Data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notebook in which I analyze the results of the BPMF experiments.\n",
    "\n",
    "# Very small example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Experiment parameters: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "N=943\n",
    "M=1682\n",
    "T=100\n",
    "cutoff=0\n",
    "\n",
    "D_list=[10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n",
    "results=pd.read_csv(\"results/N943_M1682_T50_cutoff0_D[10, 20, 30, 40, 50, 60, 70, 80, 90, 100].csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "N=943\n",
    "M=1682\n",
    "T=100\n",
    "cutoff=0\n",
    "\n",
    "D_list=[10, 20, 30, 40, 50, 60]\n",
    "results=pd.read_csv(\"results/N943_M1682_T100_cutoff0_D[10, 20, 30, 40, 50, 60].csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`train_err` and `test_err` have been saved as strings!!! Then you need to elaborate a bit to recover the data in them!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>D</th>\n",
       "      <th>train_err</th>\n",
       "      <th>test_err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2.764729</td>\n",
       "      <td>2.785983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2.605796</td>\n",
       "      <td>2.712269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1.880385</td>\n",
       "      <td>1.950126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>1.535221</td>\n",
       "      <td>1.593199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1.343844</td>\n",
       "      <td>1.395058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>1.227318</td>\n",
       "      <td>1.276447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>1.148916</td>\n",
       "      <td>1.198863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>1.088991</td>\n",
       "      <td>1.144755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>1.038512</td>\n",
       "      <td>1.103174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0.995599</td>\n",
       "      <td>1.070847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0.959365</td>\n",
       "      <td>1.046517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>0.928799</td>\n",
       "      <td>1.027513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>0.903012</td>\n",
       "      <td>1.012923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>0.880793</td>\n",
       "      <td>1.001545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>0.861821</td>\n",
       "      <td>0.991901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>0.845522</td>\n",
       "      <td>0.983910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>0.831152</td>\n",
       "      <td>0.977165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>0.818593</td>\n",
       "      <td>0.971469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>0.807550</td>\n",
       "      <td>0.966942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>0.797823</td>\n",
       "      <td>0.962955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0.789265</td>\n",
       "      <td>0.959868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0.781558</td>\n",
       "      <td>0.957123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>0.774458</td>\n",
       "      <td>0.954226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>0.768084</td>\n",
       "      <td>0.951924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>0.762286</td>\n",
       "      <td>0.949901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>0.756982</td>\n",
       "      <td>0.948065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>0.752202</td>\n",
       "      <td>0.946682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>10</td>\n",
       "      <td>0.747787</td>\n",
       "      <td>0.945101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>10</td>\n",
       "      <td>0.743788</td>\n",
       "      <td>0.943638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>0.739966</td>\n",
       "      <td>0.942358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>19</td>\n",
       "      <td>100</td>\n",
       "      <td>0.572895</td>\n",
       "      <td>0.966893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>20</td>\n",
       "      <td>100</td>\n",
       "      <td>0.558727</td>\n",
       "      <td>0.966246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>21</td>\n",
       "      <td>100</td>\n",
       "      <td>0.545876</td>\n",
       "      <td>0.964669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>22</td>\n",
       "      <td>100</td>\n",
       "      <td>0.533950</td>\n",
       "      <td>0.964481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>23</td>\n",
       "      <td>100</td>\n",
       "      <td>0.522887</td>\n",
       "      <td>0.963686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>24</td>\n",
       "      <td>100</td>\n",
       "      <td>0.512623</td>\n",
       "      <td>0.963222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>25</td>\n",
       "      <td>100</td>\n",
       "      <td>0.502929</td>\n",
       "      <td>0.963056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>26</td>\n",
       "      <td>100</td>\n",
       "      <td>0.494010</td>\n",
       "      <td>0.962797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>27</td>\n",
       "      <td>100</td>\n",
       "      <td>0.485517</td>\n",
       "      <td>0.962146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>28</td>\n",
       "      <td>100</td>\n",
       "      <td>0.477571</td>\n",
       "      <td>0.962500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>29</td>\n",
       "      <td>100</td>\n",
       "      <td>0.470074</td>\n",
       "      <td>0.962508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>30</td>\n",
       "      <td>100</td>\n",
       "      <td>0.463178</td>\n",
       "      <td>0.963260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>31</td>\n",
       "      <td>100</td>\n",
       "      <td>0.456594</td>\n",
       "      <td>0.963963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>32</td>\n",
       "      <td>100</td>\n",
       "      <td>0.450424</td>\n",
       "      <td>0.964532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>33</td>\n",
       "      <td>100</td>\n",
       "      <td>0.444544</td>\n",
       "      <td>0.964868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>34</td>\n",
       "      <td>100</td>\n",
       "      <td>0.439017</td>\n",
       "      <td>0.965204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>35</td>\n",
       "      <td>100</td>\n",
       "      <td>0.433678</td>\n",
       "      <td>0.965628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>36</td>\n",
       "      <td>100</td>\n",
       "      <td>0.428683</td>\n",
       "      <td>0.966036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>37</td>\n",
       "      <td>100</td>\n",
       "      <td>0.423948</td>\n",
       "      <td>0.966269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>38</td>\n",
       "      <td>100</td>\n",
       "      <td>0.419410</td>\n",
       "      <td>0.967043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>39</td>\n",
       "      <td>100</td>\n",
       "      <td>0.415115</td>\n",
       "      <td>0.967740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>40</td>\n",
       "      <td>100</td>\n",
       "      <td>0.410970</td>\n",
       "      <td>0.968451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>41</td>\n",
       "      <td>100</td>\n",
       "      <td>0.407037</td>\n",
       "      <td>0.969095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>0.403412</td>\n",
       "      <td>0.969840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>43</td>\n",
       "      <td>100</td>\n",
       "      <td>0.399911</td>\n",
       "      <td>0.971017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>44</td>\n",
       "      <td>100</td>\n",
       "      <td>0.396586</td>\n",
       "      <td>0.971707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>45</td>\n",
       "      <td>100</td>\n",
       "      <td>0.393329</td>\n",
       "      <td>0.972170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>46</td>\n",
       "      <td>100</td>\n",
       "      <td>0.390319</td>\n",
       "      <td>0.973324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>47</td>\n",
       "      <td>100</td>\n",
       "      <td>0.387498</td>\n",
       "      <td>0.974210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>48</td>\n",
       "      <td>100</td>\n",
       "      <td>0.384708</td>\n",
       "      <td>0.974513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>490 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0    D  train_err  test_err\n",
       "0             0   10   2.764729  2.785983\n",
       "1             1   10   2.605796  2.712269\n",
       "2             2   10   1.880385  1.950126\n",
       "3             3   10   1.535221  1.593199\n",
       "4             4   10   1.343844  1.395058\n",
       "5             5   10   1.227318  1.276447\n",
       "6             6   10   1.148916  1.198863\n",
       "7             7   10   1.088991  1.144755\n",
       "8             8   10   1.038512  1.103174\n",
       "9             9   10   0.995599  1.070847\n",
       "10           10   10   0.959365  1.046517\n",
       "11           11   10   0.928799  1.027513\n",
       "12           12   10   0.903012  1.012923\n",
       "13           13   10   0.880793  1.001545\n",
       "14           14   10   0.861821  0.991901\n",
       "15           15   10   0.845522  0.983910\n",
       "16           16   10   0.831152  0.977165\n",
       "17           17   10   0.818593  0.971469\n",
       "18           18   10   0.807550  0.966942\n",
       "19           19   10   0.797823  0.962955\n",
       "20           20   10   0.789265  0.959868\n",
       "21           21   10   0.781558  0.957123\n",
       "22           22   10   0.774458  0.954226\n",
       "23           23   10   0.768084  0.951924\n",
       "24           24   10   0.762286  0.949901\n",
       "25           25   10   0.756982  0.948065\n",
       "26           26   10   0.752202  0.946682\n",
       "27           27   10   0.747787  0.945101\n",
       "28           28   10   0.743788  0.943638\n",
       "29           29   10   0.739966  0.942358\n",
       "..          ...  ...        ...       ...\n",
       "460          19  100   0.572895  0.966893\n",
       "461          20  100   0.558727  0.966246\n",
       "462          21  100   0.545876  0.964669\n",
       "463          22  100   0.533950  0.964481\n",
       "464          23  100   0.522887  0.963686\n",
       "465          24  100   0.512623  0.963222\n",
       "466          25  100   0.502929  0.963056\n",
       "467          26  100   0.494010  0.962797\n",
       "468          27  100   0.485517  0.962146\n",
       "469          28  100   0.477571  0.962500\n",
       "470          29  100   0.470074  0.962508\n",
       "471          30  100   0.463178  0.963260\n",
       "472          31  100   0.456594  0.963963\n",
       "473          32  100   0.450424  0.964532\n",
       "474          33  100   0.444544  0.964868\n",
       "475          34  100   0.439017  0.965204\n",
       "476          35  100   0.433678  0.965628\n",
       "477          36  100   0.428683  0.966036\n",
       "478          37  100   0.423948  0.966269\n",
       "479          38  100   0.419410  0.967043\n",
       "480          39  100   0.415115  0.967740\n",
       "481          40  100   0.410970  0.968451\n",
       "482          41  100   0.407037  0.969095\n",
       "483          42  100   0.403412  0.969840\n",
       "484          43  100   0.399911  0.971017\n",
       "485          44  100   0.396586  0.971707\n",
       "486          45  100   0.393329  0.972170\n",
       "487          46  100   0.390319  0.973324\n",
       "488          47  100   0.387498  0.974210\n",
       "489          48  100   0.384708  0.974513\n",
       "\n",
       "[490 rows x 4 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the train and test error with all the possible choices of D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hI2WFEXbFIySaarOvnQ8Rkb3Iyu/EQYURatHVRyIibWVlKJQVRqgjQqC5DhKJTJcjItJrZGUolOTmUNvSKF5zXWaLERHpRbIyFPIjQepQS6kiIm1lZSgUREK79xR0XkFEJCkrQyE/EqIOrw0k7SmIiOyWlaGQl6PDRyIi7cnKUDAzXDjf61EoiIgkZWUoALhwntehcwoiIklZGwpECrx37SmIiCRlbSgEFAoiIntQKOjwkYhIUtaGQk4kjzgB3dEsIpIia0OhIBqiHj19TUQkVdaGgncDm1pKFRFJlbWh4DV1oUdyioikytpQyI+EqHEREo3aUxARaZHVoVBHlHiDQkFEpEXWhkJBJEiti+K0pyAikpS1odCyp+B0TkFEJCmrQ6HWRTGFgohIUtaGQoH/TAVrViiIiLTI2lDIz/FCIRirA+cyXY6ISK+QtaHQ8kjOgItBvCnT5YiI9ApZGwr5ET19TUSkrSwOhRC1yVDQZakiIpDFoRAJBWg07SmIiKTK2lAwM+Khluc0q/lsERHI4lAAcDktoaDDRyIikOWhQDIUdPhIRASyPBRMoSAi0kpWh0IgWuh16PCRiAiQ5aEQjBR4HdpTEBEBsjwUQlGFgohIqqwOhfxoDvXk6PCRiIgvu0MhEqLORfRMBRERn0LBRfWcZhERX1aHQoHf/lGsoTrTpYiI9AppCwUzG2Fmr5vZMjNbamY3tDONmdk9ZrbazBab2eR01dOefP9BO4kG7SmIiACE0jjvGPAN59wCMysE5pvZy865ZSnTnAUc5r8+C/zKf+8RBZEgtU7PaRYRaZG2PQXnXKVzboHfXQ0sB8rbTHYu8JjzvAOUmNnQdNXUlrenENXVRyIivh45p2Bmo4CjgXfbjCoHPk3pX8eewYGZXW1m88xs3pYtW7qtrpZnKphaSRURAXogFMysAHgWuNE5t2t/5uGce9A5V+GcqygrK+u22gr8S1IDMYWCiAikORTMLIwXCE84555rZ5L1wIiU/uH+sB7RsqcQVCiIiADpvfrIgIeA5c65n3Uw2WzgMv8qpOOAnc65ynTV1FZBjnefQiheD4l4Ty1WRKTXSufVR8cDlwJLzGyhP+w7wEgA59wDwJ+As4HVQB1weRrr2UN+JLj7Oc3NdRAp7MnFi4j0OmkLBefcm4B1Mo0Drk1XDZ0JBQM0B3O9nqZahYKIZL2svqMZIB7K8zp0r4KIiEIhEdZzmkVEWmR9KLiwHskpItIi60MhoOc0i4gkZX0oWESHj0REWigUov4VR41qPltEJOtDweX5zWbUbM5sISIivUDWh0IkN5+dLh+qN2a6FBGRjNtrKJjZ51O6R7cZd366iupJ+ZEQm10JTqEgItLpnsJdKd3Pthl3azfXkhEFkSCbXAkJhYKISKehYB10t9ffJ+VHQmzmIB0+EhGh81BwHXS3198nFfiHjwI1m8D1i1USEdlvnTWId4iZzcbbK2jpxu8f3fHH+o78nBBbXAmWaIL6HZA3INMliYhkTGehcG5K911txrXt75PyIyE2uYO8nppNCgURyWp7DQXn3F9T+/0nqY0H1jvn+sWF/S2HjwCoroRBR2W2IBGRDOrsktQHzGyc310MLAIeA94zs4t7oL60y48E2UxLKGzKbDEiIhnW2YnmE51zS/3uy4EPnHMTgGOAm9NaWQ85KC+HzcnDR7oCSUSyW2eh0JTSfRrwAoBzrt98e5bkhYmH8mgM5GlPQUSyXmehUGVm55jZ0XjPXH4RwMxCQG66i+sJZsaQ4ihVwVLvnIKISBbr7OqjfwXuAYYAN6bsIUwD/pjOwnrSkKIoW7eWMLhGewoikt06u/roA+DMdoa/BLyUrqJ62pDiKJWbixlXvT7TpYiIZNReQ8HM7tnbeOfc9d1bTmYMKY6ytqkYV7MAcw6sX7TgISKyzzo7fPQ14H3gaWAD/aS9o7aGFEXZkCjGmuugcRdEizNdkohIRnQWCkOB6cAMIAbMAp5xzlWlu7CeNLQ4ykKXcq+CQkFEstRerz5yzm1zzj3gnDsF7z6FEmCZmV3aI9X1kMFFUa+lVNC9CiKS1TrbUwDAzCYDF+PdqzAHmJ/Oonra0OLclKYuFAoikr06O9F8B/AFYDnwFPBt51ysJwrrSQMLctjasqegUBCRLNbZnsKtwEfARP/1I/OuzDHAOec+k97yekYoGCC34CCaYhFydK+CiGSxzkKhXzwzoSsGl+SyY0cpg7WnICJZrLOb1z5pb7iZBfDOMbQ7vi8aWhRl8/YShYKIZLXOms4uMrNvm9l9Zna6ea4D1gD/3DMl9owhxVE2xIp09ZGIZLXODh/9N7AD+H/AV4Hv4J1POM85tzDNtfWoIcVRNsSLcdXv98879EREuqDTZzT7z0/AzH4LVAIjnXMNaa+shw0pirLCHYQ11UBjDUQKMl2SiEiP66zp7OaWDudcHFjXHwMBvD2F5L0KugJJRLJUZ3sKE81sl99tQK7f33JJalFaq+tBQ1Lvaq7eCKVjMluQiEgGdHb1UbCnCsm01nsKOtksItmps8NHWSMaDtIQLfN6dFmqiGSptIWCmT1sZpvN7P0Oxp9sZjvNbKH/+l66aumqvKKB1AXyYduHmS5FRCQj0rmn8CjtPLWtjTecc5P81x1prKVLhpTksiYwCja1m2MiIv1e2kLBOfc3YHu65p8OQ4ujLIuPgE3LIJHIdDkiIj0u0+cUpprZIjObY2bjOprIzK42s3lmNm/Lli1pK2ZwUZQFTcOhqRqq+k0LHiIiXZbJUFgAHOycmwjcC7zQ0YTOuQedcxXOuYqysrK0FTS0OMqKxEivZ9PStC1HRKS3ylgoOOd2Oedq/O4/AWEzG5ipesDbU1jphuMwnVcQkayUsVAwsyHW8nAGsyl+LdsyVQ949yrUE6W2YKRCQUSyUpcex7k/zOxJ4GRgoJmtA24DwgDOuQeAC4Gvm1kMqAcucs65dNXTFUOLcgHYnHsYBRsVCiKSfdIWCs65izsZfx9wX7qWvz+KckOU5uewyg7mkB2vqGE8Eck6mb76qFcxM8aVF/NO7TBvwOZlmS1IRKSHKRTaGD+siNd2+Fc46byCiGQZhUIb48uL+SRRSjxcCDqvICJZRqHQxoTyYsDYVnCY7lUQkayjUGhj+EG5FEVDfBgY5YWCmrsQkSyiUGjDzBhfXsyCxmFecxc712a6JBGRHqNQaMeE8mJerxrs9ei8gohkEYVCO8aVF7M0Vq7mLkQk6ygU2jF+WBH1RNlZeBh8/GamyxER6TEKhXaMKs2nIBJiUd5x8MnbUL8j0yWJiPQIhUI7AgFj7LAi/tQ0CVwcVr+a6ZJERHqEQqED44cVM3vrEFx+Gayck+lyRER6hEKhA+PLi6hvhl0jPg+rX4Z4c6ZLEhFJO4VCB7w7m2FZweegYSesfSfDFYmIpJ9CoQOHlBWQGw7yatNYCOboEJKIZAWFQgeCAeNzY0qZ80ENbvRJ8MEcyOwzgERE0k6hsBdnjh/C+qp61g86Cbavga2rMl2SiEhaKRT24rSxgwkFjNn1n/EGfKBDSCLSvykU9qIkL4epY0r5n1UON3QiLJqlQ0gi0q8pFDpxxrghfLS1lo1HfgU2L4XVr2S6JBGRtFEodOL0cYMxg/9p/CwUDYc37850SSIiaaNQ6MSgwijHHjyAPy3bBp/7N/jkLVj7bqbLEhFJC4VCF5w5fggrNlbz8cEXQu5B8NbPM12SiEhaKBS64MzxQwCYs3IXfPZrsPJPsHlFhqsSEel+CoUuGFaSy9EjS3h63qfEK66CcB68+bNMlyUi0u0UCl101YmH8NHWWuasaYQpV8PiWXoAj4j0OwqFLjpz3BDGlOVz/+sf4v7hW1ByMMy+HprrM12aiEi3USh0USBgfP3kQ1leuYvXP6qFL/4Ctn8If/1xpksTEek2CoV9cO6kYZSX5HLfa6txh5wMky6Bt+6BykWZLk1EpFsoFPZBOBjgaycdwoK1VbyzZjuc8UPIK4UXroGm2kyXJyJywBQK+2h6xQgGFkT4+Ssf4KIlcN4vYfMyeOYKiMcyXZ6IyAFRKOyjaDjIDacexrsfbWf2og1w2Glw9k/hgxdhzs1qME9E+jSFwn74lykjmTi8mB/873J21jfDsV+F42+AeQ/BW7/IdHkiIvtNobAfggHj//zTBLbXNnLXSyu9gdNuh3Hnwyu3was/gEQiozWKiOwPhcJ+Gl9ezGVTR/H4u5+w6NMqCATg/Adh8mXwxl3w7BW6h0FE+hyFwgH4xumHU1YQ4dvPLaEploBgGL54D5x2Byx9AR79gvcYTxGRPkKhcAAKo2F+eN54llXu4icv+g3kmXnnF2b8N2xdDb/8HLx9HyTimS1WRKQLFAoH6PRxQ7j0uIP57Zsf8frKzbtHHPVFuPYdOORk+PN34aHT4JO3M1WmiEiXpC0UzOxhM9tsZu93MN7M7B4zW21mi81scrpqSbfvfuEojhxSyDeeXsSmXQ27RxQNg4ufhAsegqpP4ZGz4HdfVDiISK+Vzj2FR4Ez9zL+LOAw/3U18Ks01pJW0XCQ+/7laOqb4tz41EJi8ZQrj8xgwoVwwyI440fecxgeOQsePAUW/h6aGzqesYhID0tbKDjn/gZs38sk5wKPOc87QImZDU1XPel26KBC7jh3HP9vzTb+4w9LcW1vYsvJg6nXeuFw9l1esxgvfB1+dhT88RteM9w67yAiGRbK4LLLgU9T+tf5wyrbTmhmV+PtTTBy5MgeKW5/TK8YwZqttfzqLx9ycGkeXztpzJ4T5eTBlKu8G94+fgPmPQzvPQF//y0UDIbDTvfOQ4w+CQrKenoVRCTLZTIUusw59yDwIEBFRUWvbkfiW6cfwbod9dw5ZwXDD8rlnM8Ma39CMxj9D96rqRY+eAmWvQDLZ8N7/+1NM/BwKK+A8skwdCKUHQHR4p5bGRHJOpkMhfXAiJT+4f6wPi0QMH564WfYuLOef396EfmREKccMWjvH8rJh/Hne69EHCoXwpq/wKdzYfXLsOj3u6ctKvfCofRQKD0MSg+BklFQMgJCkXSumohkgUyGwmzg38zsKeCzwE7n3B6HjvqiaDjIby6r4JKH3uVfH5vP/V+azGljB3ftw4EglB/jvcBrYK9qLWxaCltWeK+tH8DCJ6GpuvVnC4ZA4eD23/MHes1855VCtMS7A1tEpA3b44Rod83Y7EngZGAgsAm4DQgDOOceMDMD7sO7QqkOuNw5N6+z+VZUVLh58zqdrFfYWd/MZQ/PZen6ndx78dGcNaEbz6M7BzWbvae/Va2FHZ/AzrVQvQlqNnrvtVuA9ravQaQIcou9w1GRIu8VLYJIoffKKfBf+d55kHAehHMhlOvtkYSiEMqBYA4Ewt7d3MEcb1wg5B0eE5Few8zmO+cqOp0uXaGQLn0pFACqG5r5yiN/Z+GnVdz+xbFcOnVUzy08HvOCoWYT1G2Fuu1QuxUaqqC+yntv2AWNu7z3purd/YkDfDZEMij8sAjm+N0RP0wiXn8glPIKggW991bDAl7IWADw3/cYZt47tB7XMrzV+Ha6k+8t86b1vFstO+XzyeW1WWbLsLafSXaTMu92akzWht8cezt/p63+dl37w1s+29482o5Lfq5Nd3vaLrvtPNpbbruf2cvy2q3Pf+9o3B71tbd+rVbEn19iL5/rpHHLdtexs3VK+IM72q4dbLPDz4DxF+y9ng50NRT6xInmvqwwGuaxK6Zw3ZPv8R9/WMoHm2r43hfHEg72wOGbYAiKhnqvfeEcxJu8E+BNNd57cz3EGqC5DmJNXnesAeLNkGj23uNN3ivWlDKsZbw/PN64+z3e7J1DiTV4IZSIe38sLd0t7y1/RMluBy7ezh8YKeMTbabp4I+1oy9c6YOM1nuoHQT/Hh/by4+HVsPbcG7PgG9vucnulOXRybwx/6Ntxg8a296KdyuFQg/Ij4T4zWUV/PjFFTz4tzWs2VrDvRdPZkB+TqZLa5+Zf4goAnkDMl1Nz0mGSct7gta/JNv8qvQ+tPuXa0sItfpsYvc89/glTZvpOvjlD+z1yyPZ2dkXYttpUufb2d5Ue9ouu5157bHcdj6z1+W1mVd7X6g6VNmtFAo9JBgwvnP2URw2qIDvPv+AOSZzAAAO/ElEQVQ+Z/z8b9w1fSInHa57EXoNM7BgpqsQyShdgtLDpleM4IVrj6ckN8yXH57L7bOX0tCsO5lFpHdQKGTA2GFF/N/rTuArnxvFo29/zGl3/5XXVmzKdFkiIgqFTImGg9z+j+N48qrjiISCXPHoPK56bB6fbKvNdGkiksUUChk2dUwpf7r+RGaedSRvrtrKtP/6K999fgkbd6r1VBHpebpPoRfZtKuB+15bzZNz1xIMGP/y2ZFcecJohh+Ul+nSRKSP081rfdin2+v4xaureOG99TjgCxOGcuUJo/nM8GJMl9+JyH5QKPQDG6rqeeStj3hy7qfUNMYYO7SIi6eM4NyjyymKhjNdnoj0IQqFfmRXQzN/WLiBJ99dy7LKXeSEAnz+iEH846RhfP7IQUTDurZeRPZOodAPOedYsn4nzy1Yz/8urmRrTSN5OUFOOryM08YO5vNHDqIkr5feJS0iGaVQ6Odi8QTvrNnOn96v5JVlm9hc3UjAYMLwEk44tJTjDx3I5JEHaS9CRACFQlZJJByL1+/ktRWbeWv1VhZ+WkU84QgHjYnDSzh29AAmjzyIo0eWMLBAD+IRyUYKhSxW3dDM3I+2M/fj7cz9aDtL1u0klvC284gBuXymvIRx5UWMH1bM2GFFCgqRLKCms7NYYTTMtKMGM+0o72lv9U1x3t+wk/fW7mDhp1UsXl/FH5fsfsjdwIIIRw0t5PDBhRw6qIBDBxUwpqyAg/LCugRWJMsoFLJAbk6QY0cN4NhRu5vB3lnXzPsbdrK8chcrN1azfOMunnj3ExqadzcLXZwb5pCyfEaX5jNiQB4Hl+YxYkAe5SW5DC6KEgwoMET6G4VClirOC3P8oQM5/tCByWGJhGN9VT2rN9fw4ZYa1mytZc2WGt5Zs43nF65v1dx/KGAMKY4yrCSXYcVRhpbkMrQ4yuCiKEOKogwpjlKan0OoJx4mJCLdRqEgSYGAMWKAtzdwypGDWo1raI6zvqqeT7fXsb6qnvU76llfVU9lVQPzPtnBxsWVyfMWyfkZlBZEGFQYoawwQllBhIGFEUrzcxhYEGFAfg4D8nMoLcjhoLwcXSkl0gsoFKRLouEgY8q8cw3tSSQcW2sb2bSzkcqd9WyubmRzdSObdjawtaaRLTWNrKisZlttI83x9i9uyMsJclBeDiV54eR7SV6Yklyvuzg3TFFumKJomMJoyOuPhimIhnQoS6SbKBSkWwQCxqDCKIMKo0wYXtzhdM45dtXH2FLTyI66JrbVNLG9tokddU3sqG1ie10TVXXN7KhrYkNVPTvqmthZ30yik4vk8nOCFPoBURDZ/cqPhCiMhsiPBMn3h+XlhMjPCZIX8d5zc4Lk54TIiwTJywmRFw4SUMhIllIoSI8yM4rzwhTndb3tpkTCUdMUo6q2mV0N/qu+mV0NMaobYuyqb6amMUZNQ4zqxmZqGuPUNDSzubqB2sa4N64xRryzZEkRCQXIy/FCIhoOkJsTJDccJBr23nNzgkRD3nskHEiOi4YCRMJBouEA0ZA3ruU9EgoSCXnvOaGA1x0OkBMM6NyL9BoKBen1AgGjKBo+oEYAnXM0xhLUNsaobYxT2xSjrsnrrmuKU9cUa/Puddc3JWho9robmhNUN8TYUt1IQ3Oc+uY4Dc3e+MZYovMi9raORquwyGl5BVu/h1P6w0EjHAwQTulvmSYcTJkmFCAcCBAOGaHA7s+FggHCAfPek8N2T9MyPpgyTcs4XarcfykUJCuYmfdLPhyktP3TIgckkfBCp6E5TkOsdVg0NMdpiiVojCVojO3ubhnf5L9SxzXFEjTGEzTHEjTFEzTHvWG1jTGa4i7ZH4t74xtjCWL+8LYn/NMh2BIW/ns4GPDeA0YwaIQDgWSYBAMQDAQI+dO3BE/LPILmfSbUap7e50KBAAHzPhMwS84r2O4wr66APw9vnPcKmD+sZXkp3YEAyWmTn/GHt3RbynAzWs03kLLcgLWZxu/uSyGqUBDpBoGAeYeYcjJ/BVUi4WhOJGiOO5pjiWR3S4g0J8PD604Nk1g8QbP/Hos7muIJ4ok24+OOWCJBPOG1wRVLOOIJl5yft3xv2nhynDc+Fnc0NieoScRJ+MPjid3TNccdCeeS84ynzDuRgLhz+3QYsLcImBc8XnDQKjwCyXDZPS7Qarg3zAwunjKSr554SFprVSiI9DOBgBEJBImEgH7YgolzjoRjd2i41gHSEiqJlPEJ/z3mh07LdPEEJFLGpw7f3e29tzd9IrG7lpZpkv0t45zDud210FK7czjnrU/c/5xLLm/3chwk+3uiSRqFgoj0KWaWPFQk3U+XPIiISJJCQUREkhQKIiKSpFAQEZEkhYKIiCQpFEREJEmhICIiSQoFERFJMuf61i3jZrYF+GQ/Pz4Q2NqN5fQl2bruWu/sovXu2MHOubLOZtTnQuFAmNk851xFpuvIhGxdd613dtF6HzgdPhIRkSSFgoiIJGVbKDyY6QIyKFvXXeudXbTeByirzimIiMjeZduegoiI7IVCQUREkrImFMzsTDNbaWarzWxmputJFzMbYWavm9kyM1tqZjf4wweY2ctmtsp/PyjTtaaDmQXN7D0z+1+/f7SZvetv91lmlpPpGrubmZWY2TNmtsLMlpvZ1GzY3mZ2k/9//H0ze9LMov11e5vZw2a22czeTxnW7jY2zz3+v8FiM5u8L8vKilAwsyBwP3AWMBa42MzGZraqtIkB33DOjQWOA67113Um8Kpz7jDgVb+/P7oBWJ7S/2PgbufcocAO4MqMVJVevwBedM4dCUzEW/9+vb3NrBy4Hqhwzo0HgsBF9N/t/ShwZpthHW3js4DD/NfVwK/2ZUFZEQrAFGC1c26Nc64JeAo4N8M1pYVzrtI5t8Dvrsb7gijHW9/f+ZP9DjgvMxWmj5kNB74A/NbvN+DzwDP+JP1uvc2sGPgH4CEA51yTc66KLNjeeI8TzjWzEJAHVNJPt7dz7m/A9jaDO9rG5wKPOc87QImZDe3qsrIlFMqBT1P61/nD+jUzGwUcDbwLDHbOVfqjNgKDM1RWOv0cuBlI+P2lQJVzLub398ftPhrYAjziHzb7rZnl08+3t3NuPXAXsBYvDHYC8+n/2ztVR9v4gL7vsiUUso6ZFQDPAjc653aljnPedcj96lpkMzsH2Oycm5/pWnpYCJgM/Mo5dzRQS5tDRf10ex+E94t4NDAMyGfPwytZozu3cbaEwnpgREr/cH9Yv2RmYbxAeMI595w/eFPLLqT/vjlT9aXJ8cA/mtnHeIcHP493rL3EP7wA/XO7rwPWOefe9fufwQuJ/r69TwU+cs5tcc41A8/h/R/o79s7VUfb+IC+77IlFP4OHOZfmZCDd0JqdoZrSgv/OPpDwHLn3M9SRs0Gvux3fxn4Q0/Xlk7OuW8754Y750bhbd/XnHNfAl4HLvQn64/rvRH41MyO8AdNA5bRz7c33mGj48wsz/8/37Le/Xp7t9HRNp4NXOZfhXQcsDPlMFOnsuaOZjM7G++YcxB42Dn3fzJcUlqY2QnAG8ASdh9b/w7eeYWngZF4TY//s3Ou7YmrfsHMTga+6Zw7x8wOwdtzGAC8B1zinGvMZH3dzcwm4Z1czwHWAJfj/eDr19vbzL4PzMC74u494Kt4x8773fY2syeBk/GayN4E3Aa8QDvb2A/J+/AOp9UBlzvn5nV5WdkSCiIi0rlsOXwkIiJdoFAQEZEkhYKIiCQpFEREJEmhICIiSQoFyTgzc2b2Xyn93zSz27tp3o+a2YWdT3nAy5nut1D6epvhw8zsGb97kn9pdHcts8TMrmlvWSL7S6EgvUEjcL6ZDcx0IalS7oztiiuBq5xzp6QOdM5tcM61hNIkYJ9CoZMaSoBkKLRZlsh+UShIbxDDe8bsTW1HtP2lb2Y1/vvJZvZXM/uDma0xszvN7EtmNtfMlpjZmJTZnGpm88zsA7+NpJbnLvzUzP7utzn/rynzfcPMZuPdIdu2nov9+b9vZj/2h30POAF4yMx+2mb6Uf60OcAdwAwzW2hmM8ws328nf67fmN25/me+Ymazzew14FUzKzCzV81sgb/slhZ+7wTG+PP7acuy/HlEzewRf/r3zOyUlHk/Z2YvmtcO/09S/j0e9WtdYmZ7bAvJDvvyS0gkne4HFrd8SXXRROAovCaF1wC/dc5NMe/BQtcBN/rTjcJrPn0M8LqZHQpchnf7/7FmFgHeMrM/+9NPBsY75z5KXZiZDcNrr/8YvLb6/2xm5znn7jCzz+PdRd3unaPOuSY/PCqcc//mz+9HeM1xXGFmJcBcM3slpYbP+HeohoB/cs7t8vem3vFDa6Zf5yR/fqNSFnmtt1g3wcyO9Gs93B83Ca/13EZgpZndCwwCyv1nE+DXI1lIewrSK/gtuT6G9+CUrvq7//yIRuBDoOVLfQleELR42jmXcM6twguPI4HT8dqHWYjXBEgp3kNJAOa2DQTfscBf/EbYYsATeM8y2F+nAzP9Gv4CRPGaLAB4OaVZCgN+ZGaLgVfwmnLorCnsE4DHAZxzK/CaQWgJhVedczudcw14e0MH4/27HGJm95rZmcCuduYpWUB7CtKb/BxYADySMiyG/+PFzAJ47fu0SG3TJpHSn6D1/+22bbk4vC/a65xzL6WO8NtNqt2/8veZARc451a2qeGzbWr4ElAGHOOcazavJdjoASw39d8tDoScczvMbCJwBvA14J+BKw5gGdJHaU9Beg3/l/HTtH6E4sd4h2sA/hEI78esp5tZwD/PcAiwEngJ+Lp5zYxjZoeb93CavZkLnGRmA817xOvFwF/3oY5qoDCl/yXgOr8BM8zs6A4+V4z3rIhm/9zAwR3ML9UbeGGCf9hoJN56t8s/LBVwzj0L3Ip3+EqykEJBepv/wmsJssVv8L6IFwFT2b9f8WvxvtDnAF/zD5v8Fu/QyQL/5Oyv6WTP2W9+eCZe88yLgPnOuX1pmvl1YGzLiWbgB3ght9jMlvr97XkCqDCzJXjnQlb49WzDOxfyftsT3MAvgYD/mVnAVzppLbQc+It/KOtx4Nv7sF7Sj6iVVBERSdKegoiIJCkUREQkSaEgIiJJCgUREUlSKIiISJJCQUREkhQKIiKS9P8B+xdLvNTkUnQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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X65JUEZE2ZGVS2Bgr8moK8XimwxER6VayMCmE2RQrgngUGnZkOhwRkW4lC5NCiK2uZfwjNSGJiCTLwqQQZistQ13ormYRkWRZmBRCVLlCb2anmo9ERJJlZVKoJs+baajKbDAiIt1M1iWF4kiYalfgzTSmjs8nIpLdsi4pFEVC1CZqCkoKIiLJsi4pFEfCxAjSHMxT85GISIqsSwqFEW+4p8ZgETQqKYiIJMu6pBAOBsgLB9kZKFDzkYhIiqxLCuD1K9QHCtTRLCKSImuTQi356lMQEUmRpUkhTDX5aj4SEUmRpUkhRFU8T81HIiIp0vk4zm6rOBJmRzwPmtV8JCKSLGtrCpXRPIg1QXNDpsMREek2sjgp5HozakISEUnI0qQQpjIa8WbU2SwikpClSSFENf6geLosVUQkIUuTQpga5w+Kp6EuREQSsjQphKgh35tR85GISEL2JgXXkhRUUxARaZGVSaEkz7+jGXT1kYhIkqxNCnVEcJiaj0REkmRlUijNz8ERoCmkkVJFRJJlZVIoyAkSDBgNgSL1KYiIJMnKpGBmlOaFqQ9opFQRkWRZmRQASvLD1Jmaj0REkmVvUsgLU+3yoWFHpkMREek2sjYplOaFvWcqqPlIRCQhbUnBzIaY2QtmtszMlprZN9pYx8zs12a2yswWmdnkdMWTqiQvzHY9aEdEpJV0PmQnCnzLObfAzIqA+Wb2jHNuWdI6JwOj/ddRwG/997Qrzc/xhs+OVYNzYNYVhxUR6dbSVlNwzm1wzi3wp2uA5cCglNVOBx50nteBUjMbkK6YkhXnhdkajYCLQVNdVxxSRKTb65I+BTMbDhwBvJGyaBDwUdL8WnZPHJjZZWY2z8zmbdmypVNiKm3paAY1IYmI+NKeFMysEHgMuMY5t1/fvs65u51zFc65ij59+nRKXCV54aRB8ZQUREQgzUnBzMJ4CeFh59yf2lhlHTAkaX6wX5Z2pflJg+LprmYRESC9Vx8ZcC+w3Dn3i3ZWewK4yL8K6Wigyjm3IV0xJWtVU1DzkYgIkN6rj44BLgQWm9lCv+w7wFAA59ydwD+AU4BVQD1wcRrjaUU1BRGR3aUtKTjnXgX2eJ2nc84BV6Yrhj0pyctRR7OISIqsvaO5JC+c9EhO1RRERCCLk0JOKEAgJ4+YBXX1kYiIL2uTAnhNSA2BQjUfiYj4sjwp+MNnq/lIRARQUqAWPWhHRKRFVieF0nx/qAs1H4mIAFmeFBLDZ6v5SEQEyPKkUJqfw/ZYRM1HIiK+rE4KJXlhdsTzcI2qKYiIgJKCN9RFYy3E45kOR0Qk47I6KZTme4PiGU6dzSIiZHlSSNQUQElBRIQsTwqleTl60I6ISJKsTgqtagq6LFVEJMuTQr4etCMikiyrk0JRbohay/NmVFMQEdlzUjCzTyVNj0hZdma6guoqgYDRkNvHm6nukkdDi4h0ax3VFG5Jmn4sZdmNnRxLRuTmF1MTLIUdazIdiohIxnWUFKyd6bbmD0oleWE2B/vD9tWZDkVEJOM6Sgqunem25g9KJfk5rLe+sENJQUQk1MHykWb2BF6toGUaf35E+5sdPErywqyJ94Ed/4Z4DALBTIckIpIxHSWF05Omb0lZljp/UCrNC/NBtAxcM9RsgJLBmQ5JRCRj9pgUnHMvJc+bWRgYD6xzzm1OZ2BdpSQvzKLG3pCD16+gpCAiWayjS1LvNLNx/nQJ8DbwIPCWmZ3fBfGlXWl+mDXOvyxVVyCJSJbrqKN5hnNuqT99MfCuc24CMAW4Lq2RdZHivDDrXTkOU2eziGS9jpJCU9L0CcDjAM65jWmLqIuV5oVpIky0QJelioh0lBR2mNlpZnYEcAzwFICZhYC8dAfXFUrywgDU5w9WTUFEsl5HVx99Ffg10B+4JqmGcBzw93QG1lVK83MAqMkbSMn2+RmORkQkszq6+uhd4KQ2yp8Gnk5XUF2pvNBLCpuD/RhcvQ6iTRDKyXBUIiKZscekYGa/3tNy59zVnRtO1+tdkENRJMSH0XIm46B6LfQememwREQyoqPmo68BS4BHgfX0kPGOkpkZI8sLWNbQmzPB62xWUhCRLNVRUhgAnAOcC0SBOcAfnXM70h1YVxpRXsCCD4q9GXU2i0gW2+PVR865Sufcnc65mXj3KZQCy8zswo52bGb3mdlmM1vSzvJjzazKzBb6r//erzPoBCPKC3m7Oh8XCOmyVBHJah3VFAAws8nA+Xj3KjwJ7M1lOr8Hbse7A7o9rzjnTtubGNJpeHk+MReguXAQOaopiEgW66ij+SbgVGA5MBu4wTkX3ZsdO+deNrPhBxpgVxhZXghAdWQg5RrqQkSyWEc3r92I12Q0EfgxsMDMFpnZYjNb1AnHn2Zmb5vZky1jLLXFzC4zs3lmNm/Lli2dcNjWhpfnA95lqWo+EpFs1lHzUTqfmbAAGOacqzWzU/CG0Bjd1orOubuBuwEqKio6/eE+RZEwfYpyWR3rw9i6zdBUDzn5nX0YEZFur6OO5tVtvYCPgOkHcmDnXLVzrtaf/gcQNrPyA9nngRhRVsCKxt7ejJqQRCRLdTR0drGZ3WBmt5vZiea5Cngf+NyBHNjM+puZ+dNT/VgqD2SfB2JEeQFv1+qyVBHJbh01H/0B2A78G7gU+A7eDWxnOOcW7mlDM3sEOBYoN7O1wHeBMIBz7k7gbOByM4sCO4HznHMZe+7ziD4FPD+vF0RQv4KIZK0On9HsPz8BM7sH2AAMdc41dLRj59weH8LjnLsd75LVbmFEeQFbKCGa24vQxrczHY6ISEZ0dPVRc8uEcy4GrN2bhHAwGlFeABhbeh0Bq/+d6XBERDKio6Qw0cyq/VcNcHjLtJlVd0WAXWVo73zM4N3IBNj2HtRsynRIIiJdrqOrj4LOuWL/VeScCyVNF3dVkF0hEg4yqDSPN+NjvII1r2U2IBGRDOioppBVRpQX8GrtQAjnqwlJRLKSkkKSEeUFvFfZiBt8pGoKIpKVlBSSjCgvoKYxSn3/qbBxCTRUZTokEZEupaSQxLsCCdYUTQIcfDQ3swGJiHQxJYUkLaOlLg2MhkAIVv8rwxGJiHQtJYUkg3rlkZ8TZOHGJhio+xVEJPsoKSQJBoyjR5bxr1WVMHQarF8AzT3yXj0RkTYpKaSYfkg5H2ytY2vZFIg1wbq9eciciEjPoKSQYvpob/Tul3eO8gpW69JUEckeSgopRvctpF9xLs+taYZBFbDkMcjc4K0iIl1KSSGFmXHMIeW8tmor8SMuhC3LYe2bmQ5LRKRLKCm0YcbocrbXN7O89wmQUwjzH8h0SCIiXUJJoQ3HHOL3K6xpgPFnwdI/6e5mEckKSgpt6FsU4dB+Rby6agtM+SI018Pi/8t0WCIiaaek0I7po8t588PtNPSZCP0mqAlJRLKCkkI7po8upykaZ+6H273awsZFsP6tTIclIpJWSgrtOGpEb3KCAV5csQUO/xyE8mDuPZkOS0QkrZQU2pGfE2LmmD48vnAdDcFCmPIlWPiwagsi0qMpKezBhUcPZ1tdE08u2QAzb4CCPvD3b0E8nunQRETSQklhDz4+qoyR5QX84d+rIVICJ/7AGwtpgTqdRaRnUlLYg0DA+MLRw1iwZgdL1lV5fQvDpsOz34O6rZkOT0Sk0ykpdODsyYOJhAM8/MZqMINTb4GmWnj6vzQmkoj0OEoKHSjJD3P6xEE8/tZ6qnY2Q9/DYMa3YNFs+PcdmQ5PRKRTKSnshQunDWNnc4zH5q/1Cj45C8aeDv+8EZb/NbPBiYh0IiWFvTB+UAmTh5Zyzyvv09Acg0AA/uMuGDQFHvuKHsQjIj2GksJe+vaJh7K+qoGHXl/tFYTz4PzZUNgHHjobPvxXZgMUEekESgp76eOHlDNjdDm3v7CK6oZmr7CwD1z4OOSXwYOnw4IHMxukiMgBUlLYB9efNIYd9c3c9dJ7uwrLRsGlz8KIGfDEVfDk9dC8M3NBiogcACWFfTB+UAmfnTiQe1/9gE3VDbsW5JXC5/8Pjroc3rgTfjMN3ns+c4GKiOwnJYV99K0TP0Y05vjls++2XhAMwck3w0VPgAXgD/8Bj10K297PTKAiIvshbUnBzO4zs81mtqSd5WZmvzazVWa2yMwmpyuWzjSsrIALpw1j9psf8daa7buvMPKTcPlr8InrYNkTcFsF/PlrsHVV1wcrIrKP0llT+D1w0h6WnwyM9l+XAb9NYyyd6psnfIx+RRFu+NNimmNtDI4XjsCn/guuWQRHXw5LH4fbK+D+U2HefVC/reuDFhHZC2lLCs65l4E9ffudDjzoPK8DpWY2IF3xdKaiSJjvfXYc72ys4b5XP9jDiv3h0z+EaxbDzO9A3Wb427Vwy2i47yR48Sfw0VyINnZd8CIiexDK4LEHAR8lza/1yzakrmhml+HVJhg6dGiXBNeRT4/rx/GH9ePWZ9/llAkDGNI7v/2VC/vAJ6+DT/wnbFwMS/8M778AL/4YXvwRBMLe8BkDJ0G/8dBnjDdf0Mcbb0lEpIuYS+OgbmY2HPibc258G8v+BtzsnHvVn38OuN45N29P+6yoqHDz5u1xlS6zfsdOjv/FSxw5vDe/v/hIbF+/wOu3wYevwLoFsOFt2LAQdib1U+SWQO/h0GsElA6F4kFQPACKBkJhX+8VzuvUcxKRnsnM5jvnKjpaL5M1hXXAkKT5wX7ZQWNgaR7XnzSG7z6xlIdeX82F04bv2w7ye3tjKI093Zt3Dmo3weZlsPkd2PYebPvAez70in9ArGn3feQWe/vJL/duossr9Z79ECnxlkWKIbfIny/x5nMKIJzvvYI53rAdIiJkNik8AXzdzGYDRwFVzrndmo66u4umDeP5dzbzg78v5+iRZYzuV7T/OzPz+iGK+sOoT7Ve5pxXs6heBzUboHazl0DqtkB9pfd8h5oNsOUdaKjyXuxlLTAQhlCu9wrne7WPUMR/+eWBsHfZbcu6wRw/oYQgEPQuwzUDzHu3gLfMgt52wRxv20Cw9TbJ67e8AkH/eOFd67bsKxD03wOt51181yt5f8l/v3b/7knHNmt9HiTV/lr2i6Wcb8q2iWNbG+uk/J2gdeyJOF37MbeKq2U9t/t+XFJZq/XcrjJvh0n7TZG879ZBJK+Usm7LfMo5uTi4WNtxtjq++btPPsdWQaXEl/p3SH2Pt7Msad9t/Y3a2+9u55p8zqnbt7WP1PK2zikp7pa/vYvDgEkw9CjSKW1JwcweAY4Fys1sLfBdIAzgnLsT+AdwCrAKqAcuTlcs6WRm/Oycwzn5l69w9eyFPH7lx8kNBdNxICgo814DDu94/XgcmuugoRoaq733hipvuqnOu+u6uQ6iTV4NJNYE0QavvKnO6/yO7vTe6+sg3gyxqL9uM8Qavel4HOJR/z976pdBrPP/DiLZ7Jhr0p4U0tqnkA7dqU8h2XPLN3HJA/O4dPoIbjxtbKbD6R5afuXEmv2k0gzxmJdE4tHdf53F/WQSj+5KQnF/G9eynZ9sEmX+L89Wv8Lx9xNL+fXbVp9P8i9ql5Tc9vRrL/kXXMv2yfP+sdv6dZ78q7XlbxQItl07Sa2ptIrDn06tjQSCu6bbq/kkL2vvl2ry361VrYek+FNqUS1/41Y1qqTtLKl2h7WuLbb3C7y9v0Vbn2tL7SK1xppattvfObn2RhvL29q2jWO1uQ5t7yO1PPWc2qpFY97l7jkF7I+DoU+hRznusH5cNG0Y97z6ARXDe3HS+IPi6tr0Mkv6EohkOhoR2QvqYexE/3XqYRwxtJRvPvo2KzbWZDocEZF9pqTQiXJDQe68YAoFuSEu+8M8quqbMx2SiMg+UVLoZP2KI9x5wWTW79jJVbPfItrWMBgiIt2UkkIaTBnWm++fPp6X393CDX9azMHWmS8i2UsdzWly3tShbKhq4FfPraRXQQ43nDxm3+94FhHpYkoKaXTN8aPZUd/E3S+/T6/8HC4/dlSmQxIR2SMlhTQyM777mXFsr2/mJ0+9QzhoXDpjZKbDEhFpl5JCmgUCxs8/N5FY3PGDvy+nvinGVZ86RE1JItItKSl0gXAwwK/Om0RuOMAvnnmX+qYY1590qBKDiHQ7SgpdJBQMcMvZE8kLB7nzpffYXNPAj8+ckJ5xkkQ0rhrJAAARO0lEQVRE9pOSQhcKBIwfnDGevkURbn32XVZX1nPXhVMoL8zNdGgiIoDuU+hyZsY3jh/NHZ+fzNL1VZx++79Ysq4q02GJiABKChlz6uED+L+vfpy4c5z5m9e499UPdJObiGSckkIGTRhcwj+unsEnPtaH7/9tGZc8MI/K2sZMhyUiWUxJIcN6FeTwu4um8D+fHcerK7dywq0v89e316vWICIZoaTQDZgZX/z4cP529XSG9Mrjqkfe4rI/zGdTdUOmQxORLKOk0I18rF8Rj13+cb5zyhhefncLn7rlRe586T0ao3qspYh0DSWFbiYUDHDZJ0bxz2s/wbRR5dz85Duc9MtXeGbZJjUpiUjaKSl0U8PKCrjnixX8/uIjMYOvPDiPM3/7Gq+t2prp0ESkB1NS6OaOPbQvT1/zCX585gQ2VjXw+Xve4Ly7/83L725RzUFEOp0dbF8sFRUVbt68eZkOIyMammM8/MYa7n75PTZVNzJuYDFf/eQoTh7fn3BQ+V1E2mdm851zFR2up6Rw8GmMxnj8rXXc9fL7vL+ljr5FuZw/dSifP2oo/YojmQ5PRLohJYUsEI87Xnp3Cw/8+0NeXLGFYMCYeWhfPlcxmJlj+qr2ICIJe5sUNCDeQSwQMGaO6cvMMX35cGsdj7y5hsfmr+PZ5ZsoL8zhtMMH8pmJA5k8tFTDdIvIXlFNoYdpjsV5acUW/jh/Lc+v2ExTNM7gXnmcPL4/J40fwBFDSgkElCBEso2aj4Sahmb+uXQTf120nn+t2kpzzNG3KJfjDuvLsYf25ZhDyinMVWVRJBsoKUgr1Q3NvPDOZp5aspFXVm6ltjFKOGhMGdaLGaP7MGN0OeMGlhBULUKkR1JSkHY1RePMX72dF1ds5uWVW1m+oRqA4kiII4f35qiRvZk6ooxxA4vVWS3SQ6ijWdqVEwowbVQZ00aVcQOwpaaR197byuvvV/LG+9t47p3NAOSFg0wcUsKUYb2YNKQXE4eU0LdIl7yK9GSqKchuNlU3MO/D7cxbvY35q7ezbH010bj372RgSYQJg0s4fHAp4weVMG5gsR4nKnIQUE1B9lu/4ginHj6AUw8fAHh3Ui9dX8Vba3bw9toqlqyr4umlmxLr9y3K5bABxYzpX8Sh/Yv4WL8iDulbSCQczNQpiMh+UlKQDkXCQaYM682UYb0TZVU7m1m6vopl66tZtqGa5Rtq+Pd7lTTF4gAEDIb2zueQvl6COKRvIaP6FDCyvJCS/HCmTkVEOpDWpGBmJwG/AoLAPc65m1OWfwn4GbDOL7rdOXdPOmOSzlGSF+bjo8r5+KjyRFk0FufDyjpWbKzl3U01rNrsvb/07maaY7uaKUvzwwwvK2B4WT7DygoYVpbP0N7eq09Rrm60E8mgtCUFMwsCdwAnAGuBN83sCefcspRV5zjnvp6uOKTrhIIBv2ZQxKkMSJRHY3HWbKtn1eZaVlfW80FlHR9urePND7fzl7fXk9ytlRsKMLhXHoN65Xvvpd5rQEmEgaV59C3OJTekZimRdElnTWEqsMo59z6Amc0GTgdSk4L0cKFggJF9ChnZp3C3ZY3RGB9t28lH2+r5aHs9H22rZ+32nazdvpMl66rYVte02zZlBTn0K47QvyRCv+II/Ypz6VsUoW9RLn396bLCHF1OK7If0pkUBgEfJc2vBY5qY72zzOwTwLvAtc65j1JXMLPLgMsAhg4dmoZQJVNyQ8FEn0Nb6puibKhqYP2OnWzY0cDG6gY2VDWwsWonG6saWLR2B1trd08cZtA7P4fywlzKi3LoU5hLWWEuZYU5lBXk0Lsgl94FOYlXcSSkZisRMt/R/FfgEedco5l9FXgA+FTqSs65u4G7wbsktWtDlEzKzwkxqk8ho9qoZbRojsXZWtvIpupGNlc3sKW2kS01jWyuaWRrTSNbahuZt3o72+qaqG9q+3nXoYDRqyCHXvlhSvO99175OZTkhynNy6E0P0xJXpjiiPdekhemOC9EUSSsu8ClR0lnUlgHDEmaH8yuDmUAnHOVSbP3AD9NYzzSQ4WDAQaU5DGgJK/DdXc2xdha28j2+iYq65rYVtvE9vomttUlvzfzwdY6FtTvoKq+OXFFVXsKc0MURUIUR8IURbzpwpbp3BCFuSEKckMU+vMFLfO5IQpygxTkhMjPDaqvRLqFdCaFN4HRZjYCLxmcB3w+eQUzG+Cc2+DPfhZYnsZ4RMjLCTKkdz5Deufv1frOORqa42yvb6K6oZmq+maqdjZT3RCleqc3XdMQpaahmeoGb3prbRMfbK2jtjFGTUMzjdE9J5UWoYCRnxOkIDeUeM8L++85QQpyguTneNP54SB5Of4r7L0i/nSkZT4cIBIOEgkFyQ0HyA0F1EQmHUpbUnDORc3s68DTeJek3uecW2pmNwHznHNPAFeb2WeBKLAN+FK64hHZH2bmf/nmMZCOayJtaYrGqWuMUuu/kqfrG2PUNUWpb4pR2xhlZ1OMusZooqy+KcbmmgZvujFGfVOUnc2xVpf47ovckJcccsNBcoIBP1kEW5W3TOeEWi/LCQUS2+QEA+SEgl5ZKEBO0Pz3IGF/OhwMJN7DQSMn6E+HAoQC3ryGce9+NMyFyEGoORanvilGY3OMnc1e8mjwpxuaYzQ0x5Pm4zRGd703NsdpjMZpbI7RGIv78zGaon55NE5TNJY0vWt5vJO/LoIBIxw0woEAoaD5CcRLIqGglzzCQX+Zv04oGCAcsMR0KGCEAv57Yh8py4JGMGD+vBH0l7WUBf31gillu73MXx70pgNJZS2vgO1aNxAgUdayz0zV1jTMhUgPFg4GKMkLQF7X3h0eje1KFE2x5IThEvPNsV3LojGXmG+OxWmOxmmOucR8NGW6OeYtb47Fica96WgsTjTu7ycap64p5pXFHM3xOLG4S2wbi+/aRyzuLe9uv3vN8BJGS9JoSS6B3RNNIICXZPzy844cwqUzRqY1PiUFEdlroWCAUDBAwUE0BmI8npQ8/AQSi7tEAom73cujcb+8pcy5RPKJOUfc31e8pTzulbUs87YhURaLt97G247E9rGkfe0qI2ldh3N0yeCTSgoi0qMFAkZuQFd27S3d8ikiIglKCiIikqCkICIiCUoKIiKSoKQgIiIJSgoiIpKgpCAiIglKCiIiknDQjX1kZluA1fu5eTmwtRPDOZhk67nrvLOLzrt9w5xzfTra0UGXFA6Emc3bmwGheqJsPXedd3bReR84NR+JiEiCkoKIiCRkW1K4O9MBZFC2nrvOO7vovA9QVvUpiIjInmVbTUFERPZASUFERBKyJimY2UlmtsLMVpnZrEzHky5mNsTMXjCzZWa21My+4Zf3NrNnzGyl/94r07Gmg5kFzewtM/ubPz/CzN7wP/c5ZpaT6Rg7m5mVmtkfzewdM1tuZtOy4fM2s2v9f+NLzOwRM4v01M/bzO4zs81mtiSprM3P2Dy/9v8Gi8xs8r4cKyuSgpkFgTuAk4GxwPlmNjazUaVNFPiWc24scDRwpX+us4DnnHOjgef8+Z7oG8DypPmfALc65w4BtgOXZCSq9PoV8JRzbgwwEe/8e/TnbWaDgKuBCufceCAInEfP/bx/D5yUUtbeZ3wyMNp/XQb8dl8OlBVJAZgKrHLOve+cawJmA6dnOKa0cM5tcM4t8Kdr8L4gBuGd7wP+ag8AZ2QmwvQxs8HAqcA9/rwBnwL+6K/S487bzEqATwD3AjjnmpxzO8iCzxvvccJ5ZhYC8oEN9NDP2zn3MrAtpbi9z/h04EHneR0oNbMBe3usbEkKg4CPkubX+mU9mpkNB44A3gD6Oec2+Is2Av0yFFY6/RK4Doj782XADudc1J/viZ/7CGALcL/fbHaPmRXQwz9v59w64BZgDV4yqALm0/M/72TtfcYH9H2XLUkh65hZIfAYcI1zrjp5mfOuQ+5R1yKb2WnAZufc/EzH0sVCwGTgt865I4A6UpqKeujn3QvvF/EIYCBQwO7NK1mjMz/jbEkK64AhSfOD/bIeyczCeAnhYefcn/ziTS1VSP99c6biS5NjgM+a2Yd4zYOfwmtrL/WbF6Bnfu5rgbXOuTf8+T/iJYme/nkfD3zgnNvinGsG/oT3b6Cnf97J2vuMD+j7LluSwpvAaP/KhBy8DqknMhxTWvjt6PcCy51zv0ha9ATwRX/6i8Bfujq2dHLO3eCcG+ycG473+T7vnPsC8AJwtr9aTzzvjcBHZnaoX3QcsIwe/nnjNRsdbWb5/r/5lvPu0Z93ivY+4yeAi/yrkI4GqpKamTqUNXc0m9kpeG3OQeA+59wPMxxSWpjZdOAVYDG72ta/g9ev8CgwFG/o8c8551I7rnoEMzsW+LZz7jQzG4lXc+gNvAVc4JxrzGR8nc3MJuF1rucA7wMX4/3g69Gft5n9D3Au3hV3bwGX4rWd97jP28weAY7FGyJ7E/Bd4HHa+Iz9JHk7XnNaPXCxc27eXh8rW5KCiIh0LFuaj0REZC8oKYiISIKSgoiIJCgpiIhIgpKCiIgkKClIxpmZM7OfJ81/28y+10n7/r2Znd3xmgd8nHP8EUpfSCkfaGZ/9Kcn+ZdGd9YxS83siraOJbK/lBSkO2gEzjSz8kwHkizpzti9cQnwFefczORC59x651xLUpoE7FNS6CCGUiCRFFKOJbJflBSkO4jiPWP22tQFqb/0zazWfz/WzF4ys7+Y2ftmdrOZfcHM5prZYjMblbSb481snpm964+R1PLchZ+Z2Zv+mPNfTdrvK2b2BN4dsqnxnO/vf4mZ/cQv+29gOnCvmf0sZf3h/ro5wE3AuWa20MzONbMCf5z8uf5gdqf723zJzJ4ws+eB58ys0MyeM7MF/rFbRvi9GRjl7+9nLcfy9xExs/v99d8ys5lJ+/6TmT1l3jj8P036e/zej3Wxme32WUh22JdfQiLpdAewqOVLai9NBA7DG1L4feAe59xU8x4sdBVwjb/ecLzh00cBL5jZIcBFeLf/H2lmucC/zOyf/vqTgfHOuQ+SD2ZmA/HG65+CN1b/P83sDOfcTWb2Kby7qNu8c9Q51+Qnjwrn3Nf9/f0IbziOL5tZKTDXzJ5NiuFw/w7VEPAfzrlqvzb1up+0ZvlxTvL3NzzpkFd6h3UTzGyMH+vH/GWT8EbPbQRWmNltQF9gkP9sAvx4JAuppiDdgj+S64N4D07ZW2/6z49oBN4DWr7UF+MlghaPOufizrmVeMljDHAi3vgwC/GGACnDeygJwNzUhOA7EnjRH4QtCjyM9yyD/XUiMMuP4UUggjdkAcAzScNSGPAjM1sEPIs3lENHQ2FPBx4CcM69gzcMQktSeM45V+Wca8CrDQ3D+7uMNLPbzOwkoLqNfUoWUE1BupNfAguA+5PKovg/XswsgDe+T4vkMW3iSfNxWv/bTh3LxeF90V7lnHs6eYE/blLd/oW/zww4yzm3IiWGo1Ji+ALQB5jinGs2byTYyAEcN/nvFgNCzrntZjYR+DTwNeBzwJcP4BhykFJNQboN/5fxo7R+hOKHeM01AJ8Fwvux63PMLOD3M4wEVgBPA5ebN8w4ZvYx8x5OsydzgU+aWbl5j3g9H3hpH+KoAYqS5p8GrvIHMMPMjmhnuxK8Z0U0+30Dw9rZX7JX8JIJfrPRULzzbpPfLBVwzj0G3IjXfCVZSElBupuf440E2eJ3eF/EbwPT2L9f8WvwvtCfBL7mN5vcg9d0ssDvnL2LDmrO/vDDs/CGZ34bmO+c25ehmV8AxrZ0NAPfx0tyi8xsqT/floeBCjNbjNcX8o4fTyVeX8iS1A5u4DdAwN9mDvClDkYLHQS86DdlPQTcsA/nJT2IRkkVEZEE1RRERCRBSUFERBKUFEREJEFJQUREEpQUREQkQUlBREQSlBRERCTh/wOTJjYLOazNtQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#f, a=plt.subplots()\n",
    "for D in D_list:\n",
    "    results_part=results.loc[results['D']==D]\n",
    "    plt.xlabel('Number of iterations')\n",
    "    plt.ylabel('RMSE')\n",
    "    plt.plot(np.arange(len(results_part['train_err'])), results_part['train_err'], label='Training RMSE')\n",
    "    plt.plot(np.arange(len(results_part['test_err'])), results_part['test_err'], label='Test RMSE')\n",
    "    plt.legend()\n",
    "    plt.title('D = '+ str(D))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now plot the final RMSE as function of the number of hidden features used:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# f, a=plt.subplots()\n",
    "final_results_test=[]\n",
    "final_results_train=[]\n",
    "best_results_test=[]\n",
    "best_results_train=[]\n",
    "\n",
    "for D in D_list:\n",
    "    results_part=results.loc[results['D']==D]\n",
    "    final_results_test.append(results_part['test_err'].iloc[len(results_part['test_err'])-1])\n",
    "    final_results_train.append(results_part['train_err'].iloc[len(results_part['train_err'])-1])\n",
    "    best_results_test.append(min(results_part['test_err']))\n",
    "    best_results_train.append(min(results_part['train_err']))\n",
    "    # plt.plot(np.arange(len(results_part['train_err'])),results_part['train_err'], label='D ='+str(D))\n",
    "    # plt.plot(np.arange(len(results_part['train_err'])),results_part['test_err'], label='D ='+str(D))\n",
    "\n",
    "plt.plot(D_list, final_results_test, marker='.', label='Test RMSE')\n",
    "plt.plot(D_list, final_results_train, marker='.', label='Training RMSE')\n",
    "plt.plot(D_list, best_results_test, marker='.', label='Test RMSE')\n",
    "plt.plot(D_list, best_results_train, marker='.', label='Training RMSE')\n",
    "    \n",
    "plt.xlabel('D')\n",
    "plt.ylabel('RMSE')\n",
    "plt.legend()\n",
    "#plt.title('Training losss')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[False, False, True, False, False, False]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[best_results_test[i]==final_results_test[i] for i in range(len(best_results_test))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[True, True, True, True, True, True]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[best_results_train[i]==final_results_train[i] for i in range(len(best_results_train))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Latest-small example:"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}