNb9tj2wj05dhVXrVomMOlA7gZWf8tJXfXxxJ/zuzUqHPD1Y2x7YH1JhZP1FT/7K7f732dnefcfd89efbgH4zS8Zf+R7QyujSJMSixhWn5laN7QVnnHYG+17e13DMvpf3ccZpZ7SpoqWFGkcq3a/dZ8Ub8HngIXf/80XGHFcdh5mdS1Tji+2rsre1Mro0CbGoccWpuVVje8H6Devp7+snn69/1ns+n6e/r5/1G47YedcxocaRSvdr9zv284D3AW9d8HG2zWa2xcy2VMdcAjxoZvcDnwYu806dCNCDWhldmoRY1LjiRMrGmV83PhatlBvKcdGFF1EoFph8YZLCXIFKpUJhrnq9WOCiCy9KVEhNqHGk0v3aeozd3e+k8elCuPv1wPXtqUjqaWV0aRJiUeOKEykbZ37d+Fi00ujYKJdecumh5Lnpl6cZHBjk3LPOTWzyXIhxpNL9knHqsSROK6NLkxCLGtfCmtM70g1PaIszv258LFopN5Tj7HPP5uxzz+50KcsWWhypdD81dqkr9Dz3uIp7i8zsnGHmrhkObDjAz675GUPnDTG0aYjMcKbT5YmIHKLGLkdQhvnhZn86y+QNk1SKFfqP7ccyhg0ae+/Yy/T2aca2jDF4yuDSKxIRaQN9H7scRhnmhyvuLTJ5wySpwRTZk7OkV6cxM9Kr02RPzpIaTDF5wyTFvcVOlyoiAqixSw1lmB9uZudM9E59bf09FP1r+6kUK8zsrM1ZEhHpDDV2OYwyzA83c9cM/cc2PuzQf2w/M3epsYtIMqixy2EqhQqWafiJRCxjVAqVNlXUWeV89BG0RlLZFOV8bxyaEJHkU2OXw6SyKbzYOA/Ii75kswtFX65vyRcxlUKFvlzvfVJARJKpN/46y7Ipw/xwQ+cNMb9nvuGY+T3zDJ031KaKREQaU2OXwyjD/HBDm4ZIZVLM76vf3Of3zZPKpBjapMYuIsmgxi6HUYb54TLDGca2jFGZrVDYXaC0v4S7U9pforC7QGW2wtiWMYXUiEhiKKBGjqAM88MNnjLICVefcCh5zouOzzrDFwwreU5EEkeNvQ3K5QJzc09SKDxKqVRmamob2eypDAycSF9fMo9Vx8kwXxg/WyqXmNo2FVz8bF+2j+zrs1CCvnIfQ780RPb12WDmlxSKMhZZOTX2Fpuf38v09HYqlRLp9BrMiphlyOfvZ3Z2F2vWTNDfP9zpMo9abfysFQ3LWFDxs70wxyRQlLFIc+gYewuVywWmp7djNkAmM0oqtQowUqlVZDKjmA0wPb2dcrk7w17qxc9iBBU/2wtzTAJFGYs0jxp7C83NPUmlUqKvr/7u7L6+1VQqJebmnmxzZc3RC/GzvTDHJNDjLNI8auwtVCg8Sjq9puGYdHoNhcKjbaqouXohfrYX5pgEepxFmkeNvYUqlQJmjc+YNstQqXTnH6teiJ/thTkmgR5nkeZRY2+hVCqLe+Ov83Qvkkol88z4pfRC/GwvzDEJ9DiLNI9+S1oomz2VUmm64ZhSaZps9tQ2VdRcvRA/2wtzTAI9ziLNo8beQgMDJ5JKpSmX99e9vVzeTyqVZmDgxDZX1hy9ED/bC3NMAj3OIs2jxt5CfX1Z1qyZwH2OYvF5KpUDgFOpHKBYfB73OdasmUhsSM1S6sXP4gQVP9sLc0wCRRmLNI8Calqsv3+Y4eHNh5Ln3Mu4O7ncWYlOnluu2vhZL3s0v4DiZ3thjkmgKGOR5lBjb4O+viyrV29g9eoNpNM7GBkZ73RJTbUwfja9I83I+EinS2q60OdY3Fs8lIV/YMMBfnbNzxg6b6gpWfhxYmLjRBmLSH1q7CI9bvans0zeMEmlWKH/2H4sY9igsfeOvUxvn2ZsyxiDpwwe1boVEyvSfjrGLtLDinuLTN4wSWowRfbkLOnVacyM9Oo02ZOzpAZTTN4wSXFv449t1qOYWJHOUGMX6WEzO2eid+pr679r7l/bT6VYYWbnTOx1KyZWpDPU2EV62MxdM/Qf23hXeP+x/czcFb+xKyZWpDPU2EV6WDlfXjLNLZVNUc7H312umFiRzlBjF+lhfbm+JRtrpVChLxf/o2aKiRXpDP1GifSwofOGmN8z33DM/J55hs4bir1uxcSKdIYau0gPG9o0RCqTYn5f/eY+v2+eVCbF0Kb4jV0xsSKdocYu0sMywxnGtoxRma1Q2F2gtL+Eu1PaX6Kwu0BltsLYlrGjCqlRTKxIZyigRqTHDZ4yyAlXn3Aoec6Ljs86wxcMrzh5TjGxIu2nxi4iZIYzjGweYWTzCE/teIrXv+/1TVu3YmJF2ku74kVERAKixi4iIhIQNXYREZGAqLGLiIgERI1dREQkIGrsIiIiAVFjFxERCYgau4iISEDU2EVERAKixi4iIhIQNXYREZGAKCteVqxcKB/6ko9SucTUtimyp2b1JR8iIh3Q1nfsZvY6M9tuZj8xs11mdlWdMWZmnzazx83sx2Z2TjtrlHjm986z97a95O/PY6sMSxu2ysjfn2fvbXuZ31v/e75FRKQ12r0rvgT8vrufDmwCPmhmp9eMeSewvnq5EvhMe0uU5SoXykxvn8YGjMxohtSqFBikVqXIjGawAWN6+zTlQrnTpYqI9Iy2NnZ3f9bd763+/DLwEHB8zbB3A1/0yE5gjZm9pp11yvLMPTlHpVShb3X93e19q/uolCrMPTnX5spERHpXx06eM7OTgLOBu2tuOh74+YLrT3Nk85cEKDxaIL2m8Wka6TVpCo8W2lSRiIiYu7f/Ts1ywP8G/sTdv15z27eAP3X3O6vXvwv8F3e/p2bclUS76hkdHX3T1q1b21L7SuXzeXK5XKfLaIrSvhKWNrBXls2WZxnsG3xlgYOXnPTacM7TDGkb1qP5db/Q5xj6/GB5c5yYmPihu2+sXd72v7Zm1g98DfhybVOvegZ43YLrr60uO4y73wjcCLBx40YfHx9vfrEtsGPHDrql1qVMbZvCMhYdW6+6N38v5+ReOd+xcqCCuzMyPtKJElsipG1Yj+bX/UKfY+jzg5XNsd1nxRvweeAhd//zRYZtA95fPTt+E/CSuz/btiJl2bKnZilNlxqOKU2XyJ6abVNFIiLS7nfs5wHvAx4ws/uqy/4QOAHA3W8AbgM2A48Ds8AVba5RlmngxAFmd81S3l+uewJdeX+ZVDrFwIkDHahORKQ3tbWxV4+b2xJjHPhgeyqSlejL9rFmYg3T26cp5ovRiXQe7X4vTZdIpVOsmVijkBoRkTYK54wm6Yj+4X6GNw8fSp7zsuPu5M7KKXlORKQD1NhlxfqyfazesJrVG1aT3pEO6kQ5EZFuoy+BERERCYgau4iISEDU2EVERAKixi4iIhIQNXYREZGAqLGLiIgERI1dREQkIGrsIiIiAVFjFxERCYgau4iISEDU2EVERAKixi4iIhIQNXYREZGAqLGLiIgERI1dREQkIGrsIiIiAVFjFxERCYgau4iISEDU2EVERAKixi4iIhIQNXYREZGAqLGLiIgERI1dREQkIGrsIiIiAVFjFxERCYgau4iISEDU2EVERAKixi4iIhIQNXYREZGAqLGLiIgERI1dREQkIGrsIiIiAVFjFxERCYgau4iISEDU2EVERAKixi4iIhIQNXYREZGAqLGLiIgERI1dREQkIGrsIiIiAVFjFxERCYgau4iISEDU2EVERAKixi4iIhIQNXYREZGAtLWxm9n/MrM9ZvbgIrePm9lLZnZf9XJtO+sTERHpduk2399NwPXAFxuM+b67v6s95YiIiISlre/Y3f17wN523qeIiEgvSeIx9jeb2f1m9m0zO6PTxYiIiHQTc/f23qHZScC33P0NdW4bAirunjezzcB17r5+kfVcCVwJMDo6+qatW7e2rugmyufz5HK5TpfRMqHPD8Kfo+bX/UKfY+jzg+XNcWJi4ofuvrF2eaIae52xTwAb3X2q0biNGzf6Pffc05T6Wm3Hjh2Mj493uoyWCX1+EP4cNb/uF/ocQ58fLG+OZla3sSdqV7yZHWdmVv35XKL6XuxsVSIiIt2jrWfFm9lXgHFgxMyeBj4G9AO4+w3AJcAHzKwEFIDLvN27FERERLpYWxu7u//6ErdfT/RxOBERETkKidoVLyIiIiujxi4iIhIQNXYREZGAqLGLiIgERI1dREQkIGrsIiIiAVFjFxERCYgau4iISEDU2EVERAKixi4iIhIQNXYREZGAqLGLiIgERI1dREQkIGrsIiIiAVFjFxERCYgau4iISEDU2EVERAKixi4iIhIQNXYREZGAqLGLiIgERI1dREQkIObuna5hxczsBeDJTtexTCPAVKeLaKHQ5wfhz1Hz636hzzH0+cHy5niiu6+rXRhEY+8mZnaPu2/sdB2tEvr8IPw5an7dL/Q5hj4/WNkctSteREQkIGrsIiIiAVFjb78bO11Ai4U+Pwh/jppf9wt9jqHPD1YwRx1jFxERCYjesYuIiAREjb2FzKzPzH5kZt+qc9vlZvaCmd1Xvfx2J2o8Wmb2hJk9UK39njq3m5l92sweN7Mfm9k5nahzJZYxx3Eze2nBNry2E3UeLTNbY2a3mNnDZvaQmb255vau3obLmF+3b7/TFtR+n5nNmNmHa8Z07TZc5vy6fRt+xMx2mdmDZvYVMxuouX2VmX21uv3uNrOTlrPedCuKlUOuAh4Chha5/avu/rttrKfZJtx9sc9ZvhNYX738EvCZ6r/dptEcAb7v7u9qWzXNdR1wu7tfYmYZYLDm9m7fhkvND7p4+7n7I8AbIXoTATwDfKNmWNduw2XOD7p0G5rZ8cCHgNPdvWBmNwOXATctGPZbwD53/wUzuwz4FPBvl1q33rG3iJm9FrgI+Fyna+mQdwNf9MhOYI2ZvabTRUnEzF4FvAX4PIC7F919umZY127DZc4vJG8DfurutUFdXbsNayw2v26XBrJmliZ64TlZc/u7gS9Uf74FeJuZ2VIrVWNvnb8E/jNQaTDm16q7x24xs9e1qa5mceA7ZvZDM7uyzu3HAz9fcP3p6rJustQcAd5sZveb2bfN7Ix2FrdCJwMvAH9TPVz0OTNbXTOmm7fhcuYH3bv9al0GfKXO8m7ehgstNj/o0m3o7s8A/wN4CngWeMndv1Mz7ND2c/cS8BLw6qXWrcbeAmb2LmCPu/+wwbB/AE5y938B/COvvCrrFue7+zlEu/o+aGZv6XRBLbDUHO8linQ8C/gr4JvtLnAF0sA5wGfc/WxgP/DRzpbUVMuZXzdvv0OqhxkuBv6+07W0whLz69ptaGZrid6RnwyMAavN7D80Y91q7K1xHnCxmT0BbAXeamZfWjjA3V909wPVq58D3tTeElem+moTd99DdNzr3JohzwAL90K8trqsayw1R3efcfd89efbgH4zG2l7oUfnaeBpd7+7ev0Woka4UDdvwyXn1+Xbb6F3Ave6+/N1buvmbXjQovPr8m34K8Bud3/B3eeBrwP/smbMoe1X3V3/KuDFpVasxt4C7n61u7/W3U8i2oX0z+5+2CuxmuNcFxOdZNcVzGy1mR1z8GfgHcCDNcO2Ae+vnpW7iWg307NtLvWoLWeOZnbcweNdZnYu0e/Tkr90SeDuzwE/N7PTqoveBvykZljXbsPlzK+bt1+NX2fx3dRduw0XWHR+Xb4NnwI2mdlgdQ5v48g+sA34jerPlxD1kiXDZ3RWfBuZ2SeAe9x9G/AhM7sYKAF7gcs7WVtMo8A3qr9PaeDv3P12M9sC4O43ALcBm4HHgVngig7VerSWM8dLgA+YWQkoAJct55cuQX4P+HJ1V+fPgCsC24ZLza/bt9/BF51vB35nwbJgtuEy5te129Dd7zazW4gOJ5SAHwE31vSJzwN/a2aPE/WJy5azbiXPiYiIBES74kVERAKixi4iIhIQNXYREZGAqLGLiIgERI1dREQkIGrsIh1gZu8xs++Z2R4zK5jZk2b2TTO7MAG1XW5mvtQ3SZnZTWb2dHuqisfMPmxm/7rO8o9X56aP+kqw1NhF2szMPkSUZPcY0bc3XQR8snrzWztVV2A+DBzR2EV6gV61irTfHwDfdPffWrDsn4HPmplebIvIiuiPiEj7DQPP1bvB3Q/7NkAzO9nMvmxmL5jZATO7z8zeWzPm4O7lM81su5nNmtmzZvaJhS8UzGzAzP7CzB40s7yZPWdm/2BmG1oyy+g+B83sU2a228yK1X+vqalrvFr/xWZ2vZlNVS9fMrM1NetbZ2ZfMbMZM9tnZn9T/X9uZuPVMU8AJwL/vrrczeymmtJONrNbq4/Dk2Z2rV5USSj0RBZpvx8Av2Fm/8nMTl1skEVf5Xs3cBbwEaLvFLgX+Fo1jrjWN4F/At4D/B3wR8C1C25fBRxDtNv/IuADwADwf83suJVOqk79aeAO4LeB64i+zONz1br+rM5/uY7oq3L/HfDHwK9Vly309ep6riaK15wn+lavhd5L9MLpDuDN1ct/qxnzDaK9JO8hetz+mFcyuUW6m7vroosubbwApwI/JmpiDkwRfcnFO2rGfZ7oO8VfXbP8H4H7Flz/eHU9H60Z91ngZWDNInX0AYPVMR9ZsPzy6vpOWmIeNxF9g9pit7+vup631Cy/BigCx1avj1fHfaFm3PXAHK9EX7+jOu7SmnHbqsvHFyx7AvhSnZoOPlZX1Cx/APhOp58buujSjIvesYu0mbs/CpwN/CvgT4D7iN5l3mFm/3XB0AuJvsTjJTNLH7wQvRM9y8yGalZ9c831rUAOeMPBBWZ2qZndbWbTRF88sb865jSa70LgSeD/1NT/HaAf2FQz/taa6w8Q7WUYrV7fBJSJ3m0vdMtR1FZ7Xw8CJxzFekQSRyfPiXSAu5eB71UvmNkYcDvwMTP7a3ffBxwLvL96qefVwMyC67XfV33w+vHV+/hV4KvAF4h2PU8BFaIXDwMrnFI9xxJBo/VfAAACeUlEQVQd655f5PZX11zfW3P9QPXfg7W9Btjn0XdXL1Tve8iXUu++WvEYiLSdGrtIArj7pJl9juiY8nqi4/AvAt8HPrXIf5usuT5K9PWkC68DPFP99zLgcXe//OAAM+snOpmvFV4EdgOXLnL7EzHX9yyw1sz6a5r76GL/QaQXqbGLtJmZvcbdn61z08Gz0w+eMX870Ylfu9y9sIxVXwr86YLrlwF5ol3aEB1PL9X8n/cRHWtvhduJToDLu/vDTVjfTqJa38vhhx3+TZ2xB4BsE+5TpOuosYu034Nm9k9Eu8B3A0PAZmALcLO7P1Uddy3RO/fvmdn1RO9w1xIdM3+9u/9mzXr/Y/UjW/8PuIDobPSPu/tL1dtvB95jZn8BfAvYCPweML2CuWTN7JI6yx8HvgxcAXzXzP4ncD+QAU4hOsP/Pe4+u9w7cvfvmNldwI1mNlK9j0uIPjUA0WGFg34C/LKZvYvohdKUuz8Ra2YiXUqNXaT9riFq5J8g2o1cBh4FPgr85cFB7v6UmW0kOpP7vwPriHZvP0h0nLzWu4k++vVHwEtEH2tb+DGvzwKvA34T+B2iFwC/ypEno8UxDPx9neV/7e6/a2YXVOd1JXAy0cl6PyU6ea14FPf3XqI5forocdtGNN+biOZ80NVE872Z6J37F4jO9hcJ3sGPkYhIlzKzjwMfA/rdvXZXe/CqezOuAIbd/cBS40VCp3fsItI1zOxy4FXALqLd+hcSBe38mZq6SESNXUS6yX6iL3g5hegz7ruBP6R+kp1IT9KueBERkYAoeU5ERCQgauwiIiIBUWMXEREJiBq7iIhIQNTYRUREAqLGLiIiEpD/Dyt14wRj3HIWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.zeros((n,1))\n",
    "plt.figure(0,figsize=(8,8))                #scatter plot of sepal length vs sepal width                                     \n",
    "plt.axis('equal')                                                                 \n",
    "plt.xlabel('Sepal Length', fontsize=16)                                                 \n",
    "plt.ylabel('Sepal Width', fontsize=16)                                                 \n",
    "plt.title('Sepal Plot', fontsize=25,color='b')                                            \n",
    "plt.grid() \n",
    "for center in Centers:\n",
    "  plt.scatter(center[0],center[1], marker='D',color='r')                                                                \n",
    "clr = 'b'\n",
    "for i in range(n):    \n",
    "    cNumber = np.where(final_weights[i] == np.amax(final_weights[i]))\n",
    "    if cNumber[0][0]==0:\n",
    "      clr = 'y'\n",
    "    elif cNumber[0][0]==1:\n",
    "      clr = 'g'\n",
    "    elif cNumber[0][0]==2:\n",
    "      clr = 'm'\n",
    "    plt.scatter(list(df_sepal.iloc[i:i+1,0]), list(df_sepal.iloc[i:i+1,1]), alpha=0.25,s=100,color=clr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 527
    },
    "id": "SOJ6zOi1vpWR",
    "outputId": "cba796b3-3dd5-428a-9ca8-61058dc63352"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.zeros((n,1))\n",
    "plt.figure(0,figsize=(8,8))                #scatter plot of sepal length vs sepal width                                     \n",
    "plt.axis('equal')                                                                 \n",
    "plt.xlabel('Petal Length', fontsize=16)                                                 \n",
    "plt.ylabel('Petal Width', fontsize=16)                                                 \n",
    "plt.title('Petal Plot', fontsize=25,color='b')                                            \n",
    "plt.grid() \n",
    "for center in Centers:\n",
    "  plt.scatter(center[2],center[3], marker='D',color='r')                                                                \n",
    "clr = 'b'\n",
    "for i in range(n):    \n",
    "    cNumber = np.where(final_weights[i] == np.amax(final_weights[i]))\n",
    "    if cNumber[0][0]==0:\n",
    "      clr = 'y'\n",
    "    elif cNumber[0][0]==1:\n",
    "      clr = 'g'\n",
    "    elif cNumber[0][0]==2:\n",
    "      clr = 'm'\n",
    "    plt.scatter(list(df_petal.iloc[i:i+1,0]), list(df_petal.iloc[i:i+1,1]), alpha=0.25, s=100, color=clr)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Fuzzy c-means algorithm implementation",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}