VE414Project_1202-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "rnRATgKh5g8z"
   },
   "outputs": [],
   "source": [
    "# libraries\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import math\n",
    "from scipy.stats import multivariate_normal as mvn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VIJTqttd5mpG",
    "outputId": "664e9043-204c-4b17-fecd-937c60c8ec93"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               X          Y  Potter  Weasley  Granger  Trip  Close  Far\n",
      "1       2.466284   3.582118       1        0        0     1      0    0\n",
      "2       5.508336   8.295713       1        0        0     1      0    0\n",
      "3       6.784481  11.851393       1        0        0     1      0    0\n",
      "4       7.550499  14.411812       1        0        0     1      0    0\n",
      "5      10.402096  17.218079       1        0        0     1      0    7\n",
      "...          ...        ...     ...      ...      ...   ...    ...  ...\n",
      "24090  40.615083  74.485727       0        0        1    49      4   16\n",
      "24091  41.485127  74.335721       0        0        1    49      1    8\n",
      "24092  41.275887  76.995216       0        0        1    49      1    2\n",
      "24093  42.176981  76.159014       0        0        1    49      0    1\n",
      "24094  40.530502  76.063805       0        0        1    49      1    8\n",
      "\n",
      "[24094 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('data_proj_414.csv',index_col=0)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iikk42bV5qaf"
   },
   "source": [
    "Obtain a map[107][107] from the dataframe, use \"Close\" data first and then make up the untouched grids using \"Far\" data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "zXHUi5Nd9FYT"
   },
   "outputs": [],
   "source": [
    "def estimate_from_far(map_far, map_close, x, y, means, covs, r_factor): \n",
    "  #means = (0,0)\n",
    "  #covs = [[1, 0], [0, 1]]\n",
    "  acc = 0\n",
    "  count = 0\n",
    "  pdf = []\n",
    "  for i in range(-2*r_factor, 3*r_factor):\n",
    "    for j in range(-2*r_factor, 3*r_factor): \n",
    "      if (i, j) == (0, 0): \n",
    "        continue\n",
    "      if (x+i) < 0 or (x+i) >= 107: \n",
    "        continue\n",
    "      if (y+j) < 0 or (y+j) >= 107: \n",
    "        continue\n",
    "      if (map_far[x+i][y+j] != -1): \n",
    "        \n",
    "        i1 = i\n",
    "        j1 = j\n",
    "        if (i > 0): \n",
    "          i1 = i-r_factor\n",
    "        elif (i < 0): \n",
    "          i1 = i+r_factor\n",
    "        if (j > 0): \n",
    "          j1 = j-r_factor\n",
    "        elif (j < 0): \n",
    "          j1 = j+r_factor\n",
    "        temp1 = mvn.cdf((i/r_factor, j/r_factor), mean = means, cov = covs)\n",
    "        temp2 = mvn.cdf((i1/r_factor, j1/r_factor), mean = means, cov = covs)#near the center\n",
    "        if(temp2 > temp1): \n",
    "          acc = acc + (temp2-temp1) * (map_far[x+i][y+j] - map_close[x+i][y+j])\n",
    "        else: \n",
    "          acc = acc + (temp1-temp2) * (map_far[x+i][y+j] - map_close[x+i][y+j])\n",
    "        count = count + 1\n",
    "  if count == 0: \n",
    "    val = 0\n",
    "  else: \n",
    "    val = acc/count\n",
    "  #print(\"acc, count, val: \", (acc, count, val))\n",
    "  return(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "UIQLAdfL5oqn"
   },
   "outputs": [],
   "source": [
    "resolution_factor=10\n",
    "min_resolution_x=[]\n",
    "min_resolution_y=[]\n",
    "\n",
    "\n",
    "map_close = np.zeros((107*resolution_factor, 107*resolution_factor))\n",
    "one_grid_count_close=np.zeros((107*resolution_factor, 107*resolution_factor))\n",
    "max_one_grid_count_close=0\n",
    "\n",
    "map_far = np.zeros((107*resolution_factor, 107*resolution_factor))\n",
    "one_grid_count_far=np.zeros((107*resolution_factor, 107*resolution_factor))\n",
    "max_one_grid_count_far=0\n",
    "\n",
    "map_uneven = np.zeros((107*resolution_factor, 107*resolution_factor))\n",
    "map_modified = np.zeros((107*resolution_factor, 107*resolution_factor))\n",
    "\n",
    "for i in range(1, len(df['X'])+ 1):\n",
    "  #if i<len(df['X']):\n",
    "    #min_resolution_x.append(abs(df['X'][i]- df['X'][i+1])) #min(min_resolution_x,abs(df['X'][i]- df['X'][i+1]))\n",
    "    #min_resolution_y.append(abs(df['Y'][i]- df['Y'][i+1]))#min(min_resolution_y,abs(df['Y'][i]- df['Y'][i+1]))\n",
    "  x =int(round(df['X'][i],1 )*resolution_factor)\n",
    "  y =int(round(df['Y'][i],1 )*resolution_factor)\n",
    "  #record close\n",
    "  one_grid_count_close[x][y]+=1\n",
    "  max_one_grid_count_close=max(max_one_grid_count_close,one_grid_count_close[x][y])\n",
    "  map_close[x][y] = map_close[x][y] + df['Close'][i]\n",
    "  #record far\n",
    "  one_grid_count_far[x][y] +=1\n",
    "  max_one_grid_count_far=max(max_one_grid_count_far,one_grid_count_far[x][y])\n",
    "  map_far[x][y] = map_far[x][y] + df['Far'][i]\n",
    "\n",
    "#calculate close\n",
    "for i in range(107*resolution_factor): \n",
    "  for j in range(107*resolution_factor): \n",
    "    if (one_grid_count_close[i][j] == 0): \n",
    "      map_close[i][j] = -1\n",
    "    else: \n",
    "      map_close[i][j] = map_close[i][j] / one_grid_count_close[i][j]\n",
    "\n",
    "#calculate far\n",
    "for i in range(107*resolution_factor): \n",
    "  for j in range(107*resolution_factor): \n",
    "    if (one_grid_count_far[i][j] == 0): \n",
    "      map_far[i][j] = -1\n",
    "    else: \n",
    "      map_far[i][j] = map_far[i][j] / one_grid_count_far[i][j]\n",
    "\n",
    "\n",
    "#obtain the final map with estimation using far data\n",
    "means = (0,0)\n",
    "covs = [[1, 0], [0, 1]]\n",
    "\n",
    "for i in range(107*resolution_factor): \n",
    "  for j in range(107*resolution_factor): \n",
    "    if (one_grid_count_close[i][j] == 0): \n",
    "      map_uneven[i][j] = estimate_from_far(map_far, map_close, i, j, means, covs, resolution_factor)\n",
    "      if (map_uneven[i][j] != 0): \n",
    "        map_modified[i][j] = map_uneven[i][j] # a record of modified data using far data\n",
    "    else: \n",
    "      map_uneven[i][j] = map_close[i][j]\n",
    "      \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 348
    },
    "id": "gmv1ItjiApb1",
    "outputId": "c3de747e-1c58-498d-fb0c-9f373ff6fde1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda3\\envs\\VE414\\lib\\site-packages\\matplotlib\\collections.py:1003: RuntimeWarning: invalid value encountered in sqrt\n",
      "  scale = np.sqrt(self._sizes) * dpi / 72.0 * self._factor\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ntrip = 49\n",
    "\n",
    "cmap = plt.cm.get_cmap('Greys')\n",
    "for i in range(1, ntrip+1): \n",
    "    rs_df = df.loc[df['Trip'] == i]\n",
    "    c = cmap(30/ntrip)\n",
    "    plt.plot(rs_df['X'], rs_df['Y'], color = c, alpha = 0.1, zorder = 0)\n",
    "\n",
    "X = []\n",
    "Y = []\n",
    "close = []\n",
    "modi = []\n",
    "\n",
    "for i in range(0,107*resolution_factor):\n",
    "  for j in range(0,107*resolution_factor):\n",
    "    X.append(i)\n",
    "    Y.append(j)\n",
    "    close.append(map_close[i][j])\n",
    "    modi.append(map_modified[i][j])\n",
    "\n",
    "plt.scatter(X, Y, s = close, c = close, cmap=\"winter\", alpha=1, linewidth=1, zorder = 10)\n",
    "plt.scatter(X, Y, s = modi, c = modi, cmap = \"cool\", alpha = 1, zorder = 5)\n",
    "plt.xlabel(\"the X axis\")\n",
    "plt.ylabel(\"the Y axis\")\n",
    "plt.colorbar()\n",
    "plt.title(\"Unnecessarily Mysterious Fruit Location\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "j4BTZFUucmg3"
   },
   "outputs": [],
   "source": [
    "def balance_out_map(map, x, y, r_factor): \n",
    "  left = 0\n",
    "  right = 0\n",
    "  up = 0\n",
    "  down = 0\n",
    "  if x > 0 and x < 106*r_factor: \n",
    "    if map[x-1][y] + map[x+1][y] != 0: \n",
    "      left = (map[x-1][y])/(map[x-1][y] + map[x+1][y])\n",
    "      right = (map[x+1][y])/(map[x-1][y] + map[x+1][y])\n",
    "    else: #nothing on the sides\n",
    "      left = 0\n",
    "      right = 0\n",
    "  elif x > 0: # x = 106, rightmost column\n",
    "    left = 1\n",
    "    right = 0\n",
    "  else: # x = 0, leftmost comlumn\n",
    "    left = 0\n",
    "    right = 1\n",
    "  if y > 0 and y < 106*r_factor: \n",
    "    if map[x][y-1] + map[x][y+1] != 0: \n",
    "      down = (map[x][y-1])/(map[x][y-1] + map[x][y+1])\n",
    "      up = (map[x][y+1])/(map[x][y-1] + map[x][y+1])\n",
    "    else: # nothing\n",
    "      up = 0\n",
    "      down = 0\n",
    "  elif y > 0: # y = 106, bottom row\n",
    "    up = 1\n",
    "    down = 0\n",
    "  else: # y = 0, top row\n",
    "    up = 0\n",
    "    down = 1\n",
    "  return({\"left\": left, \"right\": right, \"up\": up, \"down\": down})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "D0DosKAAbqRN"
   },
   "outputs": [],
   "source": [
    "map_proportion = [[{\"left\":0,\"right\":0, \"up\": 0, \"down\": 0} for y in range(107*resolution_factor)] for x in range(107*resolution_factor)]\n",
    "for i in range(107*resolution_factor):\n",
    "  for j in range(107*resolution_factor):\n",
    "    map_proportion[i][j]= balance_out_map(map_uneven, i, j, resolution_factor)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "CiC-DLPMtcC4"
   },
   "outputs": [],
   "source": [
    "def calculate_map(map_uneven, map_prop, x, y, r_factor): \n",
    "  count = 1\n",
    "  acc = []\n",
    "  acc.append(map_uneven[x][y] * map_prop[x][y]['left']*map_prop[x][y]['up'])\n",
    "  if x > 0: \n",
    "    acc.append(map_uneven[x-1][y]*map_prop[x-1][y]['left']*map_prop[x-1][y]['down'])\n",
    "    count = count + 1\n",
    "  if y > 0: \n",
    "    acc.append(map_uneven[x][y-1]*map_prop[x][y-1]['right']*map_prop[x][y-1]['up'])\n",
    "    count = count + 1\n",
    "  if x > 0 and y > 0: \n",
    "    acc.append(map_uneven[x-1][y-1]*map_prop[x-1][y-1]['right']*map_prop[x-1][y-1]['down'])\n",
    "    count = count + 1\n",
    "  val = max(acc)\n",
    "  return val\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "4PnIf8v7nv5S"
   },
   "outputs": [],
   "source": [
    "map_even = np.zeros((107*resolution_factor, 107*resolution_factor))\n",
    "count = 0 \n",
    "for i in range(107*resolution_factor):\n",
    "  for j in range(107*resolution_factor):\n",
    "    map_even[i][j] = calculate_map(map_uneven, map_proportion, i, j, resolution_factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 315
    },
    "id": "SpVUHmkaiaNo",
    "outputId": "9a55208b-9a17-476d-b45c-80491bcbf4a2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "even = []\n",
    "\n",
    "for i in range(0,107*resolution_factor):\n",
    "  for j in range(0,107*resolution_factor):\n",
    "    even.append(map_even[i][j])\n",
    "\n",
    "plt.scatter(X, Y, s = even, c = even, cmap=\"winter\", alpha=1, linewidth=1, zorder = 10)\n",
    "# Add titles (main and on axis)\n",
    "plt.xlabel(\"the X axis\")\n",
    "plt.ylabel(\"the Y axis\")\n",
    "plt.colorbar()\n",
    "\n",
    "plt.title(\"Unnecessarily Mysterious Fruit Location(evened)\")\n",
    "\n",
    "# Show the graph\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 315
    },
    "id": "a-l9odbXrgGh",
    "outputId": "f994508f-4a51-4343-ba9f-23ab31490696"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "uneven = []\n",
    "for i in range(0,107*resolution_factor):\n",
    "  for j in range(0,107*resolution_factor):\n",
    "    uneven.append(map_uneven[i][j])\n",
    "\n",
    "plt.scatter(X, Y, s = uneven, c = uneven, cmap=\"winter\", alpha=1, linewidth=1, zorder = 10)\n",
    "# Add titles (main and on axis)\n",
    "plt.xlabel(\"the X axis\")\n",
    "plt.ylabel(\"the Y axis\")\n",
    "plt.colorbar()\n",
    "\n",
    "plt.title(\"Unnecessarily Mysterious Fruit Location(uneven)\")\n",
    "\n",
    "# Show the graph\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YaNElhMrkkUG",
    "outputId": "691eaa4e-a3ee-4b87-bf5f-b860541063d3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(array([11101,   210,    94,    34,     8,     1,     1,     0,     0,\n            0,     0,     0], dtype=int64),\n array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]))"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.histogram(even, bins=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jCnBNVmplThW",
    "outputId": "8aee47f7-5a17-4f5a-85cb-b54a451e6457"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(array([10935,   248,   101,    58,    41,    22,    14,    17,     5,\n            5,     1,     2], dtype=int64),\n array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]))"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.histogram(uneven, bins=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1hRwCElAV4vv"
   },
   "source": [
    "Use EM algorithm to find the center of clusters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "34laD09BV8wP",
    "outputId": "c004c92c-31fd-493d-98be-fd9d5c2bcf6e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6, 18], [7, 15], [7, 19], [9, 17], [9, 20], [15, 13], [16, 13], [16, 14], [17, 12], [17, 12], [17, 13], [17, 13], [17, 13], [17, 14], [17, 14], [17, 15], [17, 16], [18, 10], [18, 11], [18, 11], [18, 12], [18, 12], [18, 12], [18, 12], [18, 12], [18, 13], [18, 13], [18, 13], [18, 14], [18, 14], [18, 15], [18, 15], [18, 16], [19, 12], [19, 12], [19, 12], [19, 13], [19, 13], [19, 13], [19, 13], [19, 14], [19, 14], [19, 15], [19, 16], [20, 13], [20, 14], [20, 15], [20, 16], [21, 11], [21, 14], [21, 15], [21, 16], [22, 12], [22, 14], [27, 47], [27, 47], [27, 47], [27, 48], [27, 50], [27, 50], [27, 51], [28, 46], [28, 46], [28, 47], [28, 47], [28, 48], [28, 48], [28, 48], [28, 48], [28, 49], [28, 50], [28, 51], [28, 52], [28, 52], [28, 52], [28, 52], [29, 46], [29, 47], [29, 47], [29, 48], [29, 48], [29, 49], [29, 50], [29, 50], [29, 51], [29, 51], [29, 52], [30, 46], [30, 47], [30, 47], [30, 48], [30, 49], [30, 49], [30, 50], [30, 50], [30, 50], [30, 51], [30, 51], [30, 52], [31, 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     ]
    }
   ],
   "source": [
    "#need to modify the even data so that it can fit the EM algo we used in homework 4\n",
    "data_em = []\n",
    "#data_em_np=np.zeros((107*resolution_factor,107*resolution_factor))\n",
    "for i in range(107*resolution_factor):\n",
    "  for j in range(107*resolution_factor):\n",
    "    for k in range(round(map_even[i][j])): \n",
    "      data_em.append([i, j])\n",
    "\n",
    "\n",
    "print(data_em)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "ACascmjOYjwM"
   },
   "outputs": [],
   "source": [
    "def compute_membership_weights(data, cluster_weights, means, covariances):\n",
    "  member_weights = np.zeros((len(data), len(means)))\n",
    "  for i in range(len(data)):\n",
    "    for k in range(len(means)):\n",
    "      member_weights[i, k] = cluster_weights[k]*mvn.pdf(data[i], mean=means[k], cov=covariances[k]) #alpha * P(data | mu, cov)\n",
    "      #usage: prob = mvn.pdf([0.5, 0.5], mean = [0, 0], cov = [[1, 0], [0, 1]])\n",
    "  sums = member_weights.sum(axis=1).reshape(-1, 1) #transpose\n",
    "  member_weights = member_weights/sums\n",
    "  return member_weights\n",
    "\n",
    "def compute_cluster_weights(member_weights):\n",
    "  return np.sum(member_weights, axis=0)/len(member_weights)\n",
    "\n",
    "def compute_cluster_means(data, member_weights, ncluster):\n",
    "  soft_counts = np.sum(member_weights, axis=0)\n",
    "  means = [np.zeros(len(data[0]))] * ncluster\n",
    "  for k in range(len(soft_counts)):\n",
    "    weighted_sum = 0.\n",
    "    for i in range(len(data)):\n",
    "      weighted_sum += np.array(data[i]) * member_weights[i, k]\n",
    "      means[k] = weighted_sum/soft_counts[k]\n",
    "  return means\n",
    "\n",
    "def compute_cluster_covariances(data, member_weights, means, ncluster):\n",
    "  soft_counts = np.sum(member_weights, axis=0)\n",
    "  ndim = len(data[0])\n",
    "  covariances = [np.zeros((ndim, ndim))] * ncluster\n",
    "  for k in range(ncluster):\n",
    "    weighted_sum = np.zeros((ndim, ndim))\n",
    "    for i in range(len(data)):\n",
    "      weighted_sum += member_weights[i, k] * np.outer((data[i]-means[k]), (data[i]-means[k]))\n",
    "    covariances[k] = weighted_sum/soft_counts[k]\n",
    "  return covariances\n",
    "\n",
    "def log_sum_exp(Z):\n",
    "  return np.max(Z) + np.log(np.sum(np.exp(Z - np.max(Z))))\n",
    "\n",
    "def loglikelihood(data, weights, means, covs, ncluster): \n",
    "  num_dim = len(data[0])\n",
    "  ll = 0\n",
    "  for d in data:\n",
    "    Z = np.zeros(ncluster)\n",
    "    for k in range(ncluster):\n",
    "      delta = np.array(d) - means[k]\n",
    "      exponent_term = np.dot(delta.T, np.dot(np.linalg.inv(covs[k]), delta))\n",
    "      Z[k] += np.log(weights[k])\n",
    "      Z[k] -= 1/2. * (num_dim * np.log(2*np.pi)+np.log(np.linalg.det(covs[k]))+exponent_term)\n",
    "  ll += log_sum_exp(Z)\n",
    "  return ll\n",
    "\n",
    "def EM(data, init_means, init_covariances, init_cluster_weights, ncluster, maxiter=100, thresh=1e-4):\n",
    "  #make copies of parameters\n",
    "  print(data)\n",
    "  means = init_means[:]\n",
    "  covariances = init_covariances[:]\n",
    "  cluster_weights = init_cluster_weights[:]\n",
    "\n",
    "  membership_weights = np.zeros((len(data), len(means)))\n",
    "  ll = loglikelihood(data, cluster_weights, means, covariances, ncluster)\n",
    "  ll_track = [ll]\n",
    "\n",
    "  for itr in range(maxiter):\n",
    "    #print(\"----iteration time: \", itr, \"----\")\n",
    "    #print(\"means: \", means)\n",
    "    #print(\"cov: \", covariances)\n",
    "    #print(\"cluster_weights: \", cluster_weights)\n",
    "    try:\n",
    "      membership_weights = compute_membership_weights(data, cluster_weights, means, covariances) #checked\n",
    "    except:\n",
    "      print(\"error: compute_membership_weights\")\n",
    "    try:\n",
    "      cluster_weights = compute_cluster_weights(membership_weights) #checked\n",
    "    except:\n",
    "      print(\"error: compute_cluster_weights\")\n",
    "    try:\n",
    "      means = compute_cluster_means(data, membership_weights, ncluster)\n",
    "    except:\n",
    "      print(\"error: compute_cluster_means\")\n",
    "    try:\n",
    "      covariances = compute_cluster_covariances(data, membership_weights, means, ncluster)\n",
    "    except:\n",
    "      print(\"error: compute_cluster_covariances\")\n",
    "    try:\n",
    "      new_ll = loglikelihood(data, cluster_weights, means, covariances, ncluster)\n",
    "    except:\n",
    "      print(\"error: loglikelihood\")\n",
    "    ll_track.append(new_ll)\n",
    "    if (new_ll-ll)<thresh and new_ll > np.inf:\n",
    "      break\n",
    "    ll = new_ll\n",
    "\n",
    "  return {\"membership_weights\":membership_weights, \"cluster_weights\": cluster_weights, \"means\": means, \"covs\": covariances, \"lll\": ll_track}  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SjiDuyjbY6rO",
    "outputId": "f192d36d-ebdf-4288-a033-f4dc309ac57e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "error: compute_membership_weights\n",
      "means:  [array([nan, nan]), array([nan, nan]), array([nan, nan])]\n",
      "cov:  [array([[nan, nan],\n",
      "       [nan, nan]]), array([[nan, nan],\n",
      "       [nan, nan]]), array([[nan, nan],\n",
      "       [nan, nan]])]\n",
      "cluster_weights: [nan nan nan]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda3\\envs\\VE414\\lib\\site-packages\\ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in true_divide\n",
      "  \n",
      "F:\\Anaconda3\\envs\\VE414\\lib\\site-packages\\numpy\\linalg\\linalg.py:2158: RuntimeWarning: invalid value encountered in det\n",
      "  r = _umath_linalg.det(a, signature=signature)\n"
     ]
    }
   ],
   "source": [
    "#means = [[19.12354, 15.0213], [7.0126, 20.123], [25.030021, 45.003],[25.01238, 55.0245], [35.0564, 50.00564], [40.213, 40.02346], [40.0456, 60.0789], [40.2137, 75.013873], [45.00120, 60.045687], [45.02376, 40.027829], [45.02786, 85.056678], [50.276, 40.02756], [60.0827, 30.02678], [60.025678, 75.05267], [70.05426, 40.02456], [70.02456, 70.0542678], [90.0452678, 40.04563]]\n",
    "means = [[19.05426, 15.07627],  [40.04526, 40.04526],  [70.04526, 60.004256]]\n",
    "covariance = [[[1, 0],[0, 1]], [[1, 0],[0, 1]], [[1, 0],[0, 1]]]\n",
    "cw = [0.3, 0.3, 0.4]\n",
    "ncluster = 3\n",
    "ans = EM(data_em, means, covariance, cw, ncluster)\n",
    "print(\"means: \", ans['means'])\n",
    "print(\"cov: \", ans['covs'])\n",
    "print(\"cluster_weights:\", ans['cluster_weights'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SOqT0lEPhQUX",
    "outputId": "4aa6e528-24cd-4f4f-83f3-775dbc8f5ce4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6, 18], [7, 15], [7, 19], [9, 17], [9, 20], [15, 13], [16, 13], [16, 14], [17, 12], [17, 12], [17, 13], [17, 13], [17, 13], [17, 14], [17, 14], [17, 15], [17, 16], [18, 10], [18, 11], [18, 11], [18, 12], [18, 12], [18, 12], [18, 12], [18, 12], [18, 13], [18, 13], [18, 13], [18, 14], [18, 14], [18, 15], [18, 15], [18, 16], [19, 12], [19, 12], [19, 12], [19, 13], [19, 13], [19, 13], [19, 13], [19, 14], [19, 14], [19, 15], [19, 16], [20, 13], [20, 14], [20, 15], [20, 16], [21, 11], [21, 14], [21, 15], [21, 16], [22, 12], [22, 14], [27, 47], [27, 47], [27, 47], [27, 48], [27, 50], [27, 50], [27, 51], [28, 46], [28, 46], [28, 47], [28, 47], [28, 48], [28, 48], [28, 48], [28, 48], [28, 49], [28, 50], [28, 51], [28, 52], [28, 52], [28, 52], [28, 52], [29, 46], [29, 47], [29, 47], [29, 48], [29, 48], [29, 49], [29, 50], [29, 50], [29, 51], [29, 51], [29, 52], [30, 46], [30, 47], [30, 47], [30, 48], [30, 49], [30, 49], [30, 50], [30, 50], [30, 50], [30, 51], [30, 51], [30, 52], [31, 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     ]
    }
   ],
   "source": [
    "print(data_em)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ERMFVK8abLz6",
    "outputId": "58eeb817-1845-4a10-bf8f-497f7f181266"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "False"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(data_em).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PEKWV5Ktd2Gy",
    "outputId": "1f185836-376c-4d1c-9083-7b9508cde290"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "False"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isinf(data_em).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pjbJdzttgmMG",
    "outputId": "2d717ba0-d341-4d05-a212-e9782ff48251",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "converged iteration: 60\n",
      "converged or not:  True\n",
      "means\n",
      "[[55.83625003 38.21149881]\n",
      " [35.71059271 60.50281951]\n",
      " [64.79651633 77.48095771]\n",
      " [17.46296296 13.75925926]\n",
      " [39.94271313 42.96744528]\n",
      " [85.04239822 44.88208489]\n",
      " [46.02564007 87.15385134]\n",
      " [30.48878059 49.76440044]\n",
      " [45.51114545 71.94576944]\n",
      " [77.32620791 71.28245687]\n",
      " [37.62233908 73.54529341]\n",
      " [37.62067496 44.82597651]\n",
      " [62.92612415 32.39382756]\n",
      " [49.93308297 62.40587071]\n",
      " [49.55279204 42.48674771]\n",
      " [74.98917817 36.84203537]]\n",
      "covariances\n",
      "[[[ 3.78573022  0.89659041]\n",
      "  [ 0.89659041  3.77144995]]\n",
      "\n",
      " [[ 4.3499272  -0.84476679]\n",
      "  [-0.84476679  4.6445227 ]]\n",
      "\n",
      " [[ 3.02042691  1.66765319]\n",
      "  [ 1.66765319  7.05981428]]\n",
      "\n",
      " [[12.10048111 -3.77743484]\n",
      "  [-3.77743484  3.88648934]]\n",
      "\n",
      " [[ 2.07841529  0.44678299]\n",
      "  [ 0.44678299  2.0231578 ]]\n",
      "\n",
      " [[ 2.9747124   0.36536549]\n",
      "  [ 0.36536549  1.76249814]]\n",
      "\n",
      " [[ 2.28139616 -0.51676085]\n",
      "  [-0.51676085  3.20707819]]\n",
      "\n",
      " [[ 5.07979808  1.81066533]\n",
      "  [ 1.81066533  3.39029941]]\n",
      "\n",
      " [[ 5.10002001  1.12029354]\n",
      "  [ 1.12029354  3.60745512]]\n",
      "\n",
      " [[ 8.23004315 -6.10615327]\n",
      "  [-6.10615327  6.84155898]]\n",
      "\n",
      " [[ 7.09280678 -0.84338898]\n",
      "  [-0.84338898  3.7019807 ]]\n",
      "\n",
      " [[ 2.22738327  0.91800358]\n",
      "  [ 0.91800358  2.17396883]]\n",
      "\n",
      " [[ 3.54808301  0.64411635]\n",
      "  [ 0.64411635  4.59377986]]\n",
      "\n",
      " [[ 2.84298492  0.25156626]\n",
      "  [ 0.25156626  2.54657583]]\n",
      "\n",
      " [[ 3.37650871  0.84581278]\n",
      "  [ 0.84581278  3.26368945]]\n",
      "\n",
      " [[ 3.77115506  2.03087225]\n",
      "  [ 2.03087225  6.48161073]]]\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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EAv5CiOuO9P1SyrellEOklENO9eYTRWkvDu5Pa2po4YUrvsIaYKHf2Z1Z9UUmMcmhlOXWYPIxYmzxxUYPjC2+xMRG0ZnhBFqD6T0ygaSu8URXDKPXoGQKN9fRR4xH1lsAaK4ycG7X6zBLvza+WuVkdzRNfOcCuVJKTUrZAswGTgfKhBAxAK2P5UdfTEVRjpX9fxDOfHIZhVmVXP7ICOb+5yc6pURQsrcak48Rs4+R/IYdbGM2uXXb8QbXYQ/4lt6XhpG9oYT6plr2iKV4zI1U7HMQGRmJo7IBAI9H4q41ERhmbcvLVE4BRxOgCoDhQgg/IYQAzgHswFzgxtZjbgS+PboiKopyrBXtqWTua+s49+YBLHh7M6ExgeyzV9AUXIbBYKDR0UxySD96WkZzzsgx1O4SdKkfi7E2iFteGUNZZiOnJY7FW2cGoMZRhdftBSAgxErp3moSequWEeXoHE0f1E/AV8Am9CHmBuBt4AVgjBBiDzCmdVtRfpOrsQVtXy1528vI+qkI+5pCsn4qIm97GeUFtTQ5m9u6iKecr/+1GpPZQHL/KPbZK/AP9qHBv4SltW9T3pRDeHwg+Y7tZDUvZdmKxYz4axw5AQtZ+sMaGutc1KGxIO9dKpxFeEyNbGr8lpq6akwWI7WaE69X0uuMhLa+TOUkd1T3QUkpnwKe+tVuF3o2pSi/IKWkaHcl25fls2dDCfnbyynJ/nlet8PxDbQQmRhMXPdwOvWKIHlQDD2GxREeG3gCSn5qaXC4WDZzB6Ov78vyWTuJ6hzCPnsFCfHdOcfndixVkVQV1THx+stxz5A8MfVvXPi3oSAEq98pJGttEWE+0QxxXY+oDSY2wYo3dwJlmQ0MHpfM8k93EGzzo89IFaCUo6Mmi1WOK03T8DrNLHhnEytmZVKao9+zHRjmS1K/SPqdk4TBaMDT4qXGUYUFfzxuL86mWvx9ghEGgTAAEpoaWsjZUsrab7LwevUZUGKSQxkwpgvDLupO/3M6Y7YYO9S0RH/GxvnZNDe5GXphd+a+s4oR5/WlLLeGisI6fOKMRCWHUrq3mi4Do/GfEcbmRTns3VzKdx+sZvTZI1k9exch0QE4a4w4a5pocblJ6G2jZIeToRO6Me3277nysVRMZuOfK2B9PVRWgtEIYWHgpwZbdFQqQCnHze4duVx14Q1E5p+Bj/BnwJgujJ88hOaGFuxrCtm5ooDGOr35zoWTTPEDg/0uRhgFmxvnMsByIQa3L26X5xfnDYsNJK5HGIFhvjhrmlj68TbmvbWRgFArgy7pxMqSz3l/xtsqSP2GnSv3YfU3U+OoYiff09eVSEh0AHtLM1lf/BGX93gQ8KWqsYTtAbPgO/CamthgmIFpuSA+rDsFJbmsYwbnht+Os8bAhqx5XDzsZtZ8k4U1wMLF9w77Y4WSEv77X/jPfyAr65evRUZCz54wZAicfjqcdRZERByr6lDaMRWglONiw7xsXpo0hzDnYK6872zG3NyfJR9u45MnluFqaCE8LpDgBBN+NT5UFtXhgz9d5Si8Tn2ocmdG4m2x4EUPTnVoBGLDbDUivZLMDdl4WjvoY/r60//0vtRVNLDm0z04XJ2YftNirnt2FF0HxRxxmeub68mqyCK7KpviumIKSgoQ/gKv9GIymAjyCSI6IJrk0GT6RfXD5n9yBsB99goS+0QiXFa6MZoAazB1/m7CSeCspKsQZaF0Sgng+39mcU7vaxjR62wyV+7DkXMhnSK646hoJMI/lsHeS5CVASR1D8edO4qRlw7mo0d/5OaXzyXY5v/HCvXmm3D33XoAeu45iIoCjwcqKiAnB3bsgDfegFde0dfnGjQILrwQLrsM+vTR9ymnHBWglGNu44K9PHvR50T2tNKUWEC/8TE8ff5nVBQ6GHZRDxwVDWzO2EVm8Q+cGX0V1gAztfU17GEpvRmPALLFMvoYxmP2+lEnNdYxg6HcQLA7kpq6KjY6v6E34wm2+bE4+xPyto8lNCiMkTd34/Pli9m2Jox7B2cz7rZB3PzSufgF+fxPOUvrS1mQvYCleUtZvW81e6r2/PyiE5gHfhf5YQo00eJpodH9y76ybmHdmNB9Ajf0v4EB0QOOa50eS/XVjYRGB1DfqNf5OUGDqSyupym4jBV5nzNUWolKGoo1uY7P17+JtsNFaHAYe1hMWGUcIZYo6mQZGxu/YULCZHJ2F9DYZzsfPxtIr9RkLr7nD2ZPAF9+CQMGwKpVerDxePSA9N13kJsLXi8kJEB4OJhMUF4OTz+t//TsCddeC9dfD4mJx7i2lLakApRyTDXWN/Pqjd/SKSWCF1fdxKb1Z/DqlfOx+puZ/PYE3rh/Dmbpx4DUHjgzKmmqFARF+OBqCCDROxwf/LFYTSR4B+BvDEJKCG6JpLdnAoHY8HokPtKPRIZjNfhj9vqS0HgG4aHhRHUOYcmbu4iKHMZD71/NzpX7mPufn9j6Yx5Pzp1IpxQbDpeDz7Z/xifbP2FVwSoAwnzD6BralbMSz6LR3UhtUy1VflVUDq+kwdKAucVMQG0APbr1IDE4kfigeAxOA9nN2UxfP51X177KmC5j+M+4/9DL1quNfwO/z2NqpMnpQ6/BXUlkOInJnVjXWMCYi0ZT83kNZ509ktytZdRVWunBeQQ0xuFpFgz2vwx/ZwTWAAstVWGcG3cTjQU+XHZ3Kiu/DcIQYOXhWZdhNBmw2+2kpKQceaHCwmDvXj0QGY0wZYr+c9ppeqbU3AzZ2VBYCEVFegADSEqCpib4xz/gySdhzBi44w604cOxxRx59qy0T0c7F5+i/MLab3ZRU+bkzjfOx+ly8LdbHqSusYanfriG9//xPTu839NnXAzrMjaz0zoHh7scV4MbS1wjW/iSOjRqjPtY3zwLh7kIq58Zl08VO0mnDg2DUVCHxha+xGWtpsZRTbZYhiGwhb2bSkkcGszOxkU8demHxHYN5f+W30BjnYsHUz/grrceJO6VOO74/g7K6ssY02UMfSL7UNVYxbridazIX8Huit1UNFTQVNKE/FISUBNAQG0Ate/VUp5fzqqCVUxfNp3Xp7yOo8rBN1d9w4vnvsjGko0M+u8gPtv+WVv/Cg5L0zRWa1+xe1sOLX41bDN8xapFa+l9ZidWfLuJ5vhcNvy4k7rKRlzBZWSxgEoKqPWUs7FhNk2WShrqmgjqZGRj0RJG3dKNjfOycTtMPP39NUTEB2G32xk3bhx2u/3ICzZpEuzbBx98oG8vWKA39/30E4wYAZ99BqtXQ0HBz8HJbIaqKsjL07ejo2HdOrTLLiMtORnt6aehpuYY1p5yoqkApRxTmZv2IgSknN4Jm81GUuOZnHXpYLT8WpylkqeeeYqNXxcw8Y7zGGG8kUHD+hHdOQQqA5l8+T+Ij0zEzxnDEL8r6JnUl/C4QMKssXTnXIKEjaikEAKxkcIY4iISMbZY6ReXiqPAQ99Riezb4GBE1/NIndCfN+6aR052Hp1ecVDlrsD+dw9nRY3m3C7nsrdqL4tyFlFUUoTVqM94IJ0SZ4uTRncjzWHNGK404Inw4I3wYr3ISpm1jMrGSoLDghly8RBymnO44NMLqGuuY8WVKxgeP5zr5lzHkpwlbftLOAybzcZTj/yT5mojHs2fp+95mZINHroPi6NrryRCCwfTpWcCPUfE0zOpH+fG3UzPhD4EGWz0l1fStUt3DEYjLTVG7r3zQTZ/W0R9dRP3fT6OroP1jCUlJYX58+f/sQzqssv0wQ/33QeZmXqz3Y4deoB54gm9z2nxYrjiCggJAYMBzWAAt/vAKbSKCqipwWY286S/P7ZnntGbBR95RG8SVE46KkApx4ymacxa+gZN0klVSR2aprG1fgE1jipcDS24cPLR12/SJJ3EJIdicoZw1jV92Lu5lPPSerN44zfU1FZx+yepFIev4u5ZI5m27Xbu/nY4u60/0O/aILQCB4ZOtWw3zCWrYAfRp3tZWPgesSMF25flM+ymGL7aPJ2YIRZCehq57bY7eGHjFPzuK8G/Kgz7Z9VkFGQQ7hcOTqiZU0NLXcuBPifRIHC5XbgcLjxrPbTUteAuc9PwbQOecg/+Zn8iGyLZMG0DtgYb1/a9lmfnPcuNt9/IB2M+oGtYV9Lmp9EeFgL9LeNuGEFYTABv3P8N6wsXMnRiInNeXosh0E1Dt20Y/FvYZ69g19YcdpauwWtuIrq3P4XGdezdVcCgsV047YpEXnvz35iC3Dz2/YVM/ej/fjFL+h8KTqD3O33yCfj7w/nnw9VXg9MJd94JwcF6v1NmJnz1FYwfj3b55aRZLGgWCxiNaEBaSwsaoLW0MKWiAg30IeovvaQ3BT7wgApUJxkVoJRjxmazMeW5Z/DBn7mvrcNms3Hthbexc2EZ4XGB+FuC6BtwHtHRUSz/dCdWm2T+O5uITAzmp8/zuGzkjQiXlX0ZTUx/9e0DX3JnnHEGX375BVM+/BvDLupOY5Y/r/37dcJI4JzzRjOAizlt8FAsVhNRwZ0YE/cXti4vZNXAr+jeci7P9HiZTSHLqOpUQGxmTxrdjTS5m8AfGA7ST4I/GIYbwB880qO/dhZYgiwExgfC5WCJttAltAt7jHsYeddIdsgdhPuFc27fc8kdkUunmE7cP/x+MrVMdlfubtPfxeH4+Jq5bep5FG+rp2fLOB7+4BpufXUspdudhOwZTuGWOoSA8NBwenrOp3JvM5V7XEwcezM3P3M+edvKWfHBHm649Hbe3Hwv/Uf0PDYLOsbHw7x54HDAHXfAPffA55/rWVBGhv4DcPPN2NLTubalBVtcHPj5YQMmAzYhsAFPAjbQRwFKiebvD6++Cl26wFNPoeXmHl1ZlRNCBSjlmNE0jbc/fp0R13Tmm3+vZeGnP7GzYRHCr5lXb5rL2bf34ItF72FLsWLfvJctjXPZay+gxeVGKy/nrY+nEtnTl2/eWsntVzzEpITneWbCLJ689AMeufU5rox8loyv7HQdE8jjjz1OHRpFFflksohadxnuFg/NooFtNUtZk5dBRGgQe1jKW0veAQkuWwV7ylfh3+xPk7sJQ4UB+aXEWGUEDeSXEmu1FR+jD1aXFb/VfvT170tNZQ3mDWb6+PdhV8UuRviMYOUbKxkTNIbPd3zOpD6TqDJUkVeTR+fQzgCUO9v3X+pnXNGLv7x4Dpu+KeT+094lPD6Qd7LvZsqcm7jy0VTOuqYPZ13Th7/+czx3vD6O0bd1Z/ayj3j/qXn4BfvwwNfnk+ezCmeTAziGCzoOHKg35Tmd8NFHcPHFsHAhdO4MX3yhN+/ddhsZjY1c1dJCxvnnQ10ddmAS+mSgGjCl9RGPR8+uKirQpISAALQpU0jr1Qvttdf0QRlKu6VG8SnHzP6lHAJ8g3ls9wze+MsSLn7mOoY/Mpinzv+Mn2blMXHMX9m9qIK4hBjQzsRH+tHs8uB1WujuezqV2S4s0o8U0zlIpwV7xj68UpIkziAoMASPxUP2ojqSxWgGDu7H8ml5nJf4V3bPd+Dja8ZgMtHNOY6603dyQe0tLGINFf1X0my0EFAWS2SoFxlShbPBSVznOIouKKJbj27srtyNZ4KHYQOGkRKRgld6+abhG36q+Yn0m9KZUDaBYd2HsWnDJrr37M6ay9cQmRhJU1YTlY2VAARaAtlauhXgQKBqzy5/6HQS+0Ty37QFvHDl1/j4mUnoFUFEJ330ZE2Zk32fVuCsaUIIuHTkrUy872yGXtgdg0HQ58xjkDUdyuDB+oCI8ePR0tOxjR4NS5dCRARaRQW2+npSw8L4vKqK1C1bAEj54AOmb99OSl4eLFzI5Pp6bAYDeL2/zKg0DZsQTA0MxHbvvfDpp2jPPYft3HOP/XUoR01lUMoxZbPZ8A2w8M9F15I4JIi/P/YPZr28mCe/uwprhOSLRe8R08ef+iYHOxoXYwz20ODQJx/9qXEWNe6yA4vj1TqrcTW5qWuoZatjAZWVGq4GN9UUsEV+y8at6wiMM7IufyEVVRV0HRJD+oubqem5j4m261k5cwfbImYzKjkV1/JAKDJir5uPs9jJoOhBFOUU4b/In6xdWQw2DSZ2eSyvDXmN185/jdsTb8fxqQM0WLxuMXwN23duB2BN1hp8N/kyd9Nczko6i/9u/C+DYgYR6BPImxveZHj8cOKD4tv4N3Fkhpzflbd23cnTP1zD2L8OICDUl+I9VZRkV2Gxmhh5dW/u+/AiZhTfxyvL7mT4xT0wGPSbYo/rTB3duqF9/z1pkZFoS5fCgAFolZWkmUxobjdaTQ0zhUBbvBisVuyPPcbdn36Kff587PX1ejbl6wv8KqOSEqTEpml6NpWZSdqYMWh33KFnbUq7ItpDZ+6QIUPkhg0b2roYyjHmcXt59+/pzHt1OyaLkfGTh+CSDax4bzeOykYCYg2Yvb5UlzoRApp9qzE3hurNcTixCn9MFiNCCFzUYzUEICW0uNxUeAsIJ4GAUCvCr5m6Ii8Wq4ninjux1SQg83zRhtiJvd1F7UozvjNTcCQU43NnDksaFjP5tMlMWz+N6UOnc8+Ge+hl68Uo/1F079GdnOoc5u6eS3ZWNr179SarIovgumAqAypJDE4kvzafZEsy+zz7GNNlDN/v+Z4F1y7g852f8/6W9/nxhh8Z3Xl0W1f/KUErK8P2wQf6SL6wMDS3G1ttrd6vJAS2/U10ZjN2ICUmBgoKsAcGkhIYCMXFEByMVluLbf9sE/u/88xmaGlB69QJ2759kJwMH34IZ5zRFpfaoQkhNkoph/zPfhWglOOtaE8lnzyxjFVfZuI2NXHWpYMItvlhz9hHzpYyAIIifDEYDdRqTuRB3QJGswGjyUAd5QQbopAS3M1uatzlBOqNNgTFG4mMjqQsr5q6iiasMYK0Vy7hkkUXMHbLzdRvMtLSu4Lc6xfi77ayR+yhT2QfsquyaSpp4ozBZ7C5dDOVBZV4wj0YhZEw3zCqC6txh7kJsARQX11PUFgQDpcDm5+N6qZqBsUMYl3ROqaMmkJWVhYzS2byxJlP8OzZz7ZFNZ/a1q+HG26AXbv0EXl5eWCx6DfwAgiBJqX+LyI4mIzaWlJjY/V7psrKsIeGkuJy6ce73fqoQYNBf91ohMBAsFr1UX5PPKHf9Gv8k5PdKn/YbwUo1cSnHHdx3cJ55PPL+WfGlWhJq1gzfxvfT99AeX4tQy7oSuoVKcT3iMDlbPlFcPLxNeEbYMFlrWa160NcvtX4BVnwBtexQczAKSpw4WRZ4Wfk7iqg98gEdl7wPaEXNjPn32s5/f1bcWR5SEyDhZe/Tp+AFLa8tIUkdxKbSjZRV1xH44eNLFq3iPK8cjwzPKDpo/hqCmtwf+QGDX0Y+jzw1Ok3iHqkhzDfMDYWb+SZUc8wb808Zj48kxtjb2TK6CltVMunuNNOg02b9Hua9u2DoCA9oMCB4JSG3oyXUVvLWCCjpkYPTsC46mrs//2vPtjCYtGzKKPx5yDldkNpqT7d0pQpMHasGpLeDqgMSjmhNE0jODCUTQv2smbOLjYtyKG6tB4Ag38znXvozXYGg8DrkbQ0e3A3e8iv3EVsQDcMRoGPnxmtJZfuCX2x+pkpq9mHtSWc7I0l1JQ5ceGke/9EIsZJnjPcx7WnXc2eqj2sKVxDd2d38oPycXlchPuGU59djyvOpRcuC+gBAoHZaKY5txkSwNfkS2NJI5YoC2F+YZTWl9Ld2p3xA8bzwZYPaHI38ViPx3jyiieP6PrVLOtHads2/f6o1av1SWVb77+ye72kCAFSkg5MaB0kQWAgGf36kXrXXXqmdNFFejPe6tX6qMD9s02EhurLfJx5Jqxbp587PR369m2rK+0wVBOf0i5JKSnMqmTN/G28OP0ZhoddTlVOC46KhgPHuHCyk+/pzXh88P/FNoBd/MCY7tfTd1h33AmV/P2LOxly9yDSb0nntZ9e49kVz9IltAuJpkSWv7Uc7zgvgaGBmJvM1MypwTvOCw3AJ8B1YI224q534/7eDedDiE8Ijm8deMd56RzXmf4B/Vk4bSENYxo4p885vDH+DbqHd//da9U0jbS0tGNzz1BH5/XCzJnw6KNQXIxms5GmaUxtDUppwFT0kXsa/GIb0OfuA3j2WUhJ0adQcrkgNlZ/PnasHggbG/UJa88888RfYweiApTS7h2cXTTWN1Oxr5aa8gacNU2UlZYRYA0GASaLkbKaQvoN6EN4XCBltYX06dP7wHneXvE2f1vxN8J8w3io/0N0T+jOP5b+gy2lWxAVgsSuiRiFkfrmehzVDhotrbOUF4B/sj9WkxW3101tYS3Y9OHjvf164xfqx9p9a2lwNzA0ZCjPjH+G85LPQ/yBpR5UBnWMOZ36EhwvvYRWV4ctMhLKy9EMBn0AxcEZ1f73hISgJSZiKyvTm/V++AEmTNDvuZozR59wdtEiuP12WLZMb1JcsEANnjiOVIBSThkHZyLAIbOSzSWbufXzW9n43kZCLg3hkkGXYMPG7FdmE3F5BDnNOWgNP0/Ns3+qI/MEM3HRcYR5w9g3ax9RV0axu3E3zd5mQqwhXNnrSm4ddCunxZ12gq9aOayKCnjxRZg+HRoa9Oa5sjIQggwpGQssBFIBzWAgzWhkalwcttNPB5sNpk7V+7fsdli5EiZOhLfe0gPWo4/q5/rpJ+j++5my8sepAKWcUg7ORH6dlezfllIye8NsZu+bzfe7v6fWVasHIn+IC4wj2BqMURjxSi+NLY1UV1ZTbaw+cB5RIRjQZwCjk0ZzXtfzGJU0CovRcqIvVfkjNE2f0mj6dH3KpOhoqKwko6WF1ING/Wm+vtg8Hn3bYoGRI+Hvf4eXX9azpfJyve+pXz99XarTToO4OH00odncxhd56lEBSukQfqufx+P1sL18O1tKt5BTnUOho5CaphpcHhcCgb/FnzBrGDGBMSSFJBElonj3uXd5Y9obqknuZFRbC++9B6+9pvcphYToo/5KS/XXAwP1/qWDZkM/4Omn4amn9Cxq7Vr9/d98A5deqgerO+88gRfSMagApXQYx6qfR/UXnQLcbvj+e31J+YUL9eHlSUn6Y36+fozZrA+OiI/Xm/CCg/WmvgUL4Kab9DWqpNSnYPL1/XnSWuWY+a0ApebiU045xyqoqOB0CjCZ9MEPF1+sZ0IzZujLemRl6a/36AEREfq9UEVFsFWfS5GEBP2G3Ucf1beF0Jv4du1qm+vooFQGpShKxyKlPoR8zhyYOxc2b9b3+/npCyP26qVnUlFReoDTNFiyBL79Fh56SB+MoRxTqolPUY6hXzf/FRcUY6oz4Sxz0lzfjPRKzH5m/CP9CU0OxSfQpw1LqxxWWRksXw6rVsHGjXqWVFX1y2OiovQ1qp54Qg9ayjGlmvgU5RjRNI3Jkydz/6X3U7Gigp1Ld/Lxro85n/Pxx/+Q7wnvEU7nszvTe2JvEs9K/EP3TinHWVSUPiBi4kR9W0p9donKSmhp0WeYiIrSm/mUE0plUMox9VtDvqmq0ptSsrP1Ibz7lzbw89PvQ0lM1JtWEhPb9RdBU20TP039ie+nfo9/hT8mXxMhiSHUe+uxNFporG7E3ehGGAQ+gT74hvlSba0m2hKNtkujtqGWpN5JnPuvc+k+Xt1ToyigMijlBPj1EG9t82bSrr+eqV4vNrv9lwfvn7CzpeWX+8PD9XtSLrhAH9YbHn7iLuB37Ji1g3lp89A0jaWhS7m82+W49rio2FWBydeEGzfuJjdIkB5Js7OZElcJHzg/4DquIyw0jIywDEIaQvhswmcM/OtAxr8xHqNFzZqtKIeiMijlqP3PTbMAjz0GH36I3eMh5ayz4Oyz9QXiCgqw1dVBZaU+NY2fn55F+ftjr6sjxWjUJ/Hct08f/nvxxXDvvZCa2mbXJ72Sz+/8nKy3s4geGI3Rx8iWtVuIC4yj2dkMXmgwNuDn8dPrAA1b66xvRh8jma5MetCDoPggdhTuYNjgYXQ6oxPrXltHwuUJ3PTlTarJT+nQVAalHBe/zppsu3fDZZdBdTXazTczJTeXqUlJ2F59Fa2mRp+0MygIAgJIq6lhanAwtqYm7NXVjAPmAyl9+sD48frQ36+/hq++0udH+/e/22Rm6dn3z+a5t5/jwasepGp1FeVl5awwruD8+vPxE344cTLPM4/zOZ8GGviET7iO67Bho9xdzg/8gM1io6KogtmG2QRsD8DkY6L/Q/35x0v/oMeMHpx+4+kn/LoUpb1TGZRy1Ox2OykpKfpcZaNH6zc8fv01rF6Ndu+92NxufTbo+nq0rVuxtU43YwdSDjpPBpDaqRMYjaTn5THBaNQ7rrt0QZs+Xc+8/v53fSbqEzSSqvCnQt4b8R6dr+5MU1YTldmVSI9EQyPEGYLBYkC6JfvkPuJlPAaTgd2G3XRt6QoSfIJ92Fa7jQHhA3A3utnRsIPzx55PzsIcrppzFTPunkHvfr25dt61J+R6lLahbvo+PLVgoXJcaJrGlClT0AoK4Oqr9bnPVq3S7y+54w59Ms6zz4alSyEnB1tSEvj6ogFT0JdCoPVxmsmEVl9Pel4elwDpsbEwZw7ayy+TFh+Pdskl+mJy48bpU9mcAOumrsM31JeB4wdSsqmEqD5R1HvrWexcjCfEg8XPQrm3nK9NX1NBBWXuMr5r/g5NanS/qDtFtUXMN82nsLqQ4oZi5pvns2vXLow+RrYv3s5Ky0pytuWckGtR2sb+VgZN037/YOUXVIBSjorNZtOb977/Xl+G+7339JF6TzwB11yjz3e2ZAmccw5UVaEVFUFMDDYfHybz8/o8NiGYYDZja2xkAvC6nx8TSkvBZMLWqxeTd+zAZjDAf/+r37MyZgzU1R3Xa9M0jYKMApLHJpO5MhOTr4nqnGoSeyYylKEYa4wEJwSTHJ/MdZ2vw2a0MXnOZF54/AWifaJprm8mISKBi6MvJtwbjg0bV8VcRZQ5Cq/bS3hoOBclXER4SPsZCKIcewf+j/xqdKvy+1SAUo6azWbTVx7t0UNv4vv3v/Wh4wMHwpo1cOONsGQJ2ogRpDU0oJWXY09KYpLBgB1ACNJ9fbmxsZH0pibsRiMvNDZib2mBsDDsmzczyWrF/uWXMG8efPmlvvz39dfrIwGPg/1/9TpcDuq99by1/C3qPfWY/cwU1RXxNV9TbammpbGFvYV7mZk/kwpZwbRLp/Ho849SYaygOruaXRW7mFk4k7KgMioNlXxW8Bl1wXVIj8Qn2YePV35M2Olhx+UalPbj18FJZVRH5qj6oIQQIcC7QB9AAjejL5z9OZAE5AETpZTVhz6DTvVBnQJ69oS+fdHeeANbv356M9zu3fpyBsXF0LkzbNpERpcupDY1QV4eM/v25dozztDX8qmq4sWlS3m4pQWSk0nfu5cJSUl6VtavHxnbtpF6ww36XGovvwwGA9x/P3z8MVx33XG5JLvdzra/b6NwTSG9n+7NT3f8ROdzO5O7OJcS/xISDYk01zVjCbCwvn49/ekPwDrW6RmWjxGPy8MO0w76uPtgspoo9i0myhFF9wndcTlcZK3J4qHMhwjtHHpcrkFpn1Sf1C8drz6o14D5UsqeQH/0fu9HgSVSym7AktZt5VTn46OP0ktLQ3M6ISBAX9ogKUkPUF27Ym9sZNK+fdj37iXj5Ze5bedOMqZNg1mzyFi4kGc8HjImTsS+dy93+/hgz8uDIUPQtm1jWnAw2g8/wPnn68sh3HijPrv0k0/qy38fY/v71rre0JWy0jKef/F5Yi+IJXdxLubOZjKcGTilfrNxTmMO6aRTQAFZZDGPeWSRhdftRUNjkXsRGhouPxcrqlcQ2D8QV62LvKV5XPX2VSo4dUAqOB2ZP51BCSGCgK1AF3nQSYQQWcAoKWWJECIGWCal7HG4c6kM6hRwww0wf74+Su/SS/Wmt8BAPTjl58OoUfDDD9ivuoqUzz+HHTvIuOMOUlet0tfY+fFHMr76itS5c+Hbb7F/9BEpqan6DNQlJWjnnostPR3+7//0e6w++kgfyXfttfp9UyNGHPNL2v9X7uLHFrPwhYUMuXwInmYPu7/bjTfMi9lpxuPyIIxCH8XnjQcgiyx6mnoi3fp/i2rfasJbwvF6vdhG2WjY2UBjVSMT/juBgX8ZeMzLrSgnm+ORQXVBH3z1gRBisxDiXSGEPxAlpSwBaH2MPIrPUE4Wl18OmkbFzJn60PB16/QMx26HYcNg8WKIiWHv0qUQFASXX07YBReAvz/cdRd89RVhVit066YvLud2Q0qKnoX16EGFw6F/TnMz+PlhX7JE7+8CfZXT42D/X7nnPH8OF/3fRez6Zhf5K/LpeXlPomKi8Lg8ABgtRqL8ojD5mTD5mgiwBmCy/DwM3l/4Ez0omsCYQLQfNYITgrl13a0qOCnK7ziaAGUCBgFvSikHoi+mfcTNeUKI24QQG4QQG1Rn4Sngwgux9+jBuEcewT5ggL6ezldf6TfWbtgAQUGkOxxcUl5OuhDYCwsZ9/jj2Pv1g6uuwj5xIuN8fLBPnoz93/9mnK8v9i++gCFDsO/axbgNG/QBFcHB2L1exs2ejX3/v5vjPJpPCMEZj57BHVvuoPPozuyavQttp0ZQpyCiB0VjSbYwj3nIMElZYBkfNn2IZtOw9bLhDfcyt2Eue9btIbxbOBNnT+SWtbcQPSD6uJZZUU4FR9PEFw2slVImtW6fiR6guqKa+DqUAx2+GzZgHz6clJEj9b6hceMgOVm/Z6m6GqxW0quqmBAaCtXVZAQHkyqEPnM0MBO4NjgYevYkfcsWJvj66u/fto30oUOZsHq1Pkji+utJf+ABJtx0kx4AP/xQ75M6QRyFDuyz7eT+mEvZ1jJq8mpw4jwwk3kWWfQ09iS0Sygxg2IIGhjEsGuGEZwQfMLKqCgnk99q4kNK+ad/gJVAj9bnTwMvtf482rrvUeDF3zvP4MGDpXJyKi8vl1dffbUsLy/Xd8yYISVIOXq0lF98IaWfn5Q2m5Q9euj7Y2KkBFluNMqrzWZZLoSUIFeB9Gt9zDQYZILJJDP9/KS0WGRmaqpMAJl57bVS9u4tMyMiZEJ8vMz8y1+kNBikLCxs0zpoaWyRNQU1siKrQmatzZKXX3y5LC0ubdMyKcrJBNggDxEbjnaY+QD0YeYWIAf4C3qz4RdAAlAAXCmlrPqtc4DKoE52/zNZ7IIFcPPNEB+P/ZZbSPn0U9i5U+9TKi7WM6rQULTmZmz7l90A0oEJ+58LwYSePfWlOSorSR81igmVlXqf1rx5ZOTkkDp5MkyahPbSS+1qVJQaQqwof8xxGWYupdwipRwipewnpbxESlktpayUUp4jpezW+njY4KSc/A4OTmlpaWjnnQfLl2N3uRj3979j79IFHn1UX7m0thbi4iA4GFtj44FzaEIw02BAMxqxA3dLiX3XLn14+ogR3L1sGfaCAkhPR6upYdrkyWixsWiPP97ubnpUwUlRjg01WaxyTP0ie6ivx37ffaR8/LE+Ku/SS7H7+pKye7c+sSyA0aivVurnhyYlNosFhMBeW0tKebm+XlRQEBnnn0/qww/Dm2/Cu++ide+ObeFCSExUGYuinOR+K4NSAUo5/goL4ZVXsL/zDuPq65kfHk7KOefow8mbm/Wh5KWlsH8oeUCAnmWlpOgByOEgbfp0ppaVYTOZ4G9/0yeN9T/08uqKopxcVIBS2l5jI/a33iLlp59g8WK0ykp9slghwGZDCwrC5u+vZ001NXrQap0lQktMxHbddXDHHfpyHoqinDLUgoVK2/P1JeW++wDQyspIu/lmpl56KbaiIrTsbNKWL2dqcjK2gAAIDv45ixo6FFuXLnogUxSlw1ABSmkTtqgopn744YG+IxswVfUlKYpyELXchtJuqOCkKMrBVIBS2oRaE0dRlN+jApRyTB1pwDnUKqOKoigHU31Qyp/maW7GkZuLIyeH+sJCivPy+NecOdxz5pmE+PlhslrxCQ3FPy6OsF69CO/TB4PZfOD9KjgpinI4KkApR8zjclG+YQOla9ZQvnEj1ZmZeN3uA6+b/Py4KCAAWVBArdGIx+WiqaoKT1MTAObAQJIuuIBet9yCf2xsW12GoignCRWglMPyut2UrFpFXno6RcuX425oQJhMBMTFEZScjMflwlVTQ3NNDVUOB7NKS7khOpogkwlfm43oESOwDRqEb2QkJRkZ7J0zh5y5cxn+7LMknn9+W1+eoijtmApQyiHNmTWLrtXVZH/xBY3l5TRarcQkJuKqrqasuBiZn4/BbMZotVLb1IS/0UgQcFl0NBFhYVhCQrAEBLB940bCli7F12ZjxAsv0D8tjdUPP8zqRx6h0deXnqNGtfWlKorSTqlBEsovNNfVMfW227j8mmv46Pnn8QkJwRsdzXvZ2RTY7VRUVTGjtBSH2430eKiqr+f93FxqXS4cbjezNY16gwGPy8X2TZt4euNGAq64AktwMMvuvJMWp5NRb75Jk58fk2+/XY3iUxTlN6kApQBQXl5OXno66ePHE5GRwcMjRnBmSgo1u3djqatjTGgoQQYDAVLqz00mhNFIoJScHhREkMlEiNVKckgIwRYLjWVl9B8+nJsHDqTh228Z/txzGIxG1r3zDuaAAJKHDuXmzp1/MVDCbre3YQ0oitLeqAClUJyby6Qzz2T+Aw/gGxWFpU8fVuzcSUWVvlJKQU0N04uKKHK5KGxoYHpRESVuNwhBltPJ60VF5EhJhsnEJ7m5fLdrFz1vvJGMjAxe37wZe00N1XY7zcHBvPjNN5SXl+PIzSW6U6cDZbDb7YwbN04FKUVRDlB9UB1cQ2kpm++5hyuNRgZfey37Fi+mpbqaW3r2xFpfj7BYiDUYeCQxkXg/PxCCRxIS6BIXR2BCAhO6dSO5qop4h4PS1aux3Xor3dasoaWhgR7+/ky94Qb8W7cNpaU8c9dduLZvx5GTQ8+DlmlPSUlh/vz5pKSktGFtKIrSnqgMqgNrqqpi8U030VBWRtLtt5Obno70ejH5+iLdboTBgNnPD7xefEJDkR4PcSNH4jdwII1lZZSvX0/ed99h6N+frldcAUBXjwfp9VK1cyfCaGTnmjX4RUez96uvsIaHY+jShXVPPUVI9+4Enn76L8qjgpOiKAdTAaqDklLqI+nKy4l+4AGue/RRipub8QkNxdHSwvt5eXiiomiuqcHdvz9TtmwhcNIkDBMncv/s2WS1LtW+yWzm+htv5IW//hXNaiVtxgw0Hx+qMzP5qamJ1zIzWaFp1BcU4H/55Vx05ZUUOp30fOIJ7n3gATVIQlGU36QCVAdVvHw5patXM/DBB4mzWHg4NpYxd91F7Z499Bk/nktCQuh2xhkAnHHlldwdF0dgURGRhYU8e8stnDV2LMHdutG3upqrbTZGRkZia2ri5k6dsLW0IMxmTg8JIa1zZ4b7+NDzppuonTmTJ3r35oZPP6XL4MFqqiNFUQ5LBagOKi89HWtEBF0nTkTbtIkusbH4RkUB4I6IYE5FBU6jEWEwsPI//2E5kL1mDd8+8QQvfvABG3/8kfqCAooaG1lUXU1hfT1FTU28X1hIUUMD5oAA3E4nY/v3J2LgQDLffZfgrl25JT2d0NamPBWcFEU5HDVIooNylpQQ3LUrBpMJk78/VTU1hLRuN6xfz5kpKZTOmkVAYiK+TU2cBfg1N+NntXJ3XBxxvr54W1qIs1p5JDGROB8fpNfLEz16EOn1gtdL1LBhaJs3U5+fT7+0NHrdfPMv5uI7UuXlsG0b5OdDVZW+4G5QECQlwfDhIKVaR0pRTkUqQHVQ/nFxlK5eTYvTSfCoUXzwf/9H4COP0Ofuu5nxwgu8nJvL4wMHMqi+npySEqYXFfFIQgKBJhOLamqI9fcnsPVcnQL1Z7K5mYTQUPxiYqjOyqJ8/XoSx4+n39/+RkBc3BGXzeOBZcvg669h4ULYu/dwR2tER6fx8cdTOfdcFaSUE0NKSeXuSoo3FFOZVUlNXg3OcifN9c1Ij8ToY8Q3zJfgxGBsKTbihsUR1TcKYVCrQv8RQkrZ1mVgyJAhcsOGDW1djA6lYutWFl13HVHDhnH6iy8y66238P/uO1ocDoK7dWNeQQHnhoXRVFmJt7mZTQ4Hg4KCwGAgu6WFnoGBuBsaACgFenbvTrPDQUNpKUarlc4XXkjPv/yFoMTEIy5Tbq7G11/beP11KCjQMJttBASAwwEejx04eJSfRvfuNvr0gSVLMnA6U5k1Cy6/XF/yoz1lVO2tPMqf4/V42btwLzs/30n2vGyc5U6cOPEX/lgCLZh8TAiDOBCEHM0OzHVmPM0enDiJjo2m73V9GX7PcAJjA3/n0zoWIcRGKeWQX+9XfVAdVET//gx79lnKN2zgpdRU7nj6aUxXX02/tDRKpWTWjh3syc3F29xMUVMTH5aWUtTURFFjI6/n5VEVGEhwt26Um0w8v3s3O7dvxz8ujtOeeopLly1j6NNPH3Fw8njgX//S6NkzjYce0qip0YA0Wlo0amr2B6dxwP6beDWESKOyUmP2bDsezyR697Zz882Qnd2+FkJUCzOe/DwtHta/uZ6pyVP59IJPyfwyE6OPkUZzI/OYh1M6aXY001jVSENlA84KJ+Vl5cyunI2j2YETJz8G/YghxsCaf6/h9W6vs/7N9W19WScFlUF1cLV797LjzTdZPGcO3axWEALfyEi2NTczIjERr9uNx+Vi6759JEuJx+Uiy+mkh78/ftHR2AYPpj4+npGTJuEbEfGHPz8/HyZNgtWrITDQTl1dCgYDeL3pwISDjnwTuLP1NbBa02lqmsCgQZJNm1bz6funMelmC1/NbKZXv92k9OlzyM87btmMlODSoG4vWnE2tmAzCAOYg9EaA7F1HgoGk8qmTjJl28uYfe1syreXE949nPKKcoxVRgxmA94WLxoaNvTf58HPEVAgC0ggAaOPkSZLE+Y6M72u7IWr1sXehXsZ+++xjLh/RBteXfuhMijlkIKTk0l9+WUe27KFkdOm0feuu2jq2pU3tmxhX10dRrMZl78/PzqdhF9wAUl33smWrl0546uvuGTJElJffJHz0tL+VHBavRoGD4bNm8Fq1airmwJoeL0ZwFVARuuR6cDfgHSsVglk0NQ0kciwZRTmlADTsC/NBKB8/Vam3HM/2vxlkJUL1Q49eHAcshlnAWS9DssvhjnRMDsK7evTSbvzBrT510DGVbBsHLafUuHLQLTZZ5H2l/FoJfnH5vOV4ypveR7vDX8PZ7mThDMTKNhdwLeOb3HiRHolTpwsZzlOnGhofMInaOj/tjSp8TVfo6Fh9jVjrjMTMySGzC8z6XRGJ3pe0pMljy/BqTnb+CrbN5VBKYdkt9t/MbPDwX/5H4ssYONGGDlSIzjYRlkZGI3Q0pIBpLYe8SZC3ImUIARI+SIhQQ9S4zBgMXmBJwkLeooWt6DSMZ9bJwzlnfRI9szcQYshm5QuXcHVrKdbQf7QIwn8fP/nuv4wrwcKv4Hd06B8mb7PLxECEkGYwNtCxvZKUnsHgckffMLRGvyxhVigbCla0W5skVEw+DVIvEplVO1UfVk9b/R6A2+4lx7De7Dt420ExASQX5ZPuDcck9WEu8lNlbmKsJYwDBYDdUF1RFujqSusw2gxstG0kaHGoTTXNxPQN4D67fUkjUrCUejgko8u4f3T3+fqb6+mx0U92vpy25zKoJQ/5Ndf4gd/iR7tF6rDARddpOH1plFVpWG1gtttByah9zNlAA8ybFgGa1ZLfvzqBwziaWocqwFodn9Gs/s5Sqs+p6llNTCRmYt2cd5ptQT7lzDlnTfRSkr04BQaBA1NsHkXWm4+U6ZM+fMZVPF8mNcPVl0B9TkQfR4E9oCGfChfAeXLse/cwqT/24U9Kwdqd6JlfkPa0x+gbXkfIs/CNuFb8E+EjKvRlj+q+qfaqfXT11NZXUlGXAZrPl5DlzFdKCspY4VpBYZYA+4mN8Y4IyuMKxj78VjiTovDv8Kf8dPHc9Pym8hszuTbhm/ZXredZv9mZhXOwin1zEsIQX1JPQA+wT5tfKXtm8qglBPuySfhn/+EUaM01qyx0boiPGBHiBQSEiA/P4MvZo2geFs1H3zuy9a9m4GD5+6biY95Ei1ugS1kGY3NZ7L5nUy6xDaj1VRjCwkFkxHcHogMg9o6MBrREiOxRUb+sQK31MH6uyHvYwhIhpD+ULoA3E7wiYQWB3gPXAT2EiMpMRLwAqC1xGLr1AfKl4I1GkYvgsznIXcGWt9Z2PpedRS1qRwPsy6ZRVV2FUOeGsK8ifPoe11ftn+ynUZTI537dqZ0cynhPcIpKSxhxPUjcJY5qSuuw8/mR+6SXLxuL7tadtE3uC+uWhcuq4uYTjFU7aki9dFUds3ZhbvJzd/2/A2j2djWl9vmfiuDUvdBKSfcZ5/BuHGwcmUF3brZsNvB7Qaows9Pz7AsFsnEqw1AOLD/j6j995BIBIk0twgiQ1uocpiY/9JeusQ2A1BRW6MHKI8HrcmJrRzoHAe5Rdh8fP9YYRuKYel5aIU7sQ24DcqWQOFsPeiYndBSDQYLWg3Yglrf4/X8/H6/Tuze5cVmXgg97kXb8iG2NdfDuSugfCW28v+i97cp7UlQpyByFuUQEBWA0WKkNr8WgOi4aEo3l2IJtFCZVYnX4mXTO5uQHr1PKsAYgPTo/17jiMNV5wLAaDFStaeKTqmd2JO+h6KcIm5fdLsKTr9DNfEpJ5SU+o238fF2nM5xGAx23G4wGDKAsTidGVRXZ9DcPBZY1fqu1cDB2xlIzsNgWImreQ0Gw1h8zKvAYMCen8u4h9Kw7ytAq3OQ9uLzaDXVBwZK0NR85IVtroEfz0ErySXt2/5o22dCSy1aYzBp/y1Ba/ADbwtanYW0GaA5wF4E4/6lPxI2mIzMRsY+VURGxVC0Da+T9nUiWt56cNgh/hLQVv9cNqXdOO2u06hz13HTpTfR996+FKwsICg+CEehA2EQtDS24MRJenM69Z56nDiZxzzqPXrT3f7tBhpoMDbwjeMbggYGUbS+iOJ9xWxP3Y5/L/82vsr2T2VQygklBK1NeCmMHfspS5akEBcHRUWpGAxv4/XuHyTxNHBG6/PTgaeAVASSIP/h1Dr/zbjTB/LOfyUfz/kHqZdeCNkFpHRJ5vnbJ5PSuTMAky+bqGdT7tasxufIp1rSFtyIrT4b29hZTC69Se97a67GFmpl8phabOGh0NiELcTM5KuHYesssRmsTH++BynDY2DHs6Se9zfeLpxK6vABkL2OJ+8ai82xFRoKwGBkfzPggc9Ugyb+tGNZd7YUGzd+cSPiaoH9LTspl6dQuLYQ6ZEYzAYMJgP+bn/O53z80QPN/ufCKPD3+nOWPItAcyCBsYGMLhiNY4uDvtf0ZcxLY2gyN6nf8xFQGZRywt19NyxcqJGdPY3+/TWKisDPz47X+zj6IIl04HHgO/Tmve+Bx/AxfYdEEBa4HIv5fh697Qc2bv6Bx5/8B+mfzQIgY8tmbnvxWTI2b8K+N5tJU57Avi9f74OymCEo4IjKqO1aQNr/zUWLfQD76plM+k899sookBJ7QQOT3jBhzy6C2POxM45JU37CnluFfcsK7n78HeyGK8E3BnvmDh7/Auy789EcMOX1uWgOwL8zlC6B0EF61Ebd1Hs0jkfd9by4Jw9sfYAu53bBPttOXVEdgXGBhCSFYA2xYrQa8Rc/Z0H++GP2M+Mb5osn1MNyluNwOaisqWRrl61cteoqLpt5GYGxgSo4HSE1SEI54bxeuPdeeP11O0FBKfTqBY2NUFRkx+VKwd0ikd43CAi4A6cTGl1G4F9cMPwO7ryonAuG1/Ly55/w8DXXAfDizI95+NrrD5x/5sL5XDt2HAjI2LaV1DNSobYeenaGqPAjK+T6ydhXvU/K3aXwTTzpBacxIW45xF8G+74ko+FSUv3mwMV5sPlhMhZ9QerATtCwj/Tc/kwYMwyy34bwoaQvWseEMxJBCLRqF7boWOj1d1h1GZz2JnS748DH7h8GL6Vk/Z717GrcxeaSzeyp2kOho5CqxiqaPc1YjBbC/cJJDE6kT2QfRiaO5KzEs/AxdaxRYVJK6vfto3bvXgp37ybIaEQYjVjDwwlOTiakRw8Mpj92g/Shjq0tqCXzq0y2zd+GM1MfEMFvfHUGJwRj62XDv68/Ay8cSKcRnaisrlRB6TDUIAml3TAY4B//0LDbpxAWNpW1a20UFMDPc+3tAv5FbMCZjB8aR3zMcl6Z+TSPXxtBat/+pK9exePvvE6vxCRCAwN5ZsZ/Se3Xj9S+/bHn5/L4u9MY1KMHEcEhTJv7Fd07JWDr3UMfzXeEtPzNTJnrz9SJ5VTk13H3KxtJvt9DSnI4mgOmzcmk+6VgW3MTmv9Ypq3eQPc+Nipccdz9n3UkW7aS0jUe+7Z13P2RgeTofaSk9MbmUwgpr8G6W/TRgMl//fkzNY37H7+f5EnJ/LDnB3I/y4XzwTfYl6SQJMJ8w4jyj8JitCCEwOV2sadqD+m703lu5XOEWEP468C/8nDqw0T6/8GRiicRT3MzxcuXU7BoEWVr19JUWfmbx1qCgwkZPZr/Zmbyxttv/26Q2J+J/XqtsuCEYLpe35Wp66cydfNUgv2CqS+tx+Vw6ZPDWoz4BPsQEB2Ayed/v1ZVcPpzjjqDEkIYgQ1AkZRyghAiDPgcSALygIlSyurDnUNlUB3TwX+p1tRAZaWeXQUEQOW6hfQJDYdYGxSVM3PJAq4957wD7z2QJQHpq1cx4fQzDryWsX0rqf0H6IMm9maTclYqJMQcaEo7IkvHYd+dR8ptdpgdSUZpT1KTm6ChCPzise/YQEr/M6FmK7TUkpEFqT0ADKRv92VCXycYrSAhY7cktW8ENJWhxT+CTXsPpAfGroHAZKSULMldwnMrn2PZ+mUYI40MjRuKscKIM8TJ7srdOFuc4AT2tyhpgA2SQ5M5t8u5JJgS2Fa/ja8yvyLAEsDbF77NxN4Tf7feTyaNFRVkffIJe7/8EldNDT6hoUSffjoh3bphDgzE5OuLJTAQ38hIahoaMFZUUPjjj+xbtIgyKbnitdeIHz36dz/ncPVzstZde3c8b9S9h59n8QR4FFgipewGLGndVpT/cfB/9JAQSE6Gbt0gJgb6jD8bAvygqBx7ZRmPvz0Ne34uAFptNelrV6DVVKPVVDNz8Q/6SD1Ac9Qy7Zsv0aqq0BqdTPn2czQ/8x8LToBmGsyUj7LQ9ixFi/gL02auQjMOAE8j2r4dTPkuCC1nNbTUYi+CSdP1kXv2Ii93v9OAvdgInia0phCmzXeh1XrQwq8n7ZH/Q6szwDlLITCZvVV7OX/m+Yz5eAy78ncRvzmezubOrMlaw6pPVlFQXICv2RdjgxHmoQcpDfgEOjV3wtfsywcZH/D3h/5OU20TGTdn0DuyN1d9dRXvbXrvf6/rJOzncjc0sHXqVOaOG4f9vfeIGDiQlL/+lcghQyjNyGDrf/7DhmefZe3jj7Pib3/jy8sv58YLLmBPRgaDH3mEYR98wJyaGr6/+25K16z53c87XABSwenEOqoMSggRD3wEPAfc35pBZQGjpJQlQogYYJmU8rBzeagMSjkkrxfyivUglbuXlOSuYDKBlGhVFdhC9CY7rboKW1AwePV/y/ZqjZQzRkB4MFpFxZ/7UmmuRpvZA1t4IIyah7b8IWx1cyG4D5j8sO/YREqM+8Dh6ZtgwiD9+cy1Plx7VpA+eazRD813JDZDPjjs2FvOIuWqL8AaycdbP+b2L27HFGDiqt5XsSR3CblFuURGRlLbVIur1IUlyoLFZMHZ7EQWSEgAozDim+uLX28/yp3lBPsE07e5Lz+Jn+ga1pUlNyzh2o+vZVXlKrbfuZ0eEb/873cyZQGla9bw05NP4iwuJv7cc/GNiCD/hx9odjho9PMjNjERYTAgvV4cXi/RMTFYbTb25ORg3LoVs78/I19/nTIpKXj2WVrq67nwhx8w+nSsvrr27nhlUP8BHuaXY2WjpJQlAK2Ph2wMF0LcJoTYIITYcDL9NaecQAYDdImH4f1IGTMKwoLBaAC3B5t/kD50XAhssTH64IceSWhdY5ny+Qw02aK/9me/iC2h2C78DlxVsOA0bF1O1wc0yBa03HVM+dqNVm8BczD28kDu/lDPoDKy4Lb/usjI8YfoseATjq1+Pkg3Wsp7TJkXTbkDnln2DDfMvIGAxQGMjxvPu5vfpdHdiE+QDxUNFYgGAcvB6/Ti8XqQmoSvAQ0sVRYa5jRQnldOgCWACGcEq15ZxY2xN5Jdlc29s+8leGkwxkYjr6599X8u7WQITl6Phy2vvMKPt9yCwWKh5003Ub5uHXtmzcInLIxGX1/e272b/O3bqS8upqKqiulr15K9di2bP/uM/y5YQLcHH8QaFsZ3t93GlH/+k8Tbb6ehtJR9ixa19eUpR+hPZ1BCiAnABVLKu4QQo4AHWzOoGillyEHHVUspQw93LpVBKcfSMc0Q6nP0aY5K5oPBArazICABrbIeW4ALPE3gbcae30BKoj94GshYv4vUztWAgKjR+ii9+MvAYETTNGbsmcGDix7khv43oGka84rn0TO8J7sqdxFqDaW6qRp/sz/OGic+QT5YjBbqm+uRmiQgLoAo/ygKcwox2ozEB8dTUFtAV09XinyKuLbvtby18S123bSLB1c/SKaWSdbkrGNTFydIi9NJxkMPUbx8OUkXXkhjWRll69bhHxdHQ3k50u3GGh6OVlHBwcv+OdxuQgMCiD/7bHI3b8ZcVcUZr77KynvvJey88xjz/PN8nZpKwnnnMfTpp9vq8pRDOB6j+FKBi4QQFwBWIEgI8QlQJoSIOaiJr/woPkNR/rBjmiEEdIHR86BqM+TOgNLFoC2DmhYI+vmwiGagTEBgV1JHjdMDU+x48Iv9xelyXDk8vPhhruh1BTEBMczYOoMR8SNYU7iGCL8IagtrMUeYMRlMUAyeAA9dbV0J8gnCGmwlrFMYs+2zifaPRvgLrCYrzZ5mkuOS2VG4g9jAWNxeN/m1+UT4RuBwOU6qJr0Wp5Nlt99Oxdat9L79dnK//Zamqip8o6JwFhXR6O+Pb0sL7oYGzIGB4HSC14swmUjq3RtreDj5P/xA10suoWj5crK/+IK4UaPIWb0aYTAcCHLKyeFPN/FJKR+TUsZLKZOAq4EfpZTXAXOBG1sPuxH49qhLqShtLWwgDH4Vxm9HOyubtEUXoA38Hs5djjZoHmmLx6OdnQ8X7obUT6Hrrf8TnKSU3LfgPqIDonks9TFeWv0SF3S7gDWFa+gb2ZfKgkpaPmohqimK2m218Cl0rujM5tLNLN+wnAVPLyC4Lpgnez1J/vR8UkhhT+UerC4r695bB06oaqoCJ7z61KtsydlCnDHupBkU4W1pYeU991CxbRv977uPPZ99htvlwmix0OJw4PB6eS87mzqgyuHgg7w8REICkaedRsLYsTSUllK+YQNJF19MzrffEnvWWZSuXUtLeDj/3bYNTdPwuFwYzUc+m4jSto7HTBIvAGOEEHuAMa3binLKsMUkMPWtD7GlXACRI7H1HMfUNz/AFt3psO+zV9hZU7iGR1If4YvMLzAIAy1lLYT7hpNVmcXpA0+HsyGpaxJdR3Tl7kfvZk/4HmIDY7nz/DvhbDBGGtni2QITYLvcTkJwAg2WBkLPCyUkPIQlOUvontCdW++9lQ21G7hs8GU8+eSTJ0UGtenll9m9ciX977+fXR99hMFsprquDmEy4W5sJCIkhDFBQURGRRFkMnGRvz9Wp5PK7dvJ/+EHet16K9LjwdvcDFLibW7G09SEv9vNxIQEwoKCqC8oIDAx8aQI2MoxClBSymVSygmtzyullOdIKbu1PlYdi89QlPbk11/4RxIA1uzThzhf0O0CNpdupofswbJnl5HUkkSzpxkKgHSQ+ZKSnSW8++q7DHUPpc5Vx5tz3kSkCxYtW8SXy7/E8L2Bopwi9jn2EWOIIfOrTAYFDWJz6Wbu6nUXtzx4C4EtgVyWcNnRrYF1ghSvXMmGDz9kto8PO9PTaWlooLa5mQ+LiqioqMAnJIS8qiqmFxeTW1pK7HXXsTYlhdQvvuDylSsJ7dWLvO++IyA+nsbWJrzGigqs4eHkrFvHt3V17Jw3D6/bjbFbt5Mmq+zo1Fx8inKCOFwOAMJ9w7EYLcgISerjqWh+GjY/Gw0xDTAR6qPr8Uv2w3W2iyz/LPwt/hg6GZBjJEUhRZijzIgrBAabAR+jDyXeElKuT+HH8h+5steV7GrYhXaWxutXvE7PxJ7/MyvCIXm90NAINXVQWaP/1NZBo+u4z7buaW5mw3PPEd+9O8/ceSctdjthKSn4NDRwRlAQ0fHxuGpqSI6N5bauXbn2gw9w/vQTnbdupWjGDEx+flgCAqiqr6cuPx9vczPCaKRq507C+vRB5ufz3IMPUjl/PtbwcHqdd96R1YnS5tRUR4pygiSHJQOwpXQLZyedTfrudK4/53oeW/IYZyedzY87f8Qv0489iXtoKG2AReDu5KYhsQHvPi8sAme0E0ukBc9aDwFRAVTLarqGdcVeZWdCtwn4GH14a+NbPDzmYW4coHcFH/KL2OuFagdU1erzFDobf7vgBgME+UN4iD5dlOXY9uHkfvMN9fv2MXL6dNY/8wxhvXujbdpESUQE/9mxA18hSE1NZUtGBh/V1zNy9WrWbdnC60VFdNuyhaiVK9mzejWfNTZyjZ8fYvduApOScOzdi6u6GnNQEEn9+rHsjTfof889GC0WFZxOEiqDUpQT5JzO5xDkDuK5lc9x88CbSQpJ4tW1r3Jt32v5Me9HEmITMI020WBpgASw3mRFJAiavc3QCbgCDAkGTIEmOAssQRYCLAHkVudyffL1FNQW8Mn2T3j6rKd54dyfu35/0ZTlakZbtwXWbIMd2Wi7svWbnyNDISoczST1IBSlTzNlb3Lo2y1u2LsP1m6D3fnQ3HLM6mX3rFmEpqSAlDSWlxPUuTNISWevl/8bPZruVit+UVEkhITw1rvvMnLSJK555hlevOEGzr30Ulbeey9RsbGMMhiIjIoCIXDk5RHevz+V27bR+9Zb2fzSS/hFR9Pj+ut/v0BKu6EClKKcIA21DXRf150lO5bw0KKH+O6a7/Az+/HZjs8YmTASb70Xx0IHOMHX5Iuliz4pLKBPcbRdv3HXv9kfsVxQVVHF4JjBjI0ey8f/+piSshLSr0nnqVFPHXjfgamNSkshpxBtwQrSHnsYze1CM0PatH+j5RdAeTVaVjZpT/4dbc9eKKvEvmYd426YhH3dBvCxQPdEPXCVVsCGnXr2dZScxcXUZGXR+cILKV27FqOvL80OB0FdutBUWcmQvn0BCO7alYKaGm67/npWf/MNVXV1zPnxRxY9/jjmgACyCwqYXlLC3sJChMGAb0QEVZmZRA0bRm1ODrV79zJsyhRMvn9wRWWlTanlNhTlBMrMzGRmyUyeX/U8A6IHkNY7je3O7byz6R3qm+sxV5mxJdqwGC00ljRiijTh9rpxNjupr6kHf70Pq6fsiTnazIr8FZgMJv7S7S+8cPELhFhD/ucztdw8bFprf1JECFptDTa30Ne2Ki4gJakLNOvTNtnzc0lJ7HzgvRn27aSeNhRcLfr9VF2SoFM0ZBegFZdgSx2iN/39+jOP8N6rfYsW8f3kyVz59dfsePNNivPyiIyKwt3URM2uXfinplK3bBmxo0Zh8vVl4Zdf0sPfX5/uymoltnUIujkggLyqKhKCgzFZrXhcLnxtNjqNHUvmO+/Q+/bb6Z+W9md/bcpxdjwni1UU5Qhomsazzz7Lvf3uZfbE2Wiaxs133sycjXO4sf+N3JVyF3Gb4ki2JGOpslD5diWWKgsJwQkMix/Gef3OY3j8cOqr68n4JIMduTt46PSHyEnL4a2r3jpkcKKiGtu+Sn2ewqRYqK3H1gL4mNFqqpny7ltoFfpAW62mmikfvfPzxLs11Uz7+nO0wmI9E3vzP2i5eZCVhxYRSNr0V9BWb4Qm1/9c55GOkivMzmZGaSkNFgu1zc28uXkz7ogIarOz8Rs6lOc/+YSwCy6gaOlStn/7LT82NNDo64vD7ebz7Gwc7p/nQ4yzWLAEBtLidGKNiCD+nHPIfOcdki68kH6TJ//ZX5vShlQGpSgn0MGZhcvt4t2V7zK3aC7L85bj8rgOLKOhH3zQcwAn9E7qzeik0ZwZcSaXDr4Us/HQAxY0TcNmMOv9TG4XtuQkyC8BHwtaeVnrRLsSe14uKZ2T9UET/CqDMhrJ2LaZ1N79wNcH+65dpAwcADUOCPDDjouUeq+elUX8cpXYI82gsr/8ksVPPMH1S5eS8803zH/lFa555x1WTJ5MwrhxbFu/npDKSoKSkzH7+5NTXEysxYKrtpb8ykoSQkIQRiNupxNvdDSG0lJsgwYREB9P7ty5JE2YwPDnnqOyuloNjGjHVAalKO3AwV+SPiYf7h59NwuuW0DNozUsvGwhI/aO4J9D/8k/R/+Tf078J6+Ne41Zl89i3iXzuDzvcpZeuZTXL3idiUMnHjY4pd11N9rqjWgtTaRNfQltayb4+6KVlZE29d9o1VVotbX/kzEd2BZgL8hl0jOPYy/eh1ZSypSZH6Dl5EF0BFpBIVOefRbNItD25JA2efIvMqYjDQbBXbsSZDJRuX07AampzKmoYNOnn9L7jjvYkZ7OrL178TvtNITBwL5du/h42zYq6+potFiYU1lJdX09PiEhNJjNvLFxIxEXXoi7qYncuXPpdcstjPi//6Oyulrd93SSUhmUorQjx2qxPG3ZWmwmH+jdBW3lemxxsVDfABYzWnk5tpBQMAi0qipsYeFgEOD2oNVUYwsNA18faGjCXryPlNhOenArKsYWFQVWCzQ0oeHG1qMb7NiDFheOrWvn3y/Yr3jdbr4dM4aAuDjOnTGDNdOnk/fWW0QNH05Maipbv/4amZd34HiH202QyYRPaCiugACMlZV4GhqIHjECd0QEVQsWYPL1ZdiUKXQ699w/VXfKiaeWfFeUk8AxWSyvzolNmCAxBoor9MUaW9z6/UvNLWA0gskIbg8VjU5s3lB9GeOIEHZvLsUWHQElGoQEQr5bXziyoUk/t68P1Dn1fVVVYNG/Qmwhh12w4DcZTCb6paXx0xNPsOWVVxjxwANExcay6cUXKVu7FndkJHHDhiGlxOty4S0vx1BZiau6GmdtLSljxuAbFUXBvHmUrVxJn/HjGfzYY9T/2bpT2hXVxKcop5ryKj0o2ULR9uaS9saraGVlYDSiOetIe/UFtKoq7OUljLvnDuxuJwzoQUbBXsZecxUZWiFEhWPfsZNxD6Vhz9uLVlWpNxVWVgACraKCtJeeQysu1T/TZPzTxe1yySV0u/pq7B98wNLbbiO0Z08uXriQLmlpzCgupry0lMayMrSKCt7PzSX8gguIve46vvB4yFq2jKwZM/BERbEgMZEercFJNemdGlQTn6Kcajbv0h87x8HWLOzOalL8Q/UZIfx8sGfaSUlIgogQMpYuJ/W2Gw7MDvHiiy/y8D336ucwGsj46SdSh5ymD0nfs5uUbj30Jj5HPfbmelK6JENROZw+4KiClJSSPZ99xtapU2mpq8M/Pp7IwYNxBQZii4jA09REo6aRv3Mn3pwcvM3NNJhM9L7gArpdfTUR/fv/ohlPNemdXFQTn6J0FG43+PmC16sPfHh7KlNvS8MWEYFWXc2UD99m6r2PgKeZaXM+p3v3bthSupO+7Ecef+wxekkfJpx+BppWy7RvvqR7pwQwGpny8XtMnfwAti6J+nlnvM3Uv9yJLSnhqIITgBCC7pMmkTRhAnnff0/p6tWUrl5NY0UFxa1/RFvDwwlNSiLs6quJHjaMqGHDfnHj7cEBSQWnU4PKoBTlVLPZrjfxdU+C9TuwNzpI8Q3S59Orb8Sel0NKjx7Q6CJj905S+ww4MHXRzEXzufaCCXqfldGAPWcvKcld9QEUtTXYYmP0efuiI/QBFU0eGNgTggKOy6V4PR6k240wmTAYjy4IKu2XGmauKB1FUAA4nGAyorkamPLKS2hNTmhsbh1K/i5aSSn2fflMeuJR7Ht2A/ow8/Q1K1r7mUCrrNSPbd22RUZCYxP4WSHQXw9OcZHHLTgBGIxGjD4+Kjh1UCqDUpRTTX0DbMyE+CgIDsD+w2JSBg3Ub7A1CNJXrWDCsFQwm8jYtJHUvv0PvNWen0tKUmdo/Vqw7ysgpVMCWH3QSkv1DComAvYW6qP8+nbT+7YU5SioDEpROooAP4iOgMIyNK2CKV9+grY3F3ytZOzYxlVPPkKGfTtaVRXT5nx+iBt1a8Bg0Lc/fBut3oFWWkraW6+htTTpwSk0CPp0VcFJOa5UBqUopyKPB7btBocTLdiKLTIKcvaBx0v65nVMOP1MaHSRvnoVE04/48Db7PmtUx+ZTdDSgj03h5RevSA0iIwlP5Lao7eemXWJ1/u5FOUYUBmUonQkRiP07Q4RIdhqm6C4HLrEo/mZmPn9XLSSUjK2b+WqZx4lY8dWEAdlUFWV4PGgSTdTPp+BppVjz1jLpKcew+4LJHdSwUk5IVQGpSinMilBq4bcIn3WcaMRzdus9yUBGevXkTpgkN7nJECrrsQWFKov/97o0kfqJXaC+Cjs5SV6NqUox9hvZVAqQClKRyAlVDmgogqq68DV/NvHmowQ6K/3M0WE6tMbKcpxpG7UVZSOTAgID9Z/QL/PqcmlP3qlPlmsyaivnGsxqyY8pV1QAUpROiKzSf9RlHZMDZJQFEVR2iUVoBRFUZR2SQUoRVEUpV1SAUpRFEVpl1SAUhRFUdolFaAURVGUdkkFKEVRFKVdUgFKURRFaZdUgFIURVHaJRWgFEVRlHZJBShFURSlXVIBSlEURWmX/nSAEkJ0EkIsFULYhRA7hRD3tO4PE0IsEkLsaX0MPXbFVRRFUTqKo8mg3MADUsoUYDhwtxCiF/AosERK2Q1Y0rqtKIqiKH/Inw5QUsoSKeWm1ud1gB2IAy4GPmo97CPgkqMs4wmhaVpbF0FRFEU5yDHpgxJCJAEDgZ+AKCllCehBDIj8jffcJoTYIITY0NbBQdM00tLSVJBSFEVpR446QAkhAoCvgXullI4jfZ+U8m0p5RAp5RCbzXa0xTgivxWAbDYbU6dO5USVQ1EURfl9RxWghBBm9OA0U0o5u3V3mRAipvX1GKD86Ip4bPxelqSCk6IoSvtyNKP4BPAeYJdSvnLQS3OBG1uf3wh8++eLd3QODkYqS1IURTm5mI7ivanA9cB2IcSW1n2PAy8AXwgh/goUAFceVQn/pP0Z08FB6VgEJ6cTsrIgPx80DWpqoLERPB4wGMBigYAACA6G0FCIioLYWP3HaDzqj1cURekwhJSyrcvAkCFD5IYNG475eTVNOyZBaedO+OgjmD8fduyAP1NlJhPEx0NyMvTsCSkp0KcPDBigBzNFUZSOSgixUUo55Nf7jyaDavf+SHA6VDCrq4O77oJPPgGzGUaOhCefhL599WBTUAC7dkFeHhQXQ3U1NDSA262/32zWM6r9mZPbrb9nzRr9uP2Sk2HoUBg+HM48E/r1U9mWoijKKR2gjtShmgOlhCuvhMWL4e9/h3vvhYgIPbA89xzceqsekAAiI8Fm05v43G5oboaWlp9/XC79fR7Pz59pMOhBLiRE/6yFC+Gzz/TXgoPhrLNgzBgYNw66dj2h1aEoitIunNJNfH/ErzOozEzo3RtefhkeeEDf53bDqFGQkaEHr2uvhbIyeOUVvV9qP6tVb9Lb/56Wll8GJwAhNAICbHg8+7MpDbPZRufOeh9WUZFGWZlenq5dNS6/3MYVV8DgwVBRcWyaLhVFUdqD32riU5PFtvr1F35zs/4YFfXzvqVL9eD07rvwxRdQWAi33w7+/vDqq3DNNXpwamqC+nr9x+MBIX79aRpSpuF0arS06NuQhsejUVoKmzZplJWlERSkMXCgRkVFGi+9pHHaadCli8bo0Wls3KhuKlYU5dTWoQLUH5kpom9f6NIF/vlPvS8Kfh4cER6uP378MQwcCOvW6cHqs8/guuv0ANajh35MS8vPfVI/swFP4vXqQVEIGzAZr9eGy/Xz6waDjcxMGzU1TxISYuPssyEy0sbOnU9y2mk2xo2D9HTwev9UdSiKorRrHSZA/dHpjIxGPdBkZ8P550NVld6817Ur3H037N2rB6Hdu2HPHv0xLg6mT9dH++3bB++/Dxs36s1yvyoNMAXQWoem24FJ6NMZ/vy6y6XhcunPTSaNlSth3TqN8PApXHqpxrZtcOGFelPkjBmHCoSKoignrw4ToP7MjbqjR+tZ0fr1epBZvx5mz9ab8EaMgLPP1pv3Ro6E006DoiK4/HI9w3K59L6k/Hw9SOkDLjTsdti0ycaLL07m1VdtXHMNxMenANOBFPz9wWDQMyqz2QbYMJufpKHBRksLxMfb8HqfZPZsG506wQMPaFgscOON+ui/H344ThWoKIpyokkp2/xn8ODBsj1bu1bKpCQphZDy9tulzMiQsk8fKUHK8eOlTE7Wnw8fLqXRKGVk5M/7Lr5Yys6dpTQYymXXrlfLtWvLZWZmpkxISJCZmZlSSikzMzNlTEyCvOeeTJmYKCVkSpMpQYaGZkool/7+V0sol2FhUtps5RKuloMGlcuoKP35pEnl8uOPpezeXf/MK66Qsry8TatMURTliAEb5CFiQ5sHJ3kSBCgppXQ4pLz3Xj0ABQRI+cgjUj74oJRWq5Rms5QDBuiPJpMeoEDKwMByabHoz+PipBQi80Agu+KKT+SqVVI6nfr5P/nkEymllM3NUr7yipTwhrz/finvu08PWBdcIGW3bvpnXHppuTQapezZU8qbby6XQkjZq5eU2dlSPveclBaLlLGxUq5f34YVpiiKcoRUgDpG7HYpr7xSz6Z8faW8/np9e38g6tRJDyJQLk0mPfMRQkofHz3bEaJc+vqukuAnYZU0GKSMjFwlhfCTw4atkmPHShkT850Eo5w48TtZXl4uO3XSz1NeLmX//nqwW7xY//yxY/XnQUFSpqRIWV8v5ebNUiYm6oF07do2rjBFUZTf8VsBSt0H9SfZ7fDSS/Dpp3p/02mn6UPSN2yA0lLw9QWzWcPh0Pu8fHxoHfBgw2KB5uYM9OkM9WOFyMBqTcVshqAgsFrTGTBgAlu2wPbtGiNH2vjuO/3eq/nz9SHs//oXPPWUfi/W9u1w7rl6mR58UJ/Z4owz9FGEdrveH6YoitIeqfugjrGUFH2UXmEhvPiiPhQ9PR0qK/Vpi/r3B7dbD04Wix6g9OHj+rBwIcIOnKuxEZqa8mho0GenyMmBnTtD+eYb/bzDh9uQUg+A6en6dEs7d8KcORAQoBEYqA/YiImBLVv0c8bG6sPgCwth5swTWjWKoijHhApQRykiAh56SJ95Yt06fQh6cTGsXasHnp499dF1fn4/v8dgsAPj+HlY+Uy83utwu2diNIKUGXi9Y6mry6CgQJ+gNjcXevXSA9H77+ujCnNzNQYOTGPvXo0774SSEn2KpP2GDdPnA9y79wRWiKIoyjGi5uI7RoTQm/lOO02f+mjjRpg7F+bN059LqTflJSWBj08KxcXzKS9PaX33tUARAQHXAmAwpOJ0LgRS8Xp/npWivFyf9y8qSmPECBshITY2bpzKgAE2vF69ae+vf9XP2NKiB86WFn0CWkVRlJON6oM6ASoq9GmSli+HlSv1jGj/7A+RkRAYaKegYBwJCfNxuVKorNSzr0PTp0WCqVitNvr3h3PO0e+D6tJF74uaN0+/yTg3FyZPhqlTDzXdkqIoSvvwW31QKkC1AYdDz6o2boRNm/SAtXNnBl5v6oFjzGY9eAUH6z9+fnoG5usLBoNGaKgNs1nvo9I0/Ybg7Gz9JmLQbx5++GEYP76NLlJRFOUIdcj1oNqroCB9lorRo/VtTdOYPHka99/fnaoqG3v36utGFRfrzXrl5frzujo9ALndNgwGfeBFYKC+1EfnznDeeTBokD4lU2xsm16ioijKUVMBqh2w2WxMm/bHpmFSFEU51alRfO2ECk6Koii/pAKUoiiK0i6pAKUoiqK0SypAKYqiKO2SClCKoihKu6QClKIoitIuqQClKIqitEsqQCmKoijtkgpQiqIoSrukApSiKIrSLqkApSiKorRLKkApiqIo7ZIKUIqiKEq7pAKUoiiK0i6pAKUoiqK0SypAKYqiKO2SClCKoihKu3TcApQQYpwQIksIkS2EePR4fY6iKIpyajouAUoIYQSmA+cDvYBrhBC9jsdnKYqiKKem45VBDQWypZQ5UspmYBZw8XH6LEVRFOUUZDpO540D9h20XQgMO/gAIcRtwG2tmy4hxI7jVJZTQQRQ0daFaMdU/Ryeqp/DU/VzeCeifhIPtfN4BShxiH3yFxtSvg28DSCE2CClHHKcynLSU/VzeKp+Dk/Vz+Gp+jm8tqyf49XEVwh0Omg7Hig+Tp+lKIqinIKOV4BaD3QTQnQWQliAq4G5x+mzFEVRlFPQcWnik1K6hRCTgQWAEXhfSrnzMG95+3iU4xSi6ufwVP0cnqqfw1P1c3htVj9CSvn7RymKoijKCaZmklAURVHaJRWgFEVRlHapzQOUmhLpl4QQnYQQS4UQdiHETiHEPa37w4QQi4QQe1ofQ9u6rG1FCGEUQmwWQqS3bqu6OYgQIkQI8ZUQYlfrv6MRqo50Qoj7Wv9f7RBCfCaEsHb0uhFCvC+EKD/4XtTD1YkQ4rHW7+ssIcR5x7NsbRqg1JRIh+QGHpBSpgDDgbtb6+RRYImUshuwpHW7o7oHsB+0rerml14D5kspewL90euqw9eRECIOSAOGSCn7oA/guhpVNx8C436175B10vpddDXQu/U9b7R+jx8XbZ1BqSmRfkVKWSKl3NT6vA79yyUOvV4+aj3sI+CSNilgGxNCxAPjgXcP2q3qppUQIggYCbwHIKVsllLWoOpoPxPgK4QwAX7o92d26LqRUq4Aqn61+7fq5GJglpTSJaXMBbLRv8ePi7YOUIeaEimujcrS7gghkoCBwE9AlJSyBPQgBkS2YdHa0n+AhwHvQftU3fysC6ABH7Q2g74rhPBH1RFSyiLgZaAAKAFqpZQLUXVzKL9VJyf0O7utA9TvTonUUQkhAoCvgXullI62Lk97IISYAJRLKTe2dVnaMRMwCHhTSjkQcNLxmqwOqbUf5WKgMxAL+AshrmvbUp10Tuh3dlsHKDUl0iEIIczowWmmlHJ26+4yIURM6+sxQHlbla8NpQIXCSHy0JuDzxZCfIKqm4MVAoVSyp9at79CD1iqjuBcIFdKqUkpW4DZwOmoujmU36qTE/qd3dYBSk2J9CtCCIHef2CXUr5y0EtzgRtbn98IfHuiy9bWpJSPSSnjpZRJ6P9WfpRSXoeqmwOklKXAPiFEj9Zd5wCZqDoCvWlvuBDCr/X/2Tnofbyqbv7Xb9XJXOBqIYSPEKIz0A1Yd7wK0eYzSQghLkDvV9g/JdJzbVqgNiaEOANYCWzn536Wx9H7ob4AEtD/o10ppfx1x2aHIYQYBTwopZwghAhH1c0BQogB6INILEAO8Bf0P0Y7fB0JIZ4BrkIfLbsZuAUIoAPXjRDiM2AU+rIaZcBTwDf8Rp0IIf4O3Ixeh/dKKecdt7K1dYBSFEVRlENp6yY+RVEURTkkFaAURVGUdkkFKEVRFKVdUgFKURRFaZdUgFIURVHaJRWgFEVRlHZJBShFURSlXfp/Fxfoz2MXu2cAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "try:\n",
    "  print(axv)\n",
    "except:\n",
    "  print(\"An exception occurred\")\n",
    "'''\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.stats import norm, multivariate_normal\n",
    "from sklearn.mixture import GaussianMixture\n",
    "\n",
    "\n",
    "class GMM:\n",
    "    \"\"\"\n",
    "    Full covariance Gaussian Mixture Model,\n",
    "    trained using Expectation Maximization.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    n_components : int\n",
    "        Number of clusters/mixture components in which the data will be\n",
    "        partitioned into.\n",
    "\n",
    "    n_iters : int\n",
    "        Maximum number of iterations to run the algorithm.\n",
    "\n",
    "    tol : float\n",
    "        Tolerance. If the log-likelihood between two iterations is smaller than\n",
    "        the specified tolerance level, the algorithm will stop performing the\n",
    "        EM optimization.\n",
    "\n",
    "    seed : int\n",
    "        Seed / random state used to initialize the parameters.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, n_components: int, n_iters: int, tol: float, seed: int):\n",
    "        self.n_components = n_components\n",
    "        self.n_iters = n_iters\n",
    "        self.tol = tol\n",
    "        self.seed = seed\n",
    "\n",
    "    def fit(self, X):\n",
    "\n",
    "        # data's dimensionality and responsibility vector\n",
    "        n_row, n_col = X.shape\n",
    "        self.resp = np.zeros((n_row, self.n_components))\n",
    "\n",
    "        # initialize parameters\n",
    "        np.random.seed(self.seed)\n",
    "        chosen = np.random.choice(n_row, self.n_components, replace = False)\n",
    "        self.means = X[chosen]\n",
    "        self.weights = np.full(self.n_components, 1 / self.n_components)\n",
    "\n",
    "        # for np.cov, rowvar = False,\n",
    "        # indicates that the rows represents obervation\n",
    "        shape = self.n_components, n_col, n_col\n",
    "        self.covs = np.full(shape, np.cov(X, rowvar = False))\n",
    "\n",
    "        log_likelihood = 0\n",
    "        self.converged = False\n",
    "        self.log_likelihood_trace = []\n",
    "\n",
    "        for i in range(self.n_iters):\n",
    "            log_likelihood_new = self._do_estep(X)\n",
    "            self._do_mstep(X)\n",
    "\n",
    "            if abs(log_likelihood_new - log_likelihood) <= self.tol:\n",
    "                self.converged = True\n",
    "                break\n",
    "\n",
    "            log_likelihood = log_likelihood_new\n",
    "            self.log_likelihood_trace.append(log_likelihood)\n",
    "\n",
    "        return self\n",
    "\n",
    "    def _do_estep(self, X):\n",
    "        \"\"\"\n",
    "        E-step: compute responsibilities,\n",
    "        update resp matrix so that resp[j, k] is the responsibility of cluster k for data point j,\n",
    "        to compute likelihood of seeing data point j given cluster k, use multivariate_normal.pdf\n",
    "        \"\"\"\n",
    "        self._compute_log_likelihood(X)\n",
    "        log_likelihood = np.sum(np.log(np.sum(self.resp, axis = 1)))\n",
    "\n",
    "        # normalize over all possible cluster assignments\n",
    "        self.resp = self.resp / self.resp.sum(axis = 1, keepdims = 1)\n",
    "        return log_likelihood\n",
    "\n",
    "    def _compute_log_likelihood(self, X):\n",
    "        for k in range(self.n_components):\n",
    "            prior = self.weights[k]\n",
    "            likelihood = multivariate_normal(self.means[k], self.covs[k]).pdf(X)\n",
    "            self.resp[:, k] = prior * likelihood\n",
    "\n",
    "        return self\n",
    "\n",
    "    def _do_mstep(self, X):\n",
    "        \"\"\"M-step, update parameters\"\"\"\n",
    "\n",
    "        # total responsibility assigned to each cluster, N^{soft}\n",
    "        resp_weights = self.resp.sum(axis = 0)\n",
    "\n",
    "        # weights\n",
    "        self.weights = resp_weights / X.shape[0]\n",
    "\n",
    "        # means\n",
    "        weighted_sum = np.dot(self.resp.T, X)\n",
    "        self.means = weighted_sum / resp_weights.reshape(-1, 1)\n",
    "        # covariance\n",
    "        for k in range(self.n_components):\n",
    "            diff = (X - self.means[k]).T\n",
    "            weighted_sum = np.dot(self.resp[:, k] * diff, diff.T)\n",
    "            self.covs[k] = weighted_sum / resp_weights[k]\n",
    "\n",
    "        return self\n",
    "\n",
    "\n",
    "\n",
    "def plot_contours(data, means, covs, title):\n",
    "    \"\"\"visualize the gaussian components over the data\"\"\"\n",
    "    plt.figure()\n",
    "    plt.plot(data[:, 0], data[:, 1], 'ko',markersize=0.3)\n",
    "\n",
    "    delta = 0.2 #0.025\n",
    "    k = means.shape[0]\n",
    "    x = np.arange(0.0, 107.0, delta)\n",
    "    y = np.arange(0.0, 107.0, delta)\n",
    "    x_grid, y_grid = np.meshgrid(x, y)\n",
    "    coordinates = np.array([x_grid.ravel(), y_grid.ravel()]).T\n",
    "\n",
    "    col = ['green', 'red', 'indigo','blue','pink','purple','orange','brown']\n",
    "    #col=[(255, 255, 0),(1,5,5)]\n",
    "    \n",
    "    for i in range(k):\n",
    "        mean = means[i]\n",
    "        cov = covs[i]\n",
    "        z_grid = multivariate_normal(mean, cov).pdf(coordinates).reshape(x_grid.shape)\n",
    "        if(i==0):\n",
    "            z_grid_sum = z_grid\n",
    "        else:\n",
    "            z_grid_sum +=z_grid\n",
    "        #print(z_grid)\n",
    "        cs=plt.contour(x_grid, y_grid, z_grid, colors = col[i%len(col)],extend='both')\n",
    "        cs.cmap.set_over('red')\n",
    "        cs.cmap.set_under('blue')\n",
    "        cs.changed()\n",
    "    \n",
    "    #cmaps = colors.LinearSegmentedColormap.from_list('mylist',col,N=3000)\n",
    "    #plt.contour(x_grid, y_grid, z_grid_sum)\n",
    "    #plt.contourf(x_grid, y_grid, z_grid_sum)#,cmap=cmaps)#, colors = col[i%len(col)])\n",
    "    plt.title(title)\n",
    "    plt.tight_layout()\n",
    "\n",
    "'''\n",
    "df = pd.read_csv('raw_data.csv',index_col=0)\n",
    "plt.scatter(df['X'],df['Y'])\n",
    "plt.xlabel('X');\n",
    "plt.ylabel('Y');\n",
    "X=df.to_numpy()\n",
    "'''\n",
    "#xx=[df['X'].values.tolist() for x in df.columns]\n",
    "#yy=[df['Y'].values.tolist() for y in df.columns]\n",
    "#df.loc['']\n",
    "'''\n",
    "X=np.zeros((  len(df['X'])+1 ,2))\n",
    "for i in range(1, len(df['X'])+ 1):\n",
    "    X[i][0]=df2[i][0] #alist['X'][i]\n",
    "    X[i][1]=df2[i][1] #alist['Y'][i] #df['Y'].iloc(i) #int(df.loc[df['Y'] == i])\n",
    "'''\n",
    "\n",
    "\n",
    "X=np.array(data_em)\n",
    "\n",
    "n_clusters=16\n",
    "gmm = GMM(n_components = n_clusters, n_iters = 1, tol = 1e-4, seed = 4)\n",
    "gmm.fit(X)\n",
    "\n",
    "plot_contours(X, gmm.means, gmm.covs, 'Initial clusters')\n",
    "\n",
    "gmm = GMM(n_components = n_clusters, n_iters = 60, tol = 1e-4, seed = 4)\n",
    "gmm.fit(X)\n",
    "\n",
    "print('converged iteration:', len(gmm.log_likelihood_trace))\n",
    "plot_contours(X, gmm.means, gmm.covs, 'Final clusters')\n",
    "\n",
    "\n",
    "# use library to confirm results\n",
    "gmm = GaussianMixture(n_components = n_clusters, covariance_type = 'full',\n",
    "                      max_iter =2000, random_state = n_clusters)\n",
    "gmm.fit(X)\n",
    "\n",
    "print('converged or not: ', gmm.converged_)\n",
    "plot_contours(X, gmm.means_, gmm.covariances_, 'Final clusters')\n",
    "print(\"means\")\n",
    "print(gmm.means_)\n",
    "print(\"covariances\")\n",
    "print(gmm.covariances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
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   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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