lab7.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 7\n",
    "\n",
    "## The purpose of this lab is\n",
    "1. to practice what we have learned so far:\n",
    "   - Methods for hard and soft clustering\n",
    "   - K-means algorithm for hard clustering\n",
    "   - EM algorithm for soft (probabilistic) clustering\n",
    "2. understand practical aspects of using clustering algorithms\n",
    "3. prepare for the electronic test\n",
    "5. to help you to make progress with Python tutorial and your DM & ML portfolio\n",
    "\n",
    "## Before lab\n",
    "\n",
    "- Watch recordings, and run the Python code accompanying tutorial P4. Clustering (Week 7).\n",
    "- Make sure you can run this code using your chosen data set. In case you have any issues, contact your lab tutor and ask for help.\n",
    "- Use k-means clustering to find clusters in your data set. Evaluate the accuracy of this clustering, visualize the clusters.\n",
    "- (optional for BSc but recommended for higher marks, mandatory for MSc) try different clustering algorithms for hard and soft clustering, such as EM, GMM, hierarchical clustering or any other algorithms of your choice. Compare their performance on your data set.\n",
    "- Try also to vary the number of clusters manually. How does it affect the accuracy of clustering?\n",
    "- (optional for BSc but recommended for higher marks, mandatory for MSc) Research some of the existing algorithms to compute the optimal number of clusters. For example, look up: Elbow method, the silhouette method, cluster validity and similarity measures. Can these algorithms help you to find the optimal number of clusters for your data set?\n",
    "\n",
    "### You might find these references useful\n",
    "- Using dimensionality reduction to visualize clustering, we recommend you look into ”PCA” and ”TSNE” links to an external site. Check also Chapter 8 ”Dimensionality Reduction”, [ Hands-On Machine Learning, Aurelien Geron ] & accompanying code.\n",
    "- For alternative clustering algorithms you could check Chapter 9 ”Unsupervised Learning techniques”, Hands-On Machine Learning, by Aurelien Geron & accompanying code\n",
    "\n",
    "## During lab\n",
    "\n",
    "- Put all your results in a suitable form: it can be a table or a series of graphs, that visualise the variations of performance between different clustering algorithms and different numbers of clusters. (Lab 5 gave an example of how machine learning experiments may be assembled into a comparative table. You can use it as a starting point. But let it not limit your creativity.)\n",
    "- Using that table (or graphs), make conclusions: What did these clustering algorithms reveal about your data set? Compare performance of clustering algorithms with the results of Bayesian classification on the same data set. Is there difference in performance?\n",
    "- Make conclusions about the optimal number of clusters for your data set.\n",
    "- (optional for BSc but recommended for higher marks, mandatory for MSc) Using your experiments as a source, explain all pros and cons of using different (hard and soft) clustering algorithms on the given data set.\n",
    "- The tutors will mark: quality of your code, completeness of your tables/graphs that summarise the results of your group experiments and your analysis of the tables/graphs, i.e. what sort of conclusions you make, how you refer to the theoretical knowledge of clustering algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# conda env create -f env.yaml \n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "os.environ[\"OMP_NUM_THREADS\"] = \"5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = pd.read_csv(\"Data/data3.csv\")\n",
    "df4 = pd.read_csv(\"Data/data4.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistical Summary and Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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>failures</th>\n",
       "      <th>higher</th>\n",
       "      <th>studytime</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>age</th>\n",
       "      <th>reason_reputation</th>\n",
       "      <th>school</th>\n",
       "      <th>address</th>\n",
       "      <th>internet</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.264368</td>\n",
       "      <td>0.914751</td>\n",
       "      <td>1.970307</td>\n",
       "      <td>2.603448</td>\n",
       "      <td>2.387931</td>\n",
       "      <td>1.494253</td>\n",
       "      <td>16.726054</td>\n",
       "      <td>0.237548</td>\n",
       "      <td>0.260536</td>\n",
       "      <td>0.272989</td>\n",
       "      <td>0.792146</td>\n",
       "      <td>11.341954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.656142</td>\n",
       "      <td>0.279386</td>\n",
       "      <td>0.834353</td>\n",
       "      <td>1.124907</td>\n",
       "      <td>1.099938</td>\n",
       "      <td>0.911714</td>\n",
       "      <td>1.239975</td>\n",
       "      <td>0.425785</td>\n",
       "      <td>0.439138</td>\n",
       "      <td>0.445708</td>\n",
       "      <td>0.405967</td>\n",
       "      <td>3.864796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          failures       higher    studytime         Medu         Fedu  \\\n",
       "count  1044.000000  1044.000000  1044.000000  1044.000000  1044.000000   \n",
       "mean      0.264368     0.914751     1.970307     2.603448     2.387931   \n",
       "std       0.656142     0.279386     0.834353     1.124907     1.099938   \n",
       "min       0.000000     0.000000     1.000000     0.000000     0.000000   \n",
       "25%       0.000000     1.000000     1.000000     2.000000     1.000000   \n",
       "50%       0.000000     1.000000     2.000000     3.000000     2.000000   \n",
       "75%       0.000000     1.000000     2.000000     4.000000     3.000000   \n",
       "max       3.000000     1.000000     4.000000     4.000000     4.000000   \n",
       "\n",
       "              Dalc          age  reason_reputation       school      address  \\\n",
       "count  1044.000000  1044.000000        1044.000000  1044.000000  1044.000000   \n",
       "mean      1.494253    16.726054           0.237548     0.260536     0.272989   \n",
       "std       0.911714     1.239975           0.425785     0.439138     0.445708   \n",
       "min       1.000000    15.000000           0.000000     0.000000     0.000000   \n",
       "25%       1.000000    16.000000           0.000000     0.000000     0.000000   \n",
       "50%       1.000000    17.000000           0.000000     0.000000     0.000000   \n",
       "75%       2.000000    18.000000           0.000000     1.000000     1.000000   \n",
       "max       5.000000    22.000000           1.000000     1.000000     1.000000   \n",
       "\n",
       "          internet           G3  \n",
       "count  1044.000000  1044.000000  \n",
       "mean      0.792146    11.341954  \n",
       "std       0.405967     3.864796  \n",
       "min       0.000000     0.000000  \n",
       "25%       1.000000    10.000000  \n",
       "50%       1.000000    11.000000  \n",
       "75%       1.000000    14.000000  \n",
       "max       1.000000    20.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA20AAAJOCAYAAAAkve/mAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABv/klEQVR4nO39f7yldV3v/z+e8UsEVAjZIZCDhRZI/mhEy7JRMlDMofNVD4YKRpEd/NXhfGLwnNJOcQ51xDTLY6TGmCiRWpCYiujOPAkkhI5AxCgjDEzgD1CGCh18ff+4rg2Lzd6z18xeP6619uN+u81tr3Wt97XW81p7r/dcr3W9r/eVqkKSJEmS1E3fN+4AkiRJkqTFWbRJkiRJUodZtEmSJElSh1m0SZIkSVKHWbRJkiRJUodZtEmSJElSh1m0rXBJnpDkn5LcneS122n3g0m2JtmlvT+b5JdHl1SSFpZkU5KfXWD5Tye5oc/nWJNk8+DTSdLyLNbH9bnuO5P85qAzafR2HXcAjd1vALNV9ZTtNaqqm4G9RxNJkpavqv4eeMK4c0hauZK8CfjhqnrZCF7rZOCXq+qn5pZV1auG/boaDY+06bHAtaN6sTT8u5M0tZL4hagkaaDceV7BknwKeDbwR+3Qx9e1QyW/neSW9tuhubarktRCOyNJ3pTkfYu1bYdSnpXk/wH/BjwuyY8kuTTJN5PckOQlPes/P8l17ZDNW5P8tyG+DZKmw5OTfDHJt5L8RZKHzR/ymOSpPcPB/7Jt97u9T5Lk9CR3JNmS5JU9y/dI8uYkNye5vR1ytGf72Jokm5OckeRfgT8b2VZL6oy2D7i17WNuSHIc8AbgP7f7WV9o2z1ouOMC+1EvT/LVJN9I8t97lv9Akn9L8v09y348ydeSHAm8E/iJ9rXuah8/b66f6+mrfqOnnzu+3e/6l3af7A09z/19SdYl+XKb5cIk+w3tDdR2WbStYFX1HODvgVdX1d7AF4BXAI8CjgN+LcnxA3q5lwOnAvsAXwMuBd4PHAC8FHhHkiPatu8GfrWq9gGeCHxqQBkkTa+XAMcChwI/Bpzc+2CS3YG/As4D9gM+APzCvOf4AeCRwEHAKcAfJ9m3fez3gMcDTwZ+uG3zW/PW3Y9m9MKpA9kiSRMjyROAVwNPa/dfjgH+GfhfwF9U1d5V9aQ+nudw4P/S7Dc9Bvh+4GCAqvpXYJamv5vzMuCCqtoAvAr4XPtaj1rkJX4AeBgP9GF/2j7HjwM/DfxWkse1bV8LHA/8TJvlTuCPl9oGDYdFm+5XVbNVtaGqvldVX6TZqfmZAT39eVV1bVVto9mx2lRVf1ZV26rqauBDwIvatt8FDk/yiKq6s31ckrbnD6vqtqr6JvA3NMVVr2fQnMf9h1X13ar6MHDlvDbfBf5n+/hHga3AE5IE+BXg16vqm1V1N82O2Ak9634PeGNV3VtV/z7wrZPUdfcBe9Dsv+xWVZuq6ss78TwvAj5SVZ+pqnuB36TpX+aspymySDM53EuBP9+B5/8ucFZVfRe4ANgfeFtV3V1V19KcMvNjbdtfBf57VW1us7wJeJFDwMfDok33S/L0JJ9uD7N/i+Ybm/0H9PS39Nx+LPD0JHfN/QNOpPn2B+D/Bzwf+GqSv0vyEwPKIGl6/WvP7X/joRMnPQa4taqqZ9kt89p8o/1iaf7zPBp4OHBVT5/1sXb5nK9V1X8sI7+kCVZVG4HX0xQ2dyS5IMljduKpHkNP31RV9wDf6Hn8IprC8HHAc4FvVdX8L6C25xtVdV97e+4Lptt7Hv93Hug/Hwv8VU+/dz1NcTqzA6+nAbFoU6/3AxcDh1TVI2nGRqeP9e6h2aGZ8wMLtJm/o/R3VfWonn97V9WvAVTVP1bVWpqhk38NXLjjmyJJD7IFOKg9ajbnkD7X/TrNjswRPX3WI9th5XNqkXUlrRBV9f525sbH0vQJv8fCfcP29pu20NM3JXk4zRDJudf4D5r9ohNphlD2HmUbdD90C/C8eftrD6uqWwf8OuqDRZt67QN8s6r+I8lRwC/2ud41wLPSXMvtkcCZS7T/CPD49kTb3dp/T0vyo0l2T3Jikke2h+6/TfOtjiQtx+do+pJXJ9k1yVrgqH5WrKrv0Zz38QdJDgBIclCSY4aWVtJESXPd2+ck2QP4D5oveu6jOYq1Kg+eOfsa4IR2/2c1D5weAvBB4AVJfqo9F/d/8tD99ffSnLf7QuB9PctvBw5u1xuEdwJnJXlsu42PbvtOjYFFm3r9F+B/Jrmb5uTUvo5wVdWlwF8AXwSuoinKttf+buDnaM4HuY1mWNPv0YwFh+abo01Jvk0zRHPo1zaRNN2q6jvAf6KZYOQumn7lI8C9fT7FGcBG4PK2b/okXgNO0gP2AM6mOTL/rzSjhd4A/GX7+DeSzJ2j/5vAD9FM7PHbNCOdAGjPKzutXbalbXP/LLhtm/9Hc57b1VW1qeehT9Gck/avSb4+gG16G80IrE+0+4aXA08fwPNqJ+TBw/slSVoZklwBvLOqnKJf0kRJc9mm91fVu8adRaPhkTZJ0oqQ5Gfa6xztmuQkmhnSPjbuXJK0I5I8DXgqzSgnrRBO2SlJWimeQDPse2/gy8CLqmrLeCNJUv+SrKe5dtrr2tNNtEI4PFLSxEryHuAFwB1V9cR22X403z6uAjYBL6mqO9vHzqQ5p+k+4LVV9fF2+Y/TXHR5T+CjNP8Z2jlKkqROcHikpEl2Hs3F2nutAy6rqsOAy9r7JDmcZvKbI9p13tFemBTg/wKnAoe1/+Y/pyRJ0th0Ynjk/vvvX6tWreqr7T333MNee+013EB9MsviupTHLAvbkSxXXXXV16vq0Uu3HK2q+kySVfMWrwXWtLfXA7M0M/+tBS6oqnuBm5JsBI5Ksgl4RFV9DiDJe2mGnvztUq/fb9/Vpd/79kxKTpicrOYcvH6zdrXfGrdJ7be6lMcsC+tSFuhWnkH0W50o2latWsXnP//5vtrOzs6yZs2a4Qbqk1kW16U8ZlnYjmRJ8tXhphmombnzlKpqy9x1tYCDaKYrnrO5XfZdHjyd8tzyBSU5leaoHDMzM7z5zW9eMtDWrVvZe++9l2w3bpOSEyYnqzkHr9+sz372syep3xqZfve5uvT/FXQrj1kW1qUs0K08/WbZ3v5WJ4o2SRqBLLCstrN8QVV1LnAuwOrVq6ufTrhL/3Fsz6TkhMnJas7Bm6SskjQontMmadrcnuRAgPbnHe3yzcAhPe0Oprm4++b29vzlkiRJnWDRJmnaXAyc1N4+CbioZ/kJSfZIcijNhCNXtkMp707yjCQBXtGzjiRJ0thN3PDIDbd+i5PXXXL//U1nHzfGNJLGKckHaCYd2T/JZuCNwNnAhUlOAW4GXgxQVdcmuRC4DtgGnFZV97VP9Ws8MOX/39LHJCSShm9Vz//3c847thsTC2hl6f1bPP3IbffPdiWNysQVbZI0p6peushDRy/S/izgrAWWfx544gCjSZIkDYzDIyVJkiSpwyzaJEmSJKnDLNokSZIkqcP6KtqS/HqSa5N8KckHkjwsyX5JLk1yY/tz3572ZybZmOSGJMcML74kSZIkTbcli7YkBwGvBVZX1ROBXYATgHXAZVV1GHBZe58kh7ePHwEcC7wjyS7DiS9JkiRJ063f4ZG7Ansm2RV4OM2FZ9cC69vH1wPHt7fXAhdU1b1VdROwEThqYIklSZIkaQVZcsr/qro1yZtprnf078AnquoTSWbai9JSVVuSHNCuchBwec9TbG6XPUiSU4FTAWZmZpidne0r8MyezfUx5vS73jBs3bp1rK/fq0tZoFt5zLKwLmWRJEnS4pYs2tpz1dYChwJ3AX+Z5GXbW2WBZfWQBVXnAucCrF69utasWdNHXHj7+RdxzoYHYm86sb/1hmF2dpZ+cw9bl7JAt/KYZWFdyiJJkqTF9TM88meBm6rqa1X1XeDDwE8Ctyc5EKD9eUfbfjNwSM/6B9MMp5QkSZIk7aB+irabgWckeXiSAEcD1wMXAye1bU4CLmpvXwyckGSPJIcChwFXDja2JEmSJK0M/ZzTdkWSDwJXA9uAf6IZ1rg3cGGSU2gKuxe37a9NciFwXdv+tKq6b0j5JUmSJGmqLVm0AVTVG4E3zlt8L81Rt4XanwWctbxokiRJkqR+p/yXJEmSJI2BRZskSZIkdZhFmyRJkiR1mEWbJEmSJHWYRZskSZIkdZhFmyRJUkck2SXJPyX5SHt/vySXJrmx/blvT9szk2xMckOSY8aXWtKwWbRJmjpJnpDkmp5/307y+iRvSnJrz/Ln96zjzo+kLngdcH3P/XXAZVV1GHBZe58khwMnAEcAxwLvSLLLiLNKGhGLNklTp6puqKonV9WTgR8H/g34q/bhP5h7rKo+Cu78SOqGJAcDxwHv6lm8Fljf3l4PHN+z/IKqureqbgI2AkeNKKqkEevr4tqSNMGOBr5cVV9Nslib+3d+gJuSzO38fG5EGSUJ4K3AbwD79CybqaotAFW1JckB7fKDgMt72m1ulz1EklOBUwFmZmaYnZ1dMsjWrVv7ajcq485z+pHb7r89syedeW/G/b706lIW6FaeQWSxaJM07U4APtBz/9VJXgF8Hji9qu5khe38LGZScsLkZDXn8vTuKM/patblSvIC4I6quirJmn5WWWBZLdSwqs4FzgVYvXp1rVmz9NPPzs7ST7tRGXeek9ddcv/t04/cxks68t6M+33p1aUs0K08g8hi0SZpaiXZHXghcGa76P8Cv0OzY/M7wDnAL7HCdn4WMyk5YXKymnN5eneU55x37F6dzDoAzwRe2J5r+zDgEUneB9ye5MD2KNuBwB1t+83AIT3rHwzcNtLEkkbGc9okTbPnAVdX1e0AVXV7Vd1XVd8D/pQHzv9w50fSWFXVmVV1cFWtohkh8KmqehlwMXBS2+wk4KL29sXACUn2SHIocBhw5YhjSxoRizZJ0+yl9AyNbL+lnvMLwJfa2+78SOqqs4HnJrkReG57n6q6FrgQuA74GHBaVd03tpSShsrhkZKmUpKH0+zg/GrP4t9P8mSaoY+b5h6rqmuTzO38bMOdH0ljVFWzwGx7+xs0Eyot1O4s4KyRBZM0NhZtkqZSVf0b8P3zlr18O+3d+ZEkSZ3k8EhJkiRJ6rC+irYkj0rywST/nOT6JD+RZL8klya5sf25b0/7M5NsTHJDkmOGF1+SJEmSplu/R9reBnysqn4EeBJwPbAOuKyqDgMua++T5HCaWY+OAI4F3pFkl0EHlyRJkqSVYMmiLckjgGcB7waoqu9U1V3AWmB922w9cHx7ey1wQVXdW1U3ARt5YFptSZIkSdIO6GcikscBXwP+LMmTgKuA1wEzVbUFoL3g4wFt+4OAy3vW39wue5AkpwKnAszMzDA7O9tX4Jk9myvRz+l3vWHYunXrWF+/V5eyQLfymGVhXcoiSZKkxfVTtO0KPBV4TVVdkeRttEMhF5EFltVDFlSdC5wLsHr16lqzZk0fUeDt51/EORseiL3pxP7WG4bZ2Vn6zT1sXcoC3cpjloV1KYskSZIW1885bZuBzVV1RXv/gzRF3O1zF6ptf97R0/6QnvUPBm4bTFxJkiRJWlmWLNqq6l+BW5I8oV10NM0FaC8GTmqXnQRc1N6+GDghyR5JDgUOA64caGpJkiRJWiH6vbj2a4Dzk+wOfAV4JU3Bd2GSU4CbgRcDVNW1SS6kKey2AadV1X0DTy5JkiRJK0BfRVtVXQOsXuChoxdpfxZw1s7HkiRJkiRB/9dpkyRJkiSNQb/DIyVJkrRCbLj1W5y87pIHLdt09nFjSiPJI22SJEmS1GEWbZIkSZLUYRZtkiRJktRhFm2SJEmS1GEWbZIkSZLUYRZtkiRJktRhFm2SJEmS1GEWbZKmUpJNSTYkuSbJ59tl+yW5NMmN7c99e9qfmWRjkhuSHDO+5JIkSQ9m0SZpmj27qp5cVavb++uAy6rqMOCy9j5JDgdOAI4AjgXekWSXcQSWJEmaz6JN0kqyFljf3l4PHN+z/IKqureqbgI2AkeNPp4kSdJD7TruAJI0JAV8IkkBf1JV5wIzVbUFoKq2JDmgbXsQcHnPupvbZQ+R5FTgVICZmRlmZ2eXDLJ169a+2o3bpOSEyclqzuU5/chtD1nW1aySNEwWbZKm1TOr6ra2MLs0yT9vp20WWFYLNWyLv3MBVq9eXWvWrFkyyOzsLP20G7dJyQmTk9Wcy3Pyuksesuy8Y/fqZFZJGiaHR0qaSlV1W/vzDuCvaIY73p7kQID25x1t883AIT2rHwzcNrq0kiRJi+u7aEuyS5J/SvKR9r6zsEnqpCR7Jdln7jbwc8CXgIuBk9pmJwEXtbcvBk5IskeSQ4HDgCtHm1qSJGlhO3Kk7XXA9T33nYVNUlfNAJ9N8gWa4uuSqvoYcDbw3CQ3As9t71NV1wIXAtcBHwNOq6r7xpJckiRpnr7OaUtyMHAccBbwX9vFa4E17e31wCxwBj2zsAE3JZmbhe1zA0stSdtRVV8BnrTA8m8ARy+yzlk0fZwkSVKn9DsRyVuB3wD26Vm2rFnYdmYGNoCZPR88m9Q4Z5Dq0gxWXcoC3cpjloV1KYskSZIWt2TRluQFwB1VdVWSNX08Z1+zsO3MDGwAbz//Is7Z8EDsTSf2t94wdGm2rS5lgW7lMcvCupRFkiRJi+vnSNszgRcmeT7wMOARSd5HOwtbe5TNWdgkSZIkaQiWnIikqs6sqoOrahXNBCOfqqqX4SxskiRJkjR0y7lOm7OwSZIkDUCSQ5J8Osn1Sa5N8rp2uZdYktT3RCQAVNUszSyRzsImSZI0ONuA06vq6vY6k1cluRQ4meYSS2cnWUdziaUz5l1i6THAJ5M83i/Kpem0nCNtkiRJGoCq2lJVV7e376a5Nu5BNJdSWt82Ww8c396+/xJLVXUTMHeJJUlTaIeOtEmSJGm4kqwCngJcwTIvsdQ+3w5fZmn+JZZgZV9mqfe9mNlzvO9Fr3G/L726lAW6lWcQWSzaJEmSOiLJ3sCHgNdX1beTha6k1DRdYNlDLrEEO3eZpfmXWIKVfZmlk9ddcv/t04/cxks6csmccb8vvbqUBbqVZxBZHB4pSZLUAUl2oynYzq+qD7eLb28vrYSXWJJWLos2SZKkMUtzSO3dwPVV9Zaeh7zEkiSHR0qSJHXAM4GXAxuSXNMuewPNJZUuTHIKcDPwYmgusZRk7hJL2/ASS9JUs2iTJEkas6r6LAufpwZeYkla8RweKUmSJEkdZtEmSZIkSR1m0SZJkiRpp61adwkbbv0Wq9ZdwqqeyyNocCzaJEmSJKnDLNokSZIkqcMs2iRNnSSHJPl0kuuTXJvkde3yNyW5Nck17b/n96xzZpKNSW5Icsz40kuSJD2YU/5LmkbbgNOr6uok+wBXJbm0fewPqurNvY2THA6cABwBPAb4ZJLHe80jSZLUBR5pkzR1qmpLVV3d3r4buB44aDurrAUuqKp7q+omYCNw1PCTSpIkLc0jbZKmWpJVwFOAK4BnAq9O8grg8zRH4+6kKegu71ltM4sUeUlOBU4FmJmZYXZ2dskMW7du7avduE1KTpicrOZcntOP3PaQZV3NKknDtGTRluQQ4L3ADwDfA86tqrcl2Q/4C2AVsAl4SbvzQ5IzgVOA+4DXVtXHh5JekrYjyd7Ah4DXV9W3k/xf4HeAan+eA/wSkAVWr4Wes6rOBc4FWL16da1Zs2bJHLOzs/TTbtwmJSdMTlZzLs/JC0wdft6xe3UyqyQNUz/DI+fODflR4BnAae35H+uAy6rqMOCy9v78c0OOBd6RZJdhhJekxSTZjaZgO7+qPgxQVbdX1X1V9T3gT3lgCORm4JCe1Q8GbhtlXkmSpMUsWbRt59yQtcD6ttl64Pj2tueGSBqrJAHeDVxfVW/pWX5gT7NfAL7U3r4YOCHJHkkOBQ4DrhxVXkmSpO3ZoXPa5p0bMlNVW6Ap7JIc0Dbr69yQnTkvBGBmzwePcR/nuPYujavvUhboVh6zLKxLWYbgmcDLgQ1JrmmXvQF4aZIn0wx93AT8KkBVXZvkQuA6mtEFpzlzpCRJ6oq+i7YFzg1ZtOkCyx5ybsjOnBcC8PbzL+KcDQ/E3nRif+sNQ5fOAehSFuhWHrMsrEtZBq2qPsvCfdFHt7POWcBZQwslSZK0k/qa8n+hc0OA2+eGGrU/72iXe26IJEmSJA3IkkXbYueG0JwDclJ7+yTgop7lnhsiSZIkSQPQz/DIxc4NORu4MMkpwM3Ai8FzQyRJkiRpkJYs2rZzbgjA0Yus47khkiRJkjQAfZ3TJkmSJEkaD4s2SZIkSeowizZJkiRJ6jCLtgm3at0lrFp3CRtu/Rar1l0y7jiSJEmSBsyiTZIkSZI6rJ8p/yVJkiRJfZg/+u28Y/da9nN6pE2SJEmSOsyiTZIkSZI6zOGRkjRkG279Fif3DJXYdPZxY0wjSZImjUfaJEmSJKnDLNokSZIkqcMs2iRJkiSpwyzaJEmSJKnDLNokSZIkqcOcPVJTa+7Chqcfue3+mftW8qx9w7jQoyRJkoZvaEfakhyb5IYkG5OsG9brSNKg2G9JmjT2W9LKMJSiLckuwB8DzwMOB16a5PBhvJYkDYL9lqRJY78lrRzDOtJ2FLCxqr5SVd8BLgDWDum1JGkQ7LckTRr7LWmFSFUN/kmTFwHHVtUvt/dfDjy9ql7d0+ZU4NT27hOAG/p8+v2Brw8w7nKYZXFdymOWhe1IlsdW1aOHGWbc+um32uU703d16fe+PZOSEyYnqzkHr9+s9lsPtJuGfqtLecyysC5lgW7lWXa/NayJSLLAsgdVh1V1LnDuDj9x8vmqWr2zwQbJLIvrUh6zLKxLWTpiyX4Ldq7vmpT3elJywuRkNefgTVLWEVgx/VaX8phlYV3KAt3KM4gswxoeuRk4pOf+wcBtQ3otSRoE+y1Jk8Z+S1ohhlW0/SNwWJJDk+wOnABcPKTXkqRBsN+SNGnst6QVYijDI6tqW5JXAx8HdgHeU1XXDujpd3hI5RCZZXFdymOWhXUpy9itoH5reyYlJ0xOVnMO3iRlHaoV1m91KY9ZFtalLNCtPMvOMpSJSCRJkiRJgzG0i2tLkiRJkpbPok2SJEmSOqyzRVuSY5PckGRjknULPJ4kf9g+/sUkTx1jljVJvpXkmvbfbw0px3uS3JHkS4s8PrL3pM88I3lf2tc6JMmnk1yf5Nokr1ugzUjenz6zjOpv5mFJrkzyhTbLby/QZqR/N9OsS/3WMnOe2Ob7YpJ/SPKkLubsafe0JPeluWbVWPSTtf3cX9N+Fv9u1BnbDEv97h+Z5G96+oxXjilnp/6/m2Zdeq/dr1hWlhW3X9FnlpH9zbSvt0uSf0rykQUeW977UlWd+0dzMu2XgccBuwNfAA6f1+b5wN/SXKPkGcAVY8yyBvjICN6XZwFPBb60yOMjeU92IM9I3pf2tQ4Entre3gf4lzH+zfSTZVR/MwH2bm/vBlwBPGOcfzfT+q9L/dYAcv4ksG97+3ldzdnT7lPAR4EXdfh3/yjgOuAH2/sHdDTnG4Dfa28/GvgmsPsYsnbq/7tp/tel97qPLCP5v7N9LfcrFs7Smf2KPrOM7G+mfb3/Crx/oddc7vvS1SNtRwEbq+orVfUd4AJg7bw2a4H3VuNy4FFJDhxTlpGoqs/Q/Ae6mFG9J/3mGZmq2lJVV7e37wauBw6a12wk70+fWUai3dat7d3d2n/zZx8a6d/NFOtSv7WsnFX1D1V1Z3v3cpprP41av33va4APAXeMMtw8/WT9ReDDVXUzQFWNI28/OQvYJ0mAvWn6+G2jjdm9/++mWZfea/crlpVlJLq0X9FnlpFJcjBwHPCuRZos633patF2EHBLz/3NPPSPs582o8oC8BPt4dm/TXLEEHL0Y1TvyY4Y+fuSZBXwFJpvXHqN/P3ZThYY0XvTHqq/hman9tKqGvv7MqW61G9tz45mOIXmm8FRWzJnkoOAXwDeOcJcC+nnPX08sG+S2SRXJXnFyNI9oJ+cfwT8KM0FmjcAr6uq740m3g7pwmdppejae+1+hfsVO5IFRvc381bgN4DF+sxlvS9DuU7bAGSBZfMr537ajCrL1cBjq2prkucDfw0cNoQsSxnVe9Kvkb8vSfam+db99VX17fkPL7DK0N6fJbKM7L2pqvuAJyd5FPBXSZ5YVb3nC3Tt72ZSdanf2p6+MyR5Nk3R9lNDTbSwfnK+FTijqu5rDgyNTT9ZdwV+HDga2BP4XJLLq+pfhh2uRz85jwGuAZ4D/BBwaZK/X6APG7cufJZWii691+5XuF+xo1lG8r4keQFwR1VdlWTNYs0WWNb3+9LVI22bgUN67h9M863fjrYZSZaq+vbc4dmq+iiwW5L9h5BlKaN6T/oy6vclyW40ndn5VfXhBZqM7P1ZKss4/maq6i5gFjh23kOd+ruZYF3qt7anrwxJfoxmiMfaqvrGiLL16ifnauCCJJuAFwHvSHL8SNI9WL+/+49V1T1V9XXgM8CoJ3jpJ+craYZxVlVtBG4CfmRE+XZEFz5LK0Vn3mv3K9yv2NEsI3xfngm8sP3/6ALgOUneN6/Nst6XrhZt/wgcluTQJLsDJwAXz2tzMfCKdiaWZwDfqqot48iS5Afa8f8kOYrmfR3HTs6o3pO+jPJ9aV/n3cD1VfWWRZqN5P3pJ8uo3pskj26/fSLJnsDPAv88r1mn/m4mWJf6rWXlTPKDwIeBl4/4SFCvJXNW1aFVtaqqVgEfBP5LVf31yJP297u/CPjpJLsmeTjwdJrzUrqW82aao4EkmQGeAHxlpCn704XP0krRmffa/Qr3K3Y0y6jel6o6s6oObv8/OgH4VFW9bF6zZb0vnRweWVXbkrwa+DjNbFfvqaprk7yqffydNDOFPR/YCPwbzbeD48ryIuDXkmwD/h04oaoGfhg4yQdoZsHZP8lm4I00J12O9D3ZgTwjeV9azwReDmxIM7YZmlnQfrAnz6jen36yjOq9ORBYn2QXmo7qwqr6yDg+S9OuS/3WAHL+FvD9NEeuALZV1eoO5uyEfrJW1fVJPgZ8keZ8h3fNG8LTiZzA7wDnJdlAM5TnjPbI4Eh17f+7adal99r9imVlWYn7Ff1kGeXfzEMM8n3JCHNLkiRJknZQV4dHSpIkSZKwaNMIJakkPzzuHJK0lCSr2j6rk6cRSNJ89lvTzaJNC0qyKcl3Mm+GnSTXtB3CqjFFk6QltX3YvyfZ2vPvMePOJUnb09N33Z3kriT/kORVSdxnX+H8A9D23AS8dO5OkiNpri8kSZPg56tq755/TgkvaRL8fFXtAzwWOBs4g2b2SK1gFm3anj8HXtFz/yTgvXN3kuyR5M1Jbk5ye5J3tlOuzj3+/yXZkuS2JL/U+8RJZpP8cs/9k5N8dojbImmFS/LIJO9u+6Vbk/xuO+sYSXZp+7OvJ/kKcNy8dTcl+dme+2/KQ6/BI0kDU1XfqqqLgf8MnJTkiUmOS/JPSb6d5JYkb1ps/ST7Jfmzdj/sziR/ParsGjyLNm3P5cAjkvxou2Pzn4HenZTfAx4PPBn4YeAgmqnCSXIs8N+A59Jcef5nkaTxWg9so+mvngL8HDD35dGvAC9ol6+mmSZaksauqq6kuTDzTwP30Hyh/iiaL5d+Lcnxi6z658DDgSOAA4A/GHZWDY9Fm5Yyd7TtuTQXLLy1XR6anZxfr6pvVtXdwP+iuaAgwEuAP6uqL1XVPcCbRppakuCv23NC7kryt8DzgNdX1T1VdQfNDkxvn/XWqrqlqr4J/O8xZZakhdwG7FdVs1W1oaq+V1VfBD4A/Mz8xkkOpOnzXlVVd1bVd6vq70acWQPk7DJayp8DnwEOpWdoJPBomm9vrmovvgtNIbdLe/sxwFU97b863JiS9BDHV9UnAZIcBRwDbOnps74PuKW9/Zie22CfJalbDgK+meTpNOe5PRHYHdgD+MsF2h8CfLOq7hxdRA2TRZu2q6q+muQmmiu4n9Lz0Ndprix/RFXdusCqW2g6jDk/OO/xe2iKvjk/MIC4krSYW4B7gf2ratsCj9tnSeqkJE+jKdo+C/w18EfA86rqP5K8Fdh/gdVuAfZL8qiqumtEUTVEDo9UP04BntMOc5zzPeBPgT9IcgBAkoOSHNM+fiFwcpLDkzwceOO857wG+E9JHt5eu+0UJGlIqmoL8AngnCSPSPJ9SX4oydywoguB1yY5OMm+wLp5T3ENcEKS3ZJ4zpukoWv7qhcAFwDvq6oNwD40R9D+ox1B8IsLrdv2eX8LvCPJvm3f9ayRhdfAWbRpSVX15ar6/AIPnQFsBC5P8m3gk8AT2nX+Fngr8Km2zafmrfsHwHeA22kmBzh/KOEl6QGvoBlOdB1wJ/BB4MD2sT8FPg58Abga+PC8dX8T+KF2vd8G3j+CvJJWpr9JcjfN0bL/DrwFeGX72H8B/mf7+G/RfOG0mJcD36WZk+AO4PXDCqzhS1WNO4MkSZIkaREeaZMkSZKkDrNokyRJkqQOs2iTJEmSpA6zaJMkSZKkDuvEddr233//WrVqVV9t77nnHvbaa6/hBhoxt6n7pm17YMe26aqrrvp6VT16yJEmTr9910r/+5kUbtNk6Heb7LcWZr/lNnXdSt6m7fVbnSjaVq1axec/v9CM8g81OzvLmjVrhhtoxNym7pu27YEd26YkXx1umsnUb9+10v9+JoXbNBn63Sb7rYXZb60Zd4yBcpsmwyD6LYdHSpIkSVKHWbRJkiRJUodZtEmSJElShy1ZtCV5WJIrk3whybVJfrtdvl+SS5Pc2P7ct2edM5NsTHJDkmOGuQGSJEmSNM36mYjkXuA5VbU1yW7AZ5P8LfCfgMuq6uwk64B1wBlJDgdOAI4AHgN8Msnjq+q+IW2DpD6sWnfJg+6fd+x0zcwkLdeGW7/FyT2fk01nHzfGNNJ4zf88gJ8JaZyWPNJWja3t3d3afwWsBda3y9cDx7e31wIXVNW9VXUTsBE4apChJUmSJGml6GvK/yS7AFcBPwz8cVVdkWSmqrYAVNWWJAe0zQ8CLu9ZfXO7bP5zngqcCjAzM8Ps7Gxfgbdu3dp320nhNnXfNGzP6Udue9D9adgmSZKklaCvoq0d2vjkJI8C/irJE7fTPAs9xQLPeS5wLsDq1aur3+sxrORrN0ySadumadie+cNczjt2r4nfJkmSpJVgh2aPrKq7gFngWOD2JAcCtD/vaJttBg7pWe1g4LblBpUkSZKklaif2SMf3R5hI8mewM8C/wxcDJzUNjsJuKi9fTFwQpI9khwKHAZcOeDckrQoZ72VJEnTpJ/hkQcC69vz2r4PuLCqPpLkc8CFSU4BbgZeDFBV1ya5ELgO2Aac5syRK4+zsGnMnPVWkiRNjSWLtqr6IvCUBZZ/Azh6kXXOAs5adjpJ2glVVcBis96uaZevpxnufQY9s94CNyWZm/X2c6NLLUmStLC+JiKRpEkzjFlv2+fd4Zlvp3Gmzmncppk9HzzL6jRs3zT+nqZxmyRpKRZtkqbSMGa9bZ93h2e+nYbZR+ebxm16+/kXcc6GB/5b3HTimvGFGZBp/D1N4zZJ0lJ2aPZISZo0znorSZImnUWbpKnjrLeSJGmaODxS0jRy1ltJkjQ1LNokTR1nvZUkSdPE4ZGSJEmS1GEWbZIkSR2Q5FFJPpjkn5Ncn+QnkuyX5NIkN7Y/9+1pf2aSjUluSHLMOLNLGi6LNkmSpG54G/CxqvoR4EnA9cA64LKqOgy4rL1PksOBE4AjaGbHfUd7Hq+kKWTRJkmSNGZJHgE8C3g3QFV9p71kyVpgfdtsPXB8e3stcEFV3VtVNwEbgaNGmVnS6DgRiSRJ0vg9Dvga8GdJngRcBbwOmKmqLQBVtSXJAW37g4DLe9bf3C57iCSnAqcCzMzMMDs7u2SYmT3h9CO3PWhZP+t12datWyd+G+ZzmybDILbJok2SJGn8dgWeCrymqq5I8jbaoZCLyALLaqGGVXUucC7A6tWra82aNUuGefv5F3HOhgfvJm46cen1umx2dpZ+tn2SuE2TYRDb5PBISZKk8dsMbK6qK9r7H6Qp4m5PciBA+/OOnvaH9Kx/MHDbiLJKGjGLNkmSpDGrqn8FbknyhHbR0cB1wMXASe2yk4CL2tsXAyck2SPJocBhwJUjjCxphBweKUnq26p1lwDNuS4nr7uETWcfN+ZE0lR5DXB+kt2BrwCvpPmC/cIkpwA3Ay8GqKprk1xIU9htA06rqvvGE1vSsFm0SdsxfwcVcCdVkjQUVXUNsHqBh45epP1ZwFnDzCSpGxweKUmSJEkdZtEmSZIkSR1m0SZJkiRJHWbRJkmSJEkdZtEmSZIkSR1m0SZJkiRJHWbRJknSlFm17hJWrbuEDbd+6/5Ll0iSJpdFmyRJkiR1mBfXliRJnbTQUcLzjt1rDEkkabyWPNKW5JAkn05yfZJrk7yuXb5fkkuT3Nj+3LdnnTOTbExyQ5JjhrkBkiRJkjTN+hkeuQ04vap+FHgGcFqSw4F1wGVVdRhwWXuf9rETgCOAY4F3JNllGOElSZIkadotWbRV1Zaqurq9fTdwPXAQsBZY3zZbDxzf3l4LXFBV91bVTcBG4KgB55YkSZKkFWGHzmlLsgp4CnAFMFNVW6Ap7JIc0DY7CLi8Z7XN7bL5z3UqcCrAzMwMs7OzfWXYunVr3227bsOt3wJgZk94+/kXceRBjxxzosGZ2RNOP3Lb/fcn9Xc2tw292zPp2zJnmj5LkiRJ06zvoi3J3sCHgNdX1beTLNp0gWX1kAVV5wLnAqxevbrWrFnTV47Z2Vn6bdt1J7cnWJ9+5DbO2bArm05cM95AA/T28y/inA0P/HlN6rbN/x3B5G/LnPOO3WtqPkvzJTkEeC/wA8D3gHOr6m1J9gP+AlgFbAJeUlV3tuucCZwC3Ae8tqo+PobokiRJD9HXlP9JdqMp2M6vqg+3i29PcmD7+IHAHe3yzcAhPasfDNw2mLiS1BfPxZUkSVOjn9kjA7wbuL6q3tLz0MXASe3tk4CLepafkGSPJIcChwFXDi6yJG2f5+JKkqRp0s/wyGcCLwc2JLmmXfYG4GzgwiSnADcDLwaoqmuTXAhcR/Nt92lVdd+gg0tSPwZ5Lm77fDt8Pu40nT84/zzPadkumJ5zcWF6fk/zz8WF6fo8SVK/lizaquqzLHyeGsDRi6xzFnDWMnJJ0rIN+lxc2LnzcT0XdzJMy7m4MD2/p/nn4sJ0n48rSYvp65w2SZo0nosrSZKmhUWbpKnjubiSJGmaWLRJmkZz5+I+J8k17b/n05yL+9wkNwLPbe9TVdcCc+fifgzPxZU0Jkl2SfJPST7S3t8vyaVJbmx/7tvT9swkG5PckOSY8aWWNGw7dHFtSZoEnosraYK9jmbG20e09+cuVXJ2knXt/TPmXarkMcAnkzzeL5yk6eSRNkmSpA5IcjBwHPCunsVeqkSSR9okSZI64q3AbwD79Cwby6VK5l8CAyb7MhgwnZeLcJsmwyC2yaJNkiRpzJK8ALijqq5KsqafVRZYNrBLlcy/BAZM9mUwYLouvzLHbZoMg9gmizZJkqTxeybwwnbSpIcBj0jyPtpLlbRH2bxUibRCeU6bJEnSmFXVmVV1cFWtoplg5FNV9TK8VIkkPNImSZLUZWcDFyY5BbgZeDE0lypJMnepkm14qRJpqlm0SZIkdUhVzQKz7e1v4KVKpBXP4ZGSJEmS1GEWbZIkSZLUYRZtkiRJktRhFm2SJEmS1GEWbZIkSZLUYRZtkiRJktRhFm2SJEmS1GEWbZIkSZLUYRZtkiRJktRhFm2SJEmS1GEWbZIkSZLUYRZtkiRJktRhFm2SJEmS1GEWbZIkSZLUYRZtkiRJktRhSxZtSd6T5I4kX+pZtl+SS5Pc2P7ct+exM5NsTHJDkmOGFVySJEmSVoJ+jrSdBxw7b9k64LKqOgy4rL1PksOBE4Aj2nXekWSXgaWVJEmSpBVmyaKtqj4DfHPe4rXA+vb2euD4nuUXVNW9VXUTsBE4ajBRJal/jhKQJEnTYtedXG+mqrYAVNWWJAe0yw8CLu9pt7ld9hBJTgVOBZiZmWF2dravF966dWvfbbvu9CO3ATCzZ3N7WrYLHtimOZO6bfN/RzD52zJnmj5LizgP+CPgvT3L5kYJnJ1kXXv/jHmjBB4DfDLJ46vqvhFnliRJeoidLdoWkwWW1UINq+pc4FyA1atX15o1a/p6gdnZWfpt23Unr7sEaHamz9mwK5tOXDPeQAP09vMv4pwND/x5Teq2zf8dweRvy5zzjt1raj5LC6mqzyRZNW/xWmBNe3s9MAucQc8oAeCmJHOjBD43krCSJEnbsbNF2+1JDmyPsh0I3NEu3wwc0tPuYOC25QSUpAEayyiBaTqq6QiByTAtv6f5IwRguj5PvZIcQjMy4AeA7wHnVtXbkuwH/AWwCtgEvKSq7mzXORM4BbgPeG1VfXwM0SWNwM4WbRcDJwFntz8v6ln+/iRvoRlidBhw5XJDStKQDXWUgCMEJsO0jBCA6fk9zR8hAFM9SmAbcHpVXZ1kH+CqJJcCJ+OwbmnF62fK/w/QDBF6QpLNSU6hKdaem+RG4LntfarqWuBC4DrgY8Bpdh6SOuT2dnQAjhKQ1CVVtaWqrm5v3w1cT3PE38nfJC19pK2qXrrIQ0cv0v4s4KzlhJKkIXGUgKTOa8/HfQpwBWMa1j1/uDBM9pBhmM6htW7TZBjENg16IhJJ6oR2lMAaYP8km4E30hRrF7YjBm4GXgzNKIEkc6MEtuEoAUljkmRv4EPA66vq28lCo7ebpgssG9iw7vnDhWFyhwyvun+48H2c89l72HT2cWNONDjTNPx+jtu0MIs2SVPJUQKSJk2S3WgKtvOr6sPtYid/k7T0OW2SJEkarjSH1N4NXF9Vb+l5aG5YNzx0WPcJSfZIcigO65ammkfaJEmSxu+ZwMuBDUmuaZe9AYd1S8KiTZIkaeyq6rMsfJ4aOKxbWvEcHilJkiRJHWbRJkmSJEkdZtEmSZIkSR1m0SZJkiRJHWbRJkmSJEkdZtEmSZIkSR1m0SZJkiRJHWbRJkmSJEkdZtEmSZIkSR1m0SZJkiRJHWbRJkmSJEkdZtEmSZIkSR1m0SZJkiRJHbbruANIkiRJ2nEbbv0WJ6+75P77m84+boxpNEweaZMkSZKkDrNokyRJkqQOs2iTJEmSpA6zaJMkSZKkDrNokyRJkqQOs2iTJEmSpA4b2pT/SY4F3gbsAryrqs4exPM6tamkYbHfkjRphtVvSaO2qv1/8vQjt3Hyuksm+v/KVT3/5wOcd+xey37OoRxpS7IL8MfA84DDgZcmOXwYryVJg2C/JWnS2G9JK8ewhkceBWysqq9U1XeAC4C1Q3otSRoE+y1Jk8Z+S1ohUlWDf9LkRcCxVfXL7f2XA0+vqlf3tDkVOLW9+wTghj6ffn/g6wOM2wVuU/dN2/bAjm3TY6vq0cMMM2799Fvt8p3pu1b638+kcJsmQ7/bZL/1QDv7rYbbNBlW8jYt2m8N65y2LLDsQdVhVZ0LnLvDT5x8vqpW72ywLnKbum/atgemc5uWacl+C3au75rG99ptmgxu09Sz39oBbtNkcJsWNqzhkZuBQ3ruHwzcNqTXkqRBsN+SNGnst6QVYlhF2z8ChyU5NMnuwAnAxUN6LUkaBPstSZPGfktaIYYyPLKqtiV5NfBxmilo31NV1w7o6Xd4SOUEcJu6b9q2B6Zzm3aa/dYOc5smg9s0xey3dpjbNBncpgUMZSISSZIkSdJgDGt4pCRJkiRpACzaJEmSJKnDJqZoS3JskhuSbEyybtx5BiHJe5LckeRL484yCEkOSfLpJNcnuTbJ68adabmSPCzJlUm+0G7Tb48706Ak2SXJPyX5yLizTLNp67vst7rPfkvLMW2fcfBzPkmm7TOeZFOSDUmuSfL55TzXRBRtSXYB/hh4HnA48NIkh4831UCcBxw77hADtA04vap+FHgGcNoU/J7uBZ5TVU8Cngwcm+QZ4400MK8Drh93iGk2pX3XedhvdZ39lpbjPKbrMw5+zifJNH7Gn11VT+7qddoG7ShgY1V9paq+A1wArB1zpmWrqs8A3xx3jkGpqi1VdXV7+26aD91B4021PNXY2t7drf038bP3JDkYOA5417izTLmp67vst7rPfkvLMW2fcfBzPin8jG/fpBRtBwG39NzfzIR/2KZdklXAU4Arxhxl2dpD9dcAdwCXVtXEbxPwVuA3gO+NOce0s++aIPZbnfdW7Le0TH7OO+2tTN9nvIBPJLkqyanLeaJJKdqywLKJ/jZhmiXZG/gQ8Pqq+va48yxXVd1XVU8GDgaOSvLEMUdaliQvAO6oqqvGnWUFsO+aEPZb3Wa/pUHwc95dU/wZf2ZVPZXmNInTkjxrZ59oUoq2zcAhPfcPBm4bUxZtR5LdaDrE86vqw+POM0hVdRcwy+SP9X8m8MIkm2iG6z0nyfvGG2lq2XdNAPutiWC/pWXxc955U/kZr6rb2p93AH9Fc9rETpmUou0fgcOSHJpkd+AE4OIxZ9I8SQK8G7i+qt4y7jyDkOTRSR7V3t4T+Fngn8caapmq6syqOriqVtF8lj5VVS8bc6xpZd/VcfZbk8F+S8vh57z7pvEznmSvJPvM3QZ+DtjpWVknomirqm3Aq4GP05w8emFVXTveVMuX5APA54AnJNmc5JRxZ1qmZwIvp/l25Jr23/PHHWqZDgQ+neSLNDvgl1bVVExDq+Gbxr7Lfmsi2G9pp03hZxz8nGs8ZoDPJvkCcCVwSVV9bGefLFWeXiFJkiRJXTURR9okSZIkaaWyaJMkSZKkDrNokySpg5K8M8lvjjuHpG5IUkl+eAjPuynJzw76eTVYFm2SJA1YkvOS/O4OtD85yWd7l1XVq6rqdwafTpI0aSzaJEk7Lcmu486wMyY1tyRpZbJo0w5Jsi7Jl5PcneS6JL/QLt8lyTlJvp7kpiSvbg/j79o+/sgk706yJcmtSX43yS7j3RpJO6MdSnNGO9X0PUl+Ksk/JLkryReSrOlp+8ok17d9xleS/GrPY/sn+Ui73jeT/H2S72sf+9Eks+1j1yZ5Yc965yX54ySXtM97RZIf6iN3JTktyY3Aje2yF7TTf9/VbsOPzdvOM9u+7s4kf5bkYe1jDzkyNjd0KcmpwInAbyTZmuRv2scX6z9/FHgn8BNt+7t6tvN3e57/V5JsbN+ri5M8Zt5rvyrJjW3WP06SJX+Zksam7UdvbfuEG5Ic3e5PvaGnr7gqySE9q/3sQp/zJN+X5H8k+WqSO5K8N8kje17rhW1felfbt/7oyDdYy2LRph31ZeCngUcCvw28L8mBwK8AzwOeDDwVOH7eeuuBbcAPA0+hucDgL48ksaRheClwHPA44CLgd4H9gP8GfCjJo9t2dwAvAB4BvBL4gyRPbR87HdgMPJrmejZvACrJbsDfAJ8ADgBeA5yf5AnzXv+3gX2BjcBZfeY+Hng6cHib4z3ArwLfD/wJcHGSPXranwgcA/wQ8Hjgfyz1AlV1LnA+8PtVtXdV/Xz70IL9Z1VdD7wK+Fzb/lHznzPJc4D/DbyE5vpMXwUumNfsBcDTgCe17Y5ZKquk8Wj7s1cDT6uqfWg+r5uA/0rTvz2fpt/8JeDfelZd7HN+cvvv2TT98t7AH7Wv9XjgA8DrafrbjwJ/k2T34WydhsGiTTukqv6yqm6rqu9V1V/QfFt9FE3H8baq2lxVdwJnz62TZIamoHt9Vd1TVXcAf0BzxXtJk+kPq+oW4GXAR6vqo22/cCnweZodDqrqkqr6cjX+jqYQ++n2Ob5LU4A8tqq+W1V/X83FQ59Bs8NxdlV9p6o+BXyEZkdmzoer6sr2Aubn03xh1I//XVXfrKp/p/my6U+q6oqquq+q1gP3tq8/54+q6paq+iZNYfjSBZ6zL9vpP/txIvCeqrq6qu4FzqQ5Mreqp83ZVXVXVd0MfJr+3xNJo3cfsAfNF0i7VdWmqvoyzRfa/6Oqbmj7zS9U1Td61lvsc34i8Jaq+kpVbaXpI05oRzz9Z5oLO19aVd8F3gzsCfzkSLZUA2HRph2S5BU9Q4nuAp4I7A88Brilp2nv7ccCuwFbetb7E5pv0CVNprnP+GOBF899ttvP90/RFGMkeV6Sy9shfXfRFHP7t+v+H5qjZJ9oh06ua5c/Brilqr7X83pfBQ7quf+vPbf/jabI25Hcc9lPn5f9kPb1F2r/1XmP7ZDt9J/9eEz7+gC0O2XfYDDviaQRq6qNNEe+3gTckeSCdsjzITRH5Rez2Of8QX1Ee3tXmlEM8/uP79H0bb39hzrOE7HVtySPBf4UOJpmGM99Sa4BAmwBDu5p3jv++haab6/3b78VlzT5qv15C/DnVfUr8xu0www/BLwCuKiqvpvkr2n6DKrqbpohkqcnOQL4dJJ/BG4DDknyfT2F2w8C/zLA3HPZz6qq7Q2t7O3LfrDNBnAP8PC5B5L8wHZeZ6n+8yHtF3AbTZE593x70QzpvHWJ9SR1VFW9H3h/kkfQfJn9ezT90g8BX9rBp3tQH0HTX20Dbm8fO3LugfY8uEOw/5goHmnTjtiLZsfia9BMMEDzTTHAhcDrkhyU5FHAGXMrVdUWmiFR5yR5RHuy7A8l+ZmRppc0DO8Dfj7JMe0J9A9LsibJwcDuNMN/vgZsS/I8mvNZgfsnAfnhdgfi2zTDhe4DrqApin4jyW5pJjb5eR56Dtdy/SnwqiRPT2OvJMcl2aenzWlJDk6yH805d3/RLv8CcESSJ6eZnORN8577dprzSuZsr/+ca3/wds4xeT/wyvb19gD+F3BFVW3awW2W1AFJnpDkOe3n+T+Af6fp/94F/E6Sw9p+6ceSfH8fT/kB4NeTHJpkb5o+4i/aL8svBI5rJzrZjebLsnuBfxjGtmk4LNrUt6q6DjgH+BzNDsaRwP9rH/5TmsLsi8A/0Zzkuo2mA4Lmm/bdgeuAO4EP0g6fkjS52vPa1tIUNF+j+Zb4/wO+rz2S9lqaHYY7gV8ELu5Z/TDgk8BWmn7lHVU1W1XfAV5Icy7s14F3AK+oqn8ecPbP05zX9kdtvo00J/L3ej9N3/aV9t/vtuv+C/A/2/w3Ap+dt967ac5VuSvJXy/RfwJ8CrgW+NckX18g62XAb9IcudxC80285wVLk2sPmvP/v04z5PEAmn70LTR95idovsx6N835Z0t5D/DnwGeAm2gKwdcAVNUNNOcfv719vZ8Hfr7tazUh0pzzLQ1W+436O6vqsUs2lqQOSrIJ+OWq+uS4s0iSVjaPtGkgkuyZ5PlJdk1yEPBG4K/GnUuSJEmadBZtGpTQXHfoTprhkdcDvzXWRJJWlCQ/nebi1A/5N+5skiQth8MjJUmSOiDJr9Ncp6uADTQXpH84zQQ4q2guvvyS9nqoJDkTOIXm/PHXVtXHR59a0ihYtEmSJI1Ze2rBZ4HDq+rfk1xIM6nX4cA3q+rs9lqG+1bVGUkOp5kx8Cia63B9Enh8Vd23yEtImmCduE7b/vvvX6tWrRp3jO2655572GuvvcYdY2RW2vbCytvmHdneq6666utV9eghR5o4/fZdk/K3NSk5YXKymnPw+s06of3WrsCeSb5Lc4TtNuBMYE37+HpgluayOmuBC6rqXuCmJBtpCrjPbe8Fpq3fgsnJas7BmpScMJh+qxNF26pVq/j85z8/7hjbNTs7y5o1a8YdY2RW2vbCytvmHdneJF8dbprBG8Uwo377rkn525qUnDA5Wc05eP1mnbR+q6puTfJm4Gaaa3Z9oqo+kWSmvd4pVbUlyQHtKgcBl/c8xeZ22UMkORU4FWBmZoY3v/nNS+bZunUre++9905vzyhNSlZzDtak5IT+sz772c9etN/qRNEmSYPUDjN6LQ8eZnQCzTCjy3qGGa0D5oYZnQAcQTvMKInDjCSNTJJ9aY6eHQrcBfxlkpdtb5UFli14zktVnQucC7B69erqp+idxkJ+3Mw5WJOSEwaT1dkjJU2ruWFGu/LAMKO1NMOLaH8e396+f5hRVd1Ec5Hlo0YbV9IK97PATVX1tar6LvBh4CeB25McCND+vKNtvxk4pGf9g2n6OUlTyCNtkqbOKIcZzc7OLpln69atfbUbt0nJCZOT1ZyDN0lZd9DNwDOSPJym3zoa+DxwD3AScHb786K2/cXA+5O8hWaEwGHAlaMOLWk0LNr6sGrdJZx+5DZOXnfJ/cs2nX3cGBNJ2p6uDTN6+/kXcc5n77n/flf7j5U21GQUzDl4k5R1R1TVFUk+CFwNbKO55um5wN7AhUlOoSnsXty2v7Yd+n1d2/60QQ7p3nDrtx603wPd7buklcCircNW2VlKO+v+YUYASR40zKg9yuYwI0mdUlVvBN44b/G9NEfdFmp/FnDWsHNJGj/PaZM0je4fZpQkNDs819MMJzqpbTN/mNEJSfZIcigOM5IkSR3ikTZJU6drw4wkSZKWw6JN0lRymJEkSZoWDo+UJEmSpA6zaJMkSZKkDrNokyRJkqQOs2iTJEmSpA6zaJMkSZKkDrNokyRJkqQOs2iTJEmSpA6zaJMkSZKkDrNokyRJkqQOs2iTJEmSpA6zaJMkSZKkDrNokyRJkqQOs2iTJEmSpA6zaJMkSZKkDrNokyRJkqQOs2iTJEnqgCSPSvLBJP+c5PokP5FkvySXJrmx/blvT/szk2xMckOSY8aZXdJwWbRJkiR1w9uAj1XVjwBPAq4H1gGXVdVhwGXtfZIcDpwAHAEcC7wjyS5jSS1p6Cza9BCr1l3Chlu/xap1l7Bq3SXjjiNJ0tRL8gjgWcC7AarqO1V1F7AWWN82Ww8c395eC1xQVfdW1U3ARuCoUWaWNDq7jjuAJA1DkkcB7wKeCBTwS8ANwF8Aq4BNwEuq6s62/ZnAKcB9wGur6uMjDy1pJXsc8DXgz5I8CbgKeB0wU1VbAKpqS5ID2vYHAZf3rL+5XfYQSU4FTgWYmZlhdnZ2yTAze8LpR2570LJ+1huHrVu3djZbL3MO1qTkhMFktWiTNK3mhhm9KMnuwMOBN9AMMzo7yTqaYUZnzBtm9Bjgk0keX1X3jSu8pBVnV+CpwGuq6ookb6MdCrmILLCsFmpYVecC5wKsXr261qxZs2SYt59/EedsePBu4qYTl15vHGZnZ+lnm8bNnIM1KTlhMFkdHilp6jjMSNIE2gxsrqor2vsfpCnibk9yIED7846e9of0rH8wcNuIskoaMY+0SZpGnR5m1NXhHCttqMkomHPwJinrjqiqf01yS5InVNUNwNHAde2/k4Cz258XtatcDLw/yVtoRggcBlw5+uSSRmHJoi3JIcB7gR8AvgecW1VvS7IfnhsiqZs6PczIIUbLNylZzTl4k5R1J7wGOL8d0v0V4JU0o6IuTHIKcDPwYoCqujbJhTRF3TbgNId0S9OrnyNt24DTq+rqJPsAVyW5FDgZzw2R1E0LDTNaRzvMqD3K5jAjSZ1SVdcAqxd46OhF2p8FnDXMTJK6Yclz2qpqS1Vd3d6+m+aaIQfhuSGSOqqq/hW4JckT2kVzw4wuphleBA8dZnRCkj2SHIrDjCRJUofs0DltSVYBTwGuYJnnhuzMeSHjcvqR28ZyTsq4ptqdv71d/t0M0rSeJ7GYFbC9DjOSJElToe+iLcnewIeA11fVt5OFTgFpmi6w7CHnhuzMeSHjcvK6Szj9yG0jPyfl5HkXth7VeTDzt7er598M2pSfJ/EQ0769DjOSJEnToq8p/5PsRlOwnV9VH24XOwWtJEmSJA1ZP7NHhuZaR9dX1Vt6Hpo7N2SkU9Cumn/06ezjBvXUkiRJktQ5/QyPfCbwcmBDkmvaZW+gKdY8N0SSJEmShmjJoq2qPsvC56mB54ZIkiRJ0lD1dU6bJEmSJGk8LNokSZIkqcMs2iRJkiSpwyzaJEmSJKnDLNokSZIkqcMs2iRJkiSpwyzaJEmSJKnDLNokSZIkqcMs2iRJkiSpwyzaJEmSJKnDLNokSZIkqcMs2iRJkiSpwyzaJEmSOiLJLkn+KclH2vv7Jbk0yY3tz3172p6ZZGOSG5IcM77UkobNok3S1HLnR9IEeh1wfc/9dcBlVXUYcFl7nySHAycARwDHAu9IssuIs0oaEYs2SdPMnR9JEyPJwcBxwLt6Fq8F1re31wPH9yy/oKruraqbgI3AUSOKKmnEdh13AEkahp6dn7OA/9ouXgusaW+vB2aBM+jZ+QFuSjK38/O5EUaWpLcCvwHs07Nspqq2AFTVliQHtMsPAi7vabe5XfYQSU4FTgWYmZlhdnZ2ySAze8LpR2570LJ+1huHrVu3djZbL3MO1qTkhMFktWiTNK3eSkd3frr6n8xK+w9wFMw5eJOUdUckeQFwR1VdlWRNP6sssKwWalhV5wLnAqxevbrWrFn66d9+/kWcs+HBu4mbTuwn1ujNzs7SzzaNmzkHa1JywmCyWrRJmjpd3/lxx2f5JiWrOQdvkrLuoGcCL0zyfOBhwCOSvA+4PcmB7RdNBwJ3tO03A4f0rH8wcNtIE0saGc9pkzSN5nZ+NgEXAM/p3fkBcOdHUpdU1ZlVdXBVraI5x/ZTVfUy4GLgpLbZScBF7e2LgROS7JHkUOAw4MoRx5Y0IhZtkqaOOz+SpsjZwHOT3Ag8t71PVV0LXAhcB3wMOK2q7htbSklD5fBISSvJ2cCFSU4BbgZeDM3OT5K5nZ9tuPMjaYyqapZmoiSq6hvA0Yu0O4tmsiVJU86iTdJUc+dHkiRNOodHSpIkSVKHWbRJkiRJUodZtEmSJElSh1m0SZIkSVKHWbRJkiRJUodZtEmSJElSh1m0SZIkSVKHLVm0JXlPkjuSfKln2X5JLk1yY/tz357HzkyyMckNSY4ZVnBJkiRJWgn6OdJ2HnDsvGXrgMuq6jDgsvY+SQ4HTgCOaNd5R5JdBpZWkiRJklaYJYu2qvoM8M15i9cC69vb64Hje5ZfUFX3VtVNwEbgqMFElSRJkqSVZ9edXG+mqrYAVNWWJAe0yw8CLu9pt7ld9hBJTgVOBZiZmWF2dravFz79yG0Put/vestx+pHbmNnzwa89qtftNYrXnHvd3u0d1euO29atW1fMtsLK215JkqRRWLXukgfdP+/YvZb9nDtbtC0mCyyrhRpW1bnAuQCrV6+uNWvW9PUCJ897Ezad2N96y3Hyuks4/chtnLPhgbdrVK/baxSvOfe6vds7qtcdt9nZWfr9O5wGK217JUmSJtXOzh55e5IDAdqfd7TLNwOH9LQ7GLht5+NJkiRJ0sq2s0XbxcBJ7e2TgIt6lp+QZI8khwKHAVcuL6IkSZIkrVxLDo9M8gFgDbB/ks3AG4GzgQuTnALcDLwYoKquTXIhcB2wDTitqu4bUnZJkiRJmnpLFm1V9dJFHjp6kfZnAWctJ5QkSdL8k/lhMCf0S9Kk2dnhkZIkSZKkEbBokyRJGrMkhyT5dJLrk1yb5HXt8v2SXJrkxvbnvj3rnJlkY5IbkhwzvvSShs2iTdLUcedH0gTaBpxeVT8KPAM4LcnhwDrgsqo6DLisvU/72AnAEcCxwDuS7DKW5JKGzqJN0jRy50fSRKmqLVV1dXv7buB64CBgLbC+bbYeOL69vRa4oKruraqbgI3AUSMNLWlkBn1xbUkau6raAmxpb9+dpHfnZ03bbD0wC5xBz84PcFOSuZ2fz402uSRBklXAU4ArgJm2T6OqtiQ5oG12EHB5z2qb22ULPd+pwKkAMzMzzM7OLplhZk84/chtD1rWz3rjsHXr1s5m62XOwepyzvmfnUFktWiTNNW6uPPT1f9kuvwf4HyTktWcyzN/xwe6m3VQkuwNfAh4fVV9O8miTRdYVgs1rKpzgXMBVq9eXWvWrFkyx9vPv4hzNjx4N3HTiUuvNw6zs7P0s03jZs7B6nLOk+fNfHvesXstO6tFm6Sp1dWdH3d8lm9Ssppzeebv+MBgdn66KsluNH3W+VX14Xbx7UkObL9oOhC4o12+GTikZ/WDgdtGl1bSKHlOm6SptL2dn/Zxd34kdUaab5XeDVxfVW/peehi4KT29knART3LT0iyR5JDgcOAK0eVV9JoWbRJmjru/EiaQM8EXg48J8k17b/nA2cDz01yI/Dc9j5VdS1wIXAd8DHgtKq6bzzRJQ2bwyMlTaO5nZ8NSa5pl72BZmfnwiSnADcDL4Zm5yfJ3M7PNtz5kTRiVfVZFh6qDXD0IuucBZw1tFCSOsOiTdLUcedHkiRNE4s2dcKqeSebbzr7uDElkSRJkrrFc9okSZIkqcMs2iRJkiSpwyzaJEmSJKnDLNokSZIkqcMs2iRJkiSpwyzaJEmSJKnDLNokSZIkqcMs2iRJkiSpwyzaJEmSJKnDLNokSZIkqcMs2iRJkiSpwyzaJEmSJKnDLNokSZIkqcMs2iRJkiSpwyzaJEmSJKnDLNokSZIkqcN2HXcAaZxWrbvk/tunH7mNNeOLIkmSJC1oaEfakhyb5IYkG5OsG9brSNKg2G9JmjT2W9LKMJQjbUl2Af4YeC6wGfjHJBdX1XXDeD1pkvQe3Zuz6ezjxpBEvey3JE0a+y1p5RjW8MijgI1V9RWAJBcAawE7EWlM5heL5x2715iSdJb9lqRJY78lrRCpqsE/afIi4Niq+uX2/suBp1fVq3vanAqc2t59AnDDwIMM1v7A18cdYoRW2vbCytvmHdnex1bVo4cZZtz66bfa5TvTd03K39ak5ITJyWrOwes3q/3WA+2mud+CyclqzsGalJwwgH5rWEfassCyB1WHVXUucO6QXn/gkny+qlaPO8eorLTthZW3zStte/uwZL8FO9d3Tcp7PSk5YXKymnPwJinrCKz4fgsmJ6s5B2tScsJgsg5rIpLNwCE99w8GbhvSa0nSINhvSZo09lvSCjGsou0fgcOSHJpkd+AE4OIhvZYkDYL9lqRJY78lrRBDGR5ZVduSvBr4OLAL8J6qunYYrzVCEzOUc0BW2vbCytvmlba92zXkfmtS3utJyQmTk9WcgzdJWYfKfut+k5LVnIM1KTlhAFmHMhGJJEmSJGkwhnZxbUmSJEnS8lm0SZIkSVKHWbQtIMl7ktyR5Evzlr8myQ1Jrk3y++PKN2gLbW+SJye5PMk1ST6f5KhxZhykJIck+XSS69vf5eva5fsluTTJje3PfceddVC2s83/J8k/J/likr9K8qgxR51oSY5t+4iNSdYt8HiS/GH7+BeTPLWjOU9s830xyT8keVIXc/a0e1qS+9prVo1FP1mTrGn71GuT/N2oM7YZlvrdPzLJ3yT5QpvzlWPKueD/wz2Pd+KzNA3stwZvUvou+62B5xxuv1VV/pv3D3gW8FTgSz3Lng18EtijvX/AuHMOeXs/ATyvvf18YHbcOQe4vQcCT21v7wP8C3A48PvAunb5OuD3xp11BNv8c8Cu7fLfm6ZtHsN7vAvwZeBxwO7AF4DD57V5PvC3NNdWegZwRUdz/iSwb3v7eV3N2dPuU8BHgRd1+Hf/KOA64Afb+yP/P6TPnG+Y6weARwPfBHYfQ9aH/L807/Gxf5am4Z/91niy9rQbW99lvzWUrEPttzzStoCq+gzNL7zXrwFnV9W9bZs7Rh5sSBbZ3gIe0d5+JFN03Zeq2lJVV7e37wauBw4C1gLr22brgePHEnAIFtvmqvpEVW1rm11Oc40f7ZyjgI1V9ZWq+g5wAc3fVK+1wHurcTnwqCQHdi1nVf1DVd3Z3h3X30U/7yfAa4APAePsk/vJ+ovAh6vqZhjb/yH95CxgnyQB9qb5v2EbI7bI/0u9uvBZmgb2W4M3KX2X/daADbvfsmjr3+OBn05yRZK/S/K0cQcastcD/yfJLcCbgTPHG2c4kqwCngJcAcxU1RZoihzggDFGG5p529zrl2i+AdLOOQi4pef+5nbZjrYZth3NcArj+btYMmeSg4BfAN45wlwL6ec9fTywb5LZJFclecXI0j2gn5x/BPwozRd1G4DXVdX3RhNvh3ThszQN7LcGb1L6Lvut0VvWZ2ko12mbUrsC+9IcznwacGGSx1V7vHMK/Rrw61X1oSQvAd4N/OyYMw1Ukr1pvuV6fVV9u/mCZrrN3+ae5f+d5lup88eVbQos9Ac0v3/op82w9Z0hybNpdn5+aqiJFtZPzrcCZ1TVfWP+/PaTdVfgx4GjgT2BzyW5vKr+ZdjhevST8xjgGuA5wA8Blyb5+97+oiO68FmaBvZbgzcpfZf91ugt67Nk0da/zTSHiAu4Msn3gP2Br4031tCcBLyuvf2XwLvGmGXgkuxGU7ycX1UfbhffnuTAqtrSHq6emiGwsOg2k+Qk4AXA0VP8JcQobAYO6bl/MA8dVtxPm2HrK0OSH6P53D+vqr4xomy9+sm5Grig3enZH3h+km1V9dcjSfiAfn/3X6+qe4B7knwGeBLN+aWj0k/OV9KcClDAxiQ3AT8CXDmaiH3rwmdpGthvDd6k9F32W6O3rM+SwyP799c0FTxJHk9zMuTXxxloyG4Dfqa9/RzgxjFmGah2zPO7geur6i09D11MU6zS/rxo1NmGZbFtTnIscAbwwqr6t3HlmxL/CByW5NAkuwMn0PxN9boYeEU7g9QzgG/NDcntUs4kPwh8GHj5iL9R7bVkzqo6tKpWVdUq4IPAfxlDwQb9/e4vohliv2uShwNPpzm3tGs5b6b5Vp0kM8ATgK+MNGV/uvBZmgb2W4M3KX2X/dboLeuz5JG2BST5ALAG2D/JZuCNwHuA96SZxvM7wEnTclRike39FeBtSXYF/gM4dXwJB+6ZwMuBDUmuaZe9ATibZtjrKTQdwIvHE28oFtvmPwT2oBlKAHB5Vb1qLAknXFVtS/Jq4OM0s129p6quTfKq9vF30swS9nxgI/BvNN8OdjHnbwHfD7yj/bvYVlWrO5izE/rJWlXXJ/kY8EXge8C7qmrBaaHHmRP4HeC8JBtohvKcUVUj/4Jykf+XduvJOfbP0jSw3xpb1rGz3xq8YfdbmZK6Q5IkSZKmksMjJUmSJKnDLNrUlySrklQ7XHKhx9+U5H2jziVpZUlybZI1484hSdIoWbRJkiZGVR1RVbNLtUuyKcnYLlPSfsn1w+N6fUnTJckJaa4VfE+SO9rb/6Wd1OL1Sb6S5NtJbkvyB4t9ya7JZdGmobPjkDRJ2p0g/3+U1AlJTgfeBvwf4AeAGeBVNJOM7Q78DfDUqnoE8ESaaflfO560Ghb/U1rhkqxL8uUkdye5LskvtMt3SfLmJF9P8hXguHnrHZrk79r1LqW5zsjcY3NDKU9JcjPwqXb5LyW5PsmdST6e5LHt8rTfCt2R5FtJvpjkie1jz29z3Z3k1iT/bURvjaQOmjuC1g7JvjDJe9v+4dokq9s2fw78IPA3SbYm+Y12+TOS/EOSu5J8oXeYZZLZJGcl+X80s3o9ru3HXpXkxrbf+uPkgSvhbqdP+0zb5Avt6//nkbw5kqZOkkcC/5PmsgAfrKq7q/FPVXViVd1bVV+uqrvmVqGZ6dEj/VPGok1fBn4aeCTw28D70lxY+ldoLrj8FJqLQL5o3nrvB66iKdZ+hweub9brZ4AfBY5JcjzNFPP/CXg08PfAB9p2Pwc8C3g88CjgPwNzF8V8N/CrVbUPzbdHn1rOxkqaKi8ELqDpNy4G/gigql5Oc9mOn6+qvavq95McBFwC/C6wH/DfgA8leXTP872c5vIm+wBfbZe9AHgazTfXLwGOAdhen1ZVz2rXfVL7+n8x6A2XtGL8BM2lebZ77dgkv5jk2zTXEH4S8CcjyKYRsmhb4arqL6vqtqr6XrtjcSNwFM3OyVur6paq+ibwv+fWSXMBy6cBv9l+w/MZmkPz872pqu6pqn8HfhX431V1fVVtA/4X8OT2m+nv0uwk/QjNZSiu77nY4HeBw5M8oqrurKqrh/JGSJpEn62qj1bVfcCf0+yoLOZlwEfb9t+rqkuBz9NcM2fOeVV1bVVtq6rvtsvOrqq7qupm4NPAk9vl2+vTJGlQ9ge+3vYzAPSMGPj3JM8CqKr3t8MjHw+8E7h9PHE1LBZtK1ySVyS5pv3w30VzNGt/4DHALT1Nv9pz+zHAnVV1zyKPz+ld/7E0F+uee51v0hzCP6iqPkXzDfkfA7cnOTfJI9r1/n80O1VfbYdj/sTObqukqfOvPbf/DXjYds6hfSzw4rk+qO2Hfgo4sKfNLQusN/819u55vgX7tB3eCkla3DdoLtZ8f99WVT9ZVY9qH3vQvnxV3QhcC7xjlCE1fBZtK1j7jfCfAq8Gvr/tAL5Es+OxBTikp/kP9tzeAuybZK9FHp/Te+X2W2iGOT6q59+eVfUPAFX1h1X148ARNN8S/X/t8n+sqrXAAcBfAxfu7PZKWlFq3v1bgD+f1wftVVVnb2ed7dlunyZJA/I54F5g7Q6ssyvwQ8OJo3GxaFvZ9qLZSfkaQJJX0hxpg6Y4em2Sg5PsC6ybW6mqvkozrOi3k+ye5KeAn1/itd4JnJnkiPa1Hpnkxe3tpyV5epLdgHuA/wDua5/7xCSPbIcqfRu4bzCbLmnK3Q48ruf++4CfT3JMO9HSw5KsSXLwTj7/on3aIq8vSTusnWDkt4F3JHlRkr2TfF+SJ9Psx5Hkl5Mc0N4+HDgTuGxMkTUkFm0rWFVdB5xD8y3O7cCRwP9rH/5T4OPAF4CrgQ/PW/0XgafTDAl6I/DeJV7rr4DfAy5oT5T9EvC89uFHtK93J80wy28Ab24fezmwqV3nVTTnpUjSUv438D/a4Yv/rapuofmm+g00X1TdQnNEf6f+H1yiTwN4E7C+ff2X7PxmSFrpqur3gf8K/AZwB80+258AZwD/QDP1/4Yk9wAfbf+9YTxpNSyp2pHRIJIkSZKkUfJImyRJkiR1mEWbJEmSJHWYRZskSZIkdZhFmyRJkiR12GIXIR2p/fffv1atWtVX23vuuYe99tpr6YZjZs7Bm5Ss05jzqquu+npVPXrIkSZOv33XNP5NjNukZDXn4PWb1X5rYdPWb+0It2kyrORt2m6/VVVj//fjP/7j1a9Pf/rTfbcdJ3MO3qRkncacwOerA31F1/7123dN49/EuE1KVnMOXr9Z7bdWRr+1I9ymybCSt2l7/ZbDIyVJkiSpwyzaJEmSJKnDLNokSZI6IMl7ktyR5Es9y96U5NYk17T/nt/z2JlJNia5Ickx40ktaRQ6MRHJjthw67c4ed0l99/fdPZxY0wjSZKGZVXP//dzzjt2uiYomOc84I+A985b/gdV9ebeBUkOB04AjgAeA3wyyeOr6r5RBFU3uF+8cnikTdJUSvLrSa5N8qUkH0jysCT7Jbk0yY3tz3172vuNtaSxqqrPAN/ss/la4IKqureqbgI2AkcNLZyksZq4I22StJQkBwGvBQ6vqn9PciHNN9KHA5dV1dlJ1gHrgDP8xlpSx706ySuAzwOnV9WdwEHA5T1tNrfLHiLJqcCpADMzM8zOzi75glu3bu2r3SSZxm2a2RNOP3Lb/fcX2r4Nt37rQfePPOiRw461LNP4exrENlm0SZpWuwJ7Jvku8HDgNuBMYE37+HpgFjiDnm+sgZuSzH1j/bkRZ5ak+f4v8DtAtT/PAX4JyAJta6EnqKpzgXMBVq9eXWvWrFnyRWdnZ+mn3SSZxm16+/kXcc6GB3bnN5245iFtTp43zHihNl0yjb+nQWyTRZukqVNVtyZ5M3Az8O/AJ6rqE0lmqmpL22ZLkgPaVfzGmsnJCZOT1ZzL03sEYU5Xsw5LVd0+dzvJnwIfae9uBg7paXowzZdTkqaQRZukqdOeq7YWOBS4C/jLJC/b3ioLLFtx31hPSk6YnKzmXJ75RwigmYiki1mHJcmBc182Ab8AzM0seTHw/iRvoRnWfRhw5RgiShoBizZJ0+hngZuq6msAST4M/CRw+9wOUJIDgTva9n5jLWnsknyAZgj3/kk2A28E1iR5Ms0XSZuAXwWoqmvb83WvA7YBp3kerjS9LNokTaObgWckeTjN8MijaU7gvwc4CTi7/XlR295vrCWNXVW9dIHF795O+7OAs4aXSFJXWLRJmjpVdUWSDwJX03wD/U80Qxr3Bi5McgpNYffitr3fWEuSpM6yaJM0larqjTRDi3rdS3PUbaH2fmMtSZI6yYtrS5IkSVKHWbRJkiRJUodZtEmSJElSh1m0SZIkSVKHWbRJkiRJUodZtEmSJElSh1m0SZIkSVKHWbRJkiRJUodZtEmSJElSh/VVtCV5VJIPJvnnJNcn+Ykk+yW5NMmN7c99e9qfmWRjkhuSHDO8+JIkSZI03fo90vY24GNV9SPAk4DrgXXAZVV1GHBZe58khwMnAEcAxwLvSLLLoINLkiRJ0kqwZNGW5BHAs4B3A1TVd6rqLmAtsL5tth44vr29Frigqu6tqpuAjcBRg40tSZIkSSvDrn20eRzwNeDPkjwJuAp4HTBTVVsAqmpLkgPa9gcBl/esv7ld9iBJTgVOBZiZmWF2dravwDN7wulHbrv/fr/rjdrWrVs7m63XpOSEyclqTkmSJA1SP0XbrsBTgddU1RVJ3kY7FHIRWWBZPWRB1bnAuQCrV6+uNWvW9BEF3n7+RZyz4YHYm07sb71Rm52dpd9tGqdJyQmTk9WckiRJGqR+zmnbDGyuqiva+x+kKeJuT3IgQPvzjp72h/SsfzBw22DiSpIkSdLKsmTRVlX/CtyS5AntoqOB64CLgZPaZScBF7W3LwZOSLJHkkOBw4ArB5pakiRJklaIfoZHArwGOD/J7sBXgFfSFHwXJjkFuBl4MUBVXZvkQprCbhtwWlXdN/DkkrQdSR4FvAt4Is0Q7V8CbgD+AlgFbAJeUlV3tu3PBE4B7gNeW1UfH3loSZKkBfRVtFXVNcDqBR46epH2ZwFn7XwsSVq2uUuVvKj9wunhwBtoLlVydpJ1NOfnnjHvUiWPAT6Z5PF+4SRJkrqg3yNtkjQxei5VcjI0lyoBvpNkLbCmbbYemAXOoOdSJcBNSeYuVfK5kQaXJGkCrFp3yYPubzr7uDElWTks2iRNo6FcqgR27nIlk3J5hUnJCZOT1ZzL03uJnzldzSpJw2TRJmkaDeVSJbBzlyuZlMsrTEpOmJys5lyek+d9mw9w3rF7dTKrJA1TP1P+S9Kk8VIlkiRpali0SZo6XqpEkiRNE4dHSppWXqpEkiRNBYs2SVPJS5VImjRJ3gO8ALijqp7YLtsPry8prXgWbZIkSd1wHvBHwHt7lq3D60tqhJzOv5s8p02SJKkDquozwDfnLV5Lc11J2p/H9yy/oKruraqbgLnrS0qaQh5pkyRJ6i6vLzkg07hNM3s++HqGC23f/OsdLvUe9NN+R59zR0zj72kQ22TRJkmSNHm8vuQOmsZtevv5F3HOhgd25zeduOYhbeZf73ChNjvafkefc0dM4+9pENvk8EhJkqTu8vqSkizaJEmSOszrS0pyeKQkSVIXJPkAsAbYP8lm4I3A2Xh9SWnFs2iTJEnqgKp66SIPeX1JaYVzeKQkSZIkdZhH2iRJkiQNzfwLdoMX7d5RHmmTJEmSpA6zaJMkSZKkDuu7aEuyS5J/SvKR9v5+SS5NcmP7c9+etmcm2ZjkhiTHDCO4JEmSJK0EO3Kk7XXA9T331wGXVdVhwGXtfZIcDpwAHAEcC7wjyS6DiStJkiRJK0tfRVuSg4HjgHf1LF4LrG9vrweO71l+QVXdW1U3ARuBowaSVpIkSdLUWbXuElatu4QNt35rwYlLVrp+Z498K/AbwD49y2aqagtAVW1JckC7/CDg8p52m9tlD5LkVOBUgJmZGWZnZ/sKMrMnnH7ktvvv97veqG3durWz2XpNSk6YnKzmlCRJ0iAtWbQleQFwR1VdlWRNH8+ZBZbVQxZUnQucC7B69epas6afp4a3n38R52x4IPamE/tbb9RmZ2fpd5vGaVJywuRkNackSZIGqZ/hkc8EXphkE3AB8Jwk7wNuT3IgQPvzjrb9ZuCQnvUPBm4bWGJJ6pMTKEmSpGmwZNFWVWdW1cFVtYpmgpFPVdXLgIuBk9pmJwEXtbcvBk5IskeSQ4HDgCsHnlySluYESpIkaeIt5zptZwPPTXIj8Nz2PlV1LXAhcB3wMeC0qrpvuUElaUc4gZIkSZoW/U5EAkBVzQKz7e1vAEcv0u4s4KxlZpOk5XgrA55ACXZuEqVJmfRlUnLC5GQ15/L0Tjw2p6tZJWmYdqhok6RJMKwJlGDnJlGalElfJiUnTE5Wcy7PyQtM+33esXt1MqskDZNFm6RpNDeB0vOBhwGP6J1AqT3K5gRKkiRpIiznnDZJ6iQnUJIkSdPEI22SVpKzgQuTnALcDLwYmgmUksxNoLQNJ1CSJEkdYtEmaao5gZIkqQtWzTtHc9PZx40piSaRwyMlSZIkqcM80iZJkiSN2fwjceDROD3AI22SJEmS1GEWbZIkSZLUYRZtkiRJktRhntMmrRDzx8qfd+xeY0oiSZKkHeGRNkmSJEnqMI+0SZIkdVySTcDdwH3AtqpanWQ/4C+AVcAm4CVVdee4MkoaHos2SZKkyfDsqvp6z/11wGVVdXaSde39MwbxQhtu/RYnezFoqTMs2iRJkibTWmBNe3s9MMuAijap61bade0s2iRJkrqvgE8kKeBPqupcYKaqtgBU1ZYkByy0YpJTgVMBZmZmmJ2dXfLFZvaE04/c9qBl/azXZVu3bh3rNiz1fs5/fKE2883/PS3Ufkd/j/20X+5zLrTOXJu5bRrEc3bFIP72LNokSZK675lVdVtbmF2a5J/7XbEt8M4FWL16da1Zs2bJdd5+/kWcs+HBu4mbTlx6vS6bnZ2ln20flocMN533fs5/fKE2883/PS3UfqnX3Zn2y33OhdaZa3P6kds4Z8OuA3nOrhjE356zR0qSJHVcVd3W/rwD+CvgKOD2JAcCtD/vGF9CScNk0SZJktRhSfZKss/cbeDngC8BFwMntc1OAi4aT0JJw7Zk0ZbkkCSfTnJ9kmuTvK5dvl+SS5Pc2P7ct2edM5NsTHJDkmOGuQGSJElTbgb4bJIvAFcCl1TVx4CzgecmuRF4bntf0hTq55y2bcDpVXV1+y3PVUkuBU5mgWlmkxwOnAAcATwG+GSSx1fVfcPZBEmSpOlVVV8BnrTA8m8AR48+kaRRW/JIW1Vtqaqr29t3A9cDB9FMM7u+bbYeOL69vRa4oKruraqbgI00464laSQcISBJkqbJDs0emWQV8BTgChafZvYg4PKe1Ta3y+Y/1w5PPwv9TW3aBeOeVrZfk5ITJidrV3POnxq3qzkHpFMjBOZfpHaaryMjSZIGr++iLcnewIeA11fVt5Ms2nSBZfWQBTsx/Sz0N7VpF4x7Wtl+TUpOmJysXc05f2rc847dq5M5B6H9QmnuS6W7k/SOEFjTNuu9EO39IwSAm5LMjRD43GiTS5IkPVRfRVuS3WgKtvOr6sPt4tuTHNgeZeudZnYzcEjP6gcDtw0qsCTtiEGOEGifb9kXqe3qEc5JOvo6KVnNuTwLXTy3q1klaZiWLNrSHFJ7N3B9Vb2l56G5aWbP5sHTzF4MvD/JW2iGGR1GM9ORJI3UoEcIwGAuUusIgeWblKzmXJ6FLp47zaMEJGkx/RxpeybwcmBDkmvaZW+gKdYuTHIKcDPwYoCqujbJhcB1NOeVnObMkZJGzRECkiStbKvmffEzyeeUL1m0VdVnWfhbaFhkmtmqOgs4axm5JGmnOUJAkiRNkx2aPVKSJoQjBCRJ0tSwaJM0dRwhIEmSpsmSF9eWJEmSJI2PR9okSZIkie5OXuKRNkmSJEnqMIs2SZIkSeowizZJkiRJ6jCLNkmSJEnqMIs2SZIkSeowZ4+UJEmSeiw1g2BXZxjU9PJImyRJkiR1mEWbJEmSJHWYRZskSZIkdZhFmyRJkiR1mEWbJEmSJHWYRZskSZIkdZhFmyRJkqbWqnWXsGrdJWy49VsPmapfmhRep02SJEmS+jCua/R5pE2SJEmSOmxoRVuSY5PckGRjknXDeh1JGhT7LUmTxn5LWhmGUrQl2QX4Y+B5wOHAS5McPozXkqRBsN+SNGnst6SVY1jntB0FbKyqrwAkuQBYC1w3pNeTpOWy35I0acbab43r3J6u5pDmzP+bPO/YvZb9nKmqZT/JQ540eRFwbFX9cnv/5cDTq+rVPW1OBU5t7z4BuKHPp98f+PoA4w6LOQdvUrJOY87HVtWjhxlm3Prpt9rlO9N3TePfxLhNSlZzDl6/We23Hmg3zf3WjnCbJsNK3qZF+61hHWnLAsseVB1W1bnAuTv8xMnnq2r1zgYbFXMO3qRkNefEWrLfgp3ruyblvZ6UnDA5Wc05eJOUdQRWfL+1I9ymyeA2LWxYE5FsBg7puX8wcNuQXkuSBsF+S9Kksd+SVohhFW3/CByW5NAkuwMnABcP6bUkaRDstyRNGvstaYUYyvDIqtqW5NXAx4FdgPdU1bUDevodHlI5JuYcvEnJas4JZL8FTE5OmJys5hy8Sco6VPZbO8xtmgxu0wKGMhGJJEmSJGkwhnZxbUmSJEnS8lm0SZIkSVKHdbZoS3JskhuSbEyyboHHk+QP28e/mOSpHc15Ypvvi0n+IcmTupizp93TktzXXvtl5PrJmWRNkmuSXJvk70adsSfHUr/7Ryb5myRfaLO+cgwZ35PkjiRfWuTxTnyOpoX91mhz9rQba7/VZpiIvmsS+q02h33XGPX72ZskSTYl2dB+Bj8/7jw7Y6HPRZL9klya5Mb2577jzLijFtmmNyW5tf1dXZPk+ePMuCOSHJLk00mub/vQ17XLl/97qqrO/aM5mfbLwOOA3YEvAIfPa/N84G9prlHyDOCKjub8SWDf9vbzupqzp92ngI8CL+piTuBRwHXAD7b3D+jw3+gbgN9rbz8a+Caw+4hzPgt4KvClRR4f++doWv7Zb40+Z0+7sfVbO/Cejr3vmpR+q31t+64x/ev3szdp/4BNwP7jzrHMbXjI5wL4fWBde3vd3Od3Uv4tsk1vAv7buLPt5PYcCDy1vb0P8C/A4YP4PXX1SNtRwMaq+kpVfQe4AFg7r81a4L3VuBx4VJIDu5azqv6hqu5s715Ocw2VUevn/QR4DfAh4I5RhuvRT85fBD5cVTcDVFWXsxawT5IAe9Ps/GwbZciq+kz7uovpwudoWthvDdak9FswOX3XRPRbYN81Zv1+9jRii3wu1gLr29vrgeNHmWm5+visT5Sq2lJVV7e37wauBw5iAL+nrhZtBwG39Nzf3C7b0TbDtqMZTqH5ZnDUlsyZ5CDgF4B3jjDXfP28n48H9k0ym+SqJK8YWboH6yfrHwE/SnOh0w3A66rqe6OJ17cufI6mhf3WYE1KvwWT03dNS78F3fgsTatpfW8L+ET7+Tt13GEGaKaqtkBTMAAHjDnPoLy6Hfr8nkkb8jknySrgKcAVDOD31NWiLQssm39tgn7aDFvfGZI8m2bn54yhJlpYPznfCpxRVfcNP86i+sm5K/DjwHHAMcBvJnn8sIMtoJ+sxwDXAI8Bngz8UZJHDDfWDuvC52ha2G8N1qT0WzA5fde09FvQjc/StJrW9/aZVfVUmiHfpyV51rgDaVH/F/ghmj5oC3DOWNPshCR704wCeX1VfXsQz9nVom0zcEjP/YNpvvXb0TbD1leGJD8GvAtYW1XfGFG2Xv3kXA1ckGQT8CLgHUmOH0m6B/T7e/9YVd1TVV8HPgOMY5KEfrK+kmY4VFXVRuAm4EdGlK9fXfgcTQv7rcGalH4LJqfvmpZ+C7rxWZpWU/neVtVt7c87gL+iGQY6DW6fGxrc/hznUPGBqKrbq+q+9ij/nzJhv6sku9EUbOdX1Yfbxcv+PXW1aPtH4LAkhybZHTgBuHhem4uBV7QzSD0D+NbcYccu5Uzyg8CHgZdX1b+MON+cJXNW1aFVtaqqVgEfBP5LVf1113ICFwE/nWTXJA8Hnk4zXnjU+sl6M3A0QJIZ4AnAV0aacmld+BxNC/utwZqUfgsmp++aln4LuvFZmlb9/J1MlCR7Jdln7jbwc8CCM5NOoIuBk9rbJ9H0NRNt3vmpv8AE/a7a84HfDVxfVW/peWjZv6ddlx9v8KpqW5JXAx+nmcXoPVV1bZJXtY+/k2amsOcDG4F/o/l2sIs5fwv4fppvgAG2VdXqDuYcu35yVtX1ST4GfBH4HvCuqhr5h7nP9/R3gPOSbKAZbnJG+w37yCT5ALAG2D/JZuCNwG49Gcf+OZoW9ltjydkJk9J3TUq/BfZd47TY38mYYy3XDPBXbX+2K/D+qvrYeCPtuEU+F2cDFyY5heZLlxePL+GOW2Sb1iR5Ms2w3E3Ar44r3054JvByYEOSa9plb2AAv6dUTcMwZUmSJEmaTl0dHilJkiRJwqJNkiRJkjrNok2SJEmSOsyiTZIkSZI6zKJNkiRJkjrMok2SJEmSOsyiTZIkSZI67P8P/AM4GB7mCIYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3.hist(bins=50, figsize=(15,10))\n",
    "plt.show()\n",
    "df4.hist(bins=50, figsize=(15,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix = df3.corr().round(1)\n",
    "\n",
    "# Resizing the heatmap\n",
    "# fig, ax = plt.subplots(figsize=(16,11))\n",
    "\n",
    "sns.heatmap(corr_matrix, annot=True, vmax=1, vmin=-1, center=0, cmap='vlag')\n",
    "plt.show()\n",
    "\n",
    "corr_matrix = df4.corr().round(1)\n",
    "\n",
    "# Resizing the heatmap\n",
    "# fig, ax = plt.subplots(figsize=(16,11))\n",
    "\n",
    "sns.heatmap(corr_matrix, annot=True, vmax=1, vmin=-1, center=0, cmap='vlag')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.mixture import GaussianMixture # GMM\n",
    "from sklearn.cluster import AgglomerativeClustering # Hieracrchical\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Means Clustering\n",
    "## Experimentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 0, 0, 0])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[-1.19817006e+00,  3.43133938e+00,  1.74673310e-02,\n",
       "        -4.14558013e-02,  7.24022065e-04, -1.32801251e-01,\n",
       "         8.71891747e-02,  5.42911267e-03,  2.61818537e-02,\n",
       "        -1.66005008e-02, -1.36984048e-02,  5.08161251e-02,\n",
       "         1.01678754e-02,  9.64302895e-03, -1.37778095e-02,\n",
       "        -2.57776172e-02],\n",
       "       [ 1.16541415e+01, -1.99651137e-01,  1.36284294e-01,\n",
       "        -5.11421234e-02, -7.02110591e-02,  1.29585872e-01,\n",
       "         7.33027794e-05,  6.17779051e-02, -5.23510996e-02,\n",
       "         3.95819160e-02,  8.23891679e-02, -7.81857818e-02,\n",
       "        -3.33465786e-02,  2.00302517e-02,  5.45298364e-02,\n",
       "         5.97627724e-02],\n",
       "       [-2.59383209e+00, -3.52953691e+00, -6.33170728e-02,\n",
       "         6.03309259e-02,  2.24317846e-02,  9.63522188e-02,\n",
       "        -9.13841668e-02, -2.60938225e-02, -1.01429231e-02,\n",
       "         4.32084859e-03, -1.28591415e-02, -2.74225038e-02,\n",
       "         3.59979999e-04, -1.67202274e-02, -3.57410909e-03,\n",
       "         7.27130280e-03]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = 3\n",
    "k_means = KMeans(n_clusters=k, random_state=0)\n",
    "predicted = k_means.fit_predict(X=df4)\n",
    "display(predicted)\n",
    "display(k_means.cluster_centers_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 0])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.05963303,  0.97247706,  2.06574924,  2.78134557,  2.55810398,\n",
       "         1.40061162, 16.56269113,  0.2706422 ,  0.19877676,  0.2324159 ,\n",
       "         0.82568807, 13.52446483],\n",
       "       [ 0.60769231,  0.81794872,  1.81025641,  2.30512821,  2.1025641 ,\n",
       "         1.65128205, 17.        ,  0.18205128,  0.36410256,  0.34102564,\n",
       "         0.73589744,  7.68205128]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = 2\n",
    "k_means = KMeans(n_clusters=k, random_state=0)\n",
    "predicted = k_means.fit_predict(X=df3)\n",
    "display(predicted)\n",
    "display(k_means.cluster_centers_)\n",
    "# Expected penultimate 4 to be heavily split due to them being binary encoded.\n",
    "# However this is now the case, instead the values tended towards the binary index with most instances.\n",
    "# In general the results don't really tell us much.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 9, ..., 2, 6, 6])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.15294118,  0.76470588,  1.29411765,  1.42352941,  1.96470588,\n",
       "         1.35294118, 17.2       ,  0.09411765,  0.57647059,  0.56470588,\n",
       "         0.64705882],\n",
       "       [ 0.06321839,  0.98275862,  1.87931034,  3.64942529,  3.62068966,\n",
       "         1.27011494, 15.53448276,  0.26436782,  0.10344828,  0.16666667,\n",
       "         0.89655172],\n",
       "       [ 0.10071942,  0.9352518 ,  2.49640288,  1.86330935,  1.51798561,\n",
       "         1.14388489, 17.77697842,  0.25899281,  0.1942446 ,  0.28057554,\n",
       "         0.75539568],\n",
       "       [ 0.13559322,  0.99435028,  2.08474576,  3.56497175,  3.46327684,\n",
       "         1.24293785, 17.59887006,  0.32768362,  0.18644068,  0.18644068,\n",
       "         0.89830508],\n",
       "       [ 0.07462687,  0.98507463,  3.62686567,  2.71641791,  1.98507463,\n",
       "         1.19402985, 15.65671642,  0.47761194,  0.14925373,  0.23880597,\n",
       "         0.8358209 ],\n",
       "       [ 0.17948718,  0.97435897,  1.8974359 ,  3.56410256,  3.61538462,\n",
       "         3.69230769, 16.82051282,  0.28205128,  0.23076923,  0.28205128,\n",
       "         0.92307692],\n",
       "       [ 0.56862745,  0.76470588,  1.49019608,  2.03921569,  1.58823529,\n",
       "         4.        , 17.96078431,  0.09803922,  0.43137255,  0.35294118,\n",
       "         0.8627451 ],\n",
       "       [ 1.9047619 ,  0.50793651,  1.52380952,  1.63492063,  1.36507937,\n",
       "         1.41269841, 18.3015873 ,  0.07936508,  0.52380952,  0.3968254 ,\n",
       "         0.57142857],\n",
       "       [ 0.18987342,  0.98734177,  1.73417722,  3.4556962 ,  1.74683544,\n",
       "         1.40506329, 16.08860759,  0.21518987,  0.26582278,  0.18987342,\n",
       "         0.7721519 ],\n",
       "       [ 0.22352941,  0.94117647,  1.63529412,  1.59411765,  1.72352941,\n",
       "         1.27647059, 15.68235294,  0.17647059,  0.29411765,  0.3       ,\n",
       "         0.7       ]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = 10\n",
    "k_means = KMeans(n_clusters=k, random_state=0)\n",
    "predicted = k_means.fit_predict(X=df3.iloc[:,:-1])\n",
    "display(predicted)\n",
    "display(k_means.cluster_centers_)\n",
    "# Seems to split some features better.\n",
    "# Need to be careful! Becuase k is larger than the posible range of values for these features its possible that some of these results\n",
    "# are interpreting the discrete feature space as continuous clustors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chosen data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_data(X):\n",
    "    sns.kdeplot(x=X[:, 0], y=X[:, 1], cmap=\"Blues\",shade=True)\n",
    "    # plt.plot(X[:, 0], X[:, 1], 'k.', markersize=2)\n",
    "\n",
    "def plot_centroids(centroids, weights=None, circle_color='w', cross_color='k'):\n",
    "    if weights is not None:\n",
    "        centroids = centroids[weights > weights.max() / 10]\n",
    "\n",
    "    plt.scatter(centroids[:, 0], centroids[:, 1],\n",
    "                marker='x', s=10, linewidths=20,\n",
    "                color=cross_color, zorder=11, alpha=1)\n",
    "\n",
    "def plot_decision_boundaries(clusterer, X, resolution=1000, show_centroids=True,\n",
    "                             show_xlabels=True, show_ylabels=True):\n",
    "    mins = X.min(axis=0) - 0.1\n",
    "    maxs = X.max(axis=0) + 0.1\n",
    "    xx, yy = np.meshgrid(np.linspace(mins[0], maxs[0], resolution),\n",
    "                         np.linspace(mins[1], maxs[1], resolution))\n",
    "    Z = clusterer.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "\n",
    "    if show_centroids:\n",
    "        if isinstance(clusterer, KMeans):\n",
    "            plot_data(X)\n",
    "            plot_centroids(clusterer.cluster_centers_)\n",
    "            plt.contour(Z, extent=(mins[0], maxs[0], mins[1], maxs[1]),\n",
    "                linewidths=1, colors='k')\n",
    "        elif isinstance(clusterer, GaussianMixture):\n",
    "            plot_centroids(clusterer.means_)\n",
    "\n",
    "    if show_xlabels:\n",
    "        plt.xlabel(\"age\", fontsize=14)\n",
    "    else:\n",
    "        plt.tick_params(labelbottom=False)\n",
    "    if show_ylabels:\n",
    "        plt.ylabel(\"$x_2$\", fontsize=14, rotation=0)\n",
    "    else:\n",
    "        plt.tick_params(labelleft=False)\n",
    "\n",
    "    # plt.contourf(Z, extent=(mins[0], maxs[0], mins[1], maxs[1]),\n",
    "    #             cmap=\"Pastel2\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'split'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-12-5495de22a67f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mk_means\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m     \u001b[0mplot_decision_boundaries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk_means\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-11-cdf8ee93733d>\u001b[0m in \u001b[0;36mplot_decision_boundaries\u001b[1;34m(clusterer, X, resolution, show_centroids, show_xlabels, show_ylabels)\u001b[0m\n\u001b[0;32m     17\u001b[0m     xx, yy = np.meshgrid(np.linspace(mins[0], maxs[0], resolution),\n\u001b[0;32m     18\u001b[0m                          np.linspace(mins[1], maxs[1], resolution))\n\u001b[1;32m---> 19\u001b[1;33m     \u001b[0mZ\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclusterer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mxx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m     \u001b[0mZ\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mZ\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X, sample_weight)\u001b[0m\n\u001b[0;32m   1332\u001b[0m         \u001b[0msample_weight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_check_sample_weight\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1333\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1334\u001b[1;33m         return _labels_inertia_threadpool_limit(\n\u001b[0m\u001b[0;32m   1335\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_squared_norms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcluster_centers_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_threads\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1336\u001b[0m         )[0]\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py\u001b[0m in \u001b[0;36m_labels_inertia_threadpool_limit\u001b[1;34m(X, sample_weight, x_squared_norms, centers, n_threads)\u001b[0m\n\u001b[0;32m    753\u001b[0m ):\n\u001b[0;32m    754\u001b[0m     \u001b[1;34m\"\"\"Same as _labels_inertia but in a threadpool_limits context.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 755\u001b[1;33m     \u001b[1;32mwith\u001b[0m \u001b[0mthreadpool_limits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muser_api\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"blas\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    756\u001b[0m         labels, inertia = _labels_inertia(\n\u001b[0;32m    757\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_squared_norms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcenters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_threads\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\sklearn\\utils\\fixes.py\u001b[0m in \u001b[0;36mthreadpool_limits\u001b[1;34m(limits, user_api)\u001b[0m\n\u001b[0;32m    312\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mcontroller\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlimit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muser_api\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muser_api\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    313\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 314\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mthreadpoolctl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mthreadpool_limits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muser_api\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muser_api\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    315\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    316\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, limits, user_api)\u001b[0m\n\u001b[0;32m    169\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muser_api\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    170\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 171\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_info\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_threadpool_limits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    172\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    173\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__enter__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m_set_threadpool_limits\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    266\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    267\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 268\u001b[1;33m         modules = _ThreadpoolInfo(prefixes=self._prefixes,\n\u001b[0m\u001b[0;32m    269\u001b[0m                                   user_api=self._user_api)\n\u001b[0;32m    270\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmodules\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, user_api, prefixes, modules)\u001b[0m\n\u001b[0;32m    338\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    339\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodules\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 340\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_load_modules\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    341\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_warn_if_incompatible_openmp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    342\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m_load_modules\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    371\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_find_modules_with_dyld\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    372\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplatform\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"win32\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 373\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_find_modules_with_enum_process_module_ex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    374\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    375\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_find_modules_with_dl_iterate_phdr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m_find_modules_with_enum_process_module_ex\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    483\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    484\u001b[0m                 \u001b[1;31m# Store the module if it is supported and selected\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 485\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_module_from_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    486\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    487\u001b[0m             \u001b[0mkernel_32\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCloseHandle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mh_process\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m_make_module_from_path\u001b[1;34m(self, filepath)\u001b[0m\n\u001b[0;32m    513\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mprefix\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprefixes\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0muser_api\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muser_api\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    514\u001b[0m                 \u001b[0mmodule_class\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mglobals\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmodule_class\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 515\u001b[1;33m                 \u001b[0mmodule\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodule_class\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprefix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muser_api\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minternal_api\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    516\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    517\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, filepath, prefix, user_api, internal_api)\u001b[0m\n\u001b[0;32m    604\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minternal_api\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minternal_api\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    605\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dynlib\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mctypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCDLL\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_RTLD_NOLOAD\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 606\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mversion\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_version\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    607\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_threads\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_num_threads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    608\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_extra_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36mget_version\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    644\u001b[0m                              lambda: None)\n\u001b[0;32m    645\u001b[0m         \u001b[0mget_config\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrestype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mctypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc_char_p\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 646\u001b[1;33m         \u001b[0mconfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_config\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    647\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34mb\"OpenBLAS\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    648\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"utf-8\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'split'"
     ]
    }
   ],
   "source": [
    "df = df3[[\"age\", \"studytime\"]]\n",
    "# df = df3[[\"age\", \"G3\"]]\n",
    "\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "df = scaler.fit_transform(df)\n",
    "\n",
    "for k in range(2,7):\n",
    "    k_means = KMeans(n_clusters=k, random_state=0)\n",
    "    k_means.fit(X=df)\n",
    "\n",
    "    plot_decision_boundaries(k_means, df)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The visualization makes clear that the discrete features do affect the k-means algorithm.\n",
    "Maybe duplicating samples with added noise/spatial-jitter could smooth the bumps caused by the discrete data or it could be interpolated into continuous data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Other methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gaussian Mixture Model Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "k=4\n",
    "clusterer = GaussianMixture(n_components=k, random_state=0)\n",
    "clusterer.fit(X=df)\n",
    "\n",
    "plot_decision_boundaries(clusterer, df)\n",
    "plt.scatter(x=df[:,0], y=df[:,1], c=clusterer.predict(df), alpha=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hieracrchical Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusterer = AgglomerativeClustering(n_clusters=k)\n",
    "plt.scatter(x=df[:,0], y=df[:,1], c=clusterer.fit_predict(X=df), alpha=0.04)\n",
    "plt.xlabel(\"age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA with n = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.56255429,  7.47905223],\n",
       "       [ 0.58779179,  8.7601376 ],\n",
       "       [ 6.15181326,  4.70358245],\n",
       "       ...,\n",
       "       [ 1.44645695, -0.45744594],\n",
       "       [ 1.89950133,  1.75966626],\n",
       "       [-0.22157593,  1.25873252]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components = 2)\n",
    "df = pd.read_csv(\"Data/processed_data.csv\")\n",
    "y = df.pop(\"G3\")\n",
    "df_pca = pca.fit_transform(df)\n",
    "df_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=2, random_state=0)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means_pca = KMeans(n_clusters=2, random_state=0)\n",
    "k_means_pca.fit(df_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'split'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-65-2807f609deeb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplot_decision_boundaries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk_means_pca\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf_pca\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-11-cdf8ee93733d>\u001b[0m in \u001b[0;36mplot_decision_boundaries\u001b[1;34m(clusterer, X, resolution, show_centroids, show_xlabels, show_ylabels)\u001b[0m\n\u001b[0;32m     17\u001b[0m     xx, yy = np.meshgrid(np.linspace(mins[0], maxs[0], resolution),\n\u001b[0;32m     18\u001b[0m                          np.linspace(mins[1], maxs[1], resolution))\n\u001b[1;32m---> 19\u001b[1;33m     \u001b[0mZ\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclusterer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mxx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m     \u001b[0mZ\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mZ\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X, sample_weight)\u001b[0m\n\u001b[0;32m   1332\u001b[0m         \u001b[0msample_weight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_check_sample_weight\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1333\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1334\u001b[1;33m         return _labels_inertia_threadpool_limit(\n\u001b[0m\u001b[0;32m   1335\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_squared_norms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcluster_centers_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_threads\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1336\u001b[0m         )[0]\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py\u001b[0m in \u001b[0;36m_labels_inertia_threadpool_limit\u001b[1;34m(X, sample_weight, x_squared_norms, centers, n_threads)\u001b[0m\n\u001b[0;32m    753\u001b[0m ):\n\u001b[0;32m    754\u001b[0m     \u001b[1;34m\"\"\"Same as _labels_inertia but in a threadpool_limits context.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 755\u001b[1;33m     \u001b[1;32mwith\u001b[0m \u001b[0mthreadpool_limits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muser_api\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"blas\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    756\u001b[0m         labels, inertia = _labels_inertia(\n\u001b[0;32m    757\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_squared_norms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcenters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_threads\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\sklearn\\utils\\fixes.py\u001b[0m in \u001b[0;36mthreadpool_limits\u001b[1;34m(limits, user_api)\u001b[0m\n\u001b[0;32m    312\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mcontroller\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlimit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muser_api\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muser_api\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    313\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 314\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mthreadpoolctl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mthreadpool_limits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muser_api\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muser_api\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    315\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    316\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, limits, user_api)\u001b[0m\n\u001b[0;32m    169\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muser_api\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    170\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 171\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_info\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_threadpool_limits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    172\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    173\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__enter__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m_set_threadpool_limits\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    266\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    267\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 268\u001b[1;33m         modules = _ThreadpoolInfo(prefixes=self._prefixes,\n\u001b[0m\u001b[0;32m    269\u001b[0m                                   user_api=self._user_api)\n\u001b[0;32m    270\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmodules\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, user_api, prefixes, modules)\u001b[0m\n\u001b[0;32m    338\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    339\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodules\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 340\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_load_modules\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    341\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_warn_if_incompatible_openmp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    342\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m_load_modules\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    371\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_find_modules_with_dyld\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    372\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplatform\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"win32\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 373\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_find_modules_with_enum_process_module_ex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    374\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    375\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_find_modules_with_dl_iterate_phdr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m_find_modules_with_enum_process_module_ex\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    483\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    484\u001b[0m                 \u001b[1;31m# Store the module if it is supported and selected\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 485\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_module_from_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    486\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    487\u001b[0m             \u001b[0mkernel_32\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCloseHandle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mh_process\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m_make_module_from_path\u001b[1;34m(self, filepath)\u001b[0m\n\u001b[0;32m    513\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mprefix\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprefixes\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0muser_api\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muser_api\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    514\u001b[0m                 \u001b[0mmodule_class\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mglobals\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmodule_class\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 515\u001b[1;33m                 \u001b[0mmodule\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodule_class\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprefix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muser_api\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minternal_api\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    516\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    517\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, filepath, prefix, user_api, internal_api)\u001b[0m\n\u001b[0;32m    604\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minternal_api\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minternal_api\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    605\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dynlib\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mctypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCDLL\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_RTLD_NOLOAD\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 606\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mversion\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_version\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    607\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_threads\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_num_threads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    608\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_extra_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36mget_version\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    644\u001b[0m                              lambda: None)\n\u001b[0;32m    645\u001b[0m         \u001b[0mget_config\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrestype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mctypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc_char_p\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 646\u001b[1;33m         \u001b[0mconfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_config\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    647\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34mb\"OpenBLAS\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    648\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"utf-8\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'split'"
     ]
    }
   ],
   "source": [
    "plot_decision_boundaries(k_means_pca, df_pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graph of Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "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>Clustering Method</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K-Means</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GaussianMixtureModel</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hierarchical</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Clustering Method Result\n",
       "0               K-Means   Good\n",
       "1  GaussianMixtureModel       \n",
       "2          Hierarchical       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "results = pd.DataFrame(\n",
    "    [\n",
    "    [\"K-Means\",\"Good\"],\n",
    "    [\"GaussianMixtureModel\",\"\"],\n",
    "    [\"Hierarchical\",\"\"],\n",
    "    ],\n",
    "    columns=[\"Clustering Method\", \"Result\"],\n",
    "    )\n",
    "display(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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"
  },
  "vscode": {
   "interpreter": {
    "hash": "b056481b58bbb69e0ed93bd5c8a46db491d86cdf31945e968d7d6a1e7317231b"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}