phishing-NN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "75f3f459-f95c-4f0c-8faf-25a8da00379a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "#Ignore the next statement -- it is just to estimate how long the exercise takes\n",
    "start = time.perf_counter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9b3080f0-693b-4fb6-aac1-9e15cf3c19e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-28T21:44:52.066476Z",
     "start_time": "2024-05-28T21:44:50.840796Z"
    }
   },
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import StratifiedShuffleSplit, learning_curve, validation_curve, cross_val_score, cross_val_predict, GridSearchCV\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import classification_report, accuracy_score, roc_auc_score, precision_score, recall_score, f1_score, confusion_matrix, ConfusionMatrixDisplay, roc_curve, auc\n",
    "from sklearn.inspection import permutation_importance\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4638403c56cfb6be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11055 entries, 0 to 11054\n",
      "Data columns (total 32 columns):\n",
      " #   Column                       Non-Null Count  Dtype\n",
      "---  ------                       --------------  -----\n",
      " 0   index                        11055 non-null  int64\n",
      " 1   having_IPhaving_IP_Address   11055 non-null  int64\n",
      " 2   URLURL_Length                11055 non-null  int64\n",
      " 3   Shortining_Service           11055 non-null  int64\n",
      " 4   having_At_Symbol             11055 non-null  int64\n",
      " 5   double_slash_redirecting     11055 non-null  int64\n",
      " 6   Prefix_Suffix                11055 non-null  int64\n",
      " 7   having_Sub_Domain            11055 non-null  int64\n",
      " 8   SSLfinal_State               11055 non-null  int64\n",
      " 9   Domain_registeration_length  11055 non-null  int64\n",
      " 10  Favicon                      11055 non-null  int64\n",
      " 11  port                         11055 non-null  int64\n",
      " 12  HTTPS_token                  11055 non-null  int64\n",
      " 13  Request_URL                  11055 non-null  int64\n",
      " 14  URL_of_Anchor                11055 non-null  int64\n",
      " 15  Links_in_tags                11055 non-null  int64\n",
      " 16  SFH                          11055 non-null  int64\n",
      " 17  Submitting_to_email          11055 non-null  int64\n",
      " 18  Abnormal_URL                 11055 non-null  int64\n",
      " 19  Redirect                     11055 non-null  int64\n",
      " 20  on_mouseover                 11055 non-null  int64\n",
      " 21  RightClick                   11055 non-null  int64\n",
      " 22  popUpWidnow                  11055 non-null  int64\n",
      " 23  Iframe                       11055 non-null  int64\n",
      " 24  age_of_domain                11055 non-null  int64\n",
      " 25  DNSRecord                    11055 non-null  int64\n",
      " 26  web_traffic                  11055 non-null  int64\n",
      " 27  Page_Rank                    11055 non-null  int64\n",
      " 28  Google_Index                 11055 non-null  int64\n",
      " 29  Links_pointing_to_page       11055 non-null  int64\n",
      " 30  Statistical_report           11055 non-null  int64\n",
      " 31  Result                       11055 non-null  int64\n",
      "dtypes: int64(32)\n",
      "memory usage: 2.7 MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>having_IPhaving_IP_Address</th>\n",
       "      <th>URLURL_Length</th>\n",
       "      <th>Shortining_Service</th>\n",
       "      <th>having_At_Symbol</th>\n",
       "      <th>double_slash_redirecting</th>\n",
       "      <th>Prefix_Suffix</th>\n",
       "      <th>having_Sub_Domain</th>\n",
       "      <th>SSLfinal_State</th>\n",
       "      <th>Domain_registeration_length</th>\n",
       "      <th>...</th>\n",
       "      <th>popUpWidnow</th>\n",
       "      <th>Iframe</th>\n",
       "      <th>age_of_domain</th>\n",
       "      <th>DNSRecord</th>\n",
       "      <th>web_traffic</th>\n",
       "      <th>Page_Rank</th>\n",
       "      <th>Google_Index</th>\n",
       "      <th>Links_pointing_to_page</th>\n",
       "      <th>Statistical_report</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
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       "      <td>1</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  having_IPhaving_IP_Address  URLURL_Length  Shortining_Service  \\\n",
       "0      1                          -1              1                   1   \n",
       "1      2                           1              1                   1   \n",
       "2      3                           1              0                   1   \n",
       "3      4                           1              0                   1   \n",
       "4      5                           1              0                  -1   \n",
       "\n",
       "   having_At_Symbol  double_slash_redirecting  Prefix_Suffix  \\\n",
       "0                 1                        -1             -1   \n",
       "1                 1                         1             -1   \n",
       "2                 1                         1             -1   \n",
       "3                 1                         1             -1   \n",
       "4                 1                         1             -1   \n",
       "\n",
       "   having_Sub_Domain  SSLfinal_State  Domain_registeration_length  ...  \\\n",
       "0                 -1              -1                           -1  ...   \n",
       "1                  0               1                           -1  ...   \n",
       "2                 -1              -1                           -1  ...   \n",
       "3                 -1              -1                            1  ...   \n",
       "4                  1               1                           -1  ...   \n",
       "\n",
       "   popUpWidnow  Iframe  age_of_domain  DNSRecord  web_traffic  Page_Rank  \\\n",
       "0            1       1             -1         -1           -1         -1   \n",
       "1            1       1             -1         -1            0         -1   \n",
       "2            1       1              1         -1            1         -1   \n",
       "3            1       1             -1         -1            1         -1   \n",
       "4           -1       1             -1         -1            0         -1   \n",
       "\n",
       "   Google_Index  Links_pointing_to_page  Statistical_report  Result  \n",
       "0             1                       1                  -1      -1  \n",
       "1             1                       1                   1      -1  \n",
       "2             1                       0                  -1      -1  \n",
       "3             1                      -1                   1      -1  \n",
       "4             1                       1                   1       1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(903967645)\n",
    "phishing_dataset = pd.read_csv('../data/phishing_dataset.csv')\n",
    "print(phishing_dataset.info())\n",
    "phishing_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "82b5bb15-2218-4f69-a40b-c3d0b9665b1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11055, 32)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check shape of dataset and for null data\n",
    "print(phishing_dataset.shape)\n",
    "phishing_dataset.isna().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c1c0f9ac-aab8-4923-9ec5-e968afff8aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate features and target\n",
    "train_X = phishing_dataset.drop(columns=['Result'])\n",
    "train_y = phishing_dataset['Result']\n",
    "\n",
    "# Normalize the feature data\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "X_scaled = min_max_scaler.fit_transform(train_X)\n",
    "\n",
    "# Convert back to DataFrame\n",
    "train_X = pd.DataFrame(X_scaled, columns=train_X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4242b4d4-b6c7-4058-a868-9a3d2e6cae2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(train_X, train_y):\n",
    "    strat_train_set = train_X.iloc[train_index]\n",
    "    strat_test_set = train_X.iloc[test_index]\n",
    "    strat_train_labels = train_y.iloc[train_index]\n",
    "    strat_test_labels = train_y.iloc[test_index]\n",
    "\n",
    "train_X = strat_train_set\n",
    "test_X = strat_test_set\n",
    "train_y = strat_train_labels\n",
    "test_y = strat_test_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8649755e-896c-4c8b-996a-c3e30aa72c34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 5s, sys: 4.05 s, total: 1min 9s\n",
      "Wall time: 8.01 s\n"
     ]
    },
    {
     "data": {
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       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
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       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
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       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
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       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
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       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
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       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
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       "  align-items: stretch;\n",
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       "#sk-container-id-1 div.sk-parallel-item {\n",
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       "  width: 50%;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MLPClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MLPClassifier(random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier(random_state=42)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Initialize and train the neural network\n",
    "neural_net = MLPClassifier(random_state=42)\n",
    "neural_net.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a2af2a5c-dd1e-4642-b79d-2fc6ddaa5df2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.968340117593849\n",
      "Training ROC AUC Score: 0.9675578345112567\n",
      "Training Precision: 0.9689140088817118\n",
      "Training Recall: 0.97442143727162\n",
      "Training F1 Score: 0.97165991902834\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.97      0.96      0.96      3918\n",
      "           1       0.97      0.97      0.97      4926\n",
      "\n",
      "    accuracy                           0.97      8844\n",
      "   macro avg       0.97      0.97      0.97      8844\n",
      "weighted avg       0.97      0.97      0.97      8844\n",
      "\n",
      "CPU times: user 292 ms, sys: 2.22 s, total: 2.51 s\n",
      "Wall time: 693 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Evaluate on training data\n",
    "train_predictions = neural_net.predict(train_X)\n",
    "train_accuracy = accuracy_score(train_y, train_predictions)\n",
    "train_roc_auc = roc_auc_score(train_y, train_predictions)\n",
    "train_precision = precision_score(train_y, train_predictions)\n",
    "train_recall = recall_score(train_y, train_predictions)\n",
    "train_f1 = f1_score(train_y, train_predictions)\n",
    "\n",
    "print(\"Training Accuracy:\", train_accuracy)\n",
    "print(\"Training ROC AUC Score:\", train_roc_auc)\n",
    "print(\"Training Precision:\", train_precision)\n",
    "print(\"Training Recall:\", train_recall)\n",
    "print(\"Training F1 Score:\", train_f1)\n",
    "train_report = classification_report(train_y, train_predictions)\n",
    "print(train_report)\n",
    "\n",
    "# Write to a text file\n",
    "with open('../results/phishing-nn/training_metrics_report.txt', 'w') as file:\n",
    "    file.write(\"Training Accuracy: \" + str(train_accuracy) + \"\\n\")\n",
    "    file.write(\"Training ROC AUC Score: \" + str(train_roc_auc) + \"\\n\")\n",
    "    file.write(\"Training Precision: \" + str(train_precision) + \"\\n\")\n",
    "    file.write(\"Training Recall: \" + str(train_recall) + \"\\n\")\n",
    "    file.write(\"Training F1 Score: \" + str(train_f1) + \"\\n\")\n",
    "    file.write(\"\\nClassification Report:\\n\")\n",
    "    file.write(train_report + \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "37c63c9e-689d-4675-9892-a29f1e2e170d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Accuracy: 0.9570330167345092\n",
      "Testing ROC AUC Score: 0.9553797310963378\n",
      "Testing Precision: 0.9536741214057508\n",
      "Testing Recall: 0.9699431356620634\n",
      "Testing F1 Score: 0.9617398308497785\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.96      0.94      0.95       980\n",
      "           1       0.95      0.97      0.96      1231\n",
      "\n",
      "    accuracy                           0.96      2211\n",
      "   macro avg       0.96      0.96      0.96      2211\n",
      "weighted avg       0.96      0.96      0.96      2211\n",
      "\n",
      "CPU times: user 291 ms, sys: 2.09 s, total: 2.38 s\n",
      "Wall time: 580 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Evaluate on testing data\n",
    "test_predictions = neural_net.predict(test_X)\n",
    "test_accuracy = accuracy_score(test_y, test_predictions)\n",
    "test_roc_auc = roc_auc_score(test_y, test_predictions)\n",
    "test_precision = precision_score(test_y, test_predictions)\n",
    "test_recall = recall_score(test_y, test_predictions)\n",
    "test_f1 = f1_score(test_y, test_predictions)\n",
    "\n",
    "print(\"Testing Accuracy:\", test_accuracy)\n",
    "print(\"Testing ROC AUC Score:\", test_roc_auc)\n",
    "print(\"Testing Precision:\", test_precision)\n",
    "print(\"Testing Recall:\", test_recall)\n",
    "print(\"Testing F1 Score:\", test_f1)\n",
    "test_report = classification_report(test_y, test_predictions)\n",
    "print(test_report)\n",
    "\n",
    "# Write to a text file\n",
    "with open('../results/phishing-nn/testing_metrics_report.txt', 'w') as file:\n",
    "    file.write(\"Testing Accuracy: \" + str(test_accuracy) + \"\\n\")\n",
    "    file.write(\"Testing ROC AUC Score: \" + str(test_roc_auc) + \"\\n\")\n",
    "    file.write(\"Testing Precision: \" + str(test_precision) + \"\\n\")\n",
    "    file.write(\"Testing Recall: \" + str(test_recall) + \"\\n\")\n",
    "    file.write(\"Testing F1 Score: \" + str(test_f1) + \"\\n\")\n",
    "    file.write(\"\\nClassification Report:\\n\")\n",
    "    file.write(test_report + \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "29e8c9bc-9a1c-429c-9d89-aee1f0edc047",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 794 ms, sys: 17.8 ms, total: 811 ms\n",
      "Wall time: 304 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "fig, ax = plt.subplots(2, 2, figsize=(8, 4))\n",
    "\n",
    "# Accuracy\n",
    "ax[0, 0].bar(['Train', 'Test'], [train_accuracy, test_accuracy], color=['blue', 'orange'])\n",
    "ax[0, 0].set_title('Accuracy')\n",
    "ax[0, 0].set_ylim([0, 1])\n",
    "ax[0, 0].set_ylabel('Accuracy Score')\n",
    "\n",
    "# ROC AUC Score\n",
    "ax[0, 1].bar(['Train', 'Test'], [train_roc_auc, test_roc_auc], color=['blue', 'orange'])\n",
    "ax[0, 1].set_title('ROC AUC Score')\n",
    "ax[0, 1].set_ylim([0, 1])\n",
    "ax[0, 1].set_ylabel('ROC AUC Score')\n",
    "\n",
    "# Precision\n",
    "ax[1, 0].bar(['Train', 'Test'], [train_precision, test_precision], color=['blue', 'orange'])\n",
    "ax[1, 0].set_title('Precision')\n",
    "ax[1, 0].set_ylim([0, 1])\n",
    "ax[1, 0].set_ylabel('Precision Score')\n",
    "\n",
    "# Recall\n",
    "ax[1, 1].bar(['Train', 'Test'], [train_recall, test_recall], color=['blue', 'orange'])\n",
    "ax[1, 1].set_title('Recall')\n",
    "ax[1, 1].set_ylim([0, 1])\n",
    "ax[1, 1].set_ylabel('Recall Score')\n",
    "\n",
    "plt.tight_layout()\n",
    "fig.savefig('../images/phishing-nn/metrics-comparison.png')\n",
    "plt.show()\n",
    "\n",
    "# Plotting  ROC Curves\n",
    "train_fpr, train_tpr, _ = roc_curve(train_y, train_predictions)\n",
    "test_fpr, test_tpr, _ = roc_curve(test_y, test_predictions)\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.plot(train_fpr, train_tpr, label=f'Train ROC (AUC = {auc(train_fpr, train_tpr):.2f})', color='blue')\n",
    "plt.plot(test_fpr, test_tpr, label=f'Test ROC (AUC = {auc(test_fpr, test_tpr):.2f})', color='orange')\n",
    "plt.plot([0, 1], [0, 1], 'k--', lw=2, label='Chance')\n",
    "plt.title('ROC Curve')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.savefig('../images/phishing-nn/roc-curve-fpr-tpr')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b46538c5-0291-471e-8b54-ed59ccc99efe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/Users/xavierelon/Library/Mobile Documents/com~apple~CloudDocs/gatech/CS7641/Assignment1/supervised_learning/images/phising-nn/testing-cm.png'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "File \u001b[0;32m<timed exec>:16\u001b[0m\n",
      "File \u001b[0;32m~/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/matplotlib/pyplot.py:1134\u001b[0m, in \u001b[0;36msavefig\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1131\u001b[0m fig \u001b[38;5;241m=\u001b[39m gcf()\n\u001b[1;32m   1132\u001b[0m \u001b[38;5;66;03m# savefig default implementation has no return, so mypy is unhappy\u001b[39;00m\n\u001b[1;32m   1133\u001b[0m \u001b[38;5;66;03m# presumably this is here because subclasses can return?\u001b[39;00m\n\u001b[0;32m-> 1134\u001b[0m res \u001b[38;5;241m=\u001b[39m fig\u001b[38;5;241m.\u001b[39msavefig(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[func-returns-value]\u001b[39;00m\n\u001b[1;32m   1135\u001b[0m fig\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mdraw_idle()  \u001b[38;5;66;03m# Need this if 'transparent=True', to reset colors.\u001b[39;00m\n\u001b[1;32m   1136\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\n",
      "File \u001b[0;32m~/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/matplotlib/figure.py:3390\u001b[0m, in \u001b[0;36mFigure.savefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m   3388\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes:\n\u001b[1;32m   3389\u001b[0m         _recursively_make_axes_transparent(stack, ax)\n\u001b[0;32m-> 3390\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mprint_figure(fname, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/matplotlib/backend_bases.py:2193\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2189\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   2190\u001b[0m     \u001b[38;5;66;03m# _get_renderer may change the figure dpi (as vector formats\u001b[39;00m\n\u001b[1;32m   2191\u001b[0m     \u001b[38;5;66;03m# force the figure dpi to 72), so we need to set it again here.\u001b[39;00m\n\u001b[1;32m   2192\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[0;32m-> 2193\u001b[0m         result \u001b[38;5;241m=\u001b[39m print_method(\n\u001b[1;32m   2194\u001b[0m             filename,\n\u001b[1;32m   2195\u001b[0m             facecolor\u001b[38;5;241m=\u001b[39mfacecolor,\n\u001b[1;32m   2196\u001b[0m             edgecolor\u001b[38;5;241m=\u001b[39medgecolor,\n\u001b[1;32m   2197\u001b[0m             orientation\u001b[38;5;241m=\u001b[39morientation,\n\u001b[1;32m   2198\u001b[0m             bbox_inches_restore\u001b[38;5;241m=\u001b[39m_bbox_inches_restore,\n\u001b[1;32m   2199\u001b[0m             \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   2200\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m   2201\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;129;01mand\u001b[39;00m restore_bbox:\n",
      "File \u001b[0;32m~/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/matplotlib/backend_bases.py:2043\u001b[0m, in \u001b[0;36mFigureCanvasBase._switch_canvas_and_return_print_method.<locals>.<lambda>\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   2039\u001b[0m     optional_kws \u001b[38;5;241m=\u001b[39m {  \u001b[38;5;66;03m# Passed by print_figure for other renderers.\u001b[39;00m\n\u001b[1;32m   2040\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfacecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124medgecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morientation\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   2041\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbbox_inches_restore\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[1;32m   2042\u001b[0m     skip \u001b[38;5;241m=\u001b[39m optional_kws \u001b[38;5;241m-\u001b[39m {\u001b[38;5;241m*\u001b[39minspect\u001b[38;5;241m.\u001b[39msignature(meth)\u001b[38;5;241m.\u001b[39mparameters}\n\u001b[0;32m-> 2043\u001b[0m     print_method \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mwraps(meth)(\u001b[38;5;28;01mlambda\u001b[39;00m \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: meth(\n\u001b[1;32m   2044\u001b[0m         \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m skip}))\n\u001b[1;32m   2045\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:  \u001b[38;5;66;03m# Let third-parties do as they see fit.\u001b[39;00m\n\u001b[1;32m   2046\u001b[0m     print_method \u001b[38;5;241m=\u001b[39m meth\n",
      "File \u001b[0;32m~/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/matplotlib/backends/backend_agg.py:497\u001b[0m, in \u001b[0;36mFigureCanvasAgg.print_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs)\u001b[0m\n\u001b[1;32m    450\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprint_png\u001b[39m(\u001b[38;5;28mself\u001b[39m, filename_or_obj, \u001b[38;5;241m*\u001b[39m, metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, pil_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    451\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    452\u001b[0m \u001b[38;5;124;03m    Write the figure to a PNG file.\u001b[39;00m\n\u001b[1;32m    453\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    495\u001b[0m \u001b[38;5;124;03m        *metadata*, including the default 'Software' key.\u001b[39;00m\n\u001b[1;32m    496\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 497\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_print_pil(filename_or_obj, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpng\u001b[39m\u001b[38;5;124m\"\u001b[39m, pil_kwargs, metadata)\n",
      "File \u001b[0;32m~/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/matplotlib/backends/backend_agg.py:446\u001b[0m, in \u001b[0;36mFigureCanvasAgg._print_pil\u001b[0;34m(self, filename_or_obj, fmt, pil_kwargs, metadata)\u001b[0m\n\u001b[1;32m    441\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    442\u001b[0m \u001b[38;5;124;03mDraw the canvas, then save it using `.image.imsave` (to which\u001b[39;00m\n\u001b[1;32m    443\u001b[0m \u001b[38;5;124;03m*pil_kwargs* and *metadata* are forwarded).\u001b[39;00m\n\u001b[1;32m    444\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    445\u001b[0m FigureCanvasAgg\u001b[38;5;241m.\u001b[39mdraw(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m--> 446\u001b[0m mpl\u001b[38;5;241m.\u001b[39mimage\u001b[38;5;241m.\u001b[39mimsave(\n\u001b[1;32m    447\u001b[0m     filename_or_obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuffer_rgba(), \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39mfmt, origin\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mupper\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    448\u001b[0m     dpi\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mdpi, metadata\u001b[38;5;241m=\u001b[39mmetadata, pil_kwargs\u001b[38;5;241m=\u001b[39mpil_kwargs)\n",
      "File \u001b[0;32m~/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/matplotlib/image.py:1656\u001b[0m, in \u001b[0;36mimsave\u001b[0;34m(fname, arr, vmin, vmax, cmap, format, origin, dpi, metadata, pil_kwargs)\u001b[0m\n\u001b[1;32m   1654\u001b[0m pil_kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mformat\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mformat\u001b[39m)\n\u001b[1;32m   1655\u001b[0m pil_kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, (dpi, dpi))\n\u001b[0;32m-> 1656\u001b[0m image\u001b[38;5;241m.\u001b[39msave(fname, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpil_kwargs)\n",
      "File \u001b[0;32m~/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/PIL/Image.py:2456\u001b[0m, in \u001b[0;36mImage.save\u001b[0;34m(self, fp, format, **params)\u001b[0m\n\u001b[1;32m   2454\u001b[0m         fp \u001b[38;5;241m=\u001b[39m builtins\u001b[38;5;241m.\u001b[39mopen(filename, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr+b\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   2455\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2456\u001b[0m         fp \u001b[38;5;241m=\u001b[39m builtins\u001b[38;5;241m.\u001b[39mopen(filename, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw+b\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   2458\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   2459\u001b[0m     save_handler(\u001b[38;5;28mself\u001b[39m, fp, filename)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Users/xavierelon/Library/Mobile Documents/com~apple~CloudDocs/gatech/CS7641/Assignment1/supervised_learning/images/phising-nn/testing-cm.png'"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# Plot and save Training Confusion Matrix\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "train_cm = confusion_matrix(train_y, train_predictions)\n",
    "ConfusionMatrixDisplay(train_cm).plot(ax=ax, colorbar=False)\n",
    "ax.set_title(\"Training Confusion Matrix\")\n",
    "plt.tight_layout()\n",
    "plt.savefig('../images/phishing-nn/training-cm.png')\n",
    "plt.show()\n",
    "\n",
    "# Plot and save Test Confusion Matrix\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "test_cm = confusion_matrix(test_y, test_predictions)\n",
    "ConfusionMatrixDisplay(test_cm).plot(ax=ax, colorbar=False)\n",
    "ax.set_title('Test Confusion Matrix')\n",
    "plt.tight_layout()\n",
    "plt.savefig('../images/phising-nn/testing-cm.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d54ccba3-a5e4-4184-ae3a-a7ad5370aa50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 18min 57s, sys: 50.7 s, total: 19min 48s\n",
      "Wall time: 2min 7s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train_sizes, train_scores, test_scores = learning_curve(neural_net, train_X, train_y, cv=5, scoring='accuracy', train_sizes=np.linspace(0.1, 1.0, 10))\n",
    "\n",
    "train_scores_mean = np.mean(train_scores, axis=1)\n",
    "train_scores_std = np.std(train_scores, axis=1)\n",
    "test_scores_mean = np.mean(test_scores, axis=1)\n",
    "test_scores_std = np.std(test_scores, axis=1)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_sizes, train_scores_mean, 'o-', color='r', label=\"Training score\")\n",
    "plt.plot(train_sizes, test_scores_mean, 'o-', color='g', label=\"Cross-validation score\")\n",
    "\n",
    "plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                 train_scores_mean + train_scores_std, alpha=0.1, color='r')\n",
    "plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                 test_scores_mean + test_scores_std, alpha=0.1, color='g')\n",
    "\n",
    "plt.title('Learning Curve')\n",
    "plt.xlabel('Training Size')\n",
    "plt.ylabel('Score')\n",
    "plt.legend(loc='best')\n",
    "plt.grid()\n",
    "plt.savefig('../images/phishing-nn/roc-curve-fpr-tpr.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ffe32fa0-e79c-4603-b2b2-be390aae04a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b1d745e05535433498c7d00dcd662793",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training progress:   0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 19min 16s, sys: 1min 7s, total: 20min 23s\n",
      "Wall time: 2min 12s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Create a wrapper function for learning_curve to show progress\n",
    "def learning_curve_with_progress(estimator, X, y, cv, scoring, train_sizes):\n",
    "    all_train_scores = []\n",
    "    all_test_scores = []\n",
    "    for train_size in tqdm(train_sizes, desc='Training progress'):\n",
    "        _, train_scores, test_scores = learning_curve(estimator, X, y, train_sizes=[train_size], cv=cv, scoring=scoring)\n",
    "        all_train_scores.append(train_scores)\n",
    "        all_test_scores.append(test_scores)\n",
    "    return train_sizes, np.array(all_train_scores).squeeze(), np.array(all_test_scores).squeeze()\n",
    "\n",
    "train_sizes = np.linspace(0.1, 1.0, 10)\n",
    "train_sizes, train_scores, test_scores = learning_curve_with_progress(neural_net, train_X, train_y, cv=5, scoring='accuracy', train_sizes=train_sizes)\n",
    "\n",
    "train_scores_mean = np.mean(train_scores, axis=1)\n",
    "train_scores_std = np.std(train_scores, axis=1)\n",
    "test_scores_mean = np.mean(test_scores, axis=1)\n",
    "test_scores_std = np.std(test_scores, axis=1)\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "\n",
    "plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                 train_scores_mean + train_scores_std, alpha=0.1, color='r')\n",
    "plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                 test_scores_mean + test_scores_std, alpha=0.1, color='g')\n",
    "\n",
    "plt.plot(train_sizes, train_scores_mean, 'o-', color='r', label='Training score')\n",
    "plt.plot(train_sizes, test_scores_mean, 'o-', color='g', label='Cross-validation score')\n",
    "\n",
    "plt.title('Learning Curve')\n",
    "plt.xlabel('Training Size')\n",
    "plt.ylabel('Score')\n",
    "plt.legend(loc='best')\n",
    "plt.grid()\n",
    "plt.savefig('../images/phishing-nn/learning-curve-training-size.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "423fb90d-1acc-4c11-859a-b84c3606287f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 336 ms, sys: 780 ms, total: 1.12 s\n",
      "Wall time: 5.08 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "param_range = [50, 75, 100]\n",
    "train_scores, test_scores = validation_curve(MLPClassifier(), train_X, train_y, param_name=\"hidden_layer_sizes\", param_range=param_range, cv=5, scoring='accuracy', n_jobs=-1)\n",
    "\n",
    "train_scores_mean = np.mean(train_scores, axis=1)\n",
    "train_scores_std = np.std(train_scores, axis=1)\n",
    "test_scores_mean = np.mean(test_scores, axis=1)\n",
    "test_scores_std = np.std(test_scores, axis=1)\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.plot(param_range, train_scores_mean, 'o-', color='r', label=\"Training score\")\n",
    "plt.plot(param_range, test_scores_mean, 'o-', color='g', label=\"Cross-validation score\")\n",
    "\n",
    "plt.fill_between(param_range, train_scores_mean - train_scores_std,\n",
    "                 train_scores_mean + train_scores_std, alpha=0.1, color='r')\n",
    "plt.fill_between(param_range, test_scores_mean - test_scores_std,\n",
    "                 test_scores_mean + test_scores_std, alpha=0.1, color='g')\n",
    "\n",
    "plt.title('Validation Curve')\n",
    "plt.xlabel('Number of Hidden Layers')\n",
    "plt.ylabel('Score')\n",
    "plt.legend(loc='best')\n",
    "plt.grid\n",
    "plt.savefig('../images/phishing-nn/validation-curve-hidden-layers.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6d5f9628-17d3-42da-a8bc-bc0dee41bbf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 518 ms, sys: 441 ms, total: 958 ms\n",
      "Wall time: 14.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Define the range for the hyperparameter hidden_layer_sizes\n",
    "param_range = [(10,), (50,), (100,), (150,), (200,), (400,)]\n",
    "\n",
    "# Calculate validation curve data\n",
    "train_scores, test_scores = validation_curve(\n",
    "    MLPClassifier(random_state=42), train_X, train_y, param_name=\"hidden_layer_sizes\", param_range=param_range, cv=5, scoring=\"accuracy\", n_jobs=-1\n",
    ")\n",
    "\n",
    "# Calculate mean and standard deviation for training and test scores\n",
    "train_scores_mean = np.mean(train_scores, axis=1)\n",
    "train_scores_std = np.std(train_scores, axis=1)\n",
    "test_scores_mean = np.mean(test_scores, axis=1)\n",
    "test_scores_std = np.std(test_scores, axis=1)\n",
    "\n",
    "# Plot validation curve\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.plot([str(x) for x in param_range], train_scores_mean, label=\"Training score\", color=\"r\")\n",
    "plt.plot([str(x) for x in param_range], test_scores_mean, label=\"Cross-validation score\", color=\"g\")\n",
    "\n",
    "plt.fill_between([str(x) for x in param_range], train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color=\"r\")\n",
    "plt.fill_between([str(x) for x in param_range], test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "\n",
    "plt.title(\"Validation Curve\")\n",
    "plt.xlabel(\"Hidden Layer Sizes\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.grid()\n",
    "plt.savefig('../images/phishing-nn/validation-curve-hidden-layer-sizes-2.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "279bbcde-0cba-4cf4-844e-81b88e4b6d2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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KPgZexHFL/s40LC6XC13X8Xq9eL3e+j6dUulHX1D7zhPAaDSWe5tRo0axYsWKKh/rzTffxGw2V/p70bJlSw4ePEhsbGyD/f4dj0p7zlSX1+tF13VcLleJ519V/p7VW8CbkpKCx+MhPj4+aHt8fDyHDx8u9TaDBg3ixRdf5LLLLqNXr15s3bqV5cuX43K5SElJISEhIWj/LVu28Ntvv7Fs2bJyz2X27NnMmDGjxPb169fX6YKCDRs21NmxRNOwYdeu+j4F0cjI35mGwWQy0bx5c3JycoLK8Bqi7Oxs/+d//PGH//N33nmHxx57jB9++MG/zWaz+ZNGQJnJqOJMJhO6rgfdtiIOh4O8vLxK798YVPb71dAFPmeqq7CwkPz8fL766ivcbnfQdVX5+dd7W7Lixci6rpdZoDx16lQOHz7MWWedha7rxMfHM2bMGJ588slSX3UuW7aM7t27c8YZZ5R7DlOmTGHixIn+r7OysmjdujUDBw4kPDz8GB5V1bhcLjZs2MCAAQOaxBNdBPB4Sl7cbnA6obCwKHtbfPWpL1trMKjPDQYI+L1weTxs2L6dAT16YC4r46LrRffv9RZdPB51na4X3aemFWWFNU19tFjUR7O57IxxBdke0XDI35mGpaCggP379xMaGlrincuGQtd1srOzCQsL8/+/HPh/YlxcHAaDgc6dOwOqzKBdu3a89tprPP/882zevJnnnnuOSy65hDvvvJNvvvmGtLQ0OnbsyOTJk7nmmmv893XBBRfQo0cPnn76aQA6dOjATTfdxO7du3nzzTeJioriwQcf5Oabb/Yfq2PHjmzdupWePXvyxRdf0L9/f9avX8+UKVPYsWMHPXv2ZNmyZXTt2tV/nEcffZSFCxeSn5/PVVddRWxsLJ988gk//fRTqd+D9PR07rzzTjZs2EBOTg6tWrVi8uTJ3HDDDQAcOHCA++67jw0bNuB0OunWrRsLFy7kzDPPBNTao3nz5rF//37at2/Pgw8+yPXXX++/f6PRyHPPPcfHH3/Mp59+yr333sv06dN5//33mTlzJr///jstWrRg1KhRPPjgg5hM9R62lau050x1FRQUYLfbOeecc0r8rlTlBVK9fediY2MxGo0lsrlHjhwpkfX1sdvtLF++nBdeeIGkpCQSEhJYsmQJYWFhxMbGBu2bl5fHmjVrmDlzZoXnYrVasVqtJbabzeY6/Y+hro8nakBpwazbrYLZwsKiMgRf0KlpRQGlyRRchnAMfxzMRmPZAW9VBAbDvs/z8koPjosHviaTCo59jycwGC4eKIt6J39nGgaPx4OmaRgMhqJ+pbqufu/qg8NR4m+Q7y1p33kW59tW/OOUKVOYO3cuK1aswGq1UlhYyGmnncbkyZMJDw/nww8/ZPTo0XTq1MkfGJZ2nHnz5jFr1iweeugh3nzzTW6//XbOO+88TjjhhKBjBn4Pp06dyty5c2nWrBm33nor48aN49tvvwXglVde4bHHHvMvlF+zZg1z586lffv2ZfaMfeSRR9i5cycfffQRsbGx7N69m/z8fAwGAzk5OZx//vm0bNmS//3vfzRv3twfOBsMBt555x3uuece5s+fz4UXXsgHH3zA2LFjadOmDeeff77/GDNmzGD27NnMnz8fo9HIhg0bGDVqFAsWLKBfv378/fff3HzzzWiaxiOPPFLZn2i9qOg5cywMBgOappX6t6sqf8vqLeC1WCz07t2bDRs2MHz4cP/2DRs2cOmll5Z7W7PZTKtWrQBYs2YNF198cYlv7Ouvv47T6eS6666r+ZMXx4/i9bNHa2j9AW3gdb7A0Bfk+QI9X6a0IbcfCgxey+PLGgdmigsKioJj3z6BgX1gABwYHBe/LvBjQ/5eCVFb8vIgNLR+jp2TA+WsdamKCRMmcPnllwdtmzRpkv/zO++8k48//pg33ngjKOAtbujQoYwfPx6ABx54gKeffpovvviCE044oczbPProo5x77rkATJ48mYsuuoiCggJsNhsLFy5k7Nix/uzstGnTWL9+PTk5OWXe3759+zj11FM57bTTAGjXrp3/uldffZXk5GR++OEHoqOjAejUqZP/+jlz5jBmzBj/Y5g4cSKbN29mzpw5QQHvyJEjufHGG/1fX3/99UyePJnRo0cDKts9a9Ys7r///gYf8DZk9ZobnzhxItdffz2nnXYaffr0YcmSJezbt49bb70VUK8SDx486O+1++eff7JlyxbOPPNM0tPTmTdvHr/99hurVq0qcd/Lli3jsssua7ATbEQDEVhSEBjUOp3qEnidL0MLRcGbwQA2m/r8eAjSfEGsr9ShPKUtunM6ITtbfR34/QoMfDWtaBGe78VCaUGx76MQokHxBYc+Ho+Hxx9/nLVr13Lw4EH/QvHyFpMDnHLKKf7PNU2jefPmHDlypNK38a3rOXLkCG3atGHXrl3+4NPnjDPO4LPPPivz/m677TauuOIKfvrpJwYOHMhll11G3759Adi2bRunnnqqP9gtbufOnf4SDJ+zzz6bZ555Jmhb8e/X1q1b+eGHH3j00Uf92zweDwUFBeTl5cmwkmNUrwHviBEjSE1NZebMmSQmJtK9e3fWrVtH27ZtAUhMTGTfvn3+/T0eD3PnzmXXrl2YzWbOP/98Nm3aFPSKC1Rg/M0337B+/fq6fDiiIdL14EDW97kvQ+tyBZcc+LKTvqDOaFRdEBwOCa6qStMqzhj7FC+ncDpVtsv3MwkUGPBqWlHG2GwuPzg+Hl6QiMbN4VCZ1vo6dg0pHsjOnTuXp59+mvnz53PyyScTEhLChAkTKlysV/ztak3TKlz5H3gbXw1p4G1KWzdUniFDhvDvv//y4YcfsnHjRvr378/tt9/OnDlzsNvt5d62rOMV31b8++X1epkxY0aJLDnQYOu9G4N6r34eP358iVdcPitXrgz6ulu3bvz8888V3meXLl0qfBKLJsKXRQwMan0BrdOpAlqvtyhD61O85MCXrRX1o7I1vqUtxPMFxpUpqTCbg1u4lVVrLMGxqA+aVmNlBQ3J119/zaWXXuovMfR6vfz1119069atTs+ja9eubNmyJWjR2I8//ljh7Zo1a8aYMWMYM2YM/fr147777mPOnDmccsopvPjii6SlpZWa5e3WrRvffPMNo0aN8m/btGlThY+7V69e7Nq1K6g8QlRfvQe8QpTLF9AWz9C6XCU7HbjdRYFKYB2t2azKDqSjQOMXmH2vSPGFeL7njG87FAXHxQNeX8bYlzUuLzgWQpSrU6dOvPXWW2zatImoqCjmzZvH4cOH6zzgvfPOO7nppps47bTT6Nu3L2vXruWXX36hQ4cOZd5m2rRp9O7dm5NOOgmn08kHH3zgP+9rrrmGxx57jMsuu4zZs2eTkJDAzz//TIsWLejTpw/33XcfV111Fb169aJ///68//77vP3222zcuLHc85w2bRoXX3wxrVu35j//+Q8Gg4FffvmFX3/9lf/+9781+j05nkjAK+pf8Qyt211yYZjv4svcB5YcSEArSlOdhXiBJRW+2m3fgsTigW9gcFxerbFkjcVxaurUqezZs4dBgwbhcDi4+eabueyyy8jMzKzT87j22mv5559/mDRpEgUFBVx11VWMGTOGLVu2lHkbi8XClClT2Lt3L3a7nX79+rFmzRr/devXr+fee+9l6NChuN1uTjzxRJ577jkALrvsMp555hmeeuop7rrrLtq3b8+KFSs477zzyj3PQYMG8cEHHzBz5kyefPJJzGYzJ5xwAuPGjaux78XxSNPlvf8SsrKyiIiIIDMzs8768K5bt46hQ4c2zXZBpS0K8y1g8mXcfNt8fPWf5fSiPZ65PB7W/fQTQ3v1qpm2ZKJyAhfiFe9tHFgDrusl64cDA2Pf87kOF+I1+b8zjUxBQQF79uyhffv2DbYu0+v1kpWVRXh4eI21mGpoBgwYQPPmzVm9enV9n0qTUBvPmfJ+V6oSr0mGV1RfdXvRBg45kIBWNGRVXYgXGAwXFqoWbuX1Ng7sUuELjMsKjqWkQogqycvL4/nnn2fQoEEYjUZee+01Nm7cKNMHjxMS8IqKHUsv2uIZWgloxfGmOiUV+fmQm1v9hXjSvk0IP03TWLduHf/9739xOp107dqVt956iwsvvLC+T03UAQl4hfSiFaI+1URv48osxPP9bmZmqk4AVqsEwuK4YrfbK1wwJpouCXiPB9KLVoimoTq9jX09Tw8dUoG11Qrh4erFqu8FqxBCNFHyF64pCGzdFRjYSi9aIY5fxWt8LRb1MSpKfXQ64cgR9ffDagW7XY21tdnUvvJujRCiCZGAtzGQXrRCiJpkNKp3bByOojZs2dmQnq5e+NpsEBZWlP2VvxtCiEZOAt6GJLC8QHrRiuLcbkhJgSNH0A4dotWvv6JlZUF8PMTFQUyM/PxF1WlaUWALRXXBiYlF14WEqODYl/0VQohGRgLehuTff4uCWQjudODLukgv2qbH41GBbFISHD6s3mZOSip5SUnxl6SYgN7F78dggNhYaNZMBcCBH4tvCwuT55Eona8VWkiIer45nSrzm5Kigt3A7K8sfBNCNBIS8DYkHg9ER0sg0lQEBrKlXXyBbXJycG11eYxGaNYMb1wcqUCs242WnFwUDB85oi6//17+/dhspQfCxT/GxhZl/sTxx2BQtb12u/ra10s4K0tdZ7Wq4NduV88TGWghhGigJOBtaCTYbfh8geyRI8EZ2eLZ2aoEsgaDCjB95Qnx8aVfjpYteDweNgVOWnO7IS1NHT85ueTHwM+zslTQsn+/ulQkIqJkcBwfXzI4joqSkoqmzmIpKmnwLYxNTi5a+OZwqMywL/srf89EI7dy5UomTJhARkYGANOnT+fdd99l27ZtZd5mzJgxZGRk8O6771br2DV1P0KRgFcIH48HUlPLzsj6srJHjlQtkPUFiL5gtnnzkp9Xt/7WZFL3FxdX8b75+UUBe2nBse8xJiergCYzU1127y7/fo3GkiUVZWWOQ0MlGGrsjMai7K+uq+dKTk7RwrfA7K/VKm3PmpjDhw/z6KOP8uGHH3Lw4EHi4uLo2bMnEyZMoH///vV9erVm0qRJ3HnnnTV6n3v37qV9+/b8/PPP9OzZ07/9mWeeQQ8scxTVIn+BRNPnC2R9GVlf4FpaRtY32aoigYGsLyPbvHnJz2NjG17W026H1q3VpTy6rgLdsoLiwI+pqep75/teVsRmK7+UIrCkwmqtmcctao+mqZ+T72flGy2elKSus1hKLnyTFzyN1t69ezn77LOJjIzkySef5JRTTsHlcvHJJ59w++2388cff5R6O5fLhbmRl72EhoYSGhpaJ8eKiIiok+PUpcLCQiz1tPBVAl7ReHm9FWdkjyWQjY0tvZwgMCPbEAPZmqZpEBmpLp07l7+v261+FpUJjrOzVUnFvn3qUpHIyMoFx1FRsoCqofAtfAtse5aRoZ4jvo4ygQvfmvrvUhMzfvx4NE1jy5YthISE+LefdNJJ3Hjjjf6vNU1j8eLFfPTRR2zcuJFJkyYxY8YMFi9ezJw5c9i/fz/t27fn4Ycf5vrrr/ffbvr06SxfvpykpCRiYmK48sorWbBgAQCLFi3i6aefZv/+/URERNCvXz/efPPNEufo9Xpp06YNDz/8MLfeeqt/+08//UTv3r35+++/6dChA/PmzWPFihX8888/REdHM2zYMJ588skyg9riJQ0ej4f77ruP5cuXYzQaGTt2bIms7Mcff8x///tffvvtN4xGI3369OGZZ56hY8eOALRv3x6AU089FYBzzz2XL774okRJg9Pp5L777mPNmjVkZWVx2mmn8fTTT3P66acD8MUXX3D++eezceNGHnjgAXbs2EHPnj1ZsWIFXbt2LfXxFBYWMnHiRN566y3S09Np3rw5t9xyC1OmTAEgIyOD+++/n/fee4/MzEw6derE448/zsUXXwzAW2+9xbRp09i9ezcJCQnccccdjBs3zn//7dq1Y9y4cezevZt33nmHyy67jFWrVrFp0yYmT57MDz/8QGxsLMOHD2f27NlBz6eaJgGvaHgqCmR92dmqBLKaFpyRDSwrKJ6Rlbdeq85kKnphUJH8/ODSidLqjH0fXS4VKGVkwJ9/ln+/Rxf0VWoxXkiIZBjrSvG2Z77+4Tk5RZnh0FAVHFutx3XbM13XyXPl1cuxHWYHWiV+J9LS0vj444959NFHSw1OIiMjg75+5JFHmD17Nk8//TRGo5F33nmHu+++m/nz53PhhRfywQcfcMMNN9CqVSvOP/983nzzTZ5++mnWrFnDSSedxOHDh9m+fTsAP/74I3fddRerV6+mb9++pKWl8fXXX5d6ngaDgauvvppXXnklKOB99dVX6dOnDx06dPDvt2DBAtq1a8eePXsYP348999/P4sWLarU923u3LksX76cZcuWceKJJzJ37lzeeecdLrjgAv8+ubm5TJw4kZNPPpnc3FymTZvG8OHD2bZtGwaDgS1btnDGGWewceNGTjrppDIzoPfffz9vvfUWq1atom3btjz55JMMGjSI3bt3Ex0d7d/voYceYu7cuTRr1oxbb72VG2+8kW+//bbU+1ywYAH/+9//eP3112nTpg379+9n/9G1HV6vlyFDhpCdnc3LL79Mx44d2bFjB8ajL1C3bt3KVVddxfTp0xkxYgSbNm1i/PjxOByOoO/5U089xdSpU3n44YcB+PXXXxk0aBCzZs1i2bJlJCcnc8cdd3DHHXewYsWKSn3fj4X8zy7qjterFlYVLysI7Fjgy8i63ZW7T00rOyMbeJFAtuGw26FNG3Upj66rQLe8bLHvc19JxeHD6lKZcyirdZvv0qyZet4cxwFYrTCbi7o5+NqepaaqunKzOXji23HW9izPlUfo7Lp5u7y4nCk5hFgqzq7t3r0bXdc54YQTKnW/I0eODMr6jhw5kjFjxjB+/HgAJk6cyObNm5kzZw7nn38++/bto3nz5lx44YWYzWbatGnDGWecAcC+ffsICQnh4osvJiwsjLZt2/qzoqW59tprmTdvHv/++y9t27bF6/WyZs0aHnzwQf8+EyZM8H/evn17Zs2axW233VbpgHf+/PlMmTKFK664AoDnn3+eTz75JGgf33U+y5YtIy4ujh07dtC9e3eaNWsGQExMDM2bNy/1OLm5uSxevJiVK1cyZMgQAJYuXcqGDRtYtmwZ9913n3/fRx99lHPPPReAyZMnc9FFF1FQUICtlI47+/bto3Pnzvzf//0fmqbRtm1b/3UbN25ky5Yt7Ny5ky5dugD4XygAzJs3j/79+zN16lQAunTpwu+//87ChQuDAt4LLriASZMm+b8eNWoUI0eO9H/vO3fuzIIFCzj33HNZvHhxqedZEyQCENUXGMiW1kM2MCN7LIFsWQu9fEGJBLJNk6apMoWoKDj6x7ZMLlfFJRW+F1O5uSrL/O+/6lKRqKiys8WBQXJk5HEVnNWI0tqe5eWV3fZMftfrne/t+spkgwFOO+20oK937tzJzTffHLTt7LPP5plnngHgP//5D/Pnz6dDhw4MHjyYoUOHMmzYMEwmEwMGDKBt27b+6wYPHszw4cNxOBy88sor3HLLLf77/Oijj+jXrx8nnHACr732GpMnT+bLL7/kyJEjXHXVVf79Pv/8cx577DF27NhBVlYWbrebgoICcnNzK3x7PTMzk8TERPr06ePfZjKZOO2004LKGv7++2+mTp3K5s2bSUlJwXt00fO+ffvo3r17pb6Pf//9Ny6Xi7PPPtu/zWw2c8YZZ7Bz586gfU855RT/5wkJCQAcOXKENqUkGcaMGcOAAQPo2rUrgwcP5uKLL2bgwIEAbNu2jVatWvmD3eJ27tzJpZdeGrStb9++PPPMM3g8HgxH/x4Wfw5s3bqV3bt388orr/i36bqO1+tlz549dOvWrcLvx7GQvx6ibF6vWnFdXkb2WAPZ0hZ6Fc/INvLFDaIOmc3quVRGdiRIbm7ZJRTFP7rd6ncgPR127Sr/fk2moud2ZUoqREnF2545nUVtzywWVfYQGlq0QK6JlaU4zA5ypuTU27Ero3Pnzmiaxs6dO7nssssq3L+0oLF4sKzrun9b69at2bVrFxs2bGDjxo2MHz+ep556ii+//JKwsDB++uknvvjiC9avX8+0adOYPn06P/zwA5dccglnnnmm/z5btmwJqCzvq6++yuTJk3n11VcZNGgQsbGxAPz7778MHTqUW2+9lVmzZhEdHc0333zD2LFjcblclfp+VMawYcNo3bo1S5cupUWLFni9Xrp3705hYWGl76OsFxqB3zufwIWBvuu8ZXQW6tWrF3v27PHXWV911VVceOGFvPnmm9h9L0TLOafSzqe44s8Br9fLLbfcwl133VVi39KC8poiAe/xKDCQLauHrC+orWwgC6WXFpSWkZVAVtSnkBB1adeu/P283sqXVKSlqd+VypZUOBzBpRNlfTyeX/gZjer75Fv4VlioFjz62p4FLnxrImPVNU2rVFlBfYqOjmbQoEE899xz3HXXXSWCmYyMjBJ1vIG6devGN998w6hRo/zbNm3aFJTVs9vtXHLJJVxyySXcfvvtnHDCCfz666/06tULk8nEhRdeyIUXXsgjjzxCZGQkn332GZdffjlhYWEljjdy5Egefvhhtm7dyptvvsnixYv91/3444+43W7mzp3rz0a+/vrrlf5eREREkJCQwObNmznnnHMAcLvdbN26lV69egGQmprKzp07eeGFF+jXrx8A33zzTdD9+Gp2PeWsSenUqRMWi4VvvvmGkSNHAqrrxY8//hhUlnEswsPDGTFiBCNGjODKK69k8ODBpKWlccopp3DgwAH+/PPPUrO8J554YonH8t1339GxY0d/nW9pevXqxe+//06nTp2qdd5VJQFvU+ILZMtb6OV7W7cqr159WaviC70CLxLIiqbGYFCTD6OjoYwVzn4uV1Fv4/KC46QkVU6Rlwd796pLRaKjK+5r3KyZKr1oYhlPv9LanjmdRS8ubLbgoRdmc9P9XjQAixYtom/fvpxxxhnMnDmTU045BbfbzYYNG1i8eHGJt9gD3XfffVx11VX06tWL/v378/777/P222+zceNGQA168Hg8nHnmmTgcDlavXo3dbqdt27Z88MEH/PPPP5xzzjlERUWxbt06vF5vmR0IQNXl9u3bl7Fjx+J2u4Pegu/YsSNut5uFCxcybNgwvv32W55//vkqfS/uvvtuHn/8cTp37ky3bt2YN2+ef0gFQFRUFDExMSxZsoSEhAT27dvH5MmTg+4jLi4Ou93Oxx9/TKtWrbDZbCVakoWEhHDbbbdx3333ER0dTZs2bXjyySfJy8tj7NixVTrnQE8//TQJCQn07NkTg8HAG2+8QfPmzYmMjOTcc8/lnHPO4YorrmDevHl06tSJP/74A03TGDx4MPfeey+nn346s2bNYsSIEXz33Xc899xzzJkzp9xjPvDAA5x11lncfvvt3HTTTYSEhLBz5042bNjAwoULj/mxVEQC3sZA18vPyPo+P3KkaoFsTEzlMrKyaEeI8pnNkJCgLhXJza24lOLIERVA+ybopaVBGb1Ng86hnJIKLSYGW2ZmzTze+uZrexYSUrTwLbDtmd2usr9WqwqApba6RrVv356ffvqJRx99lHvvvZfExESaNWtG7969gzKopbnssst45plneOqpp7jrrrto3749K1as4LzzzgNUl4fHH3+ciRMn4vF4OPnkk3n//feJiYkhMjKSt99+m+nTp1NQUEDnzp157bXXOOmkk8o95rXXXsvtt9/OqFGjgt6m79mzJ/PmzeOJJ55gypQpnHPOOcyePTso+1wR3+MfM2YMBoOBG2+8keHDh5N59HfNYDCwZs0a7rrrLrp3707Xrl1ZsGCB//GCqvtdsGABM2fOZNq0afTr148vvviixLEef/xxvF4v119/PdnZ2Zx22ml88sknREVFVfp8iwsNDeWJJ57gr7/+wmg0cvrpp7Nu3Tp/xvutt95i0qRJXHPNNeTm5vrbkoHK1L7++utMmzaNWbNmkZCQwIwZM/wZ6LKccsopfPnllzz00EP069cPXdfp2LEjI0aMOObHURmaLmM8SsjKyiIiIoLMzEzCw8Nr/XiuXbv44bXXOMNoxJSbW3of2SrU+hATU7mMrASyjZbL42Fd4Ghh0fT4SipKa91WPJMckFGq8G7POQfDDTdA//5NogygBF/bs8LC0he+NZB3ogoKCtizZw/t27evtVXp1eX1esnKyiI8PNwfAAlRntp4zpT3u1KVeE0yvA2AcfZs+q5aVfGO0dEVt9+SQFaIpiGwpKKiFlBOp8oIl1NnrB85Avv3Y/jqK/jqK2jVCkaNgmuuUcdoKkpre5aSoj73ZXwDh15I6YMQxwUJeBsAvXNnMtu2JaxlSwytW5fehksCWSFEWaxWaNlSXcrg9nj44qOPuODnnzGuXQsHDsBjj8HcuTBsGNxwA/TsWXfnXBcC2575Fr7l5amR2SZTUfbXt/BN2p4J0WTJb3cD4L3/fr7o3p2hHTpgiImp79MRQjRRefHxeB98EOOkSfD++7ByJWzfDm++qS49e8KYMSoAbqBvsx+z4gvffG3PkpLU11arqgl2ONRjt1gk+ytEEyJFOUIIcbyx2+Gqq2DdOvjgA7jiChXgbdsGEybA6afD7NkqC9xU+dqeRUUVDQ3JzIT9+1X3jH//VYvg8vIqP8JcCNFgScArhBDHs1NPhQUL4McfYcoUVRaRlgbPPgt9+qhShy+/VDWwTZWmqaxueLiqZ7bZ1OK3xETYs0cFwElJagJcVRYQV4KsGxeifDX1OyIlDUI0BLquLl5v0aX4175tvv1BtaszGtXXBoO6GI1FnwduE6I8MTFwxx1w222wcSOsWAFffw3r16tLhw4werTKDNdB95p65Vv4Ftj2LDVVfW6xqAx5aGjRwrdjWI3um4aVl5dX4UQrIY5nvol05Q2zqAwJeIWoaaUFraUFs1D0UdNKBqm+mkOjUS2mMRqLglmvF/75B9q0UV97POricqmL2130tcdTMjtX/FjFA2Vx/DIaYdAgddm9G156CV5/XT3fHnkEnngCLr9c1frW0sz7BiVw4RuoDG9+vip/MBpVABwWpsojrNZKtz0zGo1ERkZy5MgRABwOR4kxrfXN6/VSWFhIQUGBtCUTlVLTzxmv10tycjIOhwNTNReVSsArRFnKyrAWD2CL8wWrgYGrr1G+71JawFnapSy+ASMhIaX/Bxt4fr6AN/Cj260uhYVFXxcWBj8mXS96HMWzxr6vG9h/0KKGdeoEM2fCAw/AW2/BqlVqAMbLL6vLWWeprO+QIQ2mv22ts1iKOuZ4POr3JiVF/b5YLMET3ypoe9a8eXMAf9Db0Oi6Tn5+Pna7vcEF46Jhqo3njMFgoE2bNtW+Pwl4xfGhrMA18FJc8ayr72uLpezgtbRMbX0EhppWlBGuKBDR9ZJBceDnhYUqIHa51NcuFxQUFO2naaWXVPjOQUoqGr+QENWz9/rrYfNm1d3ho4/U55s3qxaK110H116rPj9eGI0l255lZ6tSI5NJBb2hoUVDL4r9HmiaRkJCAnFxcbiqMiWzjrhcLr766ivOOeccfwmGEOWpjeeMxWKpkWyxBLyicSmrPKD414GlAlB6AOoLXANLBsoKViuTdW2sfBnoyigva1y8pMLrLSqtKP6CIjAYlpKKxkPT1EK2Pn3Ugq5XXlGXpCTVz/eZZ2DoUFXucMYZx9c7AMXbnrndRW3PfH9vQkLUxWoNantmNBqrXZ9YG4xGI263G5vNJgGvqJSG/JyRgFfUj7KC1PKyroFZxNLKBSoKXIvf7nj6z7im+L5/FQXIVS2p8L01XNmSCt82+RnWn4QEmDQJ7rpLZXtXroQtW+B//1OXbt1U4Hv55ept/uON7+9SSIh6LjudagR0WlpR9jdw6IW80BOiVknAK6qvvHKBwJrXQL5Apnitq9UaXC5QVolAfZYLiIoda0lFaQGyL2scWFIRuL/v5x/4XPEFw4GBsjxPaofFApdeqi6//67qfN9+G3buVLW/jz6qOjuMHq06PRyPfG3PfMM8XC4VAOfkFP3dCw0tWvgmUzWFqHES8Ioi5S3SCrwUDxxKW2hlNgcHrsUDj6ou0hJNV22XVPiuh6KssZRU1I6TToInn4QHH1SdHVatUj1sX3xRXc49V2V9+/c/vuu6fW3PILjtWUqK2m63q+yvr0RCnpNCVJsEvE1RaZ0EyuvpCkVBQPHMqdFYlHX1lQxUlG2VbJqoLTVRUuHLEktJRe2JjISbb4Zx49TQipUr4dNP1edffgmtW6tFcFdfrQY9HM9Ka3uWl1fU9sxqVX2PfRniarZmEuJ4Jb85DV1pQWpZPV0h+D/p4oFoYLlAWf+JS7mAaApqo6SitN7GxUt1ArtSSEmFetznn68u+/apnr6vvabG9z76KMyZo0ohxoyBHj3q+2wbhuJtz5xOOHJEPdes1uChFwEL34QQ5ZOAtyFxuVQ7m0ClZU6L93QNbANVXvAqhCipJksqfFljKakoqU0bePhhuPdetaht5Ur45RdV+vD662rE8ZgxcPHFRbWuxzujUdX1OhxFC9+Ktz0LXPh2PJeJCFEBCXgbklat1Cv2impd5RW9EPWjOiUVxbPGvt7Gvsyxr6TC4wn+HQ8Mhov3Nm6MfwvsdhgxQi1k+/lnNcL4gw/U5z//DDNmwMiRqudvq1b1fbYNR/GFb762Z4mJRdeFhKjg2Jf9FUL4ScDbkERFHT/TioRoyqpaUlFW1tiXJfZljX0lFb7rq1JS0dBoGvTqpS6PPKJKHV56CQ4dgmefhUWLYMAAlfXt169xBve1KbDtmW/hW3q6WvhmsQRnf2XhmxAS8AohRL0KDI4rUt5CvMDexm530VvggfuBaoUVHt6wAsjYWLjzTrjtNti4UWV9v/kGPvlEXTp2VG3N/vMfde4iWGkL3woKICtLXWe1quDXN/FNEiviOCQBrxBCNBaVLamAkqUUhYXwzz8q2ElPV4GP3d6wAl+TCQYPVpfdu1Vbs9dfh7//hmnT4PHH4YorVNb3hBPq+2wbruIL3woLITm5aOGbw6Eyw77sb0N6DghRS+Q9DiGEaIp85RSBAQ6outhWrVSQk56uMoENUadOMGsWbN0Kjz0GXbqodl2rV6s+vldeCe+/r0o8RNmMRvXCJipKXUwmleU/cED1SN67V5VB5OaqdwaEaKIkwyuEEMcTo1H1yQ0JUb1e09PVuNvQ0Ia50Ck0VJUzjBoF332nujt8/LH6/LvvoHlzuO46uPZaiIur77Nt2HztKa1W9bWvBCYpSV1nsQQvfJPMr2hCJMMrhBDHI7NZ1c62aQPNmqlMb0ZGw83yaRr07QtLlsDmzTBhgjrvw4dVP98zzoDx42HLlpKL+UTpTCYV3EZFQUSEKpfJyFB9kvfuVVlgaLjPCSGqQAJeIYQ4nlmtEB8PbduqoCcnRy128i1ya4hatID77lPB7XPPwemnq9KG996D4cNVd4dXXlElEKJyfK3NIiLU9DubTWV/QY09bsjPByEqQQJeIYQQqs6zRQuV8XU4VKYvJ6dhZ0stFrjsMnj3XdXNYeRIFajt3An33w+9e8P06bBnTz2faCNkNhfVfaelqYsQjZgEvEIIIRRNUzWzrVqpwNfX0SE/v77PrGLdu8NTT6lFbtOmQbt2KlO9dCn83/+pOt8NG4qm3onKczjUeOOsrPo+EyGOmQS8QgghghkMqt9tmzbQsqXalpbWcDs6BIqMhFtuga+/LurooGnw+eeqndn//R8sXiwZy6qwWlU2PSlJykREo1XvAe+iRYto3749NpuN3r178/XXX5e7/3PPPUe3bt2w2+107dqVl156qcQ+GRkZ3H777SQkJGCz2ejWrRvr1q2rrYcghBBNk9GoFjS1aaO6IbjdKlD01XY2ZAYDXHCBmt727bdw660qGN63D/77X1X3e8898Msv9X2mjYPDoX7+SUmN4+cvRDH1GvCuXbuWCRMm8NBDD/Hzzz/Tr18/hgwZwr59+0rdf/HixUyZMoXp06fz+++/M2PGDG6//Xbef/99/z6FhYUMGDCAvXv38uabb7Jr1y6WLl1KS1+WQgghRNX4Ojq0bVvU0SEzs/Gs3m/bFqZOhR9/hLlz4eST1WN4/XUYMgQuvhjeektNphNlCw9X/XqTk6U0RDQ69dqHd968eYwdO5Zx48YBMH/+fD755BMWL17M7NmzS+y/evVqbrnlFkaMGAFAhw4d2Lx5M0888QTDhg0DYPny5aSlpbFp0ybMR8cntm3bto4ekRBCNGG+jg5hYaq2NyNDZYFDQ1VGtaGz2+Hqq2HECPjpJ9XT94MP4Oef1WXGDLjmGtXzV5IkJWmaypKnp6uWZnFx0qtXNBr19heqsLCQrVu3MnDgwKDtAwcOZNOmTaXexul0YrPZgrbZ7Xa2bNmC6+i0nf/973/06dOH22+/nfj4eLp3785jjz2GR16NCiFEzXA4VEeHtm1VENkYOjoE0jTVwWHhQvjhB3jgAUhIUO23nn0WzjoLxo6Fr75qPI+prhgM6gVPSor6uQvRSNRbhjclJQWPx0N8fHzQ9vj4eA4fPlzqbQYNGsSLL77IZZddRq9evdi6dSvLly/H5XKRkpJCQkIC//zzD5999hnXXnst69at46+//uL222/H7XYzbdq0Uu/X6XTiDHgrK+voSlSXy+UPpGuT7xh1cSzRNMhzRlRVrTxnrFZV2xsaqrJ+qamqLZjdXnPHqG1RUXD77XDLLWgbN2JYuRLDpk1qmtvHH6N36oT3+uvxXnmlCvSOI66jiSJX8YSRwaDKXA4dUi8efO3LxHGvrv9vqspxNF2vn5evhw4domXLlmzatIk+ffr4tz/66KOsXr2aP/74o8Rt8vPzuf3221m9ejW6rhMfH891113Hk08+SVJSEnFxcXTp0oWCggL27NmD0WgEVOnEU089RWJiYqnnMn36dGbMmFFi+6uvvorD4aihRyyEEKIxCNu/n3YffUSbzz7DdLQzhdtmY/9557Fn6FCy27Sp5zMUQgDk5eUxcuRIMjMzCQ8PL3ffegt4CwsLcTgcvPHGGwwfPty//e6772bbtm18+eWXZd7W5XKRlJREQkICS5Ys4YEHHiAjIwODwcC5556L2Wxm48aN/v0/+ugjhg4ditPpxFLKrPjSMrytW7cmJSWlwm9gTXC5XGzYsIEBAwb4646FKI88Z0RV1dlzxuVS/VrT09WitpAQlQ1sjHJyMLz9NoaVK9F27/Zv9vbpg3fUKPSBAxvvY6sEl8fDhu3bGdCjB+ajCaQSMjLUzzghQdX1iuNaXf/flJWVRWxsbKUC3np7dlosFnr37s2GDRuCAt4NGzZw6aWXlntbs9lMq1atAFizZg0XX3wxhqMLJs4++2xeffVVvF6vf9uff/5JQkJCqcEugNVqxWq1lnqcugwm6vp4ovGT54yoqlp/zpjNqsY3MlIFQxkZqiNCWJha4NaYRETADTeo/r2bNqlFbp98guG77zB8950q57juOrj2WrWAq4kyG41lB7zR0UULGOPjG8fiRVHr6ur/pqoco16fmRMnTuTFF19k+fLl7Ny5k3vuuYd9+/Zx6623AjBlyhRGjRrl3//PP//k5Zdf5q+//mLLli1cffXV/Pbbbzz22GP+fW677TZSU1O5++67+fPPP/nwww957LHHuP322+v88QkhxHHLZlMBYZs2KnDMzlaZX6+3vs+s6jQNzj5bTW3bvBnuvlu1aTt8GObMgTPOUHXAP/xw/C1y0zTVriw1VYZ5iAatXt9/GDFiBKmpqcycOZPExES6d+/OunXr/G3EEhMTg3ryejwe5s6dy65duzCbzZx//vls2rSJdu3a+fdp3bo169ev55577uGUU06hZcuW3H333TzwwAN1/fCEEEI4HGoRW0SECooyMtRiN4ejcba0atEC7r9fBb3r1qms748/wrvvqstJJ6mM8PDhjWvxXnWYTKqs4cgRNZGtDkoBhaiqei+4GT9+POPHjy/1upUrVwZ93a1bN37++ecK77NPnz5s3ry5Jk5PCCFEdWma6uTgcKhMb2qqehvcbm+8QaHVqoLa4cPht99U4PvOO/D773DffWqa24gRqqdv+/b1fba1z2pVwyiSklQALAu+RQMjxTZCCCHqhsGgMr1t2qjBDrqu3gZv7BPOundXpQ0//qgmurVtqybRLVkC//d/cP31sHFj059OJuOHRQMmAa8QQoi6ZTKp/rdt2qiFTi6Xyvg29r7SUVFw663wzTfw0ktwwQUqu/3ZZzB6tAp+n39ePdamyjd++MiRph/gi0ZFAl4hhBD1w2KBZs1U4BsdDXl5qsa3sQdKBgP07w+rV6vg95ZbVNeKfftg1iw47TSYOBF+/bW+z7Tm+cYPZ2SoaWzH2yI+0WBJwCuEEKJ+2Wyqj2vbtipDmJ2tLo2xo0Nx7drBtGmq3GHOHFX+UFAAa9fC4MEwbBi8/XbjL+sIJOOHRQMkAa8QQoiGweFQtb2tW6sgODNTvT3eFLKEdjtcc40aWfzee2qxm9kMP/0Ed94Jp58Ojz8OBw/W95nWDLNZ/QyTkiAnp77PRggJeIUQQjQgmqayg61bQ6tWKluYng75+fV9ZjVD01RJw7PPqr69992n+hWnpsLChXDWWTBunCqFaOyBvt2uHm9SkspqC1GPJOAVQgjR8Pg6OrRtq8odmkpHh0DNmsGECfD992qoRd++qozjo49US7Pzz4cVK1R5R2MVFqZ+ZklJqoODEPVEAl4hhGiidF3H7XVT6CnE6VaBotPtxO1tRIGHyQQxMUUdHQoLm0ZHh0AmEwwdCm+8UdTRISQE/voLHn4YeveGBx+EP/+s7zM9Nr5Je8nJTaMuWzRK9T54QgghROXouo5X9+LRPXh1b9DF41XbXB4Xbq8bt9eNR/eg6zoe3YPraID4b8a/WC1WTAYTNpMNq0l9bjaaMRlMGDUjWkOcgObr6BAWpgJe32Ko0FAwGuv11GpU167w2GMwZQq89ZbK8O7eDatWqUvfvmqS26BBKlBuDALHD5vNaiyzEHWskfy2CCFE0+MLYP1BaymBrC+AdXlc/gA2cP/iDJoBo8GIQTOgoWE0GDFrZmwGGwAhlhB0g45bd5NekI5X9/pvZzKYMBvMWE1WbCYbJoPJHww3mEDYZlM1r+HhKvDNylIBb0iIKoNoKsLCVGA7ejR8+62a5PbJJ7Bpk7okJMB118G116oXAg2djB8W9UwCXiGEqCHFA9jSglhf8OrLwpa4DSoA1VDBpUEzBF18Aazv68oGob7g2GgwYjQdzYiai673B9e6mwJnAWn5aejoGDCogNdgxGay+QNhs8HsD4jrPBDWNBU8ORzq7fLUVJXxtVrVtoYQmNcUTVMDK/7v/1QHh5dfhldegcREeOopmD8fLr5YBcanndawH7uMHxb1SAJeIYQoR2C5QGmBrC9w9V283uD99KP/QAWxNRXA1jSDZsBqspbY7nuMHq+HLGcW6flqSpiGpgJeowmr0YrdbPcHwL6AuNYfi6+jg8OhWl+lpKisr92uLk1Ny5bwwANqoduHH6qs79at8M476tK9u8oKX3ZZw338DodqN5eUpB6PxVLfZySOExLwCiGOK8VrXosHsR6vB5e3ZAbWt78vePXRNA2jZiwKYg0GTJrJv61BlAFUg0EzYDFawAh2ioKowEA4pzCHzIJMvHgxoEojjAYjVqMqjbCYLEHBsEGr4dIDo1FlekNCVDCVnq46OoSEqKxiU2O1wuWXq8uvv6rA99134bffYNIk+O9/VZeHUaPU4IuGxleOcuSIKs1oSjXYosGSgFcI0aiVtXgr8GuX1+UPYoNqYL0evHj95QOgAliDZggKWH11rQ2mjrUBCAyEA/k6Q7i9bnJduWQ6M/3X+QJei9GC3WT3L5Tzfax2IOzr6BAWpkoc0tPVuOLQULVYqik6+WSYOxceeghef10tbNu3D154AZYsUa3NxoxRHxtKjXPg+GGzGeLiGnYphmgSJOAVQjQoxQPY4kGsx+vxB1Qur6vU/QNLCHwBbODFbDAHfS1qjqZpmI1mzMbgADMwEM5355PlzEJHLyqNOBr42k12LMbgjLDRUMUMoMWigihfJrGpdnQIFB0Nt94KN90En3+uAt/PPiu6tG2rMr4jRkBUVH2fbfD4YYulYZyTaNIk4BVC1KqKWmkFBrC+VlrF9wP8QWzx4FVDBVgSwDZs5QXCHt2Dy+OiwF1ATmEO+tEJY0bN6L+NzWTzB8K+BXMVBsKBHR3S0lRHB1+3gIaS7axpRiNceKG67NkDL70Ea9fCv//CrFlqodvw4Srr2717/Z5r4Phhs1m9IBGilkjAK4SokuJtsUrLsLo8Ln8ngsBWWr79i/O10fIFsL5gRgLYpk/TNEyayuQW53sR5HQ7ySnMwaur8hOjZvRnf+1mu7+XcOAl4ABFHR1ycoo6OthsRaNvm6r27eGRR+D++9WitpUr4fff4bXX1KV3b7jhBrjoovpbPGa3qwlsvs4NNlv9nIdo8iTgFeI4V5VWWmX1gvW9Ne0T2IEgsBdsfXciEI1LieD1qMC67Pz8fH8Zi1Ez+rO/pQ7VCA1FczjU1K/U1Kbd0SGQ3Q4jR8I118CPP6rA98MPVYeHrVth+nR1/XXXqc4Jdc1Xc+3r3NBYBmqIRkWeVUI0MaUFsMfaSqume8EKUROMBmOp5Qz+8pgyhmr4AmGb1YapeTSmnFxMGVmYUvPQQkObZkeHQJoGp5+uLo88ovr5vvwyHD4MCxbAc8+pCW5jxqiJbnUpIkK9AElOViOkm2rJiag3EvCKRstX5xe0jVK2HeN+NXlfpe1XnftyudWY2CxnFppbKxHA6roetNDLd7++j0FttI620pIAVjR2QYFwKUM1PLqHTGdm0VANzYA5QseYlY8tIx2bZsIUGoHZcnTKnFYPQzXqSlwc3HMP3HEHrF+vRhh/9x2sW6cuXbpguP56TJ061c35yPhhUcsk4G1A8grzMOnqR9JQgi2vt2S9pW8SVHn7BTbbDzxmaUFl8f0qe8zSHkNpGmrA69sWWApQ2rbS9vG41dSsQ1mHMJgMJXrBapqGxWRpMr1ghaiOCodqWGxk2bNJz0qH5L9VXXFoOCaTBavRgt1YNGbZdLRsosn8TpnNqob3ootg1y5V7vDmm/DnnxinTuXCsDC0V1+FXr1q/1xk/LCoRRLwNiD/Zv7rH/lZmUCoKtsqo6zblfaH/Vj3q8n7Kkvx22qaVqlj1PZjL2u/Y+Fxe9jDHiLtkUVjYoUQVRI0VCPSDhHNID8fb3oq7qxMPIZCcuwWMsnGq+togNk/VMOCzWDDbDzaNeLoQrpGvciya1eYPRsefBDefBN92TKse/agX3edCoJPPLH2z0HGD4taIgFvA6JpGlF26UUohBD1QtPA4cBgs2EJj1QLqXJzVRBms6EDbt2N2+sh15VHpjcbNEAHk0EFvBaDGbvRjtloOtp9wujv+9xohIXBDTfgvvxysocPJ3rXLrj6anj7baiLEgcZPyxqgQS8QgghRCDfUARfK7P0dMjMRLPZMNtsmA2lDNU4GgjnuwvIKsxBR8XCvkDYrJmxm2xYjGZ/IGzSjmGoRl0KDeW7qVMZ+vjjaL/9poZWvPVW3YwrlvHDooZJwCuEEEKUxmhU3QN8rcwyMlTm0eEIGlWsaRpmzVxqIOzRPbh1DwWeAnLcuUVDNQxGzEcXxtlNNixH26n5tjWUQNgdGor7lVcwjxgBf/yhgt6336799mUyfljUMAl4hRBCiPKYzWp0b0iImtaWlQn5+errcjKP/qEamMAYvGhOtU/zUOgpJNedVzRUw2D01wPbjTasvjHLAcM26lxUlBpUccUV8M8/cNVVKuiNj6/d48r4YVGDJOAVQgghKsNqhWbN1AjczEwV/GoahDigihlZk8EXCAdv93g9uHQ16CXfXXC0rSAYNYPK/hpM2AxWrCYLJs3k32bUjLXbOSIuTo0ovuIK2LtX1fS++SbExNTeMUHGD4saIwGvEEIIURV2uwrCwsIgMwOyc4o6ClQz6DQajBgxlhoIu3U1WCPdXYDH6UHTDBjQ/PXANqMVm8laVCNc072EW7RQQe/w4fDnnyrofeMNVXpQm+z24M4NMn5YHAMJeIUQQoiq0jRV0mC3Q1iuf2Gbr6NDTdeb+gPhYry6F5fXrYZqFGaRVuhF18GgaaqFmmZUgbDRWlQjXJ1AuE0beP11lendsUONI37tNRX816bQUBk/LKpFnjFCCCHEsSqjowN2e52MKjZoBqzGkm271FANDx7dTVZhNulkgq6haWDS1EQ6m9GKzWDFbDRXbahGx46wZg1ceSX8/DOMHq1GFNd2z1wZPyyqQQJeIYQQorpK6+iQkaGywGZzRbeucWqohgEwYw/YHhgI57hyydTLHqrhG55T6lTLE05Qmd2rroLvv4cbb1RT2mqz3EDGD4tqkJdHQgghRE3xdXRo2VIt6HI61eI2j6e+zwzwBcJm7CY7YeZQIi3hRFsjiLSEq0yxDrmuPJLykzmUlwTAkbxk3F53yTs7+WRYvVoF+V9/DTffDIWFtfsAAscPZ2XV7rFEkyIBrxBCCFHTfB0dWrZUJQ+5uarkweut7zMrlaZpmA1qOEaYOZQoawSR5nAA0gozOJCbSL47v+QNTzsNXnpJZXY//RTuuAPcpQTHNclqVW3KkpIgL692jyWaDAl4hRBCiNpit0Pz5irwtdtVVjI3F0orE2igIs3h5LnzOZCbSFZhdskd+vSBZctUEPrhh3DPPbUf2DscKrBOSqr9rLJoEiTgFUIIIWqTr6NDQoIKfM1mtbCtoKC+z6xSNE0j0hIOOhzITSQlPw2vXiygPe88eOEFVXLw9tsweXLtB/Xh4erFw5EjDaZkRDRcEvAKIYQQdcHX0aFlS5X1BbWwzems19OqrBCzA4fRzuH8IyTmJVHoKZZZHTgQFixQj/OVV+CRR2o36A0cP5yS0qiy5qLuScArhBBC1CWjUQVqLVuqCWZutwraXK76PrMKWYxmIsxhZDizOJh7mLzidb2XXgpz56rPly2Dxx+v3UA0cPxwRkbtHUc0ehLwCiGEEPXB19GhVauijg7Z2Q3+7XmjwUikJRynx8mBnENkODODW5dddRXMnq0+f/ZZeOaZ2j2hwPHDOTm1eyzRaEnAK4QQQtSnwI4OoaEqaGvAHR1A1fWGW8IwagYO5h7mSH4KHm9AoD5qlCppAHjqKXj++do9IbtdZXuTkhpNbbSoWxLwCiGEEA2Br6NDq1YqY9kIOjrYTXZCzQ6S81M5lHcYpyegHvnmm+H++9Xns2apwRS1KTRUZcmTkmq/NZpodCTgFUIIIRoKX0eHFi3UpRF0dDAbzERaw8ly5XAgN5EcV27RlXffDXfeqT5/6CFYu7Z2TyYiQpWFJCc36Ay5qHsS8AohhBANjcGg2m75OjroeoPu6GDQDERZInB73RzITSQ1P62orveBB2DcOPX5pEnw3nu1dyKB44fT0mrvOKLRkYBXCCGEaKh8HR1atSrq6JCZ2WDfsg8zh2IxmEnMT+Zw3hE1kljTYPp0uO46lXW98074+OPaOwkZPyxKIQGvEEII0dD5Ojq0bAlRUarEISurQXZ0sBmthJtDSHWmczD3MAXuAhX0zp4NV1yhzvm22+Dzz2vvJGT8sChGAl4hhBCisbDZVKa3ZUvVfzY3t0F2dDAZTERZIsh15xWNJDYYYN48uPhiNQ543DjYtKn2TkLGD4sAEvAKIYQQjY2vo0PLlkUdHfLyGlRHB99IYl3Xi0YSGw2wcCEMGKCy1KNHw48/1t5JyPhhcZQEvEIIIURjVLyjg9HYIDs6hJgd2I02Ducnk5iXhMukqb6855yjgvTrroNffqmdg8v4YXGUBLxCCCFEY1ZWR4cG9Da+1WghwhxKujOTAzmJ5Jl0WL4czjpLtRG75hr444/aObiMHxZIwCuEEEI0DSaTyma2bKkmt7lcDaqjg9FgJMoSQYFvJLGhEH3lSjj1VBWIXn017N5dOweX8cPHPQl4hRBCiKbEYoGYGBX4RkZCfr6q8fXWfw2rpmlEHB1JfCg3iWSTE89Lq+Ckk9SwiBEjYN++2jm4jB8+rpnq+wSEEKK26LqOV/eio6PrOjpHvz5ax1fWdToB15dynf82lbgu6BzKu07X8eIFnVKv8x2nvMflu674OXl1Lx6Ph0OZh2iR04KWkS3r5wci6pbNpi7h4ZCerjKbRgPYHSrwq0d2kx2jZuJIfipOSyhxL6/EOuJa+PNPuOoqePttVZdc00JDVTY5KUm9IDBJGHS8kJ+0EA2UrutkF2aTkpdCal4qqfmppOSlkJKXQnJuMnv27SHqk6jgQMoX+BwNmkoNisq5zhcI+oKv6l4XGJihU+51xc/pWK8LDP5ESY/teYzmIc3p0bwHPZv3pGfznvSI70GELaK+T03UFrtdBb65uSrYy85Wb/Hb7WpRVz2xGM1EGsLIdGVTaLPS/OXlhFx1HezdqzK9b72lWrDVtIgI9QIgORni4+s9+Bd1o94D3kWLFvHUU0+RmJjISSedxPz58+nXr1+Z+z/33HM8++yz7N27lzZt2vDQQw8xatQo//UrV67khhtuKHG7/Px8bDZbrTwGISor35UfFLgWD2RT81JJyS/aXuipYNFJet2c9/HOoBnQ0NA0DQ3N/zXasV/n/7oK12lHg5PA6zRNw4Ch4ut0SExNZL9zP4dzD3P478N88vcn/sfYMaojPZv35NTmp9KjeQ9ObHYiNpP8zWwyNE1lNx0OlelNS1P1vb4scD3xjSTOduWwPwSar15KxDU3oP3zj1rI9sYbauBGTdI0FfSmpqrAPza2Zu9fNEjVCngLCwvZs2cPHTt2xHQMbwusXbuWCRMmsGjRIs4++2xeeOEFhgwZwo4dO2jTpk2J/RcvXsyUKVNYunQpp59+Olu2bOGmm24iKiqKYcOG+fcLDw9n165dQbeVYFfUBpfHRWq+Ck5T88oJZI9+nueq+sSfUEsoMfYYYhwxxDpiibXHEmWLIvdQLm06tsFoNBYFN5rKVJQWKJUaUFXiusDgC8oI4iq4LnBbmdf5Pq/GdYGPJej7UcZ1Gpr/+1Xadb7H1RR43B5++uonuvXpxs60nfx8+Ge2Hd7GtsPb+DfzX/5O/5u/0//mrZ1vAWA2mDmx2Yn+LPCpzU+lY3RH//dUNFK+jg4Oh6rrzcxUWV+HQ9X+1pMwcyj57gIORHpwrVxE7LU3of3xB4wcCWvXqgC1JhmNReOHzeaav3/R4BxTwJuXl8edd97JqlWrAPjzzz/p0KEDd911Fy1atGDy5MmVup958+YxduxYxo0bB8D8+fP55JNPWLx4MbNnzy6x/+rVq7nlllsYMWIEAB06dGDz5s088cQTQQGvpmk0b978WB6aOM55dS8ZBRlBgWpZgWxqXioZzowqH8NitBDriCXGrgLYGEcMsfZYYh2xRDui/Z/HOmKJtkdjN9tL3IcveOl1ai+MJmMNPHJxvLCb7Zze8nROb3m6f1tafhrbD29n2+Ft/kA4NT+V7Unb2Z60nVXb1d/6UEsop8SfwqnNT/UHwgmhCU3qhcFxw2RSmdPQUFXikJGuFreFhNRbXavdZMNsMJEUD4UrFtDi+tvQfv1V9el97TV1rjXJalXDKHxBr8NRs/cvGpRjelZPmTKF7du388UXXzB48GD/9gsvvJBHHnmkUgFvYWEhW7duLbHvwIED2VTGqEGn01kiU2u329myZQsulwuz2QxATk4Obdu2xePx0LNnT2bNmsWpp55a5rk4nU6cTqf/66ysLABcLhcul6vCx1JdvmN4PV487vpfRduU6LpOTmGOClLzi0oG0vLTgjKvqfmppOWnkZqfWuX6T4NmUBnYo1lY3+f+oNZeFMjG2GMItYRWKUAo7Tnh2ybPF1FZ5T1nIswRnNP6HM5pfQ6gfm8OZB9g2+Ft/HLkF//HnMIcNu3fxKb9RX+j40Li6Bmv6oB7xPfglLhTiLRF1sljEjXAYISISLAendaWna36+IY48Ojq75THU3c18RoGwo2hpLUC1wtzaTP2bgw//YR3zBg8K1equuOaZLWqLPehQ5CQUK9Z7qbAF8/URexU1eNoum+1SRW0bduWtWvXctZZZxEWFsb27dvp0KEDu3fvplevXv6AsTyHDh2iZcuWfPvtt/Tt29e//bHHHmPVqlUlShIAHnzwQVasWMEHH3xAr1692Lp1KxdddBFHjhzh0KFDJCQksHnzZnbv3s3JJ59MVlYWzzzzDOvWrWP79u107ty51HOZPn06M2bMKLH91VdfxSGv+Bocp9dJpjuz6OLKDPo6y51FhjvD/7Vbr3oPylBjKBGmiNIv5uCvQ42h8javaPI8uof9Bfv5K+8v/sr7i915u9mbv1d1liimhbUFnR2d/Zf29vZYDBJIiKqL3L2bvtOmYc7LI+nUU9ny4IN4jya3hMjLy2PkyJFkZmYSHh5e7r7HlOFNTk4mrpSVk7m5uVV+a6v4/rqul3kfU6dO5fDhw5x11lnouk58fDxjxozhySefxGhUb+ueddZZnHXWWf7bnH322fTq1YuFCxeyYMGCUu93ypQpTJw40f91VlYWrVu3ZuDAgRV+A2uCy+Viw4YNtOvdjuiQGi7ObwRcHhdpBWklFmz5ywmKbct15Vb5GCHmkKAsbKw9NigbG7gt2haN2diw/6B63B62b9pOj749pKRBVEpNPGdO5/Sgr/Nd+fye8jvbDm9je9J2tiWpeuBDzkMcch7iy/QvATAZTHSL7UbP+J7+bHDHqI4YDfLcbZB0HfJy8aSmsX1vCj3aRmO01XBmtZKcnZrxb+xsOtz2APE//8xFS5fiWbxYlSDUJK9XZXpjYtTQDinTOSa+eGbAgAH+d91rU2USrD7HFPCefvrpfPjhh9x5551AUdC6dOlS+vTpU6n7iI2NxWg0cvjw4aDtR44cIT4+vtTb2O12li9fzgsvvEBSUhIJCQksWbKEsLAwYstYZWkwGDj99NP566+/yjwXq9WK1Wotsd1sNtfJD8zHYDQ0ieDFVwfrr331lRIElhDkVb8ONrBswL+gK6Am1rctxh5Tah1sU2A0GZvEc0bUnZp8zoSaQjmz9Zmc2fpM/7a0/DR+SfolaFFcSl4Kvx75lV+P/MrqX1cD6kVoUD1wQk9ahLaQeuCGIiJC9evdm4LR7cHoLKiXGleH0YbnrD78u/C/tBv/IIb16zFMmADPPacWntUUo1E9Zt8Cvqiomrvv41BdxU9VOcYxBbyzZ89m8ODB7NixA7fbzTPPPMPvv//Od999x5dfflmp+7BYLPTu3ZsNGzYwfPhw//YNGzZw6aWXlntbs9lMq1atAFizZg0XX3wxhjL66Om6zrZt2zj55JMr+ehEcb46WF/wmpaXVmog6/s8LT8Nj1612lKDZiDaHl0iUC0eyPqC3DBLmPzHKEQDFG2P5rx253Feu/MA9ffjYPZBfj78s39h3Pak7eS6cvnuwHd8d+A7/23jQuL8fYF97dGkHrge+f5fjY9Tbcyys9XCsTr+22s0GDH0O4/982fS+q6H0d5/H91mQ5s3r2Z76AaOHzaba36RnKhXxxTw9u3bl02bNvHUU0/RsWNH1q9fT69evfjuu++qFFhOnDiR66+/ntNOO40+ffqwZMkS9u3bx6233gqoUoODBw/y0ksvAaobxJYtWzjzzDNJT09n3rx5/Pbbb/5uEQAzZszgrLPOonPnzmRlZbFgwQK2bdvGc889dywPtcnKd+X7F25VJpB1epwV32kxkdZItVjraCutsgLZWEcsEdYIeXtTiCZI0zRahbeiVXgrhnVR3XQ8Xg9/pf0V1BXij5Q/OJJ7hPV/r2f93+v9t28f2d6fBe7RvAcnNTupyb5j02CFhqnFXUeOqIVtYWF1PqxB0zRMFw7g8FwPzSdORXvjDbxWK4bHH6/ZANxuV50bkpJUtwppadpkVDngdblc3HzzzUydOjUo0DwWI0aMIDU1lZkzZ5KYmEj37t1Zt24dbdu2BSAxMZF9ATO1PR4Pc+fOZdeuXZjNZs4//3w2bdpEu3bt/PtkZGRw8803c/jwYSIiIjj11FP56quvOOOMM6p1rg2dy+PydxkIHGCQknc0kM1PCSolOJY6WIfZERyw2gNKCIoFstH2aCxGWaQihCjJaDByQuwJnBB7Ald3vxo4Wg+c/Lu/DOLnwz+zN2MvezL2sCdjD2//8TYQUA98tDdwz+Y96RTdSV4w1za7HZo3h5QUFfSGh6kOD3VMHzKYlEI3sfdPx/Dyy3hsVozTZ9Rs0Cvjh5ukY+rSEBkZyU8//USHDh1q45zqXVZWFhEREZVa9VcTXC4X69ato8MZHYgJjQFK1sFWFMhmFGRU+bhmgzlomEGMo/xA1mGWjhUNhb8P7znSh1dUTmN8zqTnpwfVA/98+GdS8lJK7OerBw4cktEiTOqBq6vU54zbrYLejAwVGNZTMGh7412iH5oFQOGd47FMfqhmD6DravxwTIyMH64CXzwzdOjQOlu0Vtl47ZieqcOHD+fdd98N6mwgjt1Lv7zEqr2rKEwuJMuZ5S8jOJY62ChbVFCw6u8Be/TzwFrYcGu4/IcghGiwouxRnNvuXM5tdy6g6oEPZR8KWhD3S9IvpdYDN3M0o0fzHkXjkuN7EGWXhUjVZjJBXJz6mJqq3vIvZdF3bSv4z2VkFBQQOespLAsXkW8xY7vnvpr7P03GDzc5xxTwdurUiVmzZrFp0yZ69+5NSEhI0PV33XVXjZzc8WJr4la+zvgaMkpeF2GNCFqsVd5UrkhbpLytJ4RosjRNo2V4S1qGt+TiLhcDqh54d9ruoHrgnSk7Sc5LZuM/G9n4z0b/7dtFtguqB+7erLvUAx8Lg0FlPo1Gle31eOqlg0Pe9VejOQuJePIZ7HOfIdtqxn7bnZgMNZR1lvHDTcoxPStefPFFIiMj2bp1K1u3bg26TtM0CXir6NIul1KQWEDnrp1pF9WuqD+sI0bqYIUQohxGg5GusV3pGtuVEd3V2Pl8Vz47kncE1QPvydjD3oy97M3Yyzt/vAOoeuATYk8IqgfuHN1ZEgeVoWmqdZfJBMnJkJNTL10NcseNQssvIHzhC4Q9Noc0iwnHDTdhM9XQYjPf+GFf5wYZRtVoHVPAu2fPnpo+j+PaBe0voGBnAR06F9XwCiGEODZ2s53eLXrTu0Vv/zZfPfC2pKNBcOLPJOcl89uR3/jtyG+8/MvLgFqce0rcKf7ewKc2P5WWYS2l/KssYWEq6E1KUoMbwsPrvG1Zzh03oRUUELZ0FVEznuCI1Yjr6lGEWWooAHc41GPzLWKT8cONUrXz/r41b/LHQAghRENVaj1wziG2JRZlgX31wJsPbmbzwc3+28Y6YlUAHN/TXw4RbT/+pmKWyW6HhASV6c3MrPsODppG9qQ70fILCH15LXEPzeagxUzhpVcQZYusmdHv4eFqEduRI+qx1uTQC1Enjjngfemll3jqqaf8E8y6dOnCfffdx/XXX19jJyeEEELUBk3TaBnWkpZhLbmoy0WAqgf+O/3voEVxO5J3kJKXUrIeOKKdPwvcM74n3eOO83pgq7WobVl9dHDQNLIenoRWUEDIm+/R8oFZ7LOYcA4aQjN7DGZDNTsGaBpERqrHZjarhXuS6GtUjunZOG/ePKZOncodd9zB2Wefja7rfPvtt9x6662kpKRwzz331PR5CiGEELXKaDDSJaYLXWK6MOIkVQ9c4C7g9yO/sz1puz8Q/if9H/Zm7mVv5l7e3fWuuq1mLFEP3CWmy/FVD+zr4GA0qslsdd3BwWAgc9ZDaE4njvc/ps3EGex91szBc88l3h6L3VTNFyQGgyrhSElRZQ0yfrhROaaAd+HChSxevJhRo0b5t1166aWcdNJJTJ8+XQJeIYQQTYLNZCtRD5xRkKHqgQMWxR3JPcLvyb/ze/LvvPLrKwDYTfYS/YFbhbdq2iWABoNq4WU0qpZeXq8qeagrRiMZT8xAcxZiX/8Zbe+exr7nn+DAGb2Js8cSbq7mWHoZP9xoHVPAm5iYSN++fUts79u3L4mJidU+KSGEEKKhirRFck7bczin7TmAqgdOzEkMCoB/SfqFnMIcvj/4Pd8f/N5/2xh7jD8A9l2aXD2wpkF0tAoIjxyp+w4OJhPp8x5Du/1ebF9+S5vbH+Tgi09zoHtn4uwuYmxR1avrlfHDjdIx9+F9/fXXefDBB4O2r127ls6dO9fIiQkhhBCNgaZptAhrQYuwFgztPBRQ0zL/TitZD5yan8qnez7l0z2f+m/fNqJtUBa4ydQDh4WpTO+RI2occVhY3dW9WsykLXySmJsnYN38Ay1vmYRxxbMkdYFCbyHNbNVs+ynjhxudY/oJzZgxgxEjRvDVV19x9tlno2ka33zzDZ9++imvv/56TZ+jEEII0agYNAOdYzrTOaYzV510FaDqgXcm7wwakvF3+t/8m/kv/2b+y3u73gNUPXDX2K7+WmBfPXCNDVSoSw5H/XVwsNlIWzyP6LF3YP1pO/Hj7kZ7aTGp7XScnkLi7c0IMVejr25EhOrckJws44cbgWP67bniiiv4/vvvefrpp3n33XfRdZ0TTzyRLVu2cOqpp9b0OQohhBCNns1k49SEUzk14VRu4AYAMgsy2Z60nW2Ht7H9sFoYl5SbxI7kHexI3hFUD3xy/MlBmeDW4a0bRz2w1aoCQqNRBb112MFBD3GQtvQZYkaPx/LbDuJuvAPt5aVktDRyIPcQcfZYIi0Rx/Z9lPHDjcoxP+N69+7Nyy+/XJPnIoQQQhxXImwRQfXAAInZwfXA25O2k1OYw5aDW9hycIt/v2h7dFB/4J7NexLjaKDDi3ytvEwmFSDa7XXWwUEPCyN1+bPEXn8L5l1/ETvmNrRXXiSneTSHcpMo9LiItUUfW0cNGT/caBxTwLtu3TqMRiODBg0K2v7JJ5/g9XoZMmRIjZycEEIIcbxJCEsgISyBIZ3V/6Ve3cs/6f+oMoijgzJ+T/6dtPw0PtvzGZ/t+cx/2zYRbYoWxMX35OT4k3FU5237mmQ0FnVwSEmp0w4OemQEqSsXEXPtTZj/2UvM6FvRX3kRU1wUyfmpFHoLibPHYjUeQxAu44cbhWMKeCdPnszjjz9eYruu60yePFkCXiGEEKKGGDQDnaI70Sm6E/858T8AON1OdiTvCOoPvDttN/sy97Evcx//2/U//227xgTUAyf0pGtM1/qrB/Z1cDCZVO1rbg6E1E0HB29MNKkrFxN77ThM+w8SM/pWUl9ZijEmiixXDoVeF3G22GMbSSzjhxu8Y3rG//XXX5x44okltp9wwgns3r272iclhBBCiLJZTVZ/PfAYxgCQ5czy1wNvS9zGtqRtHM45zM6UnexM2cmrv70KqFrik+NODhqS0SaiTd3WA4eHq6C3jjs4eJvHkfrSC8SMHIt5z7/E3DCelNVLiIyMIMeVy4HcROI8Mcc2kljGDzdoxxTwRkRE8M8//9CuXbug7bt37yYkJKQmzksIIYQQVRBuDadfm370a9PPvy0xOzEoC7z98HayC7P54dAP/HDoB/9+UbYoTm1+Kj2a9/CXRERZanmSmMNRNI64Djs4eFomkPrSC8ReOw7zrt3E3HgHqasWERoWRoHHyeGCFAq9LprZY6qWCZfxww3aMQW8l1xyCRMmTOCdd96hY8eOgAp27733Xi655JIaPUEhhBBCHBtfPfDgToOBonpg36I4Xz1wekE6n+39jM/2FtUDtw5vTRdTF2bnzKZlZMvaOUGbrV46OHjatiZ15WJirr0Jy287iLnpblKXPYstxIFJM5LqTMfpLaz6SGIZP9xgHdOz6qmnnmLw4MGccMIJtGrVCoD9+/dzzjnnMGfOnBo9QSGEEELUjMB64CtPvBJQ9cA7U3YGBcF/pf3F/qz97Gc/F75yIbPOn8UV3a6onbKH4h0cHI46qYF1d+pA6opFxI66BctP24m+bSKpS+ZjstmIskSQ6crmQG4i8fZmhFvCKn/HMn64QTrmkoZNmzaxYcMGtm/fjt1up0ePHvTr16/iGwshhBCiwbCarP4yBp8sZxZbD2xl+vrp7M7fzd0f382Hf33IExc+QVxIXM2fRPEODh5PnXRwcJ/YldRlzxIzZjzWzT8Qfcf9pC2ag2axEGkJJ8+dz4HcRJp5Cqs2kljGDzc4VarI/v777/noo48ANUpx4MCBxMXFMWfOHK644gpuvvlmnE5nrZyoEEIIIepGuDWcc9qewxNdnuD+PvdjNphZ//d6zl91Pu/+oQZO1ThfB4fmzVXLstycmj9GKVw9upO25Bm8Niu2r74l6p4Hwe0GwGGy4zDaScpPJjEviUJPYeXvODQUnE4V9B69P1F/qhTwTp8+nV9++cX/9a+//spNN93EgAEDmDx5Mu+//z6zZ8+u8ZMUQgghRN0zakbuOP0OPrr2I06OO5mMggxuX3c7N79/Myl5KbVz0PDwo10OTKqDQ20E18UUnn4qaYufRrdYsG/4nMgHHlEZWsBiNBNhDiPdmcnB3MPkuvIqf8cREZCdrVqweb21dPaiMqoU8G7bto3+/fv7v16zZg1nnHEGS5cuZeLEiSxYsIDXX3+9xk9SiOOZruu4PC7yXfnkFOaQnp9Oen46ABn5Gf6v0/PTSctPI6MggyxnFjmFOeS58sh35eN0O3F5XHi8ntrJzAghmrRuzbrx/jXvM6nvJEwGE+t2r+P8Vefz/p/v184BfR0c7Ha1mK0OgsXCs88kbeET6CYjjvc/JmLqo/7jGg1GoiwROD1ODuQeIsOZWbm/pYHjh9PSavkRiPJUqYY3PT2d+Ph4/9dffvklgwcP9n99+umns3///po7OyGOI7qu4/KqoNTtdeP2utHR0dAwG8wYDUYcJgc2mw1N19jDHtpGtsVgMuDVvf6L77ZurxuP14NX9+LxenDpLv8+OiX/UBs0Q9BF07QS24QQxy+z0cw9Z93DgA4DuOeTe9iRvINbP7iVD7p8wGMXPFbzY41ttuC2ZWFhtd7b1nn+OaTPe4yoCVMIefM9dJuNrKn3gaahaRrhljDy3fkczD2M01NYuZHEMn64QahSwBsfH8+ePXto3bo1hYWF/PTTT8yYMcN/fXZ2NmazucZPUoimpLSgFEBDw2QwYTQYCbWEYjPZMBvNmAwm/8UXdLpcLgDsZnuFv3Ne3Yuu60FBcfFLYJDt9rrx6B68Xi8ePEH7FVc8ONYoGSTXaTN7IUSt6x7XnQ9HfsiC7xew4PsFfPDnB3y3/zsev/BxhnYeWrMHq4cODgWDLyTjiUIi759G6MtrwWYl6767/D117SY7JoOpaiOJZfxwvatSwDt48GAmT57ME088wbvvvovD4QjqzPDLL7/4+/IKcbzzBZEevSiYBFUT5wtsw63h2Ew2TAYTZkNRcFuTQaJBM4AGRiqfGfEFyDrlB8qBQbLb60bXdf/HsrLJgZljX4As2WQhGheL0cKkvpMY1HEQ93xyDztTdnLT+zdxWdfLmHXBLKLt0TV3sOIdHLzeWu96kH/pULT8AiKnPUroiy+h22xk33WL/3qzwUykNZxMVzaFXhfx9maEmisYvCXjh+tVlQLe//73v1x++eWce+65hIaGsmrVKiwBP7Dly5czcODAGj9JIRoyj9cTVIrgC/J8ga3ZYCbUGorVZPUHtGajGaNmbLDZT03TMGpVf+swMBguK6tcVjbZrbuDAuziimeNJZssRP07Of5kPhz5IfO/n89zW57j3V3v8u3+b3niwicY1GlQzR3I18HBZILkI6qDQ0jt9rfNu/pyNGcBEY/OJezZJeg2Kzk3j/Ffb9AMRFmKRhI3s0YTbYsq/++QjB+uN1UKeJs1a8bXX39NZmYmoaGhGIv9oN544w1CpcGyaKKKZzO9uhcNLSiwDbeGBwW2vsvx4liys2UFxsWzy2VlkwOD6/KyyeVllIUQx85qsvLA2Q/4s71/pv7Jjf+7kcu7Xc7M82YSZa/BaWPh4SpITE5WHRzCwmp1fG/u6JFoBU7C5z5L+JyF6DYbuaOuDton1BxCgcdJYn5yxSOJZfxwvTnmwROliY6uwbcwhKgHviAqsBTBF9j6glebyYbNZMNitASVIlS4cEGUypdNrkrJBVBmmUVgAF1aNtm3kM9/Qf18A5W3gC8waBZCFOnZvCcfXfsRT3/3NIt+XMTbO9/m233f8sSAJxjQYUDNHSgkpGTQa6i9F645t9yAVlBA2HMvEvHfp9BtVvKuGh60j81o9Y8kViUOsdhMZZRdyPjhenH8pJ6ECBAY2AbW1wL+wNZusmM32YMWjpmNZskINhA1mU0unlkuLZvs8rpK7Af4g+WyssnFg2YhmjKbycaUflMY1GkQEz6ewN/pfzPm3TFcddJVTD93OhG2GupQ4OvgEBj01mJ5QPZdt6LlFxC6/GUipj6KbrOSf0nwAj2TwRQ0kjjOHlv2SGIZP1znJOAVTZrvrfDirb4MGPxZ2RBziMrYmiyldkQQTUdNZ5ODLl5vqdnkwHZwXopqk32BckXt4DQ0ySaLRqdXQi8+ue4T5myawwtbX+D131/nq3+/Ys6AOZzf/vyaOYjZDPHxqq43PV317K2thWCaRtYDE9AKCgh59U0iH5iObrVSMKh/sd00Ii3h5LryVNDrcRFtiyz9/xMZP1ynJOAVTYJX96rBCkfLEDxejz+wNRtVD9swa1itd0QQTVNtZZO9ujeoi0dF2WTAHwCXl02WkgvRENjNdqaeO5XBnQdzz8f3sCdjD9e9cx3XdL+GaedOI9waXv2D+Do4mExF08xqK3DUNDKnPYBW4MTx9vtETXyQtGfn4Dy/X4ldQ8wOnJ5CDucn4/Q6ibPHYjaU0kIyNFTV8/o6N5gkLKst8p0VjUrxmkzfan6DZvAHsiHWEH9gG3iRAEDUpdrMJgf+Hni8nlKzyfrRfz66W32e58rDbrAfV4spRf06vcXpbLh+A098+wQv/vQir/32Gl/++yVzB87lnLbnVP8ABoOqgzUaISUZcnNVnW9tMBjIeHQqWoET+7r1RN95P6lLnqGw7xkldrUaLZg0IxnOLAo9LuIdzXCY7CXvMyJCZaiTk1XGuhbrkY9n8hdPNEiB/5n7sl3FOyKEWcKwmqwlhjMI0ZjVVjbZWejkH/7BoBnIK8zDrbsxaAasRisWo0UWXYpaZTfbmX7edIZ0GsLETyayN3Mv17x1Ddedch1Tz5lKqKWaNay+Eb4mk2r5VZsdHIxG0p+aCU4n9k+/JPq2e0hb9iyFp51acleDkUhLONmuHA7kHCLOHkuEJTw4ARM4fthsVhlrUeMkOhD1Rtd1PLonqBTBN5vcqBkxG81YTVYiTBH+jgi+YFf+cxaiSGWyyS6Tms7XNqItukHH6XGS78onpzCH3MJc3Lobo2bEYrRIACxqzZmtzmTDqA3M/no2y7ct5+VfXuaLvV8wZ+Ac+rUpWRpQZSEhweOIw8NrJ2NqNpP+zONot03E9vV3RN90N6mrFuM65aQSu1ZqJLGMH651EvCKWleZjgg2kw27yR4U2EqrLyFqnqZpWEwWrCYr4dZwmunNKPQU4nSrADjXlUtOYQ4e3eN/gWk1WWURp6gxDrODWRfMYkhnle3dn7Wfq9+8mtE9RvNQv4cIsVSzHMFuV6UBKSm128HBYiH92aeIvvlurN9vJWbsHaS89ALubl1KP62KRhLL+OFaJX/BRI3x6l4KPYXku/LJdmaTkZ9BWn4aGQUZON1OAELMIcSHxNMqvBXto9r7L20i2tAspBkRtghCLCFYTVYJdoWoAwbNgM1kI8IWQfOw5rSPak+7yHa0Dm9NhFVlmbIKskjPTyfLmUWBu6DUSXhCVFXf1n35dNSnjO4xGoBV21dx4eoL+W7/d9W/c4tFBb3R0ZCdDS5X9e+zFLrdTtripyk89RQMmVnE3DAe0+49Ze7vG0mc5crhQG4iOa7c4B0cDrXwLikJCgtr5ZyPV5LhFVVWfOqVx+sBKBrOYDQFdUQILEWQhWNCNGwGzYDdbMduthNhi8Dj9agMsMdJbmEu+a58Ml2Z6OiYDWZ/CYT8botjEWIJ4bH+jzGk8xDu/eRe9mXu48o3ruTGnjcypd8UHOZqZDkDOzikpKjsaS10cNBDQ0hduoCY0bdi+f0PYsbcSsorL+Jp27rU/X0jibOPBr1xthiirJFFv0NhYTJ+uBZIhleUyeP14HQ7yXPlkVmQSVp+Gun56WQ7s3F5XBg0AxHWCBLCEmgT0aYoYxvZnhZhLYi2RxNuDcdhdsh/iEI0UkaDEbvZTqQtkpbhLf2/5y3DWuIwO3B73WQ61d+HnMIcnG6nvxZfiMrq16Yfn476lGtPvhaA5duWM+ClAWw5uKV6d+zr4BAfDx636uBQC/TwMFKXP4erS0eMR1KIGX0rxkOJ5d4mzByKxWDmUN4RDucdKSr3Cxw/nJIC8vtUIyTDK4Iytb5WXzo6Jq0oOxtqC8VqsvqnjZkMJoyaUYJYIY4zRoMRh8GBw+wgyh6F2+vG6Xbi9Dj9AW+eKw8Ai9GC2aiywEJUJMwaxpMDnuSizhcxacMk9mbu5fK1lzOu1zgeOPsB7OZSWnpVRh11cNCjIkldsYjY627GtOdfYkapTK83vlmZtykaSZwRPJJYxg/XOMnwHid8C8cK3AXkFOaQUZBBen466fnp5BXm4fV6sRgtxDhiaBneknaR7WgX1U7V80W1o3lYc6LsUUGlChLsCiFMBhMhlhCi7dG0iWhDu8h2tI1sS/PQ5liMFgrdhaqWPz+D3MJcXJ7aqaUUTce57c7l01Gfck33a9DRWfrTUga+PJAfD/1YvTv2dXCw21UHB2/N16J7m8WSsmox7lYtMe07QMyY2zCkpZd7GzWSOJxct5rOllWYra4wm9W5JiVBTk6Nn+vxRjK8TUxgRwRfyy8dvai+9mhHBJvJprIvARPHZJGYEKK6zEYzZqOZUEsoMY4YCj2FFHoKKXCpF9u+F90AVpPV35lFiEDh1nDmDJzD0M5DuW/9ffyT/g/D1w7n5l43M6nvpGPP9vo7OCTXWgcHb/N4UlctJnbkOMx/7yFmzHhSVr+AHlH2ZLkyRxLbbOB2y/jhGiAZ3kZK1/Wgjgjp+eklOiI4TA7iQ+JpHd6adpHtgjoixIXEEWmLlI4IQohaZTFaCLWEEhsSS9vItkF/g4yakXxXvv9vV74rP6htoRAXtL+AT0d/yn9O/A9e3cvzW59n8CuD+Snxp2O/U4sF4uJVmUAtdXDwtG5JykvP44mNwfzHn8SMvQOtElnaELMDu9HG4fxkDucdweV1qfHDTqcKet3y+3Gs5GV1A1dRRwSjwUioJRSbyVZi4pj0zRRCNCSapvm7OoRZw4h1xPo7QBS4C8h2ZvunwMkQDOETaYtk/uD5XNTlIu7fcD+703Zz6ZpLGX/aeCb2mYjVZK34ToozmaBZM/UxNbVWOjh42rcldeUiYq67GcsvvxN98wTSXlyI7ig/O+0bSZx2tK43zh6LQ8YPV5t8xxoQXdfJc+WR5cwiLT+NtPy0oI4I4dZwEsISaB3R2p+t7RDVgZbhLYlxxAR1RJBgVwjR0Gma5h+AERcS518z0Dq8NVE2tUgnpzCHtPw0spxZ5LvypQfwcWxAhwF8NuozLu92OV7dy7M/PMvgVwaz/fD2Y7tDg0H16Y2PV5nTWujg4O7SibQVz+ENC8X6489Ej79XZWsrYDQYibJEkO8u4EDOITJd2ejh4So4T0ur8fM8HkhU1IBYjBZMmolIayQtw1rSNqJtUSlCsVZfdrMds1H62gohmg4ZgiEqEmWPYuGQhSy7ZBmxjlj+TP2TYa8N44lvn/CX81WJr4ND8+YqAM7OrvE2YK6TupH64kK8DjvWTd8Tfef9UFhxGYWmaURYwjBqBg7mHCbZlYHHblOdJjIza/QcjwcS8DYgbSPbluiIYDfbpSOCEOK45BuCEWFT/b59CYCW4S0Js4Th9XrJKsjyvxsmPYCPH4M7Debz0Z9zaddL8egeFny/gItevYhfk349tjsMDVVBr9WqFrPVcAcH16mnkPbCfHSrFdsX3xA16aFK1+PaTXYcJjtH8lNJ9GRQaETV8+bl1eg5NnUS8DYgslJZCCHKVtoQjHZR7WgZ1pIQcwger8ffclGGYDR90fZoFl20iCUXLyHGHsPOlJ1c/NrFzN00l0LPMYzltdtV0BsWejTo9dTo+RaeeRppi+agm83YP/6UyMkzKh1YW4xmIi1hZLqy2a9nkOPMlvHDVSQBrxBCiEbJaDD6B2C0imhFu6h2tItsR0JYAjaTDbfXHRQAF3oKJQBugi7qchGfj/6cizpfhNvrZt7meVz86sX8nvx71e8ssINDVs13cHD260v6M4+jG404/reOiGmPVbqEwjeS2O11c8CQQ3r6IfSkJLXgTlRIAl4hhBBNQnlDMKxGK4XuQtIL0mUIRhMU44hhybAlLL5oMVG2KH5P/p2hrwzl6c1PV/3n7Ovg0KyZKhsoKKjRcy248DzS5/wX3WAg5PV3CH90TpXqhsPMoZgNZg6aC0g6vBv3kcMyfrgSJOAVQgjRJAUOwPCtkWgX2Y64kDjMBjMF7oKiiZOuPOkB3ARc0vUSPh/9OUM6DcHtdTNn0xyGvTaMP1L+qNodFe/gUMP1sgUXDSTjsakAhL60hrC5z1YpaLWbbIRbwkgxuzh0YCcFKYdr9PyaonoPeBctWkT79u2x2Wz07t2br7/+utz9n3vuObp164bdbqdr16689NJLZe67Zs0aNE3jsssuq+GzFkII0djIEIzjQ7OQZiwdtpRnhzxLpDWSX4/8yuCXB7Nwy8Kq/Uw1DSIjVV0v1HgHh/zLLyFj+mQAwpasJHTxsird3mQwEemIJsfo4cDeX8hOS6yxc2uK6jXgXbt2LRMmTOChhx7i559/pl+/fgwZMoR9+/aVuv/ixYuZMmUK06dP5/fff2fGjBncfvvtvP/++yX2/ffff5k0aRL9+vWr7YchhBCikfENwQizhtEspBntIlX2t01EG2IdsWiaRl5hHmn5aWQWZJLvyvcP/hENn6ZpDO82nM9Gf8aADgNweV08/s3jXPrapfyZ+mfV7iw0FBISaqWDQ97I/5A5+R4AwucvJmTZ6ird3qAZiAxrhq7Bgb2/kpJ2QFr1laFeA9558+YxduxYxo0bR7du3Zg/fz6tW7dm8eLFpe6/evVqbrnlFkaMGEGHDh24+uqrGTt2LE888UTQfh6Ph2uvvZYZM2bQoUOHungoQgghGrHiQzDaRbajXZQKgGUIRuMVHxrPiktX8MzgZ4iwRrAtaRuDXh7Eoh8WVe0FTC12cMi98TqyJtwGQMQT83G88nqV7yMkohk2Nxzet4PDGQekPr0U9dYHq7CwkK1btzJ58uSg7QMHDmTTpk2l3sbpdGIrNvrPbrezZcsWXC4XZrMZgJkzZ9KsWTPGjh1bYYmE736dAZNPsrKyAHC5XLhqYcZ2cb5j1MWxRNMgzxlRVfKcqTojRuwGO3abnShrFC6PC6fHSb4rnzxXHukF6ejoGA1FY5Cb0pRLj9sT9LExG95lOH1a9GHyZ5P5bO9nPPr1o3z010fMHTCXjlEdK3cnBiNEx4KuQUYmOBxwNO6orsybb4C8fMKXrCRyxhN4rFbyhg+r0n2YwqIIzUwnZf9f5Dd3EhfaHJu5ZsclV6Su/85U5Tj1FvCmpKTg8XiIj48P2h4fH8/hw6UXXw8aNIgXX3yRyy67jF69erF161aWL1+Oy+UiJSWFhIQEvv32W5YtW8a2bdsqfS6zZ89mxowZJbavX78eh8NRpcdVHRs2bKizY4mmQZ4zoqrkOSOqavumYxzd2wDdGXEnJ7U+iWUHl/HT4Z8Y9PIgRiaMZFizYRg1YxXvLadmT27IpXQ/lELHDz4g+uH/sic1l4PHWpa5Zwewo0ZPryrq6u9MXhUWE9b7pIPiE8R0XS9zqtjUqVM5fPgwZ511FrquEx8fz5gxY3jyyScxGo1kZ2dz3XXXsXTpUmJjYyt9DlOmTGHixIn+r7OysmjdujUDBw4kPDz82B5YFbhcLjZs2MCAAQP8WWohyiPPGVFV8pypXR6vh0JPIU6Pk7zCPArcBf7hByajCbPBjMVoaVRTMz1uD9s3badH3x4YTVUNBhuu3vTm2uxreeDTB/hy35esPLSSX/VfmTdgHu0j21fuTnQdsjIhJRWMBrDXUHLsyWnkOMyEvv4OvefPp1275uRfeF7V7sNVCAVO8qLDKHRYiXXEEmWLwmio/Z9hXf+d8b0jXxn1FvDGxsZiNBpLZHOPHDlSIuvrY7fbWb58OS+88AJJSUkkJCSwZMkSwsLCiI2N5ZdffmHv3r0MG1b0NoD3aHG5yWRi165ddOxY8q0Lq9WK1Wotsd1sNtfpfwx1fTzR+MlzRlSVPGdqhxkzNorePnZ73TjdTgo9heQU5lDgLiDbna32Narg12wwN4oA2GgyNqmAF6B1VGteueIVXvvtNWZ8OYOtiVsZ9OogpvzfFG489cbKlabExKiFbEeOQF6uWtxWAz/PrJkPYnAW4njvQ2ImTiFt8dM4z+lb+Tsw2kDXCcvOp9ARQqozFY/mIS4kDovRUu3zq4y6+jtTlWPUW7GRxWKhd+/eJdLeGzZsoG/f8n+wZrOZVq1aYTQaWbNmDRdffDEGg4ETTjiBX3/9lW3btvkvl1xyCeeffz7btm2jdevWtfmQhBBCCKBoCEaUPYrWEa39XSCKD8FIz0+XIRj1RNM0Rp48kk9Hfcr/tfk/CtwFPPLFI1z5+pXszdhbuTvxdXCwWGqug4PBQMbsaeQP7o/mchN9+yQsm3+o2n3Y7aDrWNIyiTQ4yCjIYH/mfnILc6t/fo1UvZY0TJw4keuvv57TTjuNPn36sGTJEvbt28ett94KqFKDgwcP+nvt/vnnn2zZsoUzzzyT9PR05s2bx2+//caqVasAsNlsdO/ePegYkZGRACW2CyGEEHXFbDRjNpoJsYQQ44ih0FNIoaeQAleBPwOcU6hqQq0mKxajBZOh3qsOjwutwlux5oo1rP5lNbO+msX3B7/nwpcu5KF+DzG65+iKs72+Dg4pKSroDQ9TC9yqw2Qifc6jaM5CbJ9/TfSt95C6/DlcvXpU/j5CQiAzE0NqGlFxcWR78jiQdYC4kDgibZGN4t2FmlSvy0lHjBjB/PnzmTlzJj179uSrr75i3bp1tG3bFoDExMSgnrwej4e5c+fSo0cPBgwYQEFBAZs2baJdu3b19AiEEEKIqpMhGA2LpmmM6jGKT0d9St/Wfcl35/Pw5w9z1RtXsS+z9NkAQaxWNZUtMhKystV0tuqymElb8AQFZ5+JIS+fmHF3Yv5tZ+Vvr2kQHg5ZWWjp6YRbwjAZTBzKPsSR3CPHXV9pTddlAHNxWVlZREREkJmZWWeL1tatW8fQoUOltk5UijxnRFXJc6bx0HXdvwDOl/ktdBfi1t0YtaIWaLW9CMnj9vDTVz/R65xeTa6Gtzxe3ctL21/iv1/9l3x3Pg6zg4fPeZjrT7m+4myv1wtpaZCaCjabCoSrScvPJ3rcXVh/+AlvZAQpq1/A3bVz5e/A7YbcXH9A7vK4yC7M9vectplqrnVZXf+dqUq81nQaBgohhBBNQPEhGO0j2/uHYETbo4GiIRi+KXAyBKPmGDQDY3qOYeOojZzV8izyXHk8+OmDXPPWNRzIOlDBjQ1qMVtcHBQWQhXaZpVFt9tJe2E+hT26Y8jIJGbMeIz/7K38HZhMKvhOSYHcXMxGM5G2SHKcORzMOki2M7va59gYSMArhBBCNGCapmEz2Qi3hhMfGk/7qPa0j2pP6/DWRNoiAcgqyPJPgStwF0gAXAPaRbbjjaveYOZ5M7GZbHyz7xv6v9SfV355hXLfHNc0iIpSdb0AOdXv16uHhpD64kJc3bpiTE0jdvRtGPdVEHwHslrVeSUnQ0GBGklsj8SjeziQdYDUvNQm/5yRgFcIIYRoRAyaAZvJRoQtgoSwBNpFtqN9VHtahbcizBKG1+v1B8DZzmycbmf5AZook0EzMLbXWDZcv4HTW5xOTmEO92+8n+vevo6D2QfLv3FYmOrgYDJBZqbq3VsNekQ4qSuew9WpA8akI8SMuQ1DYumDukoVEqKyzikp/hrjUEsoVpOVxJxEknKSmnS3EAl4hRBCiEbMaDBiN9uJtEXSMrwl7aNUCUTLsJaEmEPweD1kFGSQnp9OTmGOBMDHoENUB9666i2mnTsNm9HGF/9+Qf9V/Vn729ryv5d2uwp6j3ZMoJoLxbzRUaSuXIS7bWtMBw4RO/o2DMkplb+DsDCVcU5J8bdQ8717kJqfysGsg+S78qt1jg2VBLxCCCFEE2I0GHGYHUTZo2gV0Yp2UaoHcIuwFthNdtxed1AAXOgplAC4EowGI7f0voVPrv+EXgm9yC7MZuL6iYx6dxSJ2Yll39BqVeUNNdTBwRvXjNRVi3G3TMC0dx8xY8ZjSEuv3I01TbVNy8hQl6M/d5PBRJQtijy3al2WWZDZ5J4TEvAKIYQQTVhlhmD4AmAZglGxTtGdeHfEuzzc72GsRiuf7fmMC166gDd2vFF2kGgyqYVsMTGqY4LTWa1z8LRIIHXVYjxxzTD/9TfRN96OllXJxWcGo8o4p6RAdtFtNE3z9+c9mHWQlLyUJlXXKwGvEEIIcRwJHIDh6wHcNrIt8SHxmA1mCtwFpOenk5GfAdCkgp6aYjQYue302/j4uo/pGd+TLGcWEz6ewA3v3UBSTlLpNzIYIDYWmjVTtbT51Ssd8LRprYLe6CgsO3YRM+5OtJxKTlIzm9UlObnEeTjMDhwWB0m5SSRmJ1LoKazWeTYUEvAKIYQQxzGz0VzqEIzYkFhAdYDILcxtcm9x14QuMV1475r3mPJ/U7AYLWz4ZwMXrLqAt3e+Xfr3S9MgOlqVOHi91e7g4O7YntSVi/FGhGPZ9ivRt96DVtlA+uj4YZKTVQAewGK0EGGNIL0gnYNZB5vESGIJeIUQQggBqLe1LUYLYdYwYh0q4G0R3gKDZiA9P73JLmiqDpPBxB1n3MFH137EyXEnk+HM4M6P7mTc/8aRnJtc+o0COzhkZVWrg4P7hM6kLn8Ob2gI1i1bibp9UokAtkwhIapXcEoKeIIX1BkNRqJsUTg9Tg5kHSCjIKNRv+iRgFcIIYQQZQq3htM2si0JYQl4dS/p+elN5m3umnRC7Am8f8373Nf3PswGMx///THnrzqf93a9V3qg6HCooNfhqHYHB9fJJ5K2dAFeuw3bN5uJunsyuCpRix0wfpi0tBKBt6ZphFvDMRlMHMw62KhHEkvAK4QQQohymQwmYhwxqtTBEUuBq4DMgkzc3up1HGhqzEYzE86awIfXfshJzU4ivSCd8R+O55YPbiE1L7XkDaxWNfI3IqLaHRwKe/ck7fmn0S0W7J9+SdSkqSWytqUyGCA0VAW8mZml7mI32wm1hJKcm8yh7EM43dVbdFcfJOAVQgghRKVYTVbiQ+NpG9mWcGs4OYU5ZDmzZGFbMSc1O4kPRn7AxLMmYjKY+PCvDzl/1fl88OcHJXc2m4s6OOTkVKuDQ2GfM0h79il0swn7RxuIfHCmv99uuQLHD5dRV2w2mom0R5LlzOJA1gFyCqs/Qa4uScArhBBCiCqxm+20CGtBm4g22E12MgsyZWFbMRajhXv73suHIz+kW2w3UvNTueWDW7jtw9tIy08L3tloVB0c4uJUwFuNDg7O8/6P9Hmz0Y1GHO98QMSMxytXI+wbP5ySAgUFpe5i0AxE2aPUSOLMxjWSWAJeIYQQQlSZpmmEWkJpHdGaVuGtZGFbGbrHdWfdteu4+8y7MWpG/rfrf5y/6nw++uuj4B19HRwSEqrdwaFg0AVkPDkDXdMIee0twmc/Xbmgt5Txw6UJtYRiMVk4nHOYpJykRlHaIgGvEEIIIY6ZQTMQYYuQhW3lsBgt3H/2/bx/zft0ielCSl4K494fx53r7iQ9v9iUtBrq4JA/bAgZj04FIHTlK4Q9s7hyNyxl/HBpbCYbYdYwUvNTOZB5oMG/0JGAVwghhBDVJgvbKtajeQ8+vvZj7jj9Dgyagbf/eJsLXrqA9X+vD97R4VC9eqvZwSH/ykvJmHo/AGGLlhG6eHnFNypj/HBpio8kzi6s5LS3eiABrxBCCCFqjCxsK5/VZGVKvym8d/V7dIruxJHcI9zw3g3c/fHdZBRkFO1os9VIB4e860eQef/dAIQ//RwhK1+t+EZljB8ujW8kMcDBzIMADbKWWwJeIYQQQtQ4WdhWvl4Jvfjkuk+47bTb0NB4c8eb9F/Vn0//+bRop+IdHCo7UKKY3HGjyLrzFgAiHpuLY81bFd+onPHDpQmxhGAz2wBweSrRA7iOScArhBBCiFohC9vKZzPZePich3nn6nfoENWBw7mHGfXuKCZ+MpEsZ5baKbCDQ0HBMXdwyLnjJrJvGg1AxCOzsb9TSou04soZP1was8EMgE7De1EjAa8QQgghapUsbCvf6S1OZ/1167m5981oaKz9fS0XrLqAL/Z+oXbwdXBo3lwtJMs9hg4Omkb2pDvJuW4Emq4TOWUGto82VHw7//jh5MoNsmigJOAVQgghRJ0obWFbRn6GLGxDlYA8cu4jvD3ibdpFtiMxJ5Fr376W+zfcT7bzaB1teLjq4GA8xg4OmkbWw5PIvfJSNK+XqHsfwvrZVxXeRo0fzi51/HBjIQGvEEIIIepU4MK2CFuELGwLcEbLM9hw/QbGnjoWgFd+fYX+L/Xnq3+PBqa+Dg52+9EODlX8nhkMZM56iLxhg9HcHqLvvB/rt5srvE1F44cbOgl4hRBCCFEvZGFb6RxmBzPPn8mb/3mTNhFtOJh9kGveuobJGyerkb42mwp6IyJUpreqpQZGIxlPzCB/4AVoLhdRt03E8sNP5d+mEuOHGzIJeIUQQghRb2RhW9n6tO7Dxus3MqbHGABW/7KaC1+6kG/3fRvcwSE7u+odHEwm0uc9RsG5Z2MocBJ9892Yt/9a/m1844eTk8scP9xQScArhBBCiHonC9tKF2IJ4dH+j7L2yrW0Cm/F/qz9XPXmVTz82cPkeZ3BHRyqGoRazKQtfBLnWadjyM0jZuydmHb8UcEJhYDLVeH44YZGAl4hhBBCNBiysK10/9fm//h01Kdcd8p1AKzYtoILX7qQzQe/L+rg4HFXvYODzUba4nk4e/XAkJVNzA23Y/rr7/JvU8nxww2JBLxCCCGEaHBkYVtJoZZQnrjwCV674jVahLXg38x/ufL1K5n2+TTy7WZofrSDQ3Z2lbop6CEO0pY+Q2H3EzGmZxAz5jaMe/eVfYMqjB9uKCTgFUIIIUSDJQvbSjqn7Tl8OupTRnYfiY7Osp+XMWD1AH7I2KEyvTabWsxWheyrHhZG6vJncXXtjDE5ldhRt2I8cKjsG1Rh/HBDIAGvEEIIIRo0WdhWUrg1nKcGPsXq4atpHtqcPRl7GL52ODO/f5L82EhVdlDFDg56ZASpKxfh6tAO4+EkYkbdiuHwkbJvUMXxw/VJAl4hhBBCNAqysK2kC9pfwGejPuOqk65CR+eFrS8waM1FbPUeVLW9OTlV6uDgjYkmdeVi3K1bYjpwkJjRt2JISS37BlUcP1xfJOAVQgghRKMiC9uCRdgieHrQ06y6bBXxIfH8nf43l71xOY/uXEJBVLjKvlahg4O3eRypq57HnRCPec+/xNwwHi09o+wbhISoY6SmVP/B1BIJeIUQQgjRKMnCtmAXdriQz0Z/xhXdrsCre1m0dTGD113DNmPy0Q4OuZW+L0+rFqSueh5PsxjMu3YTM/YOtLJqdTWtqHMDNMhFbBLwCiGEEKJRk4VtRSJtkSwYsoDllyynmaMZf6X9xbD3r2H2v6/ixKPqeiv5ffG0a6OC3qhILL/tJOamu9Fy80rf2WCAEIf63NXwShsk4BVCCCFEoycL24IN6jSIz0Z/xvAThuPVvTy77QWGfn0LvxT8C5mZle7g4O7UgdQVz+END8Py03aib7un7PII7WhY2QBfaEjAK4QQQogmQxa2FYm2R/Ps0GdZOmwpMfYY/kj7k4s/HctT/75KYUZqpTs4uE88gdRlC/GGOLBu/pHo2+9r0AvUSiMBrxBCCCGaHFnYVmRo56F8PvpzhnUZhkf3MP/3ZQzdfCe/HfxZjQmuBFePk0lb8gxemxXb15uIuufBSt+2IZCAVwghhBBNlixsU2IcMTx/8fMsvmgxUbYodmb8xUXf3s687c/jyqvcOOLC03uRvngeutmMfcPnRD7wSJX6/NYnCXiFEEII0eTJwjblkq6X8PnozxnaaShu3c3cP1dy8efj2HHol0rd3nn2WaQtfBLdZMTxwSdEPvzfKk10qy8S8AohhBDiuCAL25RmIc1YMmwJi4YuItIWyW+ZfzH0y5t4Zusi3J6KyxScF5xD+txH0Q0GHG/9j4hZTzXIhWqBJOAVQoj/b+/eg6Os7z2OfzabzZVcIJzEkIRwsSTRgDkkoCC2oNy0YOFgD5QRxaJtijeaOhZLbaWFxhu0dRTGIDGE4gB2lM5kqJrGFBXOaSAQOwwYkcET5BIKJNmQQBKyz/nDw56muS7J3p59v2aYcZ/n9zy/7y9+xe/+8nx3AQQUGtu+Lv6/k/4dlT9YrpmjZ6rNuKoXqzdr7p8fUHXdsV6vv3L3DNU//5wMi0WR23Yq+sXf+3TRS8ELAAACEo1tUnxkvArvLdQrs19RTGi0/m7/XLPfX6JXDxf2+nO4PO/balj9M0nSoM1bFbux0BMhXxcKXgAAENACvbHNYrFowU0L9OGD5bprxJ1qdbQp/++vad4HD+lYw4ker21e9B9q+NlPJEmxGwt14zvveCJkl1HwAgAAiMa2GwbdoC3zi7V+xjpF26J06OIRzfrzYm08Uqx2R/efxtC0dLHseY9Kkm4uLpZ193ueCrnPKHgBAAD+T6A3tlksFi0cu0hlD5ZpWvIUtThatabq95r/l4f1hf3Lbq+7lPt91f/gQZ2aPFnt0+/0XMB9RMELAADwLwK9sW1YdJK2fne7Xv7mGg0KjlDl+b9r1p8Xq+Czbd3u9tY/9ogO/OQnUkiIh6PtHQUvAABANwK5sc1iseh7OQ/pw0Xv6ZvxE3WlvUWrD67XfWU/0InGk11dIFmtng+0Dyh4AQAAehHIjW1J8aP11ne364WcZxRpDVfFP6o0ffdCFVZv95v1U/ACAAD0UaA2tllCQ3X/5FyVzXlbt8f9u660t+jZypf0n2W5+p9LX3k7vF5R8AIAALggYBvbgoOVMvIWbf/2m1qb+aTCrWH6r3OVmr57kYo+3+nTu70UvAAAANchIBvbgoIUFDdUSyf+QGVT39SkuCw1X72sVQde0JKPV6i2pdbbEXaJghcAAKAfAq6xzWKRYmOVOnq8dk75vX6d+YTCrKHa949KPVn9pPac/NjbEXZCwQsAADAAAq6xbdAgBQ1L0vfTv6e/3PGGcuLGKcIaoXH/NtbbkXXi9YJ3w4YNGjlypMLCwpSdna2PP+75XcFrr72mjIwMhYeHKy0tTcXFxR3Ov/POO8rJyVFsbKwiIyOVlZWlrVu3unMJAAAATgHV2BYeLt1wg0bekKbt2c/rNzf+RoPDYr0dVSfB3px8x44dWrFihTZs2KDbb79dr7/+uu6++24dOXJEw4cP7zR+48aNeuaZZ7Rp0yZNmDBBFRUVeuSRRzR48GDNnTtXkjRkyBCtWrVK6enpCgkJUUlJiR566CHFx8dr1qxZnl4iAAAIQNca2yJsEWpsadT55vOqu1yncFu4wm3h3g5vYIWESPEJsjoM3XDON59f9uoO7/r167Vs2TI9/PDDysjI0O9+9zulpKRo48aNXY7funWrfvjDH2rhwoUaNWqUFi1apGXLlumFF15wjpk6darmz5+vjIwMjR49Wk8++aTGjRunTz75xFPLAgAAkBRAjW3BwdLQoV//s833vmnNazu8ra2tqqys1MqVKzscnzlzpvbt29flNS0tLQoLC+twLDw8XBUVFWpra5PNZutwzjAMffjhh6quru5QFHd135aWFudru90uSWpra1NbW5tL67oe1+bwxFwwB3IGriJn4CpyZuBF26IVZglTfUu96i/Xq9Fo1KDQQQoO8uov3AdMu+PrRzauOhwerZ/6wms/4fPnz6u9vV0JCQkdjickJOjs2bNdXjNr1iy98cYbmjdvnsaPH6/KykoVFhaqra1N58+fV2JioiSpoaFBSUlJamlpkdVq1YYNGzRjxoxuY8nPz9fq1as7Hf/ggw8UERHRj1W6prS01GNzwRzIGbiKnIGryBm4as+HezwyT3Nzc5/Hev0thcVi6fDaMIxOx6559tlndfbsWd12220yDEMJCQlaunSpXnzxRVn/6bubo6KiVFVVpUuXLqmsrEx5eXkaNWqUpk6d2uV9n3nmGeXl5Tlf2+12paSkaObMmYqOju7/InvR1tam0tJSzZgxo9MuNdAVcgauImfgKnLG/QzDUHNbsy5evqim1ibZrDZF2CK6rYN8XWtrqw7/92F9685vKTIs0u3zXfuNfF94reAdOnSorFZrp93cc+fOddr1vSY8PFyFhYV6/fXXVVtbq8TERBUUFCgqKkpDrz03IikoKEg33nijJCkrK0tHjx5Vfn5+twVvaGioQkNDOx232Wwe/Y/c0/PB/5EzcBU5A1eRM+4VEhKi6IhoZ2Obvc3ut41tVsfXm4/BwcEeyRlX5vBa01pISIiys7M7/aqktLRUkydP7vFam82m5ORkWa1Wbd++XXPmzFFQUPdLMQyjwzO6AAAAviJgGtu8yKuPNOTl5WnJkiXKycnRpEmTVFBQoJqaGuXm5kr6+lGDU6dOOT9r9/PPP1dFRYVuvfVW1dXVaf369Tp8+LC2bNnivGd+fr5ycnI0evRotba2avfu3SouLu72kx8AAAB8wbVvbBsUMkj1V+pVd7lOza3Npmps8xav/vQWLlyoCxcu6Fe/+pXOnDmjzMxM7d69W6mpqZKkM2fOqKamxjm+vb1d69atU3V1tWw2m6ZNm6Z9+/ZpxIgRzjFNTU1avny5vvrqK4WHhys9PV1/+MMftHDhQk8vDwAAwGXXvrEtOjRaFy9fVENLg4IsQRoUMkhBFq9/Z5hf8vrbheXLl2v58uVdnisqKurwOiMjQ4cOHerxfmvWrNGaNWsGKjwAAACvCLeFa1jwMMWExehC8wU1XGlQiDXErxvbvMXrBS8AAAC6FlDf2OZGFLwAAAA+7lpjW2RIpBquNOji5Yuqu1ynyJBIhVh975vNfA0FLwAAgJ+gse368JMBAADwMzS2uYaCFwAAwE/R2NY3FLwAAAB+jMa23lHwAgAAmACNbd2j4AUAADARGts6C8xVAwAAmByNbf+PghcAAMDEaGyj4AUAADC9QG9so+AFAAAIEIHa2EbBCwAAEGACrbHNfCsCAABAnwRKYxsFLwAAQIAze2MbBS8AAABM3dhGwQsAAAAnMza2UfACAACgEzM1tvlXtAAAAPAoMzS2UfACAACgV/7c2EbBCwAAgD7pqbHNKqu3w+sWBS8AAABc0lVjW1NLk7fD6pZ/PHgBAAAAn3OtsW14zHDFRcRJkizyvccbKHgBAADQL6HBoYqPjJckhQT73keXUfACAADA1Ch4AQAAYGoUvAAAADA1Cl4AAACYGgUvAAAATI2CFwAAAKZGwQsAAABTo+AFAACAqVHwAgAAwNQoeAEAAGBqFLwAAAAwtWBvB+CLDMOQJNntdo/M19bWpubmZtntdtlsNo/MCf9GzsBV5AxcRc7AVZ7OmWt12rW6rScUvF1obGyUJKWkpHg5EgAAAPSksbFRMTExPY6xGH0piwOMw+HQ6dOnFRUVJYvF0un8hAkTtH///m6v7+l8V+fsdrtSUlJ08uRJRUdH9y94D+ht/b4yx/Xew5Xr+jqWnCFnXBnb3zHkjOfmIGd8h7tzZqDu7y85cz3nPZ0zhmGosbFRw4YNU1BQz0/pssPbhaCgICUnJ3d73mq19vgvsqfzPZ2Ljo72i79Uelu/r8xxvfdw5bq+jiVnyBlXxvZ3DDnjuTnIGd/h7pwZqPv7S87057wnc6a3nd1raFq7Do8++uh1n+/tWn/giTUMxBzXew9XruvrWHKGnHFlbH/HkDOem4Oc8R3uXsNA3d9fcqa/530NjzT4ALvdrpiYGDU0NPjFu2h4HzkDV5EzcBU5A1f5cs6ww+sDQkND9ctf/lKhoaHeDgV+gpyBq8gZuIqcgat8OWfY4QUAAICpscMLAAAAU6PgBQAAgKlR8AIAAMDUKHgBAABgahS8AAAAMDUKXj/V3Nys1NRUPfXUU94OBT6usbFREyZMUFZWlsaOHatNmzZ5OyT4uJMnT2rq1Km66aabNG7cOL399tveDgl+YP78+Ro8eLDuu+8+b4cCH1RSUqK0tDR94xvf0BtvvOHx+flYMj+1atUqHTt2TMOHD9fLL7/s7XDgw9rb29XS0qKIiAg1NzcrMzNT+/fvV1xcnLdDg486c+aMamtrlZWVpXPnzmn8+PGqrq5WZGSkt0ODDysvL9elS5e0ZcsW/fGPf/R2OPAhV69e1U033aTy8nJFR0dr/Pjx+tvf/qYhQ4Z4LAZ2eP3QsWPH9Nlnn+mee+7xdijwA1arVREREZKkK1euqL29XbzPRU8SExOVlZUlSYqPj9eQIUN08eJF7wYFnzdt2jRFRUV5Owz4oIqKCt18881KSkpSVFSU7rnnHr3//vsejYGCd4B99NFHmjt3roYNGyaLxaJdu3Z1GrNhwwaNHDlSYWFhys7O1scff+zSHE899ZTy8/MHKGJ4mydypr6+XrfccouSk5P19NNPa+jQoQMUPbzBEzlzzYEDB+RwOJSSktLPqOFNnswZmE9/8+f06dNKSkpyvk5OTtapU6c8EboTBe8Aa2pq0i233KJXX321y/M7duzQihUrtGrVKh06dEh33HGH7r77btXU1DjHZGdnKzMzs9Of06dP609/+pPGjBmjMWPGeGpJcDN354wkxcbG6tNPP9WJEyf01ltvqba21iNrg3t4Imck6cKFC3rggQdUUFDg9jXBvTyVMzCn/uZPV79VtFgsbo25EwNuI8l49913OxybOHGikZub2+FYenq6sXLlyj7dc+XKlUZycrKRmppqxMXFGdHR0cbq1asHKmR4mTty5l/l5uYaO3fuvN4Q4WPclTNXrlwx7rjjDqO4uHggwoQPceffM+Xl5caCBQv6GyJ82PXkz969e4158+Y5zz3xxBPGtm3b3B7rP2OH14NaW1tVWVmpmTNndjg+c+ZM7du3r0/3yM/P18mTJ/Xll1/q5Zdf1iOPPKJf/OIX7ggXPmAgcqa2tlZ2u12SZLfb9dFHHyktLW3AY4VvGIicMQxDS5cu1Z133qklS5a4I0z4kIHIGQSuvuTPxIkTdfjwYZ06dUqNjY3avXu3Zs2a5dE4gz06W4A7f/682tvblZCQ0OF4QkKCzp4966Wo4MsGIme++uorLVu2TIZhyDAMPfbYYxo3bpw7woUPGIic2bt3r3bs2KFx48Y5n9XbunWrxo4dO9DhwgcM1P+bZs2apYMHD6qpqUnJycl69913NWHChIEOFz6mL/kTHBysdevWadq0aXI4HHr66ac9/klBFLxe8K/PrRiGcV3PsixdunSAIoKv60/OZGdnq6qqyg1RwZf1J2emTJkih8PhjrDgw/r7/yZPd93Dt/SWP/fee6/uvfdeT4flxCMNHjR06FBZrdZO75jPnTvX6Z0RIJEzcB05A1eRM+gPf8kfCl4PCgkJUXZ2tkpLSzscLy0t1eTJk70UFXwZOQNXkTNwFTmD/vCX/OGRhgF26dIlffHFF87XJ06cUFVVlYYMGaLhw4crLy9PS5YsUU5OjiZNmqSCggLV1NQoNzfXi1HDm8gZuIqcgavIGfSHKfLHo58JEQDKy8sNSZ3+PPjgg84xr732mpGammqEhIQY48ePN/bs2eO9gOF15AxcRc7AVeQM+sMM+WMxDL5jFAAAAObFM7wAAAAwNQpeAAAAmBoFLwAAAEyNghcAAACmRsELAAAAU6PgBQAAgKlR8AIAAMDUKHgBAABgahS8AODn/vrXv8pisai+vr7P1zz33HPKyspyW0wA4EsoeAHAT+zbt09Wq1WzZ8/2digA4FcoeAHATxQWFurxxx/XJ598opqaGm+HAwB+g4IXAPxAU1OTdu7cqR/96EeaM2eOioqKuh1bVFSk2NhY7dq1S2PGjFFYWJhmzJihkydPdhq7detWjRgxQjExMVq0aJEaGxud59577z1NmTJFsbGxiouL05w5c3T8+HF3LA8A3IqCFwD8wI4dO5SWlqa0tDTdf//9evPNN2UYRrfjm5ubtXbtWm3ZskV79+6V3W7XokWLOow5fvy4du3apZKSEpWUlGjPnj16/vnnneebmpqUl5en/fv3q6ysTEFBQZo/f74cDofb1gkA7hDs7QAAAL3bvHmz7r//fknS7NmzdenSJZWVlWn69Oldjm9ra9Orr76qW2+9VZK0ZcsWZWRkqKKiQhMnTpQkORwOFRUVKSoqSpK0ZMkSlZWVae3atZKkBQsWdIohPj5eR44cUWZmplvWCQDuwA4vAPi46upqVVRUOHdog4ODtXDhQhUWFnZ7TXBwsHJycpyv09PTFRsbq6NHjzqPjRgxwlnsSlJiYqLOnTvnfH38+HEtXrxYo0aNUnR0tEaOHClJPD8MwO+wwwsAPm7z5s26evWqkpKSnMcMw5DNZlNdXV2311kslh6P2Wy2Tuf++XGFuXPnKiUlRZs2bdKwYcPkcDiUmZmp1tbW/iwHADyOHV4A8GFXr15VcXGx1q1bp6qqKuefTz/9VKmpqdq2bVu31x04cMD5urq6WvX19UpPT+/TvBcuXNDRo0f185//XHfddZcyMjJ6LK4BwJexwwsAPqykpER1dXVatmyZYmJiOpy77777tHnzZv32t7/tdJ3NZtPjjz+uV155RTabTY899phuu+025/O7vRk8eLDi4uJUUFCgxMRE1dTUaOXKlQOyJgDwNHZ4AcCHbd68WdOnT+9U7EpfN5VVVVXp4MGDnc5FRETopz/9qRYvXqxJkyYpPDxc27dv7/O8QUFB2r59uyorK5WZmakf//jHeumll/q1FgDwFovR0+faAAD8TlFRkVasWOHSVw0DgJmxwwsAAABTo+AFAACAqfFIAwAAAEyNHV4AAACYGgUvAAAATI2CFwAAAKZGwQsAAABTo+AFAACAqVHwAgAAwNQoeAEAAGBqFLwAAAAwNQpeAAAAmNr/AvNTsrT7ulHKAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 940 ms, sys: 28.4 ms, total: 968 ms\n",
      "Wall time: 6.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n",
    "\n",
    "# Define the range for the hyperparameter alpha\n",
    "param_range = [0.0001, 0.001, 0.01, 0.1, 1]\n",
    "\n",
    "# Calculate validation curve data\n",
    "train_scores, test_scores = validation_curve(\n",
    "    MLPClassifier(hidden_layer_sizes=(100,), random_state=42), train_X, train_y, param_name=\"alpha\", param_range=param_range, cv=5, scoring=\"accuracy\", n_jobs=-1\n",
    ")\n",
    "\n",
    "# Calculate mean and standard deviation for training and test scores\n",
    "train_scores_mean = np.mean(train_scores, axis=1)\n",
    "train_scores_std = np.std(train_scores, axis=1)\n",
    "test_scores_mean = np.mean(test_scores, axis=1)\n",
    "test_scores_std = np.std(test_scores, axis=1)\n",
    "\n",
    "# Plot validation curve\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.plot(param_range, train_scores_mean, label=\"Training score\", color=\"r\")\n",
    "plt.plot(param_range, test_scores_mean, label=\"Cross-validation score\", color=\"g\")\n",
    "\n",
    "plt.fill_between(param_range, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color=\"r\")\n",
    "plt.fill_between(param_range, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "\n",
    "plt.title(\"Validation Curve\")\n",
    "plt.xlabel(\"Alpha\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.xscale('log')  # Use a logarithmic scale for alpha\n",
    "plt.legend(loc=\"best\")\n",
    "plt.grid()\n",
    "plt.savefig('../images/phishing-nn/validation-curve-alpha.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "35f831aa-48ec-428c-b398-bf29dd248e82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores: [0.95028249 0.96497175 0.95254237 0.94576271 0.93325792 0.94343891\n",
      " 0.95701357 0.95135747 0.95135747 0.95475113]\n",
      "Mean cross-validation score: 0.9504735792622133\n",
      "CPU times: user 8min 40s, sys: 24.1 s, total: 9min 4s\n",
      "Wall time: 59.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Perform 10-fold cross-validation\n",
    "cv_scores = cross_val_score(neural_net, train_X, train_y, cv=10, scoring='accuracy')\n",
    "\n",
    "# Print cross-validation scores\n",
    "print(\"Cross-validation scores:\", cv_scores)\n",
    "print(\"Mean cross-validation score:\", cv_scores.mean())\n",
    "\n",
    "# Write cross-validation scores to a text file\n",
    "with open('../results/phishing-nn/cross_validation_scores.txt', 'w') as file:\n",
    "    file.write(\"Cross-validation scores: \" + np.array2string(cv_scores) + \"\\n\")\n",
    "    file.write(\"Mean cross-validation score: \" + str(cv_scores.mean()) + \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ecb81fc3-c428-418c-809f-78e67b210d6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot cross-validation scores\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.plot(range(1, 11), cv_scores, marker='o', linestyle='--')\n",
    "plt.title('Cross-Validation Scores')\n",
    "plt.xlabel('Fold')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim([0.8, 1.0])\n",
    "plt.grid(True)\n",
    "plt.savefig('../images/phishing-nn/cross-validation-scores.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "73956d48-d8c2-4acb-b307-74b45f2910df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.95      0.94      0.94      3918\n",
      "           1       0.95      0.96      0.96      4926\n",
      "\n",
      "    accuracy                           0.95      8844\n",
      "   macro avg       0.95      0.95      0.95      8844\n",
      "weighted avg       0.95      0.95      0.95      8844\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7min 54s, sys: 26.9 s, total: 8min 21s\n",
      "Wall time: 55 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "y_pred = cross_val_predict(neural_net, train_X, train_y, cv=10)\n",
    "\n",
    "report = classification_report(train_y, y_pred)\n",
    "print(report)\n",
    "\n",
    "with open('../results/phishing-nn/classification_report.txt', 'w') as file:\n",
    "    file.write(report)\n",
    "    \n",
    "fpr, tpr, thresholds = roc_curve(train_y, y_pred, pos_label=1)\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.plot(fpr, tpr, marker='o', linestyle='--', label='ROC Curve')\n",
    "plt.plot([0, 1], [0, 1], 'k--', lw=2, label='Chance')\n",
    "plt.title('ROC Curve')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.savefig('../images/phishing-nn/roc-curve-fpr.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8215345a-fff1-4d4d-8620-949a7b36eb15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 81 candidates, totalling 405 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Users/xavierelon/anaconda3/envs/supervised_learning/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters found:  {'activation': 'relu', 'alpha': 0.0001, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'max_iter': 200, 'solver': 'adam'}\n",
      "Best cross-validation score:  0.9534154071247144\n",
      "CPU times: user 42.8 s, sys: 4 s, total: 46.8 s\n",
      "Wall time: 1min\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Suppress the convergence warning\n",
    "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n",
    "\n",
    "# Hyperparameter tuning using GridSearchCV with increased max_iter\n",
    "param_grid = {\n",
    "    'hidden_layer_sizes': [(50,), (75,), (100,)],\n",
    "    'activation': ['tanh', 'relu', 'logistic'],\n",
    "    'solver': ['adam'],\n",
    "    'alpha': [0.0001, 0.001, 0.01],\n",
    "    'learning_rate': ['adaptive'],\n",
    "    'max_iter': [50, 100, 200],\n",
    "}\n",
    "\n",
    "mlp = MLPClassifier()\n",
    "grid_search = GridSearchCV(estimator=mlp, param_grid=param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=2)\n",
    "grid_search.fit(train_X, train_y)\n",
    "print(\"Best parameters found: \", grid_search.best_params_)\n",
    "print(\"Best cross-validation score: \", grid_search.best_score_)\n",
    "\n",
    "# Write the best parameters and best score to a text file\n",
    "with open('../results/phishing-nn/grid_search_results.txt', 'w') as file:\n",
    "    file.write(\"Best parameters found: \" + str(grid_search.best_params_) + \"\\n\")\n",
    "    file.write(\"Best cross-validation score: \" + str(grid_search.best_score_) + \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c0b45caa-a9fd-4ea1-b632-17934ec22617",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bR48eARAcHEzFihUBKF26NCqVipiYmBy1u3LlStq3b0/79u1ZuXJlpte1YsUKmjdvjqGhIWXLlmXbtm0ax1y+fBkvLy9MTEwwNjbGxcWF6OhojWNmz56NhYUFBQsWpE+fPiQlJSn7/vrrL3x9fcmfPz+GhoY0bNiQ69evK/uDg4MxMzNjx44d2Nvbo6enx+3btzO9nsTEROLj4zU+QgghhBDiyycTqf+Ap0+fsm7dOgDy5cv30e2lpaXRrFkznjx5wqFDh9i3bx/R0dG0bt0agNatW7N//34ATp06RWxsLJaWlu9tNzo6muPHj+Pt7Y23tzfHjh3j5s2bGY4LCAjA29ubCxcu0KhRI3x8fHjy5AkA9+/fp06dOujr63Pw4EHOnj1L586dSU5OVs4PCwsjOjqasLAwQkJCCA4OJjg4WNnv5+fHmTNn2LZtG8ePHyctLY1GjRppTLZevHjBtGnTWLFiBZcvX6ZIkSKZXtO0adMwNTVVPjm5D0IIIYQQ4vMn70jlUXFxcajVatLS0njx4gUATZo0wc7O7qPb3r9/PxcuXODWrVvKxGD16tU4ODhw+vRpqlWrRsGCBQEoXLgw5ubmOWp31apVNGzYkPz58wPQoEEDVq1axeTJkzWO8/Pzo23btgBMnTqVhQsXcurUKRo0aMD333+Pqakp69evVyaNtra2Gufnz5+fRYsWoa2tjZ2dHV5eXhw4cIBu3bpx/fp1tm3bxtGjR6lZsyYAa9euxdLSktDQUL777jsAkpKSWLx4MZUrV872mkaNGsXgwYOV7/Hx8TKZEkIIIYTIA2RFKo8yNjYmIiKCs2fPsnTpUsqUKcPSpUs/SduRkZFYWlpqTAjs7e0xMzMjMjLyg9pMSUkhJCSE9u3bK9vat29PSEgIKSkpGsdWqlRJ+beRkRHGxsb8/vvvAERERODi4pLtypuDgwPa2trKdwsLC+X8yMhIdHR0+Oqrr5T9BQsWpFy5chrXpqurqzGOrOjp6WFiYqLxEUIIIYQQXz5ZkfrCmJiYEBcXl2H706dPNf5I19LSwsbGBgA7OzsePnxI69at+fXXXz96DGlpaahUqhxvz4k9e/Zw//595fHAdCkpKezdu5eGDRsq296dJKlUKqXYQ06q/GV3flpaWqbnvHttBgYGH3ytQgghhBDiyycTqS+MnZ0dv/zyS4btp0+fply5clmeN2jQIObMmcPWrVtp3rz5R43B3t6eO3fucPfuXWVV6sqVK8TFxVG+fPkPanPlypW0adOGMWPGaGyfPn06K1eu1JhIZadSpUqEhISQlJT0Qe+D2dvbk5yczMmTJ5VH+x4/fsy1a9c++NoycynAU1anhBBCCCG+YPJo3xemd+/eREdH06dPH86fP8+1a9f4/vvvWblyJcOGDcvyPBMTE7p27cqECRM0Vl3i4uKIiIjQ+Ny5cyfTNtIDdr/55hvMzc2pXbs2tra2nDp1Cl9fX1xdXXF2ds71Nf3xxx9s376djh078ueff1KxYkVKlChBhQoV6NixI9u2beOPP/7IUVt9+/YlPj6eNm3acObMGa5fv87q1atzXK2wbNmyNG3alG7dunHkyBHOnz9P+/btKV68OE2bNs31tQkhhBBCiLxJVqS+MFZWVhw+fJgxY8bwzTff8OrVK2xtbZk/fz779+9nyJAhPHjwgNTUVDw9PfH396dGjRqcO3eOiIgILl68iK6uLsWLF1fKklepUkWjj44dO2pUsYOMAbvx8fEMHjyYQ4cOUb9+fRo0aMDChQs/6Jp+/PFHjIyMqFevHkePHtXY5+7ujrGxMatXr9Yo2pBu2bJlPHv2jB49ejBw4ECsra1p3749kZGRuLq6kpSUhImJCadOncrxeIKCgnBzc8PV1RU9PT3q1KnDrl27PknFw3QSyCuE+Ld9ziGXQgjxJVClZfVSiPiiuLi4kJSUxLRp0yhdujSPHj3iwIEDVKpUiWrVqlG+fHkaN27MgAEDMDMz49atW2zbto3BgwdTsmRJYmJisLa25ty5czg6OmZo/9KlS1SsWJGbN29ibW39t11HeHg47u7u/PXXX5iZmWV77MqVK+nfvz8LFizA1dWVxMRELly4wJUrV5g0aRLwpsLf06dPCQ0NzdU4/P39CQ0NJSIi4sMuJAvx8fFvyqAP3CgTKSHEv0omUkIIkbn0v9fi4uKyfRVDVqTygKdPn3LkyBHCw8NxdXUFoFSpUlSvXh2A0NBQ4uPjWbFiBTo6b35ya2tr6tatm6P2g4OD6dSpE/AmYBfg1q1bBAcHa0w20icttWvXJjAwkNevX9OmTRvmzZunrOasWbOGefPmERUVhZGREXXr1mXevHlZ5jBlZ/v27Xh7e9OlSxdlm4ODg/Jvf39/QkJCAJTCEGFhYbi5uTFixAi2bt3KvXv3MDc3x8fHh/Hjx5MvXz6Cg4MJCAjQOC8oKAg/Pz/i4uIYNmwYoaGhvHr1CmdnZ+bOnZtlGfTExEQSExOV7xLIK4QQQgiRN8hEKg9Qq9Wo1WpCQ0P5+uuv0dPT09hvbm5OcnIyW7dupVWrVu+tNrd27Vp69OihsU1fX59Xr15hbW3NsWPHKFy4cKbnhoWFYWFhQVhYGDdu3KB169Y4Ojoyb948bt++TXJyMiqVCi0tLeLj49mwYQOXL1/m/Pnzub5uc3NzDh06xO3btylVqlSG/UOHDiUyMpL4+HiCgoIAKFCgAPCmPHxwcDDFihXj4sWLdOvWDWNjY4YPH07r1q25dOkSu3fvVoKFTU1NSUtLw8vLiwIFCrBr1y5MTU1ZtmwZ9erV49q1a0rbb5s2bZoyKRNCCCGEEHmHTKTyAB0dHYKDg+nWrRtLly7FyckJV1dX2rRpQ6VKlfj6668ZPXo07dq1o2fPnlSvXp26devi6+tL0aJFM7TXpEkTjRwleFOVr2nTpvz444/ZBuxmFXa7a9cukpKSMhx/4cIFWrZsyfPnz1Gr1bm67gkTJtCiRQusrKywtbWlRo0aNGrUiFatWqGlpYVarcbAwIDExMQMYx47dqzybysrK4YMGcKGDRsYPnw4BgYGqNVqdHR0NM47ePAgFy9e5Pfff1cmq7NnzyY0NJRNmzbRvXv3DGOUQF4hhBBCiLxJqvblES1btuTBgwds27YNT09PwsPDcXJyUopGTJkyhYcPH7J06VLs7e1ZunQpdnZ2XLx4MUNbxsbG2NjYaHxKliwJQIkSJbIdR1Zht6VKlcLGxoZnz54xZMgQ6tWrR5UqVejQoQNAlpUCs2NhYcHx48e5ePEi/fv3JykpiY4dO9KgQQMlFyormzZtonbt2pibm6NWqxk3btx7x3D27FmeP39OwYIFlVVAtVrNrVu3iI6OzvQcCeQVQgghhMibZCKVh+jr6+Ph4cH48eM5duwYfn5+TJgwQdlfsGBBvvvuOwIDA4mMjKRYsWLMnj37k44hu7DbhIQEvvnmG9RqNWvWrOH06dNs3boVgNevX39wnxUqVKBPnz6sXbuWffv2sW/fPg4dOpTl8SdOnKBNmzY0bNiQHTt2cO7cOcaMGfPeMaSmpmJhYZGhXHxUVFS2peeFEEIIIUTeI4/25WH29vZZVqvT1dWlTJkyJCQk/GPjuXr1Kn/++SfTp09XHm87c+bMJ+3D3t4eQLkuXV1dUlJSNI45evQopUqV0gj/vX37tsYxmZ3n5OTEw4cP0dHRwcrK6qPGKYG8QgghhBBfNlmRygMeP35M3bp1WbNmDRcuXODWrVv8/PPPzJw5k6ZNm7Jjxw7at2/Pjh07uHbtGlFRUcyePZtdu3ZlCJmNiorKsOKSm9WimJgYLC0t0dLSYt68eZw4cUKZLJUsWRJdXV0WLlzIzZs32bZtm1Km/EP06tWLSZMmcfToUW7fvs2JEyfw9fWlcOHC1KhRA3jz/tOFCxeIiorizz//JCkpCUtLS27duoWBgQEqlYoZM2awdetWXrx4gYmJCSqVisKFC3P58mWGDRvGn3/+SWJiIvXr16dGjRo0a9aMPXv2EBMTw7Fjxxg7duwnnxAKIYQQQojPm6xI5QFqtZqvvvqKuXPnEh0drUwWunXrxujRo4mNjcXQ0JAhQ4Zw9+5d9PT00NbWJiUlBV9fXzp37qwUVWjTpk2G9m/dupWjcSQlJXHhwgUWLFhAy5YtMTU15fr168o7U4ULFyY4OJjRo0ezYMECnJycmD17Nk2aNMnxtS5btozFixdz48YN4E2hjVmzZpGYmEihQoWoUaMGBw4coGDBggB069aN8PBwnJ2def78OWFhYTx+/Bh9fX3lPpw7d466deuyefNmjh07RqFChTAzM2Pbtm2sWLGC2bNnK+XPd+3axZgxY+jcuTN//PEH5ubm1KlTJ9OiHdmRQF4h/nskt0kIIfIWCeT9j/Lz8+PRo0cEBQWRlJTE4cOH6dq1Kx07dmTJkiUaxyYlJWV49ykzf3dob04CeHNi6NChnD59WuM9qsy2/R0kkFeI/y6ZSAkhxJchp4G88mjff5ienh7m5uZYWlrSrl07fHx8CA0Nxd/fH0dHR1atWkXp0qXR09MjLS2NuLg4unfvTpEiRTAxMaFu3bpK/lNwcDAVK1YE3oT2qlQqYmJilLYAXr16hYODg0aZ8Fu3bmFqasry5cvfO963A3htbGxwcHCgbdu2GpMoNzc3Bg4cqHFes2bN8PPzU/YHBgby66+/olKpcHNzy3QbvHkscN68eQCEh4ejq6vL4cOHlXYDAwMpVKgQsbGxubntQgghhBAiD5BH+4TCwMBAyXq6ceMGGzduZPPmzcqjedmF0bZu3RpLS0vq16/PqVOnsLS0zBDaq6+vz9q1a/nqq69o1KgRjRs3pkOHDri7u9OtW7cM42nYsKHGxCUxMZGUlBQ2btzImDFjGD16dK6vccuWLYwcOZJLly6xZcsWdHV1ATLd9rb0CVqHDh04f/48MTExjBkzhp9++gkLC4ss+0tMTCQxMVH5Hh8fn+sxCyGEEEKIz49MpAQAp06dYt26ddSrVw94U4589erVymQoJ2G06e8lFS5cOMvQXkdHRyZPnky3bt1o27Yt0dHRWVYWXLFiBS9fvlS+//777/Tp04eIiAhWrVrFtWvXNAJ4c6JAgQIYGhqiq6urMcbMtr1r8uTJ7N+/n+7du3P58mU6dOhA8+bNs+1v2rRpBAQE5GhsQgghhBDiyyGP9v2H7dixA7Vajb6+PjVq1KBOnTosXLgQgFKlSmmsKH1IGG1WhgwZQrly5Vi4cCFBQUEUKlQo0+OKFy+uEQpcs2ZNzp07x8WLFxk0aFCuAng/BV1dXdasWcPmzZt5+fKl8thfdkaNGkVcXJzyuXv37t8+TiGEEEII8feTFan/MHd3d5YsWUK+fPkoVqyYRkEJIyMjjWPTw2jDw8MztGNmZparfn///XeioqLQ1tbm+vXrNGjQIFfnV6hQQQnhPXLkCC4uLhw6dAh3d3e0tLR4t35K+uOKn8KxY8cAePLkCU+ePMlwn96lp6enrOAJIYQQQoi8QyZS/2FGRkbY2Njk6NhPGUbbuXNnKlSoQLdu3ejSpQv16tVTgnRz690A3sKFC2sUf0hJSeHSpUu4u7t/1JgBoqOjGTRoEMuXL2fjxo34+vpy4MCBHD9W+DYJ5BVCCCGE+LLJo30iRz5FGK2VlRWtWrXi+PHj/Pjjj7Rr145WrVrh4+OTo9DfnATw1q1bl507d7Jz506uXr1K7969efr0aabtXb16la+//lopgvHutgcPHvDkyRNUKhVnz56lQ4cOfPPNN3Tq1ImgoCAuXbpEYGBgzm6gEEIIIYTIU2RFSuSISqX66DDapKQktm3bRkhICJaWlgB8//33VK5cmXHjxjFjxoxsz1+6dClVq1ZlyZIlPH78ONMA3s6dO3P+/Hl8fX3R0dFh0KBBWa5GTZgwASMjI6Kiopg2bRpXr17V2Obi4oKZmRmxsbEsXbqUmJgYtm/fDoC5uTkrVqzA29sbDw8PpcR7Tkkgr/hQkkUkhBBCfB4kkFf8Y6ysrBg4cGCGnKecUqlUbN26lWbNmmV5TE7DgwGcnZ3x8vLSqKqX2bZPSQJ5xceSiZQQQgjx95JAXvHRtm/fjpmZmVIRLyIiApVKxbBhw5RjevToQdu2bYE3hRjq1KmDgYEBlpaW9O/fX3l3Kd2zZ89o164darWaYsWKKVUC3yf9vazmzZujUqmU71mFB+/evZvatWtjZmZGwYIF+fbbbzWqC6Y/rjdx4kRUKhX+/v6ZbouJiUGlUhEREaGce/nyZby8vDAxMcHY2BgXF5dcVy4UQgghhBBfNplIiSzVqVOHZ8+ece7cOQAOHTpEoUKFOHTokHJMeHg4rq6uXLx4EU9PT1q0aMGFCxfYsGEDR44coW/fvhptzpo1i0qVKvHbb78xatQoBg0axL59+4A3Abxvl1Z/++Pj4wNAUFAQsbGxnD59Wmnz7fDg9AlPQkICgwcP5vTp00pBiObNmyuTwtjYWBwcHBgyZAixsbEMHTo0023vun//PnXq1EFfX5+DBw9y9uxZOnfuTHJycqb3MDExkfj4eI2PEEIIIYT48sk7UiJLpqamODo6Eh4eTtWqVQkPD2fQoEEEBATw7NkzEhISuHbtGm5ubkydOpV27dopj+2VLVuWBQsW4OrqypIlS9DX1wegVq1ajBw5EgBbW1uOHj3K3Llz8fDwyBDA+7YCBQowdepUzMzMMoTmvhseDNCyZUuNY1auXEmRIkW4cuUKFSpUwNzcHB0dHdRqtdKeWq3OsO3PP//UaOf777/H1NSU9evXK48Q2traZnkPJZBXCCGEECJvkhUpkS03NzfCw8NJS0vj8OHDNG3alAoVKnDkyBHCwsIoWrQodnZ2nD17luDgYI1VJE9PT1JTU7l165bSXnp1vbe/R0ZGAhkDeN/+FChQIMsxvhseDG9Klbdr147SpUtjYmKCtbU1AHfu3Pmo+xEREYGLi0uO38OSQF4hhBBCiLxJVqREttzc3Fi5ciXnz59HS0sLe3t7XF1dOXToEH/99Reurq7Am8DeHj160L9//wxtlCxZMts+VCrVR40xs1Dcxo0bY2lpyfLlyylWrBipqalUqFAhR2XWs2NgYJCr4yWQVwghhBAib5KJlMhW+ntS8+bNw9XVFZVKhaurK9OmTeOvv/5iwIABwJvA3suXL7834PfEiRMZvtvZ2eVoLPny5SMlJeW9xz1+/JjIyEiWLVuGi4sLAEeOHMlRH+9TqVIlQkJCclUdMDMSyCuEEEII8WWTR/tEttLfk1qzZg1ubm7Am8nVb7/9prwfBTBixAiOHz9Onz59iIiI4Pr162zbto1+/fpptHf06FFmzpzJtWvX+P777/n555+Vydj7WFlZceDAAR4+fMhff/2V5XH58+enYMGC/PDDD9y4cYODBw8yePDgD7r+d/Xt25f4+HjatGnDmTNnuH79OqtXryYqKuqTtC+EEEIIIb4MsiIl3svd3Z3ffvtNmTTlz58fe3t7Hjx4QPny5YE3KzWHDh1izJgxuLi4kJaWRpkyZWjdurVGW0OGDOHs2bMEBARgbGxMYGAgnp6eORpHYGAggwcPZvny5RQvXpyYmJhMj9PS0mL9+vX079+fChUqUK5cOQICAmjevDldu3aldevW5M+fnxcvXijvLFlZWXH79m0Azp8/T0BAAMWLF1dWsho1asTw4cMZOHAgBw8eZNiwYbi6upKcnEy+fPm4cOFCru6pBPLmjmQnCSGEEOJzI4G84j/BxcWFpKQkpk2bRunSpXn06BEHDhygUqVKeHl5YWVlRZcuXejWrZtyjra2tlLEIqswYX9/f0JDQzVyprIjgbwfRiZSQgghhPin5DSQV1akRJ739OlTjhw5omRewZtKf9WrV9c4ztjYOENpdSGEEEIIITIj70iJz8LatWuzDON1cHD4qLbT2wkNDSUxMfETjThnJJBXCCGEECJvkkf7xGfh2bNnPHr0KNN9+fLlo1SpUh/V/ubNm+nWrRsvX77EyckJV1dX2rRpQ6VKlYA3j+7FxsZqVOKbOnWqUs49s/3wJgzY3t4+y0f7/P39Mw3klUf7ckce7RNCCCHEPyWnj/bJREr8Z7x69YrDhw9z/Phxdu/ezalTp1ixYgV+fn5YWVnRvn17/Pz8lOMLFSqEmZkZQKb7ARYsWMCvv/6a5UQqMTFRYxUsPj4eS0tLmUjlkkykhBBCCPFPkXekhHiHvr4+Hh4eeHh4MH78eLp27cqECROUyVGhQoWyzcHKbH+BAgWy7VMCeYUQQggh8iaZSIn/LHt7e0JDQ/+VviWQVwghhBDiyybFJv7DVCpVriYSwcHByqNun1JMTAwqlSrHJcRz6/Hjx9StW5c1a9Zw4cIFbt26xc8//8zMmTNp2rTp39KnEEIIIYTI22RFKo/z8/Pj6dOnmU6YYmNjyZ8//z8/qHdYWloSGxtLoUKFPkl772Y7qdVqvvrqK+bOnUt0dDRJSUlYWlrSrVs3Ro8e/Un6zK3/aiCvvOskhBBCiLxCJlL/YZ9LZpK2tvbfOhY9PT2mTZvGtGnTsjwmJiYm2zay2u/v74+/v/+HD04IIYQQQnyR5NG+/7C3H+1Lf7xuy5YtuLu7Y2hoSOXKlTl+/HiW5z9+/Jjq1avTpEkTXr16xV9//YWPjw+FCxfGwMCAsmXLEhQU9N5xvPtoX3h4OCqVigMHDuDs7IyhoSE1a9YkKirqvW0FBwcTEBDA+fPnUalUqFQqgoODAZgzZw4VK1bEyMgIS0tLevfuzfPnzzXOX758OZaWlhgaGtK8eXPmzJmj8Tjj+fPncXd3x9jYGBMTE6pWrcqZM2feOy4hhBBCCJG3yERKaBgzZgxDhw4lIiICW1tb2rZtS3Jycobj7t27h4uLC3Z2dmzZsgV9fX3GjRvHlStX+OWXX4iMjGTJkiUf9bjemDFjCAwM5MyZM+jo6NC5c+f3ntO6dWuGDBmCg4MDsbGxxMbG0rp1awC0tLRYsGABly5dIiQkhIMHDzJ8+HDl3KNHj9KzZ08GDBhAREQEHh4eTJkyRaN9Hx8fSpQowenTpzl79iwjR47MkC31NgnkFUIIIYTIm+TRPqFh6NCheHm9eY8lICAABwcHbty4gZ2dnXLMtWvX8PDwoGnTpsyfPx+VSgXAnTt3qFKlCs7OzsCb7KWPMWXKFFxdXQEYOXIkXl5evHr1Cn19/SzPMTAwQK1Wo6Ojk+FxwYEDByr/tra2ZtKkSfTq1YvFixcDsHDhQho2bMjQoUMBsLW15dixY+zYsUM5786dOwwbNky5H2XLls32GqZNm5ZpIK8QQgghhPiyyYqU0FCpUiXl3xYWFgD8/vvvyraXL19Su3ZtmjVrxoIFC5RJFECvXr1Yv349jo6ODB8+nGPHjv2tY8mtsLAwPDw8KF68OMbGxvj6+vL48WMSEhIAiIqKonr16hrnvPt98ODBdO3alfr16zN9+nSio6Oz7XPUqFHExcUpn7t3737w+IUQQgghxOdDJlJCw9uPqaVPklJTU5Vtenp61K9fn507d3Lv3j2Ncxs2bMjt27cZOHAgDx48oF69esrqzt8xlty4ffs2jRo1okKFCmzevJmzZ8/y/fffA5CUlARAWlqaxsQwfdvb/P39uXz5Ml5eXhw8eBB7e3u2bt2aZb96enqYmJhofIQQQgghxJdPHu0TuaKlpcXq1atp164ddevWJTw8nGLFiin7CxcujJ+fH35+fri4uDBs2DBmz579j45RV1eXlJQUjW1nzpwhOTmZwMBAtLTe/O8HGzdu1DjGzs6OU6dOZTjvXba2ttja2jJo0CDatm1LUFAQzZs3z9UYJZBXCCGEEOLLJhOp/4C4uLgMYbcFChT44Pa0tbVZu3Ytbdu2VSZT5ubmjB8/nqpVq+Lg4EBiYiI7duygfPnyHzn6zLm5ueHo6Mi8efMy7LOysuLWrVtERERQokQJjI2NKVOmDMnJySxcuJDGjRsTGhrKuHHjNM7r168fderUYc6cOTRu3JiDBw/yyy+/KKtUL1++ZNiwYbRq1Qpra2vu3bvH6dOnadmy5d9yjUIIIYQQ4vMlE6n/gPDwcKpUqaKxrWPHjh/Vpo6ODj/99BOtW7dWJlO6urqMGjWKmJgYDAwMcHFxYf369R/VT1a2bNmSZbW8li1bKmXcnz59SlBQEH5+fsyZM4cZM2YwatQoqlWrluG8WrVqsXTpUgICAhg7diyenp4MGjSIRYsWAW8mkI8fP8bX15dHjx5RqFAhWrRo8UHFJPJiIK+E7QohhBDiv0SV9u5LIEL8B8TExGBtbc25c+dwdHTM8rhu3bpx9epVDh8+/En6jY+Px9TUFMuBG2UiJYQQQgjxGUr/ey0uLi7bVzGk2IT4Irm5uSnlzK2srJg6dSqdO3fG2NiYkiVL8sMPP2gcf+rUKapUqYK+vj7Ozs6cO3cuQ5tXrlzBzs4OAwMDChYsSLVq1QgJCaFjx47KitvbE6rAwEAKFSpEbGzs33qtQgghhBDi8yMrUuJvN3XqVKZOnZrpPhcXF3755Zdctefg4MDVq1fR1tZGV1eXFy9ekJaWhq6uLlOnTiUpKYkxY8Zw+fJl7OzsSEhIwNramrp16zJu3Dhu3brFgAEDuHnzprIiFRsbS6VKlShUqBC///47CQkJ6OjoULx4caKiogAYPnw4Gzdu5Pz588TExPDVV1/x008/ZVtoIjExkcTEROV7fHw8lpaWsiIlhBBCCPGZyumKlLwjJf52PXv2xNvbO9N9BgYGuW5v165deHt7U758ecaOHYurqyvVqlVj9uzZFC1aFLVazdy5cwkPD8fOzo61a9eSkpLCqlWrMDQ0xMHBgXv37tGrVy+lzSVLluDk5MSePXuUbffu3cPS0pJr165ha2vL5MmT2b9/P927d+fy5ct06NDhvdX6JJBXCCGEECJvkomU+NsVKFDgo6oEvqtUqVIYGBhgZmaGjY0N+fLlo1atWtjY2CjHmJubK+G9kZGRVK5cGUPD/18BqlGjhkabZ8+eJSwsDLVanaG/6OhobG1t0dXVZc2aNVSqVIlSpUplWjHwXaNGjWLw4MHK9/QVKSGEEEII8WWTiZTIE96t4KdSqZTw3pw8vZqamkrjxo2ZMWNGhn0WFhbKv48dOwbAkydPePLkCUZGRtm2q6enh56e3nv7F0IIIYQQXxYpNiHyPHt7e86fP8/Lly+VbSdOnNA4xsnJicuXL2NlZYWNjY3GJ32yFB0dzaBBg1i+fDlff/01vr6+ymRNCCGEEEL8t/wnVqRUKhVbt26lWbNmOTo+ODiYgQMH8vTp0791XB8quzDa3PDz8+Pp06eEhoZ+knF9rtq1a8eYMWPo0qULY8eOJSYmhtmzZ2sc06dPH5YvX07btm0ZNmwYhQoV4saNG6xfv57ly5cD0KFDB7755hs6depEw4YNqVixIoGBgQwbNizXY7oU4Jnty4tCCCGEEOLzlmcmUtlNCmJjY8mfP/8/P6i/SXZhtJnJKjNp/vz5OXrs7VPJaXbTp6ZWq9m+fTs9e/akSpUq2NvbM2PGDFq2bKkcU6xYMY4ePcqIESPw9PQkMTGRUqVK0aBBA7S0tJg0aRIxMTFs374dePMO1ooVK/D29sbDwyPX1/MlBPJKFT4hhBBCiKzlmYlUdszNzf/tIXxSn6pwg6mp6Sdp598QHh6u/DsmJibD/oiICI3vX3/9dYZt704iy5Yty5YtWzLtb/z48YwfP15jW9OmTTVKmwshhBBCiP+O/8Q7UiqVSlmpiomJQaVSsWXLFtzd3TE0NKRy5cocP348y/MfP35M9erVadKkCa9eveKvv/7Cx8eHwoULY2BgQNmyZQkKCnrvONL7Xr9+PTVr1kRfXx8HBweNSQHAoUOHqF69Onp6elhYWDBy5EiSk5OV/W+H0cL7A2mtra0BqFKlCiqVCjc3N+DNKt7bjzu6ubnRv39/hg8fToECBTA3N8ff319jbFevXqV27dro6+tjb2/P/v37Ne5vdrIaR2pqKhMnTqREiRLo6enh6OjI7t2739se5OyepqSk0KVLF6ytrTEwMKBcuXLMnz9fo53k5GT69++PmZkZBQsWZMSIEXTs2FHj/qSlpTFz5kxKly6NgYEBlStXZtOmTTkapxBCCCGEyFv+ExOpzIwZM4ahQ4cSERGBra0tbdu21ZispLt37x4uLi7Y2dmxZcsW9PX1GTduHFeuXOGXX34hMjKSJUuWUKhQoRz3PWzYMIYMGcK5c+eoWbMmTZo04fHjxwDcv3+fRo0aUa1aNc6fP8+SJUtYuXIlkydPzrbNwMBAnJ2dOXfuHL1796ZXr15cvXoVgFOnTgGwf/9+YmNjs1x1AQgJCcHIyIiTJ08yc+ZMJk6cyL59+4A3E55mzZphaGjIyZMn+eGHHxgzZkyOrzurccyfP5/AwEBmz57NhQsX8PT0pEmTJly/fj3HbWd3T1NTUylRogQbN27kypUrjB8/ntGjR7Nx40bl/BkzZrB27VqCgoI4evQo8fHxGSaHY8eOJSgoiCVLlnD58mUGDRpE+/btOXToUJbjSkxMJD4+XuMjhBBCCCG+fP/ZidTQoUPx8vLC1taWgIAAbt++zY0bNzSOuXbtGrVq1aJ+/fqEhISgo/PmScg7d+5QpUoVnJ2dsbKyon79+jRu3DjHffft25eWLVtSvnx5lixZgqmpKStXrgRg8eLFWFpasmjRIuzs7GjWrBkBAQEEBgZmWyGuUaNG9O7dGxsbG0aMGEGhQoWUVZnChQsDULBgQczNzbN9NLBSpUpMmDCBsmXL4uvri7OzMwcOHABg7969REdH8+OPP1K5cmVq167NlClTcnzdWY1j9uzZjBgxgjZt2lCuXDlmzJiR62Ia2d3TfPnyERAQQLVq1bC2tsbHxwc/Pz+NidTChQsZNWoUzZs3x87OjkWLFmFmZqbsT0hIYM6cOaxatQpPT09Kly6Nn58f7du3Z9myZVmOa9q0aZiamiofyZASQgghhMgb/rMTqUqVKin/Ts8JSg9wBXj58iW1a9emWbNmLFiwAJVKpezr1asX69evx9HRkeHDhyvZQjn1dhisjo4Ozs7OREZGAm/CY2vUqKHRX61atXj+/Dn37t3L0fWoVCqNQNrceLsdeHNv0tuJiorC0tJS452z6tWr57qPt8XHx/PgwQNq1aqlsb1WrVrKPcmJ7O4pwNKlS3F2dqZw4cKo1WqWL1/OnTt3AIiLi+PRo0ca16KtrU3VqlWV71euXOHVq1d4eHigVquVz48//kh0dHSW4xo1ahRxcXHK5+7duzm+JiGEEEII8fn6TxSbyMzbVe/SJy1vr/jo6elRv359du7cybBhwyhRooSyr2HDhty+fZudO3eyf/9+6tWrR58+fTKU1M6N9DGkpaVpTKLSt719zPuuJ/3YD8k4el+wbXZj+BiZXfPH9pV+/saNGxk0aBCBgYHUqFEDY2NjZs2axcmTJ987hnTp92Dnzp0UL15c47jsAnclkFcIIYQQIm/6z65IvY+WlharV6+matWq1K1blwcPHmjsL1y4MH5+fqxZs4Z58+ZpFHd4n7fDYJOTkzl79ix2dnbAm/DYY8eOafwRf+zYMYyNjTP8AZ9Turq6wJuiCx/Dzs6OO3fu8OjRI2Xb6dOnP2ocJiYmFCtWjCNHjmgce+zYMcqXL5/jtrO7p4cPH6ZmzZr07t2bKlWqYGNjo7GKZGpqStGiRZV3uNLHeO7cOeW7vb09enp63LlzJ0NgrzyuJ4QQQgjx35OnVqTi4uIylLj+mFLh9erVo1KlSlSuXJm6desSHh6Oubk548ePp2rVqjg4OJCYmMiOHTty9Uf/999/T9myZTExMaF+/foYGhrSuXNnAHr37s28efPo168fffv2JSoqigkTJjB48GC0tD5s3lukSBEMDAzYvXs3JUqUQF9f/4NKn3t4eFCmTBm8vb359ddfCQoKYunSpUD2q2XvG8ewYcOYMGECZcqUwdHRkaCgICIiIli7dm2Ox5Z+T8uXL8/cuXP566+/lHtqY2PDjz/+yJ49e7C2tmb16tWcPn1aqSII0K9fP6ZNm4aNjQ12dnYsXLiQO3fuKKG5xsbGDB06lEGDBpGamkrt2rWJj4/n2LFjqNVqOnbsmJtbKYG8QgghhBBfuDw1kQoPD6dKlSoa23L7B+67tLS0+Omnn2jdurUymdLV1WXUqFHExMRgYGCAi4sL69evz3Gb06dPZ8aMGZw7d46yZcvy/fffK1X/ihcvzq5duxg2bBiVK1emQIECdOnShbFjx37wNejo6LBgwQImTpzI+PHjKV68ODo6Oty+fRsdHR3c3NwYOnToe9vR1tYmNDSUDh06ADBx4kQWLFhA48aN0dfXz/E4JkyYoHE9RkZGGBoa0qVLF16/fo29vT3btm2jbNmyOb7Gt+9pmTJl+N///qfc0549exIREUHr1q1RqVS0bduW3r1788svvyjnjxgxgocPH+Lr64u2tjbdu3encePGyioawKRJkyhSpAjTpk3j5s2bmJmZ4eTkxOjRo3M8znSfeyCvhPEKIYQQQmRPlfZuKqlQuLm55bp6XHZiYmKwtrbm3LlzODo6fpI2c6tDhw6cOnWKuXPnYm9vz+PHjzl27BgmJiY5nnS+fR0JCQnUrl2bGzduUKZMmVydv3//fhwcHHjx4gUXL15k/vz5nDhxgu3bt1OvXr1cj+VT3tPU1FTKly+Pt7c3kyZN+mTtxsfHv6neN3CjTKSEEEIIIT5D6X+vxcXFZfsEkbwj9R6pqalZBtTOmTOHihUrYmRkhKWlJb179+b58+fAm8cM0x9je1eNGjV4/vy5Eiab/jhieHg4KpWKAwcO4OzsjKGhITVr1iQqKkrj/MmTJ1OkSBGMjY3p2rUrI0eOzPEkYvv27YwePZpGjRphZWVF1apV6devn8YkKrOAXTMzM4KDg9m6dSuHDx8G4Oeff8bDwwOVSkWTJk0yBAu/T3oZ9NKlS9O0aVP279/PV199RZcuXTTeo1qyZAllypRBV1eXcuXKsXr16gxtbdq0iW+//RZDQ0PKly/P8ePHuXHjBm5ubhgZGVGjRg2N96Kio6Np2rQpRYsWRa1WU6lSJQYPHsy1a9e4ePEivXr14tq1axrZYiqVihUrVtC8eXMMDQ0pW7Ys27Zty9U1CyGEEEKIvEEmUu+RXUCtlpYWCxYs4NKlS4SEhPDzzz+TP39+1Go1xYsXJykpicaNG6NWq2nYsKHSppubG2q1Oss+x4wZQ2BgIGfOnEFHR0d51wdg7dq1TJkyhRkzZnD27FlKlizJkiVLcnw95ubm7Nq1i2fPnn3A3YBnz54xfvx44E2I7VdffcXRo0epWbMmnp6eGqXB3/68ff1Z0dLSYsCAAdy+fZuzZ88C4OPjQ+/evbl37x758uUjJiYGX19fDAwMNNr84Ycf8PX1JSIiAjs7O9q1a0ePHj0YNWoUZ86cAd5kTaV7/vw5jRo1Yv/+/Zw7dw43NzfmzZuHs7MztWrV4uLFixQtWpSiRYtqjDEgIABvb28uXLhAo0aN8PHx4cmTJ1lekwTyCiGEEELkTXnqHam/Q3pALUDZsmVZtGgRBw4cwMPDg4EDByrHWVtbM3PmTAYPHqxUstu7dy/Dhg3j+PHjFChQAGNjY/T19enXr1+2fU6ZMgVXV1cARo4ciZeXF69evUJfX5+FCxfSpUsXOnXqBMD48ePZu3evshL2Pj/88AM+Pj4ULFhQCdVt1apVhhynrPj6+lKnTh2sra2ZMmUKI0aMAKBatWrs2rULHx8funfvnuE8AwODHLWfXmkvJiaG6tWrc/36dby9vTWCf/v378+LFy9YsWKFUsmwR48eeHt7A2/ed6pRowbjxo3D09MTgAEDBij3DKBy5cpUrlxZ+b5gwQIOHDhAr169lAmXlZVVhvH5+fnRtm1bAKZOncrChQs5deoUDRo0yPR6pk2bRkBAQI6uXQghhBBCfDlkReo9sguoDQsLw8PDg+LFi2NsbEzv3r15+vQpFhYW2NjY0LVrV3R1dbl8+TLFixdn8+bNGBsb88033+S4z3fDgqOiojKE4OYmFLdOnTrcvHmTAwcO0LJlSy5fvoyLi8sHvQf0bghu9erV+eOPPzKUB7exsclx6fZ3M7Nu3LhBo0aNNNry9PTkzp07Gm2+fc/SV5EqVqyose3Vq1fKilBCQgLDhw/H3t4eMzMz1Go1V69eVUJ6s/J2P0ZGRhgbG2cbfCyBvEIIIYQQeZNMpN4jq4Da27dv06hRIypUqMDmzZs5e/Ys33//PQBJSUnAm9ykVq1asW7dOgDWrVtH69at0dHJfiHwfWHB2QXH5vSaXFxcGDlyJHv37mXixIlMmjSJ169fK+2/22b6Nb3Px4boRkZGAmiUJs9JWG9m9yy7+zhs2DA2b97MlClTOHz4MBEREVSsWFG5B1nJbfCxnp4eJiYmGh8hhBBCCPHlk4nUBzpz5gzJyckEBgby9ddfY2trmyG0F96847N7924uX75MWFgYPj4+H9VvuXLlNIJj08fyMezt7UlOTubVq1fAm7Dh2NhYZf/169d58eJFhvOyC8H9EKmpqSxYsABra2uljH358uU/Oqw3M4cPH8bPz4/mzZtTsWJFzM3NiYmJ+ag2hRBCCCHEf4e8I/WBypQpQ3JyMgsXLqRx48YcPXpUCad9m6urK0WLFsXHxwcrKyu+/vrrj+q3X79+dOvWDWdnZ2rWrMmGDRu4cOECpUuXztH5bm5utG3bFmdnZwoWLMiVK1cYPXo07u7uympJ3bp1WbRoEV9//TWpqamMGDEiw0oMZB+CmxOPHz/m4cOHvHjxgkuXLjFv3jxOnTrFzp070dbWBt6sHHl7e+Pk5ES9evXYvn07W7ZsYf/+/TnuJzM2NjZs2bKFxo0bo1KpGDduXLYrS5nx8/MjISHhg/qXQF4hhBBCiC+bTKQ+kKOjI3PmzGHGjBmMGjWKOnXqMG3aNHx9fTWOSw+AnTVrllLt7mP4+Phw8+ZNhg4dyqtXr/D29sbPzy/DKlVWPD09CQkJYfTo0bx48YJixYrx7bffaowtMDCQatWqUbVqVeBNNb20tDRmzZpFamoqbm5uwJsQ3DZt2vD69WusrKw0QnAHDhxIRESEUhI9ISGBiRMn8vPPP3P//n0A6tevD4ChoSGlSpUiLi6Oly9fUrduXfLly4elpSXe3t4EBgYya9Ys+vfvj7W1NUFBQcoYPtTcuXPp3LkzNWvWpFChQowYMUIq6gkhhBBCiByTQN48wMPDA3Nz80zzlT6Un58fjx49IigoiJSUFB49esTu3buZNm0aLi4ubNu2DR0dHaysrHj06BHVq1fn0KFDyvnvTqRyEgTs5uaGra0tEydO5PXr15w+fZpOnTrRp08fpk2b9smuLbeSkpIyXZHz8/Pj6dOnGTK3siOBvEIIIYQQnzcJ5M2jXrx4wZw5c7h8+TJXr15lwoQJ7N+/XyNQ91PR09PD3Nyc4sWL4+TkxOjRo/nf//7HL7/8QnBwsHJcjx49OHHiBLt27cqyrZwEAcOb1Slzc3NKlixJy5Yt8fDwYO/evcr+tLQ0Zs6cSenSpTEwMKBy5cps2rRJo43Lly/j5eWFiYkJxsbGuLi4KGG8qampTJw4kRIlSqCnp4ejo6NGaHJ6SPLGjRtxc3NDX1+fNWvWkJKSwuDBgzEzM6NgwYIMHz4810U+hBBCCCFE3iETqS+MSqVi165duLi4ULVqVbZv387mzZuVx+SyCsRVq9UcPnz4o/uvW7culStXZsuWLco2KysrevbsyahRo0hNTaVnz54sXryYw4cPK33Hx8fTtWtXunTpkuO+zp8/z9GjRzVWg8aOHUtQUBBLlizh8uXLDBo0iPbt2yurYffv36dOnTro6+tz8OBBzp49S+fOnUlOTgZg/vz5BAYGMnv2bC5cuICnpydNmjTh+vXrGn2PGDGC/v37ExkZiaenJ4GBgaxatYqVK1dy5MgRnjx5wtatW997DRLIK4QQQgiRN8k7Ul8YAwODbAstREREZLkvp1lO72NnZ8eFCxc0tqVPcNauXcvEiRN59eoVkZGRrF27FoBTp04xZMgQfvzxRy5cuJBlEPDixYtZsWIFSUlJvH79Gi0tLaWsfEJCAnPmzOHgwYNKhlXp0qU5cuQIy5Ytw9XVle+//x5TU1PWr1+vTMBsbW2V9mfPns2IESNo06YNADNmzCAsLIx58+Yp/cCbRxNbtGihfJ83bx6jRo2iZcuWACxdupQ9e/a8915JIK8QQgghRN4kE6k8xsbG5m/vI7Mcp8KFCzN06FDGjx9P69atMTMzw8DAQBmPjY0N3333HSdOnODo0aMcPHiQ+fPnExAQwLhx45R2fHx8GDNmDPHx8cyYMQMTExNl8nLlyhVevXqFh4eHRt+vX79WyqVHRETg4uKS6TtN8fHxPHjwIMPkrVatWpw/f15jm7Ozs/LvuLg4YmNjMwQQOzs7v/fxvlGjRjF48GCNMVhaWmZ7jhBCCCGE+PzJRErkWmRkpEZgbrrBgwezePFiFi9enOl56UHA6WHAkydPZuLEiYwYMQJdXV0ATE1NlcnXmjVrcHBwYOXKlXTp0kUpT75z584Mq2t6enrAmxW798lJwK+RkdF728kJPT09ZWxCCCGEECLvkHekRK4cPHiQixcvKqtEb1Or1YwbN44pU6bk6F2gd4OA35UvXz5Gjx7N2LFjefHiBfb29ujp6XHnzh1sbGw0PumrPJUqVeLw4cMkJSVlaM/ExIRixYrlOuDX1NQUCwuLTAOIhRBCCCHEf5OsSIksJSYm8vDhwwzlz7/99tsMeVnpunfvzty5c/npp5/46quvlO05CQLOTLt27Rg9ejSLFy9m6NChDB06lEGDBpGamkrt2rWJj4/n2LFjqNVqOnbsSN++fVm4cCFt2rRh1KhRmJqacuLECapXr065cuUYNmwYEyZMoEyZMjg6OhIUFERERITyLldWBgwYwPTp05UA4jlz5vD06dMPuq8ggbxCCCGEEF86mUiJLO3evRsLCwt0dHTInz8/lStXZsGCBXTs2BEtrcwXM0+dOkViYiKvXr3i0qVLyvacBAED3Lt3D5VKxV9//YWZmRm6urr07duXmTNn0rNnTyZNmkSRIkWYNm0aN2/exMzMTCnNDlCwYEEOHjzIsGHDcHV1RVtbG0dHR+W9qP79+xMfH8+QIUP4/fffsbe3Z9u2bZQtWzbDtbi5ueHo6Mi8efMYMmQIsbGx+Pn5oaWlRefOnWnevDlxcXGf6nYLIYQQQogviATyik/qq6++wtbWlmnTpqFWqzEzM8vV+eHh4bi7uysTqX/TkydPyJcvH8bGxp+szc85kFdCeIUQQgghJJBX/Euio6OpW7cuJUqU+NcnQh+rQIECn3QSJYQQQggh8g6ZSH1Bdu/eTe3atTEzM6NgwYJ8++23REdHK/uPHTuGo6Mj+vr6ODs7Exoaikql0siWunLlCo0aNUKtVlO0aFE6dOjAn3/+maP+ExMT6d+/P0WKFEFfX5/atWtz+vRpAGJiYlCpVDx+/JjOnTujUqkIDg5+b5u7du3C1tYWAwMD3N3diYmJyXDM5s2bcXBwQE9PDysrKwIDAzX2W1lZMXnyZHx9fVGr1ZQqVYr//e9//PHHHzRt2hS1Wk3FihU5c+aMcs7jx49p27YtJUqUwNDQkIoVK/LTTz9ptOvm5sbAgQM1+pk6dSqdO3fG2NiYkiVL8sMPP7z3nkkgrxBCCCFE3iMTqS9IQkICgwcP5vTp0xw4cAAtLS2aN29Oamoqz549o3HjxlSsWJHffvuNSZMmMWLECI3zY2NjcXV1xdHRkTNnzrB7924ePXqEt7d3jvofPnw4mzdvJiQkhN9++w0bGxs8PT158uQJlpaWxMbGYmJiwrx584iNjaV169bZtnf37l1atGhBo0aNiIiIoGvXrowcOVLjmLNnz+Lt7U2bNm24ePEi/v7+jBs3LsMkbe7cudSqVYtz587h5eVFhw4d8PX1pX379spYfX19ldynV69eUbVqVXbs2MGlS5fo3r07HTp04OTJk9mOOTAwEGdnZ86dO0fv3r3p1asXV69ezfL4adOmYWpqqnwkQ0oIIYQQIm+Qd6S+YH/88QdFihTh4sWLHDlyhLFjx3Lv3j309fUBWLFiBd26dePcuXM4Ojoyfvx4Tp48yZ49e5Q27t27h6WlJVFRUdja2mbZV0JCAvnz5yc4OJh27doBkJSUhJWVFQMHDmTYsGEAmJmZMW/ePPz8/N47/tGjRxMaGsrly5eVHKeRI0cyY8YM5R0pHx8f/vjjD/bu3aucN3z4cHbu3Mnly5eBNytFLi4urF69GoCHDx9iYWHBuHHjmDhxIgAnTpygRo0axMbGYm5unul4vLy8KF++PLNnzwY0i01k1k9aWhrm5uYEBATQs2fPTNtMTEwkMTFR+Z4eyCvvSAkhhBBCfJ7kHak8KDo6mnbt2lG6dGlMTEyUUNw7d+4QFRVFpUqVlEkUQPXq1TXOP3v2LGFhYajVauVjZ2entP2+vpOSkpTqd/Am56l69epERkZ+0PVERkby9ddfa4Th1qhRI8Mxb/cJUKtWLa5fv05KSoqyrVKlSsq/ixYtCkDFihUzbPv9998BSElJYcqUKVSqVImCBQuiVqvZu3cvd+7cyXbMb/ejUqkwNzdX2syMnp4eJiYmGh8hhBBCCPHlk/LnX5DGjRtjaWnJ8uXLKVasGKmpqVSoUIHXr1+TlpamMSEBeHexMTU1lcaNGzNjxowMbVtYWGTbd3pbmfXx7racysliaE6uC95M6tKlH5/ZttTUVODNI3pz585l3rx5VKxYESMjIwYOHMjr16+zHc/bbaa3m96mEEIIIYT475CJ1Bfi8ePHREZGsmzZMlxcXAA4cuSIst/Ozo61a9eSmJiInp4egEZxBQAnJyc2b96MlZUVOjq5++ltbGzQ1dXlyJEjGo/2nTlzRqMgQ27Y29sTGhqqse3EiRMZjnn7OuFNUQ1bW1u0tbU/qF+Aw4cP07RpU9q3bw+8mWBdv36d8uXLf3CbuSGBvEIIIYQQXzZ5tO8LkT9/fgoWLMgPP/zAjRs3OHjwIIMHD1b2t2vXjtTUVLp3705kZCR79uxR3vVJX43p06cPT548oW3btpw6dYqbN2+yd+9eOnfurPGYXGaMjIzo1asXw4YNY/fu3Vy5coVu3brx4sULunTp8kHX1LNnT6Kjoxk8eDBRUVGsW7cuQxGJIUOGcODAASZNmsS1a9cICQlh0aJFDB069IP6TGdjY8O+ffs4duwYkZGR9OjRg4cPH35Um0IIIYQQ4r9DVqS+EFpaWqxfv57+/ftToUIFypUrx4IFC3BzcwPAxMSE7du306tXLxwdHalYsSLjx4+nXbt2+Pv7Kys/Wlpa7Ny5k23btqGtrU2pUqVo0KABWlrvn1NPnz6d1NRUOnTowLNnz3B2dmbPnj3kz5//g66pZMmSbN68mUGDBrF48WKqV6+ulBdP5+TkxMaNGxk/fjzjx48nf/78TJw4MUfFLA4ePIifnx9Pnz7NsG/lypVK1UFDQ0PlPaf//e9/qNVqypQpo1EkAt5U+hs0aBB+fn4fnZFVYcIeKTYhhBBCCPEFk6p9edjatWvp1KkT3t7ePH78mKCgIJKTk7ly5QqdO3fGxcUlQ3bS5yy9QmBOHyUMDg5m4MCBmU6k3q0uqFKpCAoKokGDBiQkJLBhwwbGjBnD7t278fT0BCA8PBx3d3elouCHSK8CI1X7hBBCCCE+T1K17z/oxx9/5MiRI9y6dYvQ0FBGjBiBt7c3Ojo66OnpYW5uTokSJfjmm29o3bq1RknxoKAgypcvj76+PnZ2dixevFij7VOnTlGlShUl7Hfr1q0aYb/BwcEZJhfpgcBv2759O1WrVkVfX5/SpUsTEBBAcnKyst/f35+SJUuip6dHsWLF6N+/P/CmFPnt27cZNGgQKpXqgwtcZMfMzAxzc3PKlCnD6NGjKVCggMY9+hASyCuEEEIIkTfJo315yMOHDxk/frySo/Tdd98xZcoUevfurXHczZs32b17t1KBbvny5YwdO5b4+Hi0tbW5ffs2ffr0YfDgwUpRCj09PTw8PFizZg23bt1iwIAB7x3PkiVLAFCr1cCbkuOvXr1CT0+PFi1a0KlTJ7p37w7AhAkT2LRpE3PnzmX9+vU4ODjw8OFDzp8/D8CWLVuoXLky3bt3p1u3bp/mhmUhJSWFzZs38+TJkwxV+nJr2rRpBAQEfKKRCSGEEEKIz4VMpPKQ4cOHM3z48Ez37dixA7VarUxmAObMmQPApEmTCAwM5Ouvv1aO//777wkPD+fnn39m/fr1zJkzh1WrVmFoaIiDgwP37t2jV69e2Y6nbdu27N27V1m1atu2La6urvTs2RMTExOKFCnCpEmTGD58OBMmTODOnTuYm5tTv3598uXLR8mSJZUsrAIFCqCtrY2xsXGWgbofq23btmhra/Pq1StSUlIoUKAAXbt2/ag2R40apVEUJD2QVwghhBBCfNlkIvUf4e7uzpIlS3jx4gUrVqzg2rVr9OvXjz/++IO7d+/SvXt3jYITycnJmJqaYmNjw+PHj3F0dMTQ8P/f6Xk3ODcz6Y/62djYAHDlyhUuXbrE0qVLlWPSJ3YvXrzgu+++Y968eZQuXZoGDRrQqFEjGjdunOtS7R9q7ty51K9fn7t37zJ48GAGDRqkjP1D6enpKeXohRBCCCFE3iETqf8IIyMjZVKwYMEC3N3dCQgIoG/fvsCbx/u++uorjXPSc5pyUo9ES0srw3FJSUka31NTUwkICKBFixYZztfX18fS0pKoqCj27dvH/v376d27N7NmzeLQoUMf9IidiYkJz58/JyUlRSNzKiUlhefPn2NqaqpxvLm5OTY2NtjY2PDzzz9TpUoVnJ2dsbe3z3XfQgghhBAib5OJ1H/UhAkTaNiwIb169aJ48eLcvHkTHx+fTI+1t7dn9erVvHz5EgMDAyBjcG7hwoV59uwZCQkJGBkZASiP9KVzcnIiKioq21UeAwMDmjRpQpMmTejTpw92dnZcvHgRJycndHV135t39TY7OztSUlI4d+4czs7OyvbffvuNlJQUypUrl+W5NjY2tGzZklGjRvG///0vx33mlATyCiGEEEJ82aRq3z9IpVIpeU7/BH9/fxwdHTPd5+bmhoODA1OnTsXf359p06Yxf/58ihcvzvDhwwkKClLeoWrXrh1aWlp06dKFK1eusGvXLiXsN91XX32FoaEho0eP5saNG5mG644fP54ff/wRf39/Ll++TGRkJBs2bGDs2LHAm8p/K1eu5NKlS9y8eZPVq1djYGBAqVKlgDflz3/99Vfu37/Pn3/++d7rt7e3p2HDhnTu3Jn9+/dz69Yt9u/fT5cuXWjYsCGGhoYalQffNWTIELZv386ZM2c0tl+8eJGIiAiNjxBCCCGE+G+RHKlP6Pfff2fcuHH88ssvPHr0iPz581O5cmX8/f2pUaMGKpWKrVu30qxZs0/ed2ZtP3/+nMTERIYMGcLTp08zTOLWrVtHp06duHHjBocPH2bWrFlcuXIFIyMjKlasyMCBA2nevDnwZgWqZ8+eREZGYm9vz7hx42jZsiXnzp1TJmuhoaEMGzaMe/fuUb9+fZo0aUL37t01Hvnbs2cPEydO5Ny5c+TLlw87OzvKli3LxYsXuXr1KikpKaSlpaGjo4OTkxOTJ0+mXr16yhh69OhBVFQUiYmJOXrkMD4+Hn9/f7Zv3869e/coUaIE3377Lf7+/qjVav744w8KFSpEvnz5Mv1tvvnmG3R0dNi1a5eSI5WZnP6fkeRICSGEEEJ83nKaIyUTqU/IxcWFpKQkpk2bRunSpXn06BEHDhygUqVKeHl5/S0TqdevX6Orq/u3TtIyExMTg7W1tcZE6kOsXLmS/v37s2DBAlxdXUlMTOTChQtcuXKFSZMmfXC7SUlJH126/O8gEykhhBBCiM+bBPL+w54+fcqRI0eYMWMG7u7ulCpViurVqzNq1Ci8vP7/D9Q///yT5s2bY2hoSNmyZdm2bZtGO4cOHaJ69ero6elhYWHByJEjNQJr3dzc6Nu3L4MHD6ZQoUJ4eHhgZWUFQPPmzVGpVMr3dx/t8/Pzo1mzZsyePRsLCwsKFixInz59NIpCWFlZMW/ePOW7SqVixYoV2Y5527ZtlC1bFgMDA9zd3QkJCUGlUvH06dP33rft27fj7e1Nly5dsLGxwcHBgbZt22aYRGUXGBwTE4NKpWLjxo24ubmhr6/P4sWLMTAwYPfu3RrtbNmyBSMjI54/f66c9/ajeZcvX8bLywsTExOMjY1xcXEhOjo6R+PIjATyCiGEEELkTTKR+kTUajVqtZrQ0FASExOzPC4gIABvb28uXLhAo0aN8PHx4cmTJwDcv3+fRo0aUa1aNc6fP8+SJUtYuXIlkydP1mgjJCQEHR0djh49yrJlyzh9+jTw5o/82NhY5XtmwsLCiI6OJiwsjJCQEIKDgzO8y5STMadPkh48eECrVq1o1qwZERER9OjRgzFjxuTgjr1hbm7OiRMnuH37dpbHLF++nDFjxjBlyhQiIyOZOnUq48aNo1KlSqjVahwcHABo06YNJ0+eRFtbm9jYWLy8vFi7dq1GW+vWraNp06ZKSPDb7t+/T506ddDX1+fgwYOcPXuWzp07KxPZrMYREhKS5dinTZuGqamp8pEMKSGEEEKIvEGq9n0iOjo6BAcH061bN5YuXYqTkxOurq60adOGSpUqKcf5+fnRtm1bAKZOncrChQs5deoUDRo0YPHixVhaWrJo0SJUKhV2dnY8ePCAESNGMH78eCXnycbGhpkzZ2YYg5mZ2XvDavPnz8+iRYvQ1tbGzs4OLy8vDhw4QLdu3bI8J7MxP3z4kLS0NEaOHEm5cuWYNWsWAOXKlePSpUtMmTIlR/dtwoQJtGjRAisrK2xtbalRowaNGjWiVatWyvWmBwanl023trbmypUrbN26lYiICO7du4e7uzujR4/Gz88PeBPge+jQIXx9fXnx4gWGhobEx8ezc+dONm/enOlYvv/+e0xNTVm/fr3yWKCtra2yP6txLFu2jI4dO2bapgTyCiGEEELkTbIi9Qm1bNmSBw8esG3bNjw9PQkPD8fJyUljxeftSZWRkRHGxsb8/vvvAERGRipFKdLVqlWL58+fc+/ePWXb26W8c8vBwUEjU8nCwkLpPyvZjTkqKopq1appHF+9evUcj8fCwoLjx49z8eJF+vfvT1JSEh07dqRBgwakpqYqgcFdunRRVv3UajWTJ0/m3r172NjYKI8yNmzYUMmBKlCgAF5eXujo6CiPIm7evBljY2O++eabTMcSERGBi4tLpu9WZTeOtx/9e5eenh4mJiYaHyGEEEII8eWTFalPTF9fHw8PDzw8PBg/fjxdu3ZlwoQJykrJu3+kq1QqUlNTgTeV396eRKVvSz8uXXpO04fIrv8POSe7MedGhQoVqFChAn369OHIkSO4uLhw6NAhJQw3u8DgdO/eF11dXVq1asW6deto06YN69ato3Xr1ujoZP6ffXpGVmbSrzcn4xBCCCGEEHmfTKT+Zvb29jnOjrK3t2fz5s0ak5Njx45hbGxM8eLFsz03X758uQqr/VTs7OzYtWuXxrZ3c5dyK33ylJCQQNGiRd8bGJwdHx8fvvnmGy5fvkxYWFi2lQArVapESEhIphX/PnYc75JAXiGEEEKIL5s82veJPH78mLp167JmzRouXLjArVu3+Pnnn5k5cyZNmzbNURu9e/fm7t279OvXj6tXr/K///2PCRMmMHjwYOV9oaxYWVlx4MABHj58yF9//fUpLilHevTowdWrVxkxYgTXrl1j48aNyqOM765UZaZXr15MmjSJo0ePcvv2bU6cOIGvry+FCxemRo0aABqBwdeuXePixYsagcHZcXV1pWjRovj4+GBlZcXXX3+d5bF9+/YlPj6eNm3acObMGa5fv87q1auJiopSxjF+/Hi6dOmS63EIIYQQQoi8RVakPhG1Ws1XX33F3LlziY6OJikpCUtLS7p168bo0aNz1Ebx4sXZtWsXw4YNo3LlyhQoUIAuXbowduzY954bGBjI4MGDWb58OcWLFycmJuYjr+j/LViwQAnm1dHRITU1lfXr1/Pdd99hbW3Npk2bGDJkCPPnz6dGjRqMGTOGXr16oaen996269evz6pVq1iyZAmPHz+mUKFC1KhRgwMHDlCwYEEAunbtiqGhIbNmzWL48OEagcHwplofQJUqVciXLx+WlpZ4e3vj7++Pnp4ebdu2ZdasWYwfPz7bsRQsWJCDBw8ybNgwXF1d0dbWxtHRkVq1ainj6NatG2FhYaxZsybDOHKjwoQ9/3qOlORGCSGEEEJ8OAnkFe/l5+fHo0ePCAoKIikpicOHD9O1a1c6duzIkiVLMhw/ZcoUli5dyt27d/+R8bm5uWFra8vEiRN5/fo1p0+fplOnTvTp04dp06Z90r4+Nvj4cwrklYmUEEIIIURGEsgrPik9PT3Mzc2xtLSkXbt2+Pj4EBoaypo1ayhZsiSGhobKatLMmTM1yoHnJLD32LFj1KlTBwMDAywtLenfvz8JCQk5Hp+hoSHm5uaULFmSli1b4uHhwd69e5X9jx8/pm3btpQoUQJDQ0MqVqzITz/9pNGGm5sb/fv3Z/jw4RQoUABzc3P8/f2z7XfixIkULVpUI9RXCCGEEELkfTKREh/EwMCApKQkXr9+TdWqVTE2NubZs2dERERQtGhRZQLi5uZG06ZNuX37NlpaWhw/fpxOnToBbx5HBLh48SKenp60aNGCCxcusGHDBo4cOULfvn0/aGznz5/n6NGjGgUjXr16RdWqVdmxYweXLl2ie/fudOjQgZMnT2qcGxISgpGRESdPnmTmzJlMnDiRffv2ZegjLS2NAQMGsHLlSo4cOYKjo2OmY0lMTCQ+Pl7jI4QQQgghvnzyaJ94Lz8/P54+fapUHzx16hSNGjWiXr16bNiwQePY06dPU716dZ49e4ZaraZPnz7s27dPo7LfnDlzWLJkCTdv3sTa2hpfX18MDAxYtmyZcsyRI0dwdXUlISEBfX39bMfn5ubGsWPH0NXVVSZ3WlpabNy4kZYtW2Z5npeXF+XLl2f27NlKOykpKRw+fFg5pnr16tStW5fp06cDbx7t+/nnn/nf//7HmTNn2LdvHyVKlMiyD39/fwICAjJsl0f7hBBCCCE+Tzl9tE+KTYgc2bFjB2q1muTkZJKSkmjatCkLFy7k3Llz+Pv7ExERwZMnT5S8pTt37mBvb8+DBw+oXbs2NjY2SlsNGjRgyZIl5M+fH4CzZ89y48YN1q5dqxyTlpZGamoqt27donz58u8dn4+PD2PGjCE+Pp4ZM2ZgYmKiMYlKSUlh+vTpbNiwgfv375OYmEhiYmKG7Km3w4ch88DiQYMGoaenx4kTJyhUqFC24xo1ahSDBw9WvsfHx2Npafne6xFCCCGEEJ83mUiJHHF3d2fJkiXky5ePYsWKkS9fPhISEvjmm2/45ptvWLNmDYULF+bOnTt4enry+vVrIGeBvampqfTo0YP+/ftn6LdkyZI5Gp+pqakyWVuzZg0ODg6sXLmSLl26AG8eI5w7dy7z5s2jYsWKGBkZMXDgQGWc6XISWOzh4cFPP/3Enj173psppaenl6PqhUIIIYQQ4ssiEymRI0ZGRhqrSgBXr17lzz//ZPr06coqy7thvDkJ7HVycuLy5csZ2v9Q+fLlY/To0YwaNYq2bdtiaGjI4cOHadq0Ke3btwfeTN6uX7+eo9WudzVp0oTGjRvTrl07tLW1lfLruSGBvEIIIYQQXzYpNiE+WMmSJdHV1WXhwoXcvHmTbdu2MWnSJI1jchLYO2LECI4fP06fPn2IiIjg+vXrbNu2jX79+n3w2Nq1a4dKpWLx4sUA2NjYsG/fPo4dO0ZkZCQ9evTg4cOHuWrTyspK+Xfz5s1ZvXo1nTp1YtOmTR88TiGEEEII8WWSFanPmJubG46OjsybN09je2hoKM2bNyctLY3g4GClCh5AkSJFqF69OtOnT8fBwUHZ/m7BiNz0k17KPLO+HBwcWLduHQsWLMDJyYnZs2fTpEkTBg0aRFhYWI4CeytVqsShQ4cYM2YMTk5O5MuXDzs7O1q3bv3B905XV5e+ffsyc+ZMevbsybhx47h16xaenp4YGhrSvXt3mjVrRlxcXIZzg4ODGThwoEZ59sy0atWK1NRUOnTogJaWFi1atMjx+P7tQF4pNCGEEEII8XFkIpUHmJiYEBUVRVpaGvfv32f48OF4eXlx7do1dHV1P7r99BWk4ODgTPt68uQJ8fHxSl8dO3bUmIQ0adKEJk2aKN+nTJlCiRIlNKrxVatWjb1792JlZcXAgQMZOHBgjscXHh6e6fbRo0czevRo5Xtmk8jM2km/3szOmTt3rkYYr7e3N97e3jkeqxBCCCGEyBvk0b48QKVSYW5ujoWFBc7OzgwaNIjbt28TFRX1WfS1ePFiTp8+zc2bN1m9ejWzZs3SCOzNje3bt1O1alX09fUpXbo0AQEBJCcna4xvxYoVNG/eHENDQ8qWLcu2bds02sguIDg8PJxOnToRFxeHSqVCpVJphPK+ePGCzp07Y2xsTMmSJfnhhx8+6DqEEEIIIcSXTSZSeczTp09Zt24dkLEC3b/V1/Xr12natCn29vZMmjSJIUOGaExOsnP48GHUajVqtRoDAwOaNGnC5cuX0dbWJjY2luDgYKZMmaJxTkBAAN7e3ly4cIFGjRrh4+PDkydPAIiJiaFVq1Y0a9aMiIgIevTowZgxY5Rza9asybx58zAxMSE2NpbY2FiGDh2q7A8MDMTZ2Zlz587Ru3dvevXqxdWrV7McvwTyCiGEEELkTfJoXx4QFxeHWq0mLS2NFy9eAG8ep7Ozs/ss+po7dy5z5879oP6cnZ2JiIgAoG3btri6utKzZ09l/4kTJxg+fDgTJkxQtvn5+dG2bVsApk6dysKFCzl16hQNGjRg6dKllCtXjlmzZgFQrlw5Ll26pEzGdHV1MTU1VVbe3tWoUSN69+4NvCmSMXfuXMLDw7O8/mnTpmUayCuEEEIIIb5sMpHKA4yNjfntt99ITk7m0KFDzJo1i6VLl37xfQEYGBgoZdGvXLnCpUuXNPpLSUnh1atXvHjxAkPDN8Ub3g7VNTIywtjYWAnVjYqKolq1ahp9VK9ePcfjebvt9MnWu4G9b5NAXiGEEEKIvEkmUp8xExOTTKvKPX36VCODSEtLS5ls2NnZ8fDhQ1q3bs2vv/76Sfv5FH19jNTUVAICAjKtjvd24YrsQnVzEhCcnZwE9r5NAnmFEEIIIfImmUh9xuzs7Pjll18ybD99+jTlypXL8rxBgwYxZ84ctm7dSvPmzf+2fj6kr4/h5OREVFTURwX35iQgWFdXl5SUlA/uIyckkFcIIYQQ4suWp4pNBAcHY2Zm9m8PI0esrKwy5Da9q3fv3kRHR9OnTx/Onz/PtWvX+P7771m5ciXDhg3L8jwTExO6du3KhAkTNFZb4uLiiIiI0PjcuXMnx/106tSJpKSkj+orJ+7fv09ERATFihVj2LBhRERE8OTJE8aPH8+PP/6Iv78/ly9fJjIykg0bNjB27NgctQs5Cwi2srLi+fPnHDhwgD///JMXL14QHByc4/ELIYQQQoi8L1crUn5+foSEhLw5UUeHAgUKUKlSJdq2bYufnx9aWv/uvKx169Y0atToXx1DTp0+fRojI6Nsj7GysuLw4cPUrl2bH3/8ES0tLWxtbQkODua7777L9twBAwawYMECfv75ZyXnKDw8nCpVqmgc17FjR4KDgzl8+DBjxozhm2++IS4uDi0trQz9zJs3T6Oow4f29T6zZ89m9uzZGv8OCgrCz8+PHTt2MHHiRGbOnKkE93bt2vW9babLSUBwu3btqFmzJq1bt+bx48dMmDABKyurHPeRE/9WIK8E8QohhBBCfBqqtFy8IOLn58ejR48ICgoiJSWFR48esXv3bqZNm4aLiwvbtm1DRyfvPi34+vXrTxJwm1sfElKbmZyO39/fn9DQUKVa3r/lU133+0yZMoWlS5dy9+7dLPsNDg5m4MCBGkHDHyI+Ph5TU1MsB26UiZQQQgghxGco/e+1uLi4bF/FyPUSkp6eHubm5hQvXhwnJydGjx7N//73P3755RdlteHOnTs0bdoUtVqNiYkJ3t7ePHr0SGnD398fR0dHVq1aRcmSJVGr1fTq1YuUlBRmzpyJubk5RYoUyZAPNGfOHCpWrIiRkRGWlpb07t2b58+fK/vffbQvvZ/Vq1djZWWFqakpbdq04dmzZzm6Vjc3N/r27cvgwYMpVKgQHh4ewJvqcY0aNUKtVlO0aFE6dOjAn3/+qZz37NkzfHx8MDIywsLCgrlz5+Lm5qbxh/m7j/b5+/tTsmRJ9PT0KFasGP3791fGcPv2bQYNGqQExKY7duwYderUwcDAAEtLS/r3709CQoJGH5MnT8bPzw9TU1O6desGvCnbbWtri6GhIaVLl2bcuHHKI3vBwcEEBARw/vx5pb+3H30LDQ1V2r948SJ169bFwMCAggUL0r17d43fw8/Pj2bNmjF79mwsLCwoWLAgffr0yfB4YE7FxcXRvXt3ihQpgomJCXXr1uX8+fMa9/B9v/ezZ8+oVq0a+vr6FC5cGB8fHyZMmICFhcV77zfAnj17KF++PGq1mgYNGhAbG/tB1yKEEEIIIb5sn+RZvLp161K5cmW2bNlCWloazZo148mTJxw6dIh9+/YRHR1N69atNc6Jjo7ml19+Yffu3fz000+sWrUKLy8v7t27x6FDh5gxYwZjx47lxIkT/z9YLS0WLFjApUuXCAkJ4eDBgwwfPjzbsUVHRxMaGsqOHTvYsWMHhw4dYvr06Tm+tpCQEHR0dDh69CjLli0jNjYWV1dXHB0dOXPmDLt37+bRo0fKI20AgwcP5ujRo2zbto19+/Zx+PBhfvvttyz72LRpE3PnzmXZsmVcv36d0NBQKlasCMCWLVsoUaIEEydOVAJi4c0kxtPTkxYtWnDhwgU2bNjAkSNH6Nu3r0bbs2bNokKFCpw9e5Zx48YBb0qYBwcHc+XKFebPn8/y5cuVnKfWrVszZMgQHBwclP7e/e0AXrx4QYMGDcifPz+nT5/m559/Zv/+/Rn6DwsLIzo6mrCwMDp16sTixYsxNjZWQnbTPw4ODtn+DmlpaXh5efHw4UN27drF2bNncXJyol69ekrYLrz/9x48eDBRUVGo1Wri4+P53//+h7a2Nl9//XW29zv9mmfPns3q1av59ddfuXPnjkZYb2YkkFcIIYQQIm/6ZM/h2dnZceHCBfbv38+FCxe4deuWkpezevVqHBwcOH36tJLhk5qayqpVqzA2Nsbe3h53d3eioqLYtWsXWlpalCtXjhkzZhAeHq78kfv2io61tTWTJk2iV69eLF68OMtxpaamEhwcjLGxMQAdOnTgwIEDGVa7smJjY8PMmTOV7+PHj8fJyYmpU6cq21atWoWlpSXXrl3DwsKCkJAQ1q1bR7169QAICgqiWLFiWfZx584dzM3NqV+/Pvny5aNkyZJKtlGBAgXQ1tbG2NhYIyB21qxZtGvXTrknZcuWZcGCBbi6urJkyRKlHHjdunUz/LH/dnEGKysrhgwZwoYNGxg+fDgGBgao1Wp0dHQyDaRNt3btWl6+fMmPP/6ovOu1aNEiGjduzIwZMyhatCgA+fPnZ9GiRWhrazNhwgSuXLmCSqVi/vz5Gu29W1b8XWFhYVy8eJHff/9deZdp9uzZhIaGsmnTJrp37w5k/3s/e/ZM+W1atWoFvFnlKlasmPJ+X1b3GyApKYmlS5dSpkwZAPr27cvEiROzHbcE8gohhBBC5E2fbCKVns8TGRmJpaWlRuiovb09ZmZmREZGKhMpKysr5Y9dgKJFi6Ktra1RsKJo0aIaYadhYWFMnTqVK1euEB8fT3JyMq9evSIhISHLwg3v9mNhYZFtgOq7nJ2dNb6fPXuWsLAw1Gp1hmOjo6N5+fIlSUlJGiGvpqam2ZYR/+6775g3bx6lS5emQYMGNGrUiMaNG2f7vtnZs2e5ceMGa9euVbalpaWRmprKrVu3KF++fKbjhzcrYPPmzePGjRs8f/6c5OTkXJfijoyMpHLlyhr3vVatWqSmphIVFaVMpBwcHNDW1gberISVLVuWixcv5rqE+dmzZ3n+/DkFCxbU2P7y5Uuio6OV79n93jdv3sz1b/M2Q0NDZRL1bttZkUBeIYQQQoi86ZNNpCIjI7G2ts408BQyBqFmFmyaXdjp7du3adSoET179mTSpEkUKFCAI0eO0KVLl2zfucltgOq73p2gpaamKqsu77KwsOD69etKP2/LrqaHpaUlUVFR7Nu3j/3799O7d29mzZrFoUOHslypSU1NpUePHsq7VG8rWbJkluM/ceIEbdq0ISAgAE9PT0xNTVm/fj2BgYFZji8zWf3OwHt/59zc/3SpqalYWFgQHh6eYd/b78W9L4z33fG9vf19Mmv7fedKIK8QQgghRN70SSZSBw8e5OLFiwwaNIgSJUpw584d7t69q/wv71euXCEuLk5ZJfkQZ86cITk5mcDAQGXVauPGjZ9i+Lni5OTE5s2bsbKyynTFqEyZMuTLl49Tp04p1x8fH8/169dxdXXNsl0DAwOaNGlCkyZN6NOnD3Z2dly8eBEnJ6dMA2KdnJy4fPlyrld2jh49SqlSpRgzZoyy7fbt2xrH5CSQ1t7enpCQEI3VwKNHjyol2j81JycnHj58iI6OzgeXIs/pbyOBvEIIIYQQ4n1yXWwiMTGRhw8fcv/+fX777TemTp1K06ZN+fbbb/H19aV+/fpUqlQJHx8ffvvtN06dOoWvry+urq6ZPmaWU2XKlCE5OZmFCxdy8+ZNVq9ezdKlSz+4vQ/Vp08fnjx5Qtu2bTl16hQ3b95k7969dO7cmZSUFIyNjenYsSPDhg0jLCyMy5cv07lzZ7S0tLJcwQkODmblypVcunRJuTYDAwNKlSoFvHlc7ddff+X+/ftKdcARI0Zw/Phx+vTpQ0REBNevX2fbtm3069cv2/Hb2Nhw584d1q9fT3R0NAsWLGDr1q0ax1hZWXHr1i0iIiL4888/SUxMzNCOj48P+vr6dOzYkUuXLhEWFka/fv3o0KGD8ljf22JiYlCpVPzxxx85us/vql+/PjVq1KBZs2bs2bOHmJgYjh07xtixYzlz5kyO2sjstzE1NSU1NVXjt8nsfgshhBBCCPG2XK9I7d69GwsLC3R0dMifPz+VK1dmwYIFdOzYUVkpCg0NpV+/ftSpUwctLS0aNGjAwoULP2qgjo6OzJkzhxkzZjBq1Cjq1KnDtGnT8PX1/ah2c6tYsWIcPXqUESNG4OnpSWJiIqVKlaJBgwbK9c+ZM4eePXvy7bffYmJiwvDhw7l7965SAOJdZmZmTJ8+ncGDB5OSkkLFihXZvn278j7QxIkT6dGjB2XKlCExMZG0tDQqVarEoUOHGDNmDC4uLqSlpVGmTJkMFfbi4+MZMGAAO3fu5N69e5iamlK0aFG6du2KSqXCy8uLcePG4e/vr5zTsmVLtmzZgru7O0+fPlXCcN9maGjInj17GDBgANWqVcPQ0JCWLVsyZ86cT3ez36JSqdi1axdjxoyhc+fOPHz4ED09PVq0aJHpxC0r7/42ACVKlND4bTK735/aPx3IK/lRQgghhBCfVq4CecWHSUhIoHjx4gQGBtKlS5d/rN+bN29Sq1YtzMzMCAgIoGLFiiQnJ3Pt2jVWrVpFjx49aNKkyT8ylpiYGKytrTl37hyOjo4f3Z6fnx9Pnz7VyLX6ECqVCkNDQxYsWPCP/Db/ViCvTKSEEEIIIXLmbwvkFe937tw5fvrpJ6Kjo/ntt9/w8fEBoGnTpv/oOHr37o2Ojg5nzpzB29ub8uXLU7FiRVq2bMnOnTtp3Lgx8P4AZYAlS5ZQpkwZdHV1KVeuHKtXr9bYf/XqVWrXro2+vj729vbs378/Q4Dvu94XbJwbbm5u9O/fn+HDh1OgQAHMzc01VtkArl+/jpOTE/ny5cPGxkajbH76b3P//n1at25N/vz5KViwIE2bNiUmJka5RkNDQ9atW6ect2XLFvT19bl48eIHjVsIIYQQQnyZ/rMTqTt37mQIhX37c+fOnY9qf/bs2VSuXJn69euTkJDA4cOHKVSo0Cca/fs9fvyYvXv30qdPnyxLw6dXnXtfgPLWrVsZMGAAQ4YM4dKlS/To0YNOnToRFhYGvKmo16xZMwwNDTl58iQ//PCDRjGLzISGhuLg4MD+/ftJS0vj2bNn/PTTT5ibm2daWj4nQkJCMDIy4uTJk8ycOZOJEyeyb98+ZYwtWrRAS0uLsmXL8uDBA6Xi4dSpUylUqBAvXrzA3d0dtVrNr7/+ypEjR1Cr1TRo0IDXr19jZ2fH7Nmz6d27N7dv3+bBgwd069aN6dOnKwHK75JAXiGEEEKIvOmTlT//0hQrVoyIiIhs93+oKlWqcPbs2Q8+/1O4ceMGaWlpGTKSChUqxKtXr4A3hTPq16//3gDl2bNn4+fnR+/evQEYPHgwJ06cYPbs2bi7u7N3716io6MJDw9XQmynTJmCh4dHluM7efIktWvXJigoSNkWGxtLnTp1MhS/yKlKlSoxYcIE4E1A8aJFizhw4AAeHh7s37+fyMhIYmJiKFGiBPDmfb+GDRsqRT3Wr1+PlpYWK1asUIpPBAUFYWZmRnh4ON988w29e/dm165ddOjQAV1dXapWrcqAAQOyHJME8gohhBBC5E3/2YmUjo5OrkuHf4nerRR46tQpUlNT8fHxITExMUcBypGRkXTv3l2jnVq1ajF//nwAoqKisLS0VCZRgEbobWYuXLjAyZMnM31fKjk5ObeXCbyZSL3t7cDcyMhISpYsqUyiAGrUqKFxfHrI8duBvgCvXr3SCP1dtWoVtra2aGlpcenSpSyrMYIE8gohhBBC5FX/2YlUXmdjY4NKpeLq1asa20uXLg28ya2CrIN1392eWYht+rbswnmz8r5g4w+RkzDed/e/O6aqVauydu3aDMcWLlxY+ff58+dJSEhAS0uLhw8fZrt6KYG8QgghhBB5k0yk8qiCBQvi4eHBokWL6NevX5bvSdnb2783QLl8+fIcOXJEo9T8sWPHlP12dnbcuXOHR48eKaXIT58+ne343hds/KmlX+eDBw+Uic/x48czjGnDhg0UKVIkywotT548wc/PjzFjxvDw4UMlLy19YppTEsgrhBBCCPFl+88Wm/i7hYeHo1KpePr06Sdv28rKinnz5r33uMWLF5OcnIyzszMbNmwgMjISlUrFwIEDuXr1Ktra2jkKUB42bBjBwcEsXbqU69evM2fOHLZs2cLQoUMB8PDwoEyZMnTs2JELFy5w9OhRpdhEVitV7ws2/tTq169PuXLl8PX15fz58xw+fDhDQQwfHx8KFSpE06ZNOXz4MLdu3eLQoUMMGDCAe/fuAWBpaUm+fPkYO3Ysc+bM4erVq7Ro0eKTj1cIIYQQQnzeZEUqC7///jvjxo3jl19+4dGjR0r4sL+/f4Z3a/5pp0+f1lhhUqlUbN26lWbNminb/P39CQ0N5dy5c0ydOpVRo0Zx7949jIyMOHbsGEOHDqV3795KifLsApSbNWvG/PnzmTVrFv3798fa2pqgoCDc3NwA0NbWJjQ0lK5du1KtWjVKly7NrFmzaNy4MT///DMTJkzIUAY9J8HGn5KWlhZbt26lS5cuVK9eHSsrKxYsWECDBg2UYwwNDfn1118ZMWIELVq04NmzZxQvXpx69ephYmLCjz/+CMC2bdvQ0dFRVtL279/Prl27aNSoUY7H808G8kqGlBBCCCHEpyeBvFlwcXEhKSmJadOmUbp0aR49esSBAweoVKkSXl7v/8M0PDwcd3d3/vrrL8zMzP7WsWY3kcquMuHf6ejRo9SuXZt+/frx66+//mvj+Ltldu+z828E8spESgghhBAi5ySQ9yM8ffqUI0eOMGPGDNzd3SlVqhTVq1dn1KhReHl5ERMTg0ql0pgcPH36FJVKRXh4uEZbR48epXLlyujr6/PVV19pBLcGBwdjZmbGjh07KFeuHIaGhrRq1YqEhARCQkKwsrIif/789OvXT+Nxt7cf7bOysgKgefPmqFQqrKysCA4OJiAggPPnz6NSqVCpVAQHBwNohOSmX8eWLVtwd3fH0NCQypUrZ3h3aPny5VhaWmJoaEjz5s2ZM2dOhsnh1q1b2bdvHzExMezfv5/u3btTtmxZFi5cmOk4chICnJ3t27dTtWpV9PX1KV26NAEBARrV/lQqFcuWLePbb7/F0NCQ8uXLc/z4cW7cuIGbmxtGRkbUqFFDoxpfdHQ0TZs2pWjRoqjVaqpVq8b+/fs1+s3pY5VCCCGEECJvk4lUJtJDeUNDQ0lMTPyotoYNG8bs2bM5ffo0RYoUoUmTJiQlJSn7X7x4wYIFC1i/fj27d+8mPDycFi1asGvXLnbt2sXq1av54Ycf2LRpU6btpxd1CAoKIjY2ltOnT9O6dWuGDBmCg4MDsbGxxMbGagTsvmvMmDEMHTqUiIgIbG1tadu2rTIpOXr0KD179mTAgAFERETg4eHBlClTMrTx7NkzevfujZ2dHX5+flSrVo2DBw9mOo73hQC/Lyx59erVtG/fnv79+3PlyhWWLVtGcHBwhnFNmjQJX19fIiIisLOzo127dvTo0YNRo0Zx5swZAPr27asc//z5cxo1asT+/fs5d+4cnp6eNG7c+KPCmSWQVwghhBAib5J3pDKho6NDcHAw3bp1Y+nSpTg5OeHq6kqbNm0yZBW9z4QJE5Rg2pCQEEqUKMHWrVvx9vYGICkpiSVLllCmTBkAWrVqxerVq3n06BFqtRp7e3vc3d0JCwvLdDKUXpbbzMxMI8dJrVajo6OjsS0rQ4cOVR5XDAgIwMHBgRs3bmBnZ8fChQtp2LChUljC1taWY8eOsWPHDo02fH19Nar6ZTeOffv2ZRsCXKVKlWwfBezUqRMjR46kY8eOwJuS7pMmTWL48OFKIG/6cen3ecSIEdSoUYNx48bh6ekJwIABA+jUqZNyfOXKlalcubLyffLkyWzdupVt27ZpTLhyQwJ5hRBCCCHyJlmRykLLli158OAB27Ztw9PTk/DwcJycnJRH03Lq7cIUBQoUoFy5ckRGRirbDA0NlUkUQNGiRbGyskKtVmtsSw+W/Tu8PTlMz3BK7y8qKipDuO77wnbf530hwOlhyVl9fvvtNyZOnKixStWtWzdiY2N58eJFpteVXpa9YsWKGttevXqlrBIlJCQwfPhwZSxqtZqrV69+1IrUqFGjiIuLUz5379794LaEEEIIIcTnQ1aksqGvr4+HhwceHh6MHz+erl27MmHCBA4fPgxohry+/bje+7xdEjyzENnsgmX/Dm/3lz62t4NsMwvj/Rg5DQHOSmpqKgEBAZmWHdfX11f+ndl1ZXetw4YNY8+ePcyePRsbGxsMDAxo1aoVr1+/zuGVZSSBvEIIIYQQeZNMpHLB3t6e0NBQ5XG62NhYqlSpApDlo2gnTpygZMmSAPz1119cu3YNOzu7TzqufPnyZche0tXV/SR5THZ2dpw6dUpjW/r7RTmR2ThyEgKcHScnJ6KiorCxscnxOHLi8OHD+Pn50bx5c+DNO1MxMTGftA8hhBBCCJE3fJKJlJubG46Ojn9bNbOYmBisra05d+4cjo6Of0sfb3v8+DHfffcdnTt3plKlShgbG3PmzBlmzpxJ06ZNMTAw4Ouvv2b69OlYWVnx559/MmzYMAClKly6iRMnUrBgQYoWLcqYMWMoVKhQjktlZ8bf358HDx5obLOysuLAgQPUqlULPT098ufPj5WVFbdu3SIiIoISJUpgbGz8QSsj6flSc+bMoXHjxhw8eJBffvklRytH6WN7dxxvhwDPmzeP5ORkevfurREC/K63y8mPHz+eb7/9FktLS7777ju0tLS4cOECa9as4fDhwx8cgmxjY8OWLVto3LgxKpWKcePG/W0rgZcCPLMtpymEEEIIIT5vX8SKlKWlJbGxsRQqVOgf6U+tVvP69Wu6d++Orq4uSUlJGBgY8PjxY1atWkVwcDCFCxcmNTUVZ2dnypUrx5AhQzIttjB9+nQGDBjA9evXqVy5Mtu2bUNXV/eDxzZ06FBWrVqlsS0wMJDBgwezfPlyihcvTkxMDC1btlTKmj99+pSgoCD8/Pxy3V+tWrUwMzNj4sSJjB07Fk9PT1JSUpRy7wYGBpQuXZp+/frRo0ePDOdnNY7169fj4OBA1apVMTY2zhACnB1PT0927NjBxIkTmTlzJvny5cPOzg57e/tcX9/b5s6dS+fOnalZsyaFChVixIgRf1uVPQnkFUIIIYT4sn2SQN6/e0Xq3/BuoK2/vz+bNm1i//79pKSkcO7cObp06YKTkxO//PLLP75q9k+ysrJi4MCBDBw4EABjY2MKFCjAyZMnef78uVJ6fP369dmWWX/bmjVrWLZsGWlpafTq1QsfH59sj89JwHFwcDADBw7MckUqKSkpw/tn/zQJ5BVCCCGE+Lz944G8qampDB8+nAIFCmBubo6/v7+yb86cOVSsWBEjIyMsLS3p3bs3z58/ByAuLg4DAwN2796t0d6WLVswMjJS3lN5OwA3PDwclUrFgQMHcHZ2xtDQkJo1axIVFaXRxuTJkylSpAjGxsZ07dqVkSNHftQkJ72Md/Hixfn222/p378/e/fu5eXLl8oxN2/ezDLc9vHjx7Rt25YSJUpgaGhIxYoV+emnn5T9y5Yto3jx4hkeJ2vSpIlS6tvf31/jGvz8/GjWrBmzZ8/GwsKCggUL0qdPH43iF7GxsXh5eWFgYIC1tTXr1q3LVbBsXFwc9+/f58aNGyxcuJDnz59To0YNzM3NsbGxYfLkyZQtW1YJ+s2JlStX0r59e9q3b8/KlSsz7N+1axe2trYYGBjg7u6e6btKwcHBlCxZUgkKfvz4scb+9Hu1atUqSpcujZ6eHmlpacTFxdG9e3eKFCmCiYkJdevW5fz588p558+fx93dHWNjY0xMTKhataryXtjt27dp3Lgx+fPnx8jICAcHB3bt2pXj6xZCCCGEEHnDJ5tIhYSEYGRkxMmTJ5k5cyYTJ05k3759bzrR0mLBggVcunSJkJAQDh48yPDhwwEwNTXFy8uLtWvXarS3bt06mjZtqlEG/F1jxowhMDCQM2fOoKOjQ+fOnZV9a9euZcqUKcyYMYOzZ89SsmRJlixZ8qkuFwADAwNSU1OV8Nr0MWUVbvvq1SuqVq3Kjh07uHTpEt27d6dDhw6cPHkSgO+++44///yTsLAwpb2//vqLPXv2ZLtiExYWRnR0NGFhYYSEhBAcHKxRpt3X15cHDx4QHh7O5s2b+eGHH3JVTv3169csXbqUihUrsnTpUgoUKMDXX38NgIODA2q1mujoaH7++WeNkuTv/qbpoqOjOX78ON7e3nh7e3Ps2DFu3ryp7Le1tcXLy4uYmBi0tLQ4fvy48ttu3LgRgJMnT9K5c2d69+5NREQE7u7uTJ48OUNfN27cYOPGjWzevFmZiHt5efHw4UN27drF2bNncXJyol69ejx58gQAHx8fSpQowenTpzl79iwjR45UVrL69OlDYmIiv/76KxcvXmTGjBnZ/jcqgbxCCCGEEHnTJ3tHqlKlSkoYatmyZVm0aBEHDhzAw8NDeSQMwNramkmTJtGrVy8WL14MvPnD1dfXlxcvXmBoaEh8fDw7d+5k8+bN2fY5ZcoUXF1dARg5ciT/196dx+WU/o8ff93tu5KdFFIpkWUQQ1mzDBlmbFmyM7bsjMm+k3UwM5Y0xr5+G0zWMtlChFETkyVmsg7ZS3V+f/h1Pm4VRdnm/Xw87sfDuc51rnNdl2bu3q5zrnezZs14+vQpRkZGLFy4kO7du6sJV8eOHcvu3bvVlbC39eeff7JkyRKqVauGubm5uhryquS2xYsXVxPbwvONHEJCQti4cSPVq1cnf/78NG7cmDVr1lC/fn0ANm7cSP78+dXjzFhZWfH999+jq6uLk5MTzZo1Y9++ffTs2ZM///yTvXv3cvz4cXUjh2XLllG2bNlsj7VgwYJaj/bZ2dmp54KDg9m0aROjRo1i/PjxWgFfeu6ml61YsYImTZpgZWUFQOPGjVmxYoUaCDVs2JC0tDStTS1mzZrFTz/9RJMmTQCYP38+Xl5ejBo1CvhfouCXVzaTk5NZtWqVutPi/v37OXv2LDdv3lQ335g9ezbbtm1j06ZN9OrVi/j4eIYPH67urvjiXMXHx9O6dWs1H1Xp0qVfOXeSkFcIIYQQ4tOUaytSLyY/heeJXdNXPUJDQ2nYsCHFixfH3Nyczp07c+fOHR49egQ8XyHQ09MjODgYgM2bN2Nubk6jRo2yfc93kUj27NmzmJmZYWxsjLOzMzY2NhlWXV7Vp9TUVKZMmUKFChWwtrbGzMyM3bt3ayV89fHxYfPmzSQlJQHPV9batWuHrq5ulv1ycXHROv/i3MfGxqKnp0flypXV8/b29moQ86ZGjhyJmZkZTk5OTJw4keHDh+Pv76+VONfc3DzDdampqQQFBdGxY0e1rGPHjgQFBanbpP/zzz/UqVOHsmXLqm01bdoUQG0zJiZGK9kxkOEYwNbWVg2iACIjI3n48KE6/+mfS5cuERcXB8CQIUPo0aMHDRo0YPr06Wo5wMCBA5k8eTK1atVi3LhxnDlz5pXzJAl5hRBCCCE+TbkWSGWVRPbKlSs0bdqU8uXLs3nzZiIjI1m0aBHwvyS2BgYGfPXVV6xZswZ4/lhf27Zt0dN79YLZq5KrvliW7m331XB0dCQqKoro6GiePHnC/v37M+QyelWfAgICmDt3LiNGjGD//v1ERUXh5eWllfC1efPmpKWlsWPHDq5evUp4eLhW0JGZVyXwzWrMbzsXw4cPJyoqiitXrvDw4UNmzpyJjs7rf5x27drF33//rf796unp0a5dO65du8bu3buz3bfs9t/U1FTrOC0tjaJFixIVFaX1iY2NVbewHz9+POfOnaNZs2bs378fZ2dntm7dCkCPHj24ePEinTp14uzZs1StWvWVuw0aGhpiYWGh9RFCCCGEEB+/XAuksnLixAlSUlIICAigRo0aODg4ZMiDBM9XYkJCQjh37hyhoaGv3cXtdRwdHd8qkWxmDAwMsLe3p1SpUm+Ukyk8PBxvb286duxIxYoVKV26NBcuXNCqY2xsTKtWrVi9ejVr167FwcGBKlWqvHGfnZycSElJ4dSpU2rZX3/99ca5ltIVKFAAe3t7ihUrlu2cUvB8k4l27dplCGR8fHzUTSecnZ05evSo1nUvH2enTmYqV67M9evX0dPT01o9s7e319pe38HBgcGDB7N7925atWpFYGCges7GxoY+ffqwZcsWhg4dytKlS7M9fiGEEEII8WnI8zxSZcqUISUlhYULF9K8eXMOHTrEDz/8kKGeh4cHhQsXxsfHBzs7O3Uzgzc1YMAAevbsSdWqValZsybr16/nzJkzr32nJS/Z29uzefNmDh8+jJWVFXPmzOH69euUK1dOq56Pjw/Nmzfn3Llzr12Neh0nJycaNGhAr169WLJkCfr6+gwdOhRjY+McBUC54datW/z6668EBwdTvnx5rXNdunShWbNm3Lp1iz59+qi5sXr37k1kZCSzZs3Sqj9w4EBq1qzJzJkzadmyJbt372bdunWYmLx6S/EGDRrg7u5Oy5YtmTFjBo6Ojvzzzz/s3LmTli1b4uLiwvDhw/nqq68oVaoU165d4/jx47Ru3RoAPz8/mjRpgoODA3fv3mX//v0Z/v6yQxLyCiGEEEJ83PI8kHJzc2POnDnMmDGD0aNHU6dOHaZNm5Yhea1Go6F9+/bMmjWLsWPHvvV9fXx8uHjxIsOGDePp06e0adMGX1/fDKtU75K/vz+XLl3Cy8sLExMTevXqRcuWLUlMTNSqV69ePfLnz09sbCwdOnR46/v+/PPPdO/enTp16lCkSBGmTZvGuXPnMDIyytb19+7dY9asWVqbhqSXW1lZERoaiqen52vbWbp0Kc+ePcuwTXnbtm3ZsGEDlpaWrFq1iiFDhrB582a+/PJLFixYQM2aNZkzZw6DBg1Sr6lRowbLli1j3LhxjB8/ngYNGmRrLBqNhp07dzJmzBi6devGrVu3KFKkCHXq1KFw4cLo6upy584dOnfuzI0bNyhQoACtWrVSN4xITU2lX79+XLt2DQsLCxo3bszcuXOzde8X5XVCXskdJYQQQgiRt3IlIe/HomHDhhQpUoRVq1a97668V9euXcPGxoa9e/e+cjfAdC8nJ06X00AKnm8IUbFiRa1VySJFimBgYMDYsWPp0aOHVh93795Nw4YNs9W2RqNh69attGzZMlv134d3lZBXAikhhBBCiDfzzhPyfmgeP37MnDlzOHfuHH/++Sfjxo1j7969amLbT4Wnpyf9+/enf//+WFpaYm1tzXfffaduxnD37l0aNmyImZkZRkZG1KhRA29vb+zs7KhTpw4rV67E0tKSbdu24eDggJGREQ0bNnyj3eXSEyXv2LGDihUrYmRkRPXq1Tl79qxap27duoSFhanHMTExPHnyhG+++UarPDQ0FH19fWrVqgWQIYHwhQsXqFOnDkZGRjg7O6s5y9KlJ3HesmVLlgmS4fkOkS4uLhgaGmJnZ0dAQIB6buHCheo25wDbtm1Do9Gom6UAeHl5MXr06BzPlRBCCCGE+Lh9soFU+iNctWvXpkqVKvz6669s3rxZfQTsxa2vX/6Eh4e/597nTFBQEHp6ekRERLBgwQLmzp3LsmXLAPD19SU2NpZChQqh0Wg4deoUsbGx7NmzB319fY4cOUJiYiKtW7fm2rVr6OjosH//fuzs7HBxcXmj/jRv3pzz58+jo6NDZGQkFStWVBP01q1bl9jYWBISEoDnAVPt2rWpV69ehkCqevXqmb7zlJaWRqtWrdDV1eXo0aP88MMPjBw5MtO+vCpBcmRkJG3atKFdu3acPXuW8ePH4+/vryYz9vT05Ny5c9y+fRuAAwcOUKBAAQ4cOABASkoKhw8fVnOZZUYS8gohhBBCfJry/B2p98XY2Ji9e/dmef7lx9ReVLx48TzoUd6xsbFh7ty5aDQaHB0dOXv2LHPnzsXT05Pg4GAOHTpEzZo1Abhz5w42NjacOnUKe3t73NzcAFi/fr3657i4OBo3bszUqVPfqD9z585VkxLfu3ePzz//nKlTp9KiRQt0dXXR19cnLCyM9u3bExYWhoeHB5UrVyYxMZELFy5QtmxZwsLCstxoY+/evcTExHD58mVKlCgBwNSpU9VkvS96VYLkOXPmUL9+ffz9/YHnO/VFR0cza9YsfH19KV++PNbW1hw4cIDWrVsTFhbG0KFD1Xeijh8/ztOnT/n888+znAtJyCuEEEII8Wn6ZFekXuflra9f/BgbG7/v7uVIjRo1tHbgc3d358KFC0RHR6Onp0f16tXVc9bW1jg6OhITEwM8Dzj19PT48ssv1fF7eXlhaWn5xlukv9hW1apVKVeuHHfv3sXc3BwTExOqVaumrj4dOHAAT09P9PT0qFWrFmFhYcTHx3Pp0iXq1auXafsxMTGULFlSDaLSx5yZVyVIjomJUR8dTFerVi0uXLhAamoqGo2GOnXqEBYWxr179zh37hx9+vQhNTWVmJgYwsLCqFy5MmZmZlnOhSTkFUIIIYT4NH2yK1Iia4qiZNj6PLOt0NPLLCwsMuwsCKiBVr58+V57zxfbr1u3LuvXr+fcuXM8efKEypUrA8+3wA8NDcXAwEB9nyur/r+q/Re9KkFyZvPwctuenp789NNPhIeHU7FiRSwtLalTpw4HDhwgLCzstZtsGBoavlHOMSGEEEII8WH7z65IfUoyS0xbtmxZnJ2dSUlJISIiQj13584dzp8/r5X7KCUlRStZcWxsLPfu3cPJyQl4novq2rVrXL9+Xes+x48fR0dHB3t7+yz7c/fuXc6fP6+2Bc8DqQsXLrBmzRo+//xzdHV1geeBVFhYGGFhYbi7u2e5PbuzszPx8fFaiZ1f3kQiO5ydnTl48KBW2eHDh3FwcFD7lP6e1KZNm9SgycPDg7179772/SghhBBCCPEJU8RHzcPDQzEzM1MGDx6s/Pnnn8qaNWsUU1NT5YcfflAURVG8vb0VZ2dnJTw8XImKilIaN26s2NvbK8nJyYqiKEpgYKCir6+vVKtWTTl69KgSGRmpuLu7KzVq1FDv8ezZM8XV1VXx8PBQDh48qFy8eFHZtm2bUrJkSeWbb75R64WGhiqA4uLiouzdu1c5e/as0qJFC6VkyZJKUlKSWu/JkyeKoaGhYm5urkyfPl0tT05OVkxMTBRzc3Nl4sSJWuO0tbVV5s6dqyiKoqSmpirOzs5K/fr1laioKOX3339XqlSpogDK1q1bFUVRlPDwcAVQ1q1bp7Zx9+5dBVBCQ0MVRVGUyMhIRUdHR5k4caISGxurrFy5UjE2NlYCAwPVa9LS0pQCBQoourq6yvbt2xVFUZSoqChFV1dX0dXVVRITE3P095WYmKgAOb5OCCGEEEK8G9n9fU1WpD4BnTt35smTJ1SrVo1+/foxYMAAevXqBUBgYCBVqlThiy++wN3dHUVR2Llzp9YjbyYmJowcOZIOHTrg7u6OsbEx69atU8/r6emxe/duSpcujY+PDy4uLowaNYoePXowZ86cDP2ZPn06gwYNokqVKiQkJBAcHIyBgYF6Pv2xvQcPHvD777+j0WjQaDQYGBjw+PFjHjx4QNmyZbMcr46ODlu3biUpKYlq1arRo0cPpkyZolUn/X2oMmXKZNlO5cqV2bBhA+vWraN8+fKMHTuWiRMn4uvrq9bRaDTqqlPt2rWB5+9d5cuXj0qVKr0yt4AQQgghhPh0/acS8n6KPD09cXNz08qxlBMrV67Ez8/vjTeWeFFYWBh169bl7t27WFpaZusaX19fbty4QWBgoFZ5wYIF1cfrPiXvIiGvJOMVQgghhHhz//mEvOLjYWhoSJEiRbQ+8+fPx9XVFVNTU2xsbPjmm294+PAhAImJiRgbGxMSEqLVzpYtWzA1NeXhw4dqQt4Xt7k/d+4czZo1w8LCAnNzc2rXrk1cXBzwfAOKiRMnUqJECQwNDXFzc9NqP7sJfoUQQgghxH+DBFLig6Sjo8OCBQv4448/CAoKYv/+/YwYMQJ4vktgs2bNWL16tdY1a9aswdvbO9PtyP/++2/q1KmDkZER+/fvJzIykm7duqnJeefPn09AQACzZ8/mzJkzeHl50aJFCy5cuKDVzqsS/GZGEvIKIYQQQnya5NE+8V75+vryyy+/aO3Q16RJEzZu3KhVb+PGjfTt25fbt28DsHXrVjp37syNGzcwMTHh/v37FC5cmM2bN9O0aVMuX75MqVKlOHXqFG5ubnz77besW7eO2NhYrffD0hUvXpx+/frx7bffqmXVqlXjs88+Y9GiRWp7y5Yto3v37gBER0fj4uJCTEyM1q6ELxo/fnymCXnl0T4hhBBCiA+TPNonPhp169YlKipK/SxYsIDQ0FAaNmxI8eLFMTc3p3Pnzty5c4dHjx4B0KxZM/T09AgODgZg8+bNmJub06hRo0zvERUVRe3atTMNou7fv88///yTaXLe9MTF6V6V4DczkpBXCCGEEOLTJIGUeO9MTU2xt7dXP8nJyTRt2pTy5cuzefNmIiMjWbRoEQDPnj0DwMDAgK+++oo1a9YAzx/ra9u2LXp6meeYNjY2fm0/MkvO+3LZqxL8ZsbQ0BALCwutjxBCCCGE+PhJICU+OCdOnCAlJYWAgABq1KiBg4ODVvLddD4+PoSEhHDu3DlCQ0Px8fHJss0KFSoQHh6uBmIvsrCwoFixYpkm530xcbEQQgghhBDpMv/neyHeozJlypCSksLChQtp3rw5hw4d4ocffshQz8PDg8KFC+Pj44OdnR01atTIss3+/fuzcOFC2rVrx+jRo8mXLx9Hjx6lWrVqODo6Mnz4cMaNG0eZMmVwc3MjMDCQqKioDBta5JY/JnjJ6pQQQgghxEdMVqTEB8fNzY05c+YwY8YMypcvz+rVq5k2bVqGehqNhvbt23P69OlXrkYBWFtbs3//fh4+fIiHhwdVqlRh6dKl6qN6AwcOZOjQoQwdOhRXV1dCQkIIDg7OMjGwr6/va+8phBBCCCE+XbJrnxA5kJqaikajoVu3bty7d49t27bl6PrcSMgru/IJIYQQQuQd2bVPCMDT05P+/fvTv39/LC0tsba25rvvviP93w/u3r1L586dsbKywsTEhCZNmmjljlq5ciWWlpZs374dZ2dnDA0N6dq1K0FBQfzf//0fGo0GjUZDWFjYexqhEEIIIYR4H+QdKfHJCwoKonv37kRERHDixAl69eqFra0tPXv2xNfXlwsXLhAcHIyFhQUjR46kadOmREdHq4/9PX78mGnTprFs2TKsra0pUqQIT58+5f79+wQGBgKQP3/+TO+dlJREUlKSeiwJeYUQQgghPg0SSIlPno2NDXPnzkWj0eDo6MjZs2eZO3cunp6eBAcHc+jQIWrWrAnA6tWrsbGxYdu2bXz99dfA8y3XFy9eTMWKFdU2jY2NSUpKokiRIq+897Rp0zJNyCuEEEIIIT5u8mif+OTVqFFDKx+Uu7s7Fy5cIDo6Gj09PapXr66es7a2xtHRUSsRr4GBgVYi3pyQhLxCCCGEEJ8mWZES4iUvJ+I1NjbOkJg3uwwNDTE0NMytrgkhhBBCiA+ErEiJT97Ro0czHJctWxZnZ2dSUlKIiIhQz925c4fz58+/NhGvgYEBqampedJfIYQQQgjx4ZMVKfHJu3r1KkOGDKF3796cPHmShQsXEhAQQNmyZfH29qZnz578+OOPmJubM2rUKIoXL463t/cr27Szs2PXrl3ExsZibW1Nvnz51M0pskMS8gohhBBCfNxkRUp88jp37szNmzdxcnKiT58+DBgwgF69egEQGBhIlSpV+OKLL3B3d0dRFHbu3PnaoKhnz544OjpStWpVChYsyKFDh97FUIQQQgghxAdCEvL+R/j6+maaQDYsLIy6devi7e3N//3f/73VPQIDA7Gzs6Nu3bpqWYECBahatSrTp09Xd727ePEiY8aM4cCBA/z7778UKFCAKlWqMGvWLBwcHF55j8uXL1OqVClOnTqFm5vba/vk6emJm5sbfn5+Obour0hCXiGEEEKID5sk5BU5Mn/+fBISEtQPPA+M0o+vXLmidb5NmzY0btxYq6xt27Zqe7GxsSQkJLBjxw7u3r1L48aNSUxMJDk5mYYNG3L//n22bNlCbGws69evp3z58iQmJr6v4QshhBBCCJEjEkgJAPLly0eRIkXUD4ClpaV6XLJkSa3zxsbGGBoaZihLV6hQIYoUKUK1atUICAjg+vXrHD16lOjoaC5evMjixYupUaMGtra21KpViylTpvDZZ5+9tp+lSpUCoFKlSmg0Gjw9PQFIS0tj4sSJlChRAkNDQ9zc3AgJCcmynbS0NHr27ImDgwNXrlwB4Ndff6VKlSoYGRlRunRpJkyYQEpKinqNRqNh2bJlfPnll5iYmFC2bFmCg4Nf2d+kpCTu37+v9RFCCCGEEB8/CaREnksPsJ49e0bBggXR0dFh06ZNb7Tr3bFjxwDYu3cvCQkJbNmyBXi+ohYQEMDs2bM5c+YMXl5etGjRgqVLlzJv3jytNpKTk2nTpg0nTpzg4MGD2NrasmvXLjp27MjAgQOJjo7mxx9/ZOXKlUyZMkXr2gkTJtCmTRvOnDlD06ZN8fHx4d9//82yv9OmTSNfvnzqx8bGJsdjFkIIIYQQHx4JpP5Dtm/fjpmZmdanSZMmeXrPO3fuMGHCBMzNzalWrRrFixdnwYIFjB07FisrK+rVq8ekSZO4ePFittorWLAg8DxxbpEiRcifPz8As2fPZuTIkbRr1w5HR0dmzJiBm5tbhiDq4cOHNGvWjOvXrxMWFkahQoUAmDJlCqNGjaJLly6ULl2ahg0bMmnSJH788Uet6319fWnfvj329vZMnTqVR48eqcFdZiQhrxBCCCHEp0kCqf+QunXrEhUVpfVZtmxZntyrRIkSmJmZUaBAAWJiYti4caMatPTr14/r16/zyy+/4O7uzsaNG3FxcWHPnj1vdK/79+/zzz//UKtWLa3yWrVqERMTo1XWvn17Hj58yO7du8mXL59aHhkZycSJE7WCzJ49e5KQkMDjx4/VehUqVFD/bGpqirm5OTdv3syyb4aGhlhYWGh9hBBCCCHEx0/ySP2HmJqaYm9vr1V27dq1PLlXeHg4FhYWFCxYMNPgwdzcnBYtWtCiRQsmT56Ml5cXkydPpmHDhm98T41Go3WsKEqGsqZNm/LLL79w9OhR6tWrp5anpaUxYcIEWrVqlaFdIyMj9c8vb4uu0WhIS0t74z4LIYQQQoiPkwRSIk+UKlUKS0vLbNXVaDQ4OTlx+PDh19Y1MDAA0Hq/ysLCgmLFinHw4EHq1Kmjlh8+fJhq1appXd+3b1/Kly9PixYt2LFjBx4eHgBUrlyZ2NjYDIFmXpGEvEIIIYQQH7eP9tE+T09P/Pz8cq09X19fWrZs+U7vmZWwsDA0Gg337t3LlfZ8fX3Zv39/rrT1tooVK4arqyubNm0iOjoajUZD//79WbFiBd7e3q+9vlChQhgbGxMSEsKNGzfULdOHDx/OjBkzWL9+PbGxsYwaNYqoqCgGDRqUoY0BAwYwefJkPD09mTp1KgBjx47l559/Zvz48Zw7d46YmBjWr1/Pd999l7sTIIQQQgghPgmyIiXeKV1dXaysrJgwYQKXL1/GzMyMAwcOMGHCBAYPHvza6/X09FiwYAETJ05k7Nix1K5dm7CwMAYOHMj9+/cZOnQoN2/exNnZmeDgYMqWLcv48ePZsGGDVjt+fn7cu3eP6dOn4+npiZeXF9u3b2fixInMnDkTfX19nJyc6NGjR57MQ/lxu94oIa8k4xVCCCGE+DBIIPUfUblyZbZt25ah3NPTE0VRMpRnVpYuOTmZlStXZnouq/bS6erq0qpVq2yv7D179izDe0k9evTIEODo6OgwduxYxo4dm2k7BgYGGfo1fvx4xo8frx57eXnh5eWVZV8yG1durRoKIYQQQoiPy0fxaN+jR4/o3LkzZmZmFC1alICAAK3zd+/epXPnzlhZWWFiYkKTJk24cOGCen78+PG4ublpXTNv3jzs7Owy3GvChAkUKlQICwsLevfuTXJycpb9Sk5OZsSIERQvXhxTU1OqV69OWFhYtsZ05coVmjdvjpWVFaampri4uLBz585M6965c4f27dtTokQJTExMcHV1Ze3atVp1Nm3ahKurK8bGxlhbW9OgQQMePXqkVWf27NkULVoUa2tr+vXrx7Nnz7LVVzs7OyZPnoyvry/58uWjZ8+ewPN3kOrUqYOxsTE2NjYMHDhQ6543b96kefPmGBsbU6pUKVavXp2hbY1GowZ4ly9fRqPRsGHDBjw9PTEyMuKXX34BIDAwkHLlymFkZISTkxOLFy/WaufatWu0a9eO/PnzY2pqStWqVYmIiGDlypVMmDCB06dPo9Fo0Gg0ahCY2b23bNlC3bp1MTExoWLFihw5ckTrPkuXLsXGxgYTExO+/PJL5syZ88p3wSQhrxBCCCHEp+mjCKSGDx9OaGgoW7duZffu3YSFhREZGame9/X15cSJEwQHB3PkyBEURaFp06bZDhTS7du3j5iYGEJDQ1m7di1bt25lwoQJWdbv2rUrhw4dYt26dZw5c4avv/6axo0bawVxWenXrx9JSUn8/vvvnD17lhkzZmBmZpZp3adPn1KlShW2b9/OH3/8Qa9evejUqRMREREAJCQk0L59e7p160ZMTAxhYWG0atVKawUlNDSUuLg4QkNDCQoKYuXKlVmuKmVm1qxZlC9fnsjISPz9/Tl79ixeXl60atWKM2fOsH79eg4ePEj//v3Va3x9fbl8+TL79+9n06ZNLF68+JVbhadr164dERER6Orq0r9/f4yMjOjevTvGxsbExMQwdepU/P39CQoKAp7nhvLw8OCff/4hODiY06dPM2LECNLS0mjbti1Dhw7FxcWFhIQEEhISaNu2bZb3HjNmDMOGDSMqKgoHBwfat29PSkoKAIcOHaJPnz4MGjSIqKgoGjZsmCFh78skIa8QQgghxKfpg3+07+HDhyxfvpyff/5Z3Ro7KCiIEiVKAHDhwgWCg4M5dOgQNWvWBGD16tXY2Niwbds2vv7662zfy8DAgBUrVmBiYoKLiwsTJ05k+PDhTJo0CR0d7ZgzLi6OtWvXcu3aNYoVKwbAsGHDCAkJITAwUN3EICvx8fG0bt0aV1dXAEqXLp1l3eLFizNs2DD1eMCAAYSEhLBx40aqV69OQkICKSkptGrVCltbWwC13XRWVlZ8//336Orq4uTkRLNmzdi3b5+6uvQ69erV0+pD586d6dChg/qIXtmyZVmwYAEeHh4sWbKE+Ph4fvvtN44ePUr16tUBWL58OeXKlXvtvb799lt8fX3V49q1azNixAjatGlD8eLFKVWqFNHR0fz444906dKFNWvWcOvWLY4fP64m6H1x9z0zMzP09PQoUqTIa+89bNgwmjV7/h7ShAkTcHFx4a+//sLJyYmFCxfSpEkTdR4cHBw4fPgw27dvz7K90aNHM2TIEPX4/v37EkwJIYQQQnwCPvhAKi4ujuTkZNzd3dWy/Pnz4+joCEBMTAx6enrqL+sA1tbWODo6ZkjG+joVK1bExOR/GwC4u7vz8OFDrl69qgYo6U6ePImiKDg4OGiVJyUlYW1t/dp7DRw4kL59+7J7924aNGhA69attZK9vig1NZXp06ezfv16/v77b5KSkkhKSsLU1FTtd/369XF1dcXLy4tGjRrx1VdfYWVlpbbh4uKCrq6uely0aFHOnj37+kn5/6pWrap1HBkZyV9//aX1uJ6iKKSlpXHp0iXOnz+Pnp6e1nVOTk7Z2hK9SZMmaiB069Ytrl+/zpgxY/D391frpKSkqAl1o6KiqFSpkhpEvY0X/w6KFi0KPH9E0cnJidjYWL788kut+tWqVXtlIGVoaIihoeFb90sIIYQQQnxYPvhA6lUbF7zq/IvJWHV0dDLUy8ljfy8ndYXnCVx1dXWJjIzUClCALB/Re1GPHj3w8vJix44d7N69m2nTphEQEMCAAQMy1A0ICGDu3LnMmzcPV1dXTE1N8fPzU9/f0tXVZc+ePRw+fJjdu3ezcOFCxowZQ0REBKVKlQLePpFsetD24vh79+7NwIEDM9QtWbIksbGx6n1y6sV7pfdx6dKlWsEyoM67sbFxju+RlRfnKb3v6X3ILMHv634+hRBCCCHEp+mDD6Ts7e3R19fn6NGjlCxZEni+ucT58+fx8PDA2dmZlJQUIiIi1Ef77ty5w/nz59XHyAoWLMj169e1fhGOiorKcK/Tp0/z5MkT9Rfzo0ePYmZmpj5G+KJKlSqRmprKzZs3qV279huNzcbGhj59+tCnTx9Gjx7N0qVLMw2kwsPD8fb2pmPHjsDzX+wvXLig9ZicRqOhVq1a1KpVi7Fjx2Jra8vWrVu1HivLTZUrV+bcuXNZJrAtV64cKSkpnDhxQk2KGxsbm+Nd7goXLkzx4sW5ePEiPj4+mdapUKECy5Yt499//810VcrAwEArge+bcnJy4tixY1plJ06ceKO2JCGvEEIIIcTH7YPfbMLMzIzu3bszfPhw9u3bxx9//IGvr6/6zlLZsmXx9vamZ8+eHDx4kNOnT9OxY0eKFy+uJnj19PTk1q1bzJw5k7i4OBYtWsRvv/2W4V7Jycl0796d6OhofvvtN8aNG0f//v0zvB8Fz9+P8fHxoXPnzmzZsoVLly5x/PhxZsyYkeXuey/y8/Nj165dXLp0iZMnT7J///4s3x+yt7dXV5xiYmLo3bs3169fV89HREQwdepUTpw4QXx8PFu2bOHWrVvZeh/pTY0cOZIjR47Qr18/oqKi1HfV0gNBR0dHGjduTM+ePYmIiCAyMpIePXq80erR+PHjmTZtGvPnz+f8+fOcPXuWwMBA5syZw+XLl/H19cXKyoqWLVuycOFCNBoNP//8s7rjnp2dHZcuXSIqKorbt2+TlJT0RmMeMGAAO3fuZM6cOVy4cIEff/yR33777Y1W3YQQQgghxMftg1+Rguc7xj18+JAWLVpgbm7O0KFDSUxMVM8HBgYyaNAgvvjiC5KTk6lTpw47d+5UH9MqV64cixcvZurUqUyaNInWrVszbNgwfvrpJ6371K9fn7Jly1KnTh2SkpJo166dVp6hlwUGBjJ58mSGDh3K33//jbW1Ne7u7jRt2vS1Y0pNTaVfv35cu3YNCwsLGjduzNy5czOt6+/vz6VLl/Dy8sLExIRevXrRsmVLdQ4sLCz4/fffmTdvHvfv38fW1paAgACaNGny2n68qQoVKnDgwAHGjBlD7dq1URSFMmXKaO2IFxgYSI8ePfDw8KBw4cKULFmSJ0+eMHjwYIYNG6Zu0vHw4cNX3qtHjx6YmJgwa9YsRowYgampKa6urlq5qBYvXsyKFSv47rvvMDY2ZuHChSxatAiA1q1bq9ua37t3j8DAQK3NLLKrVq1a/PDDD0yYMIHhw4dTqlQpBg8ezPfff5/jtt4kIa8k4xVCCCGE+HBoFHnJQ7wjvr6+3Lhxg8DAQFJSUoiOjqZbt27Url07Q16s7Lp8+TKlSpXi1KlTGXKFZSY1NRWNRpPpKmNOeHp64ubmxqNHj/jzzz8JDw/P1nX3799/vg263wYJpIQQQgghPkDpv68lJia+8lWMD/7RPvFpMTQ0pEiRIpQoUYJGjRrRtm1bdu/erZ5/XeLdY8eOUalSJYyMjKhatSqnTp3SOh8WFoZGo1HfxVq5ciWWlpZs374dZ2dnDA0NuXLlSraSKR86dAgPDw9MTEywsrLCy8uLiRMn0qJFCw4cOMD8+fNZtmwZBw8e5PLly3kxXUIIIYQQ4gMlgVQeadKkCWZmZpl+Xpdj6l0KDw/Psp/Z2X3wbVy8eJGQkBD1EcylS5cyZswYpkyZkmni3UePHvHFF1/g6OhIZGQk48eP18ptlZXHjx8zbdo0li1bxrlz5yhUqNBrkylHRUVRv359XFxcOHLkCAcPHqR58+ZERkZy5MgRNBoNlpaWTJ8+nYSEhCxzQyUlJXH//n2tjxBCCCGE+Ph9FO9IfYyWLVvGkydPMj2XG/mOckvVqlUz3cEwr2zfvh0zMzNSU1N5+vQpAHPmzAFg0qRJBAQE0KpVK4AMiXdXr15NamqqVtLka9eu0bdv31fe89mzZyxevJiKFSsC2UumPHPmTKpWraq1Iubi4kL//v2B/z3aN3LkyFfee9q0aUyYMOENZkoIIYQQQnzIJJDKI8WLF3/fXcgWY2PjLLcwzwt169ZlyZIlPH78mGXLlnH+/HkGDBjArVu3uHr1Kt27d6dnz55q/RcT78bExGSaNPl1DAwMtBLtZieZclRUFF9//fVbjRVg9OjRWlvQ379/P8vVKyGEEEII8fGQQEq8U6ampmrgtmDBAurWrcuECRPUlZ5XJd59031RjI2NtbYoz04y5dxK8mtoaIihoWGutCWEEEIIIT4cEkiJ92rcuHE0adKEvn37vjbxrrOzM6tWrcqQNDmnspNMuUKFCuzbty/Lx/LeNsmvJOQVQgghhPi4yWYT4r3y9PTExcWFqVOnvjLxLkCHDh3Q0dFRkybv3LmT2bNn5/ie2UmmPHr0aI4fP84333zDmTNn+PPPP1myZAm3b98Gnif5jYiI4PLly9y+fZu0tLTcmxQhhBBCCPHBkxUp8d4NGTKErl278tdff7Fs2bIsE++amZnx66+/0qdPHypVqoSzszMzZsygdevWOb7n65IpOzg4sHv3br799luqVauGsbEx1atXp3379sDzzSm6dOmCs7MzT5484dKlS9jZ2WX7/tlJyCt5o4QQQgghPlySkFeIXJCcnIyBgcFr6+UkIa8EUkIIIYQQ754k5P0PSEpKYuDAgRQqVAgjIyM+//xzjh8/DvwvMe2+ffuoWrUqJiYm1KxZk9jY2Gy1PX78eNzc3FixYgUlS5bEzMyMvn37kpqaysyZMylSpAiFChViypQpWtfFx8fj7e2NmZkZFhYWtGnThhs3bqjnfX19admypdY1fn5+eHp6qsebNm3C1dUVY2NjrK2tadCgAY8ePVLPvy5p79mzZ6lXr556fa9evXj48CEAu3btwsjISE3Ym27gwIF4eHiox4cPH6ZOnToYGxtjY2PDwIEDtfpgZ2fH5MmT8fX1JV++fFo7DQohhBBCiE+fBFIfsREjRrB582aCgoI4efIk9vb2eHl58e+//6p1xowZQ0BAACdOnEBPT49u3bplu/24uDh+++03QkJCWLt2LStWrKBZs2Zcu3aNAwcOMGPGDL777jt1wwdFUWjZsiX//vsvBw4cYM+ePcTFxdG2bdts3zMhIYH27dvTrVs3YmJiCAsLo1WrVuqOfa9L2vv48WMaN26MlZUVx48fZ+PGjezdu1fdFbBBgwZYWlqyefNm9Z6pqals2LBB3eTi7NmzeHl50apVK86cOcP69es5ePCg2ka6WbNmUb58eSIjI/H39890PJKQVwghhBDiE6WIj9LDhw8VfX19ZfXq1WpZcnKyUqxYMWXmzJlKaGioAih79+5Vz+/YsUMBlCdPnry2/XHjxikmJibK/fv31TIvLy/Fzs5OSU1NVcscHR2VadOmKYqiKLt371Z0dXWV+Ph49fy5c+cUQDl27JiiKIrSpUsXxdvbW+tegwYNUjw8PBRFUZTIyEgFUC5fvpxpv2xsbJQ1a9ZolU2aNElxd3dXFEVRfvrpJ8XKykp5+PCh1rh1dHSU69evK4qiKAMHDlTq1aunnt+1a5diYGCg/Pvvv4qiKEqnTp2UXr16ad0jPDxc0dHRUefO1tZWadmyZaZ9fNG4ceMUIMPHxm+DYjty+ys/QgghhBDi3UtMTFQAJTEx8ZX1ZEXqIxUXF8ezZ8+oVauWWqavr0+1atWIiYlRy15MRFu0aFEAbt68ma172NnZYW5urh4XLlwYZ2dndHR0tMrS24uJicHGxkYr4ayzszOWlpZafXqVihUrUr9+fVxdXfn6669ZunQpd+/eBdBK2mtmZqZ+Jk+eTFxcnNqHihUrYmpqqrZZq1Yt0tLS1McafXx8CAsL459//gFg9erVNG3aFCsrKwAiIyNZuXKl1j28vLxIS0vj0qVLartVq1Z97XhGjx5NYmKi+rl69Wq25kEIIYQQQnzYZNe+j5Ty/x91ezHRbHr5i2X6+vrqn9PLs7tV94vXpl+fWVl6ey/fO7M+6ejoZEis++zZM/XPurq67Nmzh8OHD7N7924WLlzImDFjiIiIwMTk+eYMr0vam1kf0vsKUK1aNcqUKcO6devo27cvW7duJTAwUK2XlpZG7969GThwYIY2SpYsqf75xWAtK5KQVwghhBDi0ySB1EfK3t4eAwMDDh48SIcOHYDnAcmJEyfU7cLfNWdnZ+Lj47l69aq6KhUdHU1iYiLlypUDoGDBgvzxxx9a10VFRWUI+GrVqkWtWrUYO3Ystra2bN26lSFDhmQraW9QUBCPHj1SA51Dhw6ho6ODg4ODWq9Dhw6sXr2aEiVKoKOjQ7Nm/9shr3Llypw7dw57e/vcmZhMSEJeIYQQQoiPmzza95EyNTWlb9++DB8+nJCQEKKjo+nZsyePHz+me/fuREVFAZCYmPjO+tSgQQMqVKiAj48PJ0+e5NixY3Tu3BkPDw/1Mbh69epx4sQJfv75Zy5cuMC4ceO0AquIiAimTp3KiRMniI+PZ8uWLdy6dUsNxCpXrsy4ceOyTNrr4+ODkZERXbp04Y8//iA0NJQBAwbQqVMnChcurN4nvY9Tpkzhq6++wsjISD03cuRIjhw5Qr9+/YiKiuLChQsEBwczYMCAdzGNQgghhBDiIyArUh+x6dOn8/fff9OkSRN0dHRwd3dn165d6rs+75pGo2Hbtm0MGDCAOnXqoKOjQ+PGjVm4cKFax8vLC39/f0aMGMHTp0/p1q0bnTt35uzZswBYWFjw+++/M2/ePO7fv4+trS0BAQE0adIEeB5InT59mpUrV2aatNfExIRdu3YxaNAg3Nzc0NHRwdfXVw204PkKWKVKlahQoQJnzpzB19dX63HA/Pnz4+zszPHjx/n5559RFIUyZcpQoEAB3Nzc1CD1bbwuIa/kkBJCCCGE+LBJQt6PXI8ePTAzM2PZsmVER0er7/CEhYVRt25d7t69i6Wl5Tvt07NnzzK8S5Vbxo8fz7Zt27IVzPj6+nLv3j22bdumVZ4eSF26dAk7Ozt1rmJjY7GwsODWrVtMnjyZXbt2cf78eQoVKpTje2cluwl5JZASQgghhHg/JCHvf8CjR4/YsGEDffv25YsvvmDlypUZ6hw6dIiKFStiZGRE9erV1ZUfgJUrV2JpacmuXbsoV64cZmZmNG7cmISEBLVOWloaEydOpESJEhgaGuLm5kZISIh6/vLly2g0GjZs2ICnpydGRkb88ssvauLdqVOnUrhwYSwtLZkwYQIpKSkMHz6c/PnzU6JECVasWKHV35EjR+Lg4ICJiQmlS5fG399fazOKvFSoUCGKFCmCq6sr3333HYmJiURERLyTewshhBBCiI+LBFIfsfXr1+Po6IijoyMdO3YkMDAww454w4cPZ/bs2Rw/fpxChQrRokULnj17houLC3369CExMZFmzZpx5coV0tLS2L17N61bt1avnz9/PgEBAcyePZszZ87g5eVFixYtuHDhgtZ9Ro4cycCBA4mJicHLywuA/fv3888///D7778zZ84cxo8fzxdffIGVlRURERH06dOHPn36aG0Jbm5uzsqVK4mOjmb+/PksXbqUuXPn5uEsZvT48WN1F7+3XVmThLxCCCGEEJ8mCaQ+YsuXL6djx44ANG7cmIcPH7Jv3z6tOuPGjaNhw4a4uroSFBTEjRs32Lp1Kzt37mTChAkA7Nq1izNnznDmzBnGjh3LX3/9pV4/e/ZsRo4cSbt27XB0dGTGjBm4ubkxb948rfv4+fnRqlUrSpUqRbFixYDn7xotWLAAR0dHunXrhqOjI48fP+bbb7+lbNmyjB49GgMDAw4dOqS2891331GzZk3s7Oxo3rw5Q4cOZcOGDXkxfRmUKFFCzRs1d+5cqlSpQv369d+qzWnTppEvXz7182KOLSGEEEII8fGSQOojFRsby7Fjx2jXrh0Aenp6tG3bNsOjcu7u7uqf8+fPj6OjIzExMdja2lK4cGFMTEyoX78+9vb22NvbU7FiRW7fvg08fz70n3/+0Ur6C88T3L6cYDez5LQuLi4Zkve6urqqx7q6ulhbW2slCN60aROff/45RYoUwczMDH9/f+Lj43M6PW8kPDyckydPsnbtWmxtbVm5cuVbr0hJQl4hhBBCiE+T7Nr3kVq+fDkpKSkUL15cLVMUBX19fe7evfvKa7NK2Jt+7uXHA1+X9BcyT06b04S+R48epV27dkyYMAEvLy/y5cvHunXrCAgIeOV4smJhYcGVK1cylN+7dw+AfPnyaZWXKlUKS0tLHBwcePr0KV9++SV//PHHWyXUlYS8QgghhBCfJgmkPkIpKSn8/PPPBAQE0KhRI61zrVu3ZvXq1ZQvXx54Hpyk7+R39+5dzp8/j5OTU7buY2FhQbFixTh48CB16tRRyw8fPky1atVyaTT/c+jQIWxtbRkzZoxallkglF1OTk6sXbuWp0+fauWJOn78OAULFnzlNvGdOnVi4sSJLF68mMGDB79xH7IiCXmFEEIIIT5u8mjfR2j79u3cvXuX7t27U758ea3PV199xfLly9W6EydOZN++fUyePJlChQpRoEABWrZsme17DR8+nBkzZrB+/XqKFy9O/fr1iYqKYtCgQW89Dk9PT/7991/12N7envj4eNatW0dcXBwLFixg69atb9y+j48Penp6dOrUiRMnThAXF8cvv/zCtGnTGD58+Cuv1dHRwc/Pj+nTp/P48WO1/MmTJ0RFRWl9XnynTAghhBBC/DdIIPUe3bx5k969e1OyZEkMDQ0pUqQIXl5eHDlyBPhfgtuXLV++nAYNGmR4NA2er0hFRUWpm0FMnz6dQYMGMWnSJMqXL09wcDAGBgav7Vt67qmBAwcydOhQhg4dyq1bt7h16xbBwcGULVv2jcedFW9vbwYPHkz//v1xc3Pj8OHD+Pv7v3F7+fLlIzw8HEVRaNmyJRUrVmTmzJlMmjSJoUOHvvb6bt268ezZM77//nu17Pz581SqVEnr06NHjxz3rfy4XdiN2pHhI4QQQgghPg6SkPc9ql27Ns+ePWPatGmULl2aGzdusG/fPipUqECzZs3QaDRs3bo1RytIAHZ2dvj5+eHn5/dG/Vq5ciV+fn7qu0R5xdPTM9MdALMjOTk5WwFhXkhPOPwm/X9dQl5JxCuEEEII8X5JQt4P3L179zh48CAzZsygbt262NraUq1aNUaPHk2zZs2ws7MD4Msvv0Sj0ajHcXFxeHt7U7hwYczMzPjss8/Yu3ev2q6npydXrlxh8ODBaDQadVOI9OS76U6fPk3dunUxNzfHwsKCKlWqcOLECcLCwujatSuJiYnq9ePHjweeB2gvBg337t2jV69eFC5cGCMjI8qXL8/27dsBuHPnDu3bt6dEiRKYmJjg6urK2rVr33i+7OzsmDx5Mr6+vuTLl4+ePXsCz9/XqlOnDsbGxtjY2DBw4EAePXqkdd2kSZPo0KEDZmZmFCtWjIULF2q1HR8fj7e3N2ZmZlhYWNCmTRtu3Lihnh8/fjxubm6sWLGC0qVLY2hoSJcuXThw4ADz589X5+ny5ctvPD4hhBBCCPFxkUDqPUnPV7Rt2zaSkpIynD9+/DgAgYGBJCQkqMcPHz6kadOm7N27l1OnTuHl5UXz5s3VLcK3bNlCiRIlmDhxIgkJCSQkJGR6fx8fH0qUKMHx48eJjIxk1KhR6OvrU7NmTebNm4eFhYV6/bBhwzJcn5aWRpMmTTh8+DC//PIL0dHRTJ8+HV1dXQCePn1KlSpV2L59O3/88Qe9evWiU6dOREREvPGczZo1i/LlyxMZGYm/vz8mJibUqlWLiIgIdHR0uHPnDosWLcLS0pLw8HCt6ypUqMDJkycZPXo0gwcPZs+ePQDqY3///vsvBw4cYM+ePcTFxdG2bVute//1119s2LCBzZs3ExUVxYIFC3B3d6dnz57qPGWWI0oS8gohhBBCfJpk1773RE9Pj5UrV9KzZ09++OEHKleujIeHB+3ataNChQoULFgQeP6uUpEiRdTrKlasSMWKFdXjyZMns3XrVoKDg+nfvz/58+dHV1cXc3NzreteFh8fz/Dhw9Ud/F585ylfvnxoNJpXXr93716OHTtGTEwMDg4OAJQuXVo9X7x4ca0AbMCAAYSEhLBx40aqV6+e3WnSUq9ePa02GzVqhJGREZMnT1bLTpw4QYcOHdRdC+F53qtRo0YB4ODgwKFDh5g7dy4NGzZk7969nDlzhkuXLqmB0KpVq3BxceH48eN89tlnwPNHCVetWqX+vQAYGBhgYmLyynmaNm2amvhYCCGEEEJ8OmRF6j1q3bo1//zzD8HBwXh5eREWFkblypVZuXJlltc8evSIESNG4OzsjKWlJWZmZvz55585Tlo7ZMgQevToQYMGDZg+fTpxcXE5uj4qKooSJUqoQdTLUlNTmTJlChUqVMDa2hozMzN27979Vsl1X076e+HCBbZu3Yqbm5v66d69O4qicP36dbXei0mJ04/TEwrHxMRgY2OjtZqUPrcvJh22tbXVCqKySxLyCiGEEEJ8miSQes+MjIxo2LAhY8eO5fDhw/j6+jJu3Lgs6w8fPpzNmzczZcoUwsPDiYqKwtXVleTk5Bzdd/z48Zw7d45mzZqxf/9+nJ2dc7TVuLGx8SvPBwQEMHfuXEaMGMH+/fuJiorCy8srx/180ctJf9PS0ujdu7fWVuSnT5/mwoULlClT5pVtpb87llly4czKM0s4nB2GhoZYWFhofYQQQgghxMdPHu37wDg7O6tbnuvr65Oamqp1Pjw8HF9fX7788kvg+TtTL29yYGBgkOG6zDg4OODg4MDgwYNp3749gYGBfPnll9m6vkKFCly7do3z589nuioVHh6Ot7c3HTt2BJ4HPRcuXKBcuXKv7Vd2Va5cmXPnzmFvb//KekePHs1wnP5Io7OzM/Hx8Vy9elVdlYqOjiYxMfG1fc3uPGdGEvIKIYQQQnzcZEXqPblz5w716tXjl19+Ud/R2bhxIzNnzsTb2xt4vuPcvn37uH79Onfv3gWeJ63dsmWLuvrSoUMH0tLStNq2s7Pj999/5++//+b27dsZ7v3kyRP69+9PWFgYV65c4dChQxw/flwNHOzs7Hj48CH79u3j9u3bWglp03l4eFCnTh1at27Nnj17uHTpEr/99hshISFqP/fs2cPhw4eJiYmhd+/eWo/bvYn169eTP39+NBoNUVFRjBw5kiNHjtCvXz+ioqK4cOECwcHBDBgwQOu6Q4cOMXPmTM6fP8+iRYvYuHGjmlC4QYMGVKhQAR8fH06ePMmxY8fo3LkzHh4eGR4lfJmdnR0RERFcvnyZ27dvZ/h7EEIIIYQQny4JpN4TMzMzqlevzty5c6lTpw7ly5fH39+fnj17qglgAwIC2LNnDzY2NlSqVAmAuXPnYmVlRc2aNWnevDleXl5UrlxZq+2JEydy+fJlypQpk+l7Pbq6uty5c4fOnTvj4OBAmzZtaNKkibopQs2aNenTpw9t27alYMGCzJw5M9MxbN68mc8++4z27dvj7OzMiBEj1BUaf39/KleujJeXF56enhQpUiTH+bB8fX3Va548ecKxY8fYvn07CQkJlC9fngoVKnDgwAEuXLhA7dq1qVSpEv7+/hQtWlSrnaFDhxIZGUmlSpWYNGkSAQEBeHl5Af9LemxlZUWdOnVo0KABpUuXZv369a/t37Bhw9DV1cXZ2ZmCBQvm6P2v9IS8QgghhBDi4yQJecUHy9fXl3v37rFt2za+//57Zs2axZUrV7Ksn1mS3rdNTpzbXk7IKwl4hRBCCCE+LJKQV3wyfH19GTBgAPHx8VrJiT09Penfvz9DhgyhQIECNGzYEIA5c+bg6uqKqakp165dY+PGjTx8+FBtLz058fbt23F0dMTExISvvvqKR48eERQUhJ2dHVZWVgwYMEDrHajk5GRGjBhB8eLFMTU1pXr16oSFhb3LqRBCCCGEEB8I2WxCvHfh4eE0adIkQ3lSUhKKonDnzh3KlCnDTz/9xPHjx9WkvwBBQUH07duXQ4cOkb64qqOjw4IFC7Czs8Pd3Z0LFy4wYsQIFi9erF73+PFjFixYwLp163jw4AGtWrWiVatWWFpasnPnTi5evEjr1q35/PPP1eS8Xbt25fLly6xbt45ixYqxdetWGjduzNmzZ7XycL08hhcTLktCXiGEEEKIT4MEUuK9q1q1KlFRURnKR4wYoS6tmpubo6urmyH5rb29fYZ3uF58jO/69ets3LiRvn37agVSz549Y8mSJeo26V999RWrVq3ixo0bmJmZ4ezsTN26dQkNDaVt27bExcWxdu1arl27RrFixYDn70iFhIQQGBjI1KlTMx2bJOQVQgghhPg0SSAl3jtjY+NMtzC3sLB47U54me2sFxoaytSpU4mOjub+/fukpKTw9OlTHj16pOaDMjEx0co1VbhwYezs7DAzM9Mqu3nzJgAnT55EUZQMW70nJSVhbW2dZf9Gjx7NkCFD1OP79+9rJf8VQgghhBAfJwmkxEft5US5V65coWnTpvTp04dJkyaRP39+Dh48SPfu3Xn27JlaT19fX+s6jUaTaVl6IJeWloauri6RkZFajxYCWsHXywwNDTE0NHyjsQkhhBBCiA+XBFLik3LixAlSUlIICAhAR+f5XiobNmx463YrVapEamoqN2/epHbt2m/dnhBCCCGE+LjJrn15wNPTM0+32758+bKalFZoK1OmDCkpKSxcuJCLFy+yatUqfvjhhwz1kpOTsbS0zHa7Dg4O+Pj40LlzZ7Zs2cKlS5c4fvw4Go0Gf3//HPfzjwlesvW5EEIIIcRHTAKpj5CNjY2alPZdu3btGgYGBjg5OWU4l76teE5cvHiR9u3bU6xYMYyMjChRogTe3t6cP3/+jfrn5ubGnDlzmDFjBuXLl2f16tVMmzbtjdp6WWBgIJ07d2bo0KE4OjrSokULAAoUKJDjtiQhrxBCCCHEx00S8uYBT09P3NzcmDdv3vvuSq6bPHkyf/75J7///jtr166lVq1a6rmVK1fi5+fHvXv3stVWcnIy5cqVw8nJCX9/f4oWLcq1a9fYuXMnLVu25LPPPsujUeS8r1nRaDRs3bqVli1bZqu+JOQVQgghhPiwSULe9ywtLY0RI0aQP39+ihQpwvjx49VzLyaMtbGx4ZtvvlETxiYmJmJsbExISIhWe1u2bMHU1JSHDx9meLQvLCwMjUbDvn37qFq1KiYmJtSsWZPY2FitNiZPnkyhQoUwNzenR48ejBo1Cjc3t2yPSVEUAgMD6dSpEx06dGD58uXqubCwMLp27UpiYiIajQaNRqM15sxER0dz8eJFFi9eTI0aNbC1taVWrVpMmTJFDaLq1atH//79ta67c+cOhoaG7N+/HwA7OzsmT55M586dMTMzw9bWlv/7v//j1q1beHt7Y2ZmhqurKydOnMjQh23btuHg4ICRkRENGzbk6tWrWufTt0g3MDDA0dGRVatWZXu+hBBCCCHEp0sCqTwSFBSEqakpERERzJw5k4kTJ7Jnzx7gfwlj//jjD4KCgti/fz8jRowAIF++fDRr1ozVq1drtbdmzRo1KMjKmDFjCAgI4MSJE+jp6dGtWzf13OrVq5kyZQozZswgMjKSkiVLsmTJkhyNKTQ0lMePH9OgQQM6derEhg0bePDgAQA1a9Zk3rx5WFhYkJCQQEJCAsOGDXtlewULFkRHR4dNmzaRmpqaaZ0ePXqwZs0araS2q1evplixYtStW1ctmzt3LrVq1eLUqVM0a9aMTp060blzZzp27MjJkyext7enc+fOvLgA+/jxY6ZMmUJQUBCHDh3i/v37tGvXTj2/detWBg0axNChQ/njjz/o3bs3Xbt2JTQ0NNtzlpSUxP3797U+QgghhBDiE6CIXOfh4aF8/vnnWmWfffaZMnLkyEzrb9iwQbG2tlaPt2zZopiZmSmPHj1SFEVREhMTFSMjI2XHjh2KoijKpUuXFEA5deqUoiiKEhoaqgDK3r171TZ27NihAMqTJ08URVGU6tWrK/369dO6b61atZSKFStme1wdOnRQ/Pz81OOKFSsqS5cuVY8DAwOVfPnyZbs9RVGU77//XjExMVHMzc2VunXrKhMnTlTi4uLU80+fPlXy58+vrF+/Xi1zc3NTxo8frx7b2toqHTt2VI8TEhIUQPH391fLjhw5ogBKQkKC2ldAOXr0qFonJiZGAZSIiAhFURSlZs2aSs+ePbX6+/XXXytNmzZVjwFl69atWY5v3LhxCpDhY+O3QbEduT270ySEEEIIId6RxMREBVASExNfWU9WpPJIhQoVtI6LFi2qJncNDQ2lYcOGFC9eHHNzczp37sydO3d49OgRAM2aNUNPT4/g4GAANm/ejLm5OY0aNcr2PYsWLQqg3jM2NpZq1app1X/5+FXu3bvHli1b6Nixo1rWsWNHVqxYke02MtOvXz+uX7/OL7/8gru7Oxs3bsTFxUVdvTM0NNS6T1RUFKdPn8bX11ernRfHXrhwYQBcXV0zlKXPB4Cenp5WQl8nJycsLS2JiYkBICYmRusdMIBatWqp57Nj9OjRJCYmqp+XHx0UQgghhBAfJwmk8khWyV3TE8aWL1+ezZs3ExkZyaJFiwDUhLEGBgZ89dVXrFmzBnj+WF/btm3R03t12q8X76nRaADUhLIvlqVTcrDPyJo1a3j69CnVq1dHT08PPT09Ro4cyZEjR4iOjs52O5kxNzenRYsWTJkyhdOnT1O7dm0mT56snu/Rowd79uzh2rVrrFixgvr162Nra6vVRmZjf918vFieVVlmc5bZNVkxNDTEwsJC6yOEEEIIIT5+Eki9Yy8mjK1RowYODg78888/Ger5+PgQEhLCuXPnCA0NxcfH563u6+joyLFjxzL0JbuWL1/O0KFDiYqKUj+nT5+mbt266mqRgYFBlu86ZZdGo8HJyUldnYPnK0tVq1Zl6dKlrFmzRuvdr7eRkpKiNQexsbHcu3dP3dq9XLlyHDx4UOuaw4cPU65cuVy5vxBCCCGE+Hi9eolD5LoXE8Y2b96cQ4cOZZow1sPDg8KFC+Pj44OdnR01atR4q/sOGDCAnj17UrVqVWrWrMn69es5c+YMpUuXfu21UVFRnDx5ktWrV2fIH9W+fXvGjBnDtGnTsLOz4+HDh+zbt4+KFStSsGDBV24NHhUVxbhx4+jUqRPOzs6cOnWKjh07YmpqysiRI7Xq9ujRg/79+2NiYsKXX375xvNQqVIl7t69C4Curi41a9bk0KFD6Ovr079/f2rUqKE+8jh8+HDatGlD5cqVqV+/PtWrVycxMZF9+/a98f3T/THBS1anhBBCCCE+YrIi9Y5lN2GsRqOhffv2nD59+q1Xo+D5Ctfo0aMZNmwYlStX5tKlS/j6+mJkZPTaa5cvX46zs7NWEOXr64tGo6FXr17cunWLokWLsmrVKrp27Urbtm0pWLAgQ4cOpUmTJlm2W6JECezs7JgwYQLVq1enV69eAIwaNYoxY8Zo1W3fvj16enrY29tnGVSeOnWKr7/+Wn0f6ptvvqFnz55ZJvc1MzNj6dKldOjQAXd3d4yNjVm3bp16vmXLlsyfP59Zs2bh4uLCgwcPaNeuHZ6enq+dMyGEEEII8WmThLz/YQ0bNqRIkSJvlBvJ19eXGzduEBgYSEpKCtHR0XTr1o3atWuzdu3aN+pPWFgYdevW5e7du1haWmqdu3r1KnZ2dvTo0YOIiAg1h1a67du307p1a7y8vBg4cCBlypTh5s2bbNy4katXr7J+/fpXtp8ddnZ2+Pn54efn90bjg+wneBNCCCGEEO+HJOQVWh4/fsycOXM4d+4cf/75J+PGjWPv3r106dLljds0NDSkSJEilChRgkaNGtG2bVt2796tntdoNGzbtk09Pnz4MG5ubhgZGVG1alW2bdumlVg4XWRkpJpYuEaNGoSGhjJy5EhKly7NTz/9xOnTp9WkvytXruTx48d07dqVpk2bEhwcTIMGDShVqhTVq1dn9uzZ/Pjjj5n2f+XKlRkCquDgYKpWrYqRkREFChSgVatWWY4/MDCQfPnyqTsMCiGEEEKI/w55R+o/QqPRsHPnTiZPnkxSUhKOjo5s3ryZBg0aALwy0e9vv/1G7dq1X9n+xYsXCQkJybBbYfv27dHV1QXg0aNH6Onpoa+vz7lz5zK8B5UuPbFwwYIFadeuHfXq1cPBwYE1a9awfv16QkJC2Lt3L/A8gXFISAi3b99Wkxq/LLurTzt27KBVq1aMGTOGVatWkZyczI4dOzKtO3v2bKZNm8auXbte+f5aUlKSVjJhScgrhBBCCPFpkEDqP8LY2FgNPjLz8qrQi4oXL55p+fbt2zEzMyM1NZWnT58CMGfOHK06c+bMoWHDhqxZs4a5c+dy8OBBDA0NgeeP8vXs2TNDu1OmTMHDwwOA6dOn06xZM06fPo2RkRE7duxAT0+PIkWKqPUvXLgAkGEjjJyaMmUK7dq1Y8KECWpZxYoVM9QbPXo0QUFBhIWFaeWqysy0adO02hNCCCGEEJ8GCaQEAPb29jm+pm7duixZsoTHjx+zbNkyzp8/z4ABA7TqFC1aFHt7e+7evUulSpVwcXFRzz1+/DjTdrNKLFyyZMlM6+fWa35RUVGZBnYvCggI4NGjR5w4cSJbOx6OHj2aIUOGqMf379/HxsbmrfsqhBBCCCHeL3lHSrwxU1NT7O3tqVChAgsWLCApKSnL1ZfMEtlmFQBlJ5HuixwcHAD4888/c9T/lxkbG7+2Tu3atUlNTWXDhg3ZalMS8gohhBBCfJokkBK5Zty4ccyePTvTBMNOTk6cOXNG632hnCQETpdZ0t9GjRpRoEABZs6cmek19+7dy1bbFSpUeG2OqGrVqhESEsLUqVOZNWtWttoVQgghhBCfHgmkRK7x9PTExcWFqVOnZjjXoUMH0tLS6NWrFzExMezatYvZs2cDZFipehU7OzsuXbpEVFQUt2/fJikpCVNTU5YtW8aOHTto0aIFe/fu5fLly5w4cYIRI0bQp0+fbLU9btw41q5dy7hx44iJieHs2bOZBmfu7u789ttvTJw4kblz52a770IIIYQQ4tMhgZTIVUOGDGHp0qVcvXpVq9zCwoJff/2VqKgo3NzcGDNmDGPHjgXIVlLgdK1bt6Zx48bUrVuXggULqjmrvL29OXz4MPr6+nTo0AEnJyfat29PYmIikydPzlbbnp6ebNy4keDgYNzc3KhXrx4RERGZ1q1VqxY7duzA39+fBQsWZLv/QgghhBDi0yAJecV7s3r1arp27UpiYmK23k/6FEhCXiGEEEKID1t2f1+TXfvEO/Pzzz9TunRpihcvzunTpxk5ciRt2rT5zwRRQgghhBDi0yGBlHhnrl+/ztixY7l+/TpFixbl66+/ZsqUKe+7W0IIIYQQQuSYPNonxDskj/YJIYQQQnzYsvv7mmw2IYQQQgghhBA5JIGUEEIIIYQQQuSQBFJCCCGEEEIIkUMSSAkhhBBCCCFEDkkgJYQQQgghhBA5JIGUEEIIIYQQQuSQBFJCCCGEEEIIkUOSkFeIdyg9bdv9+/ffc0+EEEIIIURm0n9Pe126XQmkhHiH7ty5A4CNjc177okQQgghhHiVBw8ekC9fvizPSyAlxDuUP39+AOLj41/5H6bIvvv372NjY8PVq1dfmX1cZJ/Mae6TOc19Mqe5T+Y098mc5o28nldFUXjw4AHFihV7ZT0JpIR4h3R0nr+WmC9fPvkfai6zsLCQOc1lMqe5T+Y098mc5j6Z09wnc5o38nJes/MP3rLZhBBCCCGEEELkkARSQgghhBBCCJFDEkgJ8Q4ZGhoybtw4DA0N33dXPhkyp7lP5jT3yZzmPpnT3CdzmvtkTvPGhzKvGuV1+/oJIYQQQgghhNAiK1JCCCGEEEIIkUMSSAkhhBBCCCFEDkkgJYQQQgghhBA5JIGUEEIIIYQQQuSQBFJCvIXFixdTqlQpjIyMqFKlCuHh4a+sf+DAAapUqYKRkRGlS5fmhx9+yFBn8+bNODs7Y2hoiLOzM1u3bs2r7n+QcntOly5dSu3atbGyssLKyooGDRpw7NixvBzCBycvfk7TrVu3Do1GQ8uWLXO51x++vJjXe/fu0a9fP4oWLYqRkRHlypVj586deTWED05ezOm8efNwdHTE2NgYGxsbBg8ezNOnT/NqCB+cnMxpQkICHTp0wNHRER0dHfz8/DKtJ99TuTun8j2VNz+n6fL0e0oRQryRdevWKfr6+srSpUuV6OhoZdCgQYqpqaly5cqVTOtfvHhRMTExUQYNGqRER0crS5cuVfT19ZVNmzapdQ4fPqzo6uoqU6dOVWJiYpSpU6cqenp6ytGjR9/VsN6rvJjTDh06KIsWLVJOnTqlxMTEKF27dlXy5cunXLt27V0N673KizlNd/nyZaV48eJK7dq1FW9v7zweyYclL+Y1KSlJqVq1qtK0aVPl4MGDyuXLl5Xw8HAlKirqXQ3rvcqLOf3ll18UQ0NDZfXq1cqlS5eUXbt2KUWLFlX8/Pze1bDeq5zO6aVLl5SBAwcqQUFBipubmzJo0KAMdeR7KvfnVL6ncn9O0+X195QEUkK8oWrVqil9+vTRKnNyclJGjRqVaf0RI0YoTk5OWmW9e/dWatSooR63adNGady4sVYdLy8vpV27drnU6w9bXszpy1JSUhRzc3MlKCjo7Tv8EcirOU1JSVFq1aqlLFu2TOnSpct/LpDKi3ldsmSJUrp0aSU5OTn3O/wRyIs57devn1KvXj2tOkOGDFE+//zzXOr1hy2nc/oiDw+PTH9Ble+p3J/Tl8n3VO7M6bv4npJH+4R4A8nJyURGRtKoUSOt8kaNGnH48OFMrzly5EiG+l5eXpw4cYJnz569sk5WbX5K8mpOX/b48WOePXtG/vz5c6fjH7C8nNOJEydSsGBBunfvnvsd/8Dl1bwGBwfj7u5Ov379KFy4MOXLl2fq1KmkpqbmzUA+IHk1p59//jmRkZHqY1IXL15k586dNGvWLA9G8WF5kznNDvmeyv05fZl8T+XOnL6L7ym9PGtZiE/Y7du3SU1NpXDhwlrlhQsX5vr165lec/369Uzrp6SkcPv2bYoWLZplnaza/JTk1Zy+bNSoURQvXpwGDRrkXuc/UHk1p4cOHWL58uVERUXlVdc/aHk1rxcvXmT//v34+Piwc+dOLly4QL9+/UhJSWHs2LF5Np4PQV7Nabt27bh16xaff/45iqKQkpJC3759GTVqVJ6N5UPxJnOaHfI9lftz+jL5nnr7OX1X31MSSAnxFjQajdaxoigZyl5X/+XynLb5qcmLOU03c+ZM1q5dS1hYGEZGRrnQ249Dbs7pgwcP6NixI0uXLqVAgQK539mPSG7/rKalpVGoUCF++ukndHV1qVKlCv/88w+zZs365AOpdLk9p2FhYUyZMoXFixdTvXp1/vrrLwYNGkTRokXx9/fP5d5/mPLiO0W+p/Ju/PI99dzbzOm7/J6SQEqIN1CgQAF0dXUz/GvJzZs3M/yrSroiRYpkWl9PTw9ra+tX1smqzU9JXs1putmzZzN16lT27t1LhQoVcrfzH6i8mNNz585x+fJlmjdvrp5PS0sDQE9Pj9jYWMqUKZPLI/mw5NXPatGiRdHX10dXV1etU65cOa5fv05ycjIGBga5PJIPR17Nqb+/P506daJHjx4AuLq68ujRI3r16sWYMWPQ0fl033B4kznNDvmeyv05TSffU//zNnMaFxf3zr6nPt3/gwiRhwwMDKhSpQp79uzRKt+zZw81a9bM9Bp3d/cM9Xfv3k3VqlXR19d/ZZ2s2vyU5NWcAsyaNYtJkyYREhJC1apVc7/zH6i8mFMnJyfOnj1LVFSU+mnRogV169YlKioKGxubPBvPhyKvflZr1arFX3/9pX7hA5w/f56iRYt+0kEU5N2cPn78OEOwpKuri/J8s61cHMGH503mNDvkeyr35xTkeyo35/Sdfk/l+vYVQvxHpG/XuXz5ciU6Olrx8/NTTE1NlcuXLyuKoiijRo1SOnXqpNZP36p38ODBSnR0tLJ8+fIMW/UeOnRI0dXVVaZPn67ExMQo06dP/09uK5ubczpjxgzFwMBA2bRpk5KQkKB+Hjx48M7H9z7kxZy+7L+4a19ezGt8fLxiZmam9O/fX4mNjVW2b9+uFCpUSJk8efI7H9/7kBdzOm7cOMXc3FxZu3atcvHiRWX37t1KmTJllDZt2rzz8b0POZ1TRVGUU6dOKadOnVKqVKmidOjQQTl16pRy7tw59bx8T+X+nMr3VO7P6cvy6ntKAikh3sKiRYsUW1tbxcDAQKlcubJy4MAB9VyXLl0UDw8PrfphYWFKpUqVFAMDA8XOzk5ZsmRJhjY3btyoODo6Kvr6+oqTk5OyefPmvB7GByW359TW1lYBMnzGjRv3DkbzYciLn9MX/RcDKUXJm3k9fPiwUr16dcXQ0FApXbq0MmXKFCUlJSWvh/LByO05ffbsmTJ+/HilTJkyipGRkWJjY6N88803yt27d9/BaD4MOZ3TzP5/aWtrq1VHvqdyd07leypvfk5flFffU5r/3xkhhBBCCCGEENkk70gJIYQQQgghRA5JICWEEEIIIYQQOSSBlBBCCCGEEELkkARSQgghhBBCCJFDEkgJIYQQQgghRA5JICWEEEIIIYQQOSSBlBBCCCGEEELkkARSQgghhBBCCJFDEkgJIYQQ/zF2dnbMmzfvfXdDCCE+ahJICSGE+M/x9fVFo9Gg0WjQ19endOnSDBs2jEePHr3vrmVq/PjxuLm55fi6lStXYmlpmaH8+PHj9OrV6+079gphYWFoNBru3buXp/d5G56envj5+b3vbgghPlJ677sDQgghxPvQuHFjAgMDefbsGeHh4fTo0YNHjx6xZMmSHLelKAqpqano6X0cX6sFCxZ83114r549e4a+vv777oYQ4iMnK1JCCCH+kwwNDSlSpAg2NjZ06NABHx8ftm3bBjwPjGbOnEnp0qUxNjamYsWKbNq0Sb02fbVl165dVK1aFUNDQ8LDw/H09GTAgAH4+flhZWVF4cKF+emnn3j06BFdu3bF3NycMmXK8Ntvv6ltZbZqtG3bNjQajXp+woQJnD59Wl1FW7lyJQBz5szB1dUVU1NTbGxs+Oabb3j48KHax65du5KYmKheN378eCDjo33x8fF4e3tjZmaGhYUFbdq04caNG+r59BWxVatWYWdnR758+WjXrh0PHjzI9nynj3P79u04OjpiYmLCV199xaNHjwgKCsLOzg4rKysGDBhAamqqep2dnR2TJk2iQ4cOmJmZUaxYMRYuXKjVdnb7v2LFCkqXLo2hoSFdunThwIEDzJ8/X52fy5cvk5qaSvfu3SlVqhTGxsY4Ojoyf/58rfv5+vrSsmVLZs+eTdGiRbG2tqZfv348e/ZMrZOUlMSIESOwsbHB0NCQsmXLsnz5cvV8dHQ0TZs2xczMjMKFC9OpUydu376d7fkUQrx/EkgJIYQQgLGxsfqL8HfffUdgYCBLlizh3LlzDB48mI4dO3LgwAGta0aMGMG0adOIiYmhQoUKAAQFBVGgQAGOHTvGgAED6Nu3L19//TU1a9bk5MmTeHl50alTJx4/fpytfrVt25ahQ4fi4uJCQkICCQkJtG3bFgAdHR0WLFjAH3/8QVBQEPv372fEiBEA1KxZk3nz5mFhYaFeN2zYsAztK4pCy5Yt+ffffzlw4AB79uwhLi5OvUe6uLg4tm3bxvbt29m+fTsHDhxg+vTpOZrjx48fs2DBAtatW0dISAhhYWG0atWKnTt3snPnTlatWsVPP/2kFbQCzJo1iwoVKnDy5ElGjx7N4MGD2bNnT476/9dff7FhwwY2b95MVFQUCxYswN3dnZ49e6rzY2NjQ1paGiVKlGDDhg1ER0czduxYvv32WzZs2KDVXmhoKHFxcYSGhhIUFMTKlSvVABegc+fOrFu3jgULFhATE8MPP/yAmZkZAAkJCXh4eODm5saJEycICQnhxo0btGnTJkfzKYR4zxQhhBDiP6ZLly6Kt7e3ehwREaFYW1srbdq0UR4+fKgYGRkphw8f1rqme/fuSvv27RVFUZTQ0FAFULZt26ZVx8PDQ/n888/V45SUFMXU1FTp1KmTWpaQkKAAypEjRxRFUZTAwEAlX758Wu1s3bpVefErety4cUrFihVfO64NGzYo1tbW6nFmbSuKotja2ipz585VFEVRdu/erejq6irx8fHq+XPnzimAcuzYMfX+JiYmyv3799U6w4cPV6pXr55lX9Ln6O7du2pfAOWvv/5S6/Tu3VsxMTFRHjx4oJZ5eXkpvXv31upr48aNtdpu27at0qRJkxz1X19fX7l586ZWOx4eHsqgQYOyHEO6b775RmndurV63KVLF8XW1lZJSUlRy77++mulbdu2iqIoSmxsrAIoe/bsybQ9f39/pVGjRlplV69eVQAlNjb2tf0RQnwYPo6HuYUQQohctn37dszMzEhJSeHZs2d4e3uzcOFCoqOjefr0KQ0bNtSqn5ycTKVKlbTKqlatmqHd9JUpAF1dXaytrXF1dVXLChcuDMDNmzffegyhoaFMnTqV6Oho7t+/T0pKCk+fPuXRo0eYmppmq42YmBhsbGywsbFRy5ydnbG0tCQmJobPPvsMeP6Inbm5uVqnaNGiOR6DiYkJZcqUUY8LFy6MnZ2dulKTXvZyu+7u7hmO0x9NzG7/bW1ts/1u2A8//MCyZcu4cuUKT548ITk5OcNmHy4uLujq6qrHRYsW5ezZswBERUWhq6uLh4dHpu1HRkYSGhqqNe50cXFxODg4ZKufQoj3SwIpIYQQ/0l169ZlyZIl6OvrU6xYMXXzgUuXLgGwY8cOihcvrnWNoaGh1nFmwcrLmxik7wz44jFAWloa8PzxPEVRtK558V2brFy5coWmTZvSp08fJk2aRP78+Tl48CDdu3fP1vXpFEVR+/Sq8szGlT6G7Hrd3OSk3fS+Zbf/2Q0sN2zYwODBgwkICMDd3R1zc3NmzZpFRETEa8eS3m9jY+NX3iMtLY3mzZszY8aMDOeKFi2arX4KId4/CaSEEEL8J5mammJvb5+h3NnZGUNDQ+Lj47NcUchNBQsW5MGDB1qrSFFRUVp1DAwMtDZgADhx4gQpKSkEBASgo/P8leeX3+PJ7LqXOTs7Ex8fz9WrV9VVnejoaBITEylXrtzbDC3XHD16NMOxk5MT8Hb9z2x+wsPDqVmzJt98841aFhcXl6P+urq6kpaWxoEDB2jQoEGG85UrV2bz5s3Y2dl9NDs9CiEyks0mhBBCiBeYm5szbNgwBg8eTFBQEHFxcZw6dYpFixYRFBSU6/erXr06JiYmfPvtt/z111+sWbNGa9MCeP5Y3aVLl4iKiuL27dskJSVRpkwZUlJSWLhwIRcvXmTVqlX88MMPGa57+PAh+/bt4/bt25lucNGgQQMqVKiAj48PJ0+e5NixY3Tu3BkPD49MH118Hw4dOsTMmTM5f/48ixYtYuPGjQwaNAh4u/7b2dkRERHB5cuXuX37Nmlpadjb23PixAl27drF+fPn8ff35/jx4znqr52dHV26dKFbt25s27aNS5cuERYWpga6/fr1499//6V9+/YcO3aMixcvsnv3brp16/bawFcI8eGQQEoIIYR4yaRJkxg7dizTpk2jXLlyeHl58euvv1KqVKlcv1f+/Pn55Zdf2LlzJ66urqxdu1bdpjxd69atady4MXXr1qVgwYKsXbsWNzc35syZw4wZMyhfvjyrV69m2rRpWtfVrFmTPn360LZtWwoWLMjMmTMz3F+j0bBt2zasrKyoU6cODRo0oHTp0qxfvz7Xx/qmhg4dSmRkJJUqVWLSpEkEBATg5eUFvF3/hw0bhq6uLs7OzhQsWJD4+Hj69OlDq1ataNu2LdWrV+fOnTtaq1PZtWTJEr766iu++eYbnJyc6Nmzp5rwuVixYhw6dIjU1FS8vLwoX748gwYNIl++fOrqohDiw6dRXn4wWwghhBDiA2FnZ4efnx9+fn7vuytCCKFF/tlDCCGEEEIIIXJIAikhhBBCCCGEyCF5tE8IIYQQQgghckhWpIQQQgghhBAihySQEkIIIYQQQogckkBKCCGEEEIIIXJIAikhhBBCCCGEyCEJpIQQQgghhBAihySQEkIIIYQQQogckkBKCCGEEEIIIXJIAikhhBBCCCGEyKH/B5/PBTgrshi8AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 47.5 s, sys: 3.42 s, total: 50.9 s\n",
      "Wall time: 5.64 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Fit the model with the best found parameters\n",
    "best_mlp = grid_search.best_estimator_\n",
    "best_mlp.fit(train_X, train_y)\n",
    "\n",
    "# Calculate feature importance\n",
    "result = permutation_importance(best_mlp, test_X, test_y, n_repeats=10, random_state=42, n_jobs=-1)\n",
    "\n",
    "# Plot feature importance\n",
    "sorted_idx = result.importances_mean.argsort()\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.barh(train_X.columns[sorted_idx], result.importances_mean[sorted_idx])\n",
    "plt.xlabel(\"Permutation Importance\")\n",
    "plt.title(\"Feature Importance\")\n",
    "plt.savefig('../images/phishing-nn/feature-importance.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3930aadb-1f77-40a7-8a5e-b85fd322247f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 402 ms, sys: 41.2 ms, total: 443 ms\n",
      "Wall time: 22.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "best_net = MLPClassifier(activation='tanh', alpha=0.0001, \n",
    "              hidden_layer_sizes=(75,), learning_rate='adaptive',\n",
    "              max_iter=2000, solver='adam')\n",
    "\n",
    "# Assuming best_mlp, train_X, train_y are already defined\n",
    "train_sizes, train_scores, test_scores = learning_curve(\n",
    "    best_net, train_X, train_y, cv=5, scoring='f1_weighted',\n",
    "    train_sizes=np.linspace(0.1, 1.0, 10), n_jobs=-1\n",
    ")\n",
    "\n",
    "# Calculate mean and standard deviation\n",
    "train_scores_mean = np.mean(train_scores, axis=1)\n",
    "train_scores_std = np.std(train_scores, axis=1)\n",
    "test_scores_mean = np.mean(test_scores, axis=1)\n",
    "test_scores_std = np.std(test_scores, axis=1)\n",
    "\n",
    "# Plot learning curve\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                 train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                 color=\"r\")\n",
    "plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                 test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Training score\")\n",
    "plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\", label=\"Test score\")\n",
    "\n",
    "plt.title('F1_weighted score learning curve')\n",
    "plt.xlabel('Number of Training Instances Used')\n",
    "plt.ylabel('Score')\n",
    "plt.legend(loc=\"best\")\n",
    "plt.grid()\n",
    "plt.savefig('../images/phishing-nn/best-nn-training-vs-test-set')\n",
    "plt.show()\n",
    "\n",
    "# Write to a text file\n",
    "with open('../results/phishing-nn/test_classification_results.txt', 'w') as file:\n",
    "    file.write(\"Classification Report:\\n\")\n",
    "    file.write(test_report + \"\\n\")\n",
    "    file.write(\"Confusion Matrix:\\n\")\n",
    "    file.write(np.array2string(test_cm) + \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "57502727-9f34-4b04-996f-e0f82216b383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken: in min 8.21197472916668\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.4s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.9s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.4s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.8s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.5s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.9s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.5s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.8s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.7s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.0s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.6s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.0s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.4s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.8s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.6s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.1s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.5s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.8s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.9s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.3s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.8s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.8s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.3s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.5s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.4s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.9s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.8s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.6s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.0s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.5s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   3.0s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.1s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.5s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.0s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.4s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.8s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.6s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.0s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.3s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.4s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.0s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.9s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.4s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.8s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.8s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.4s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.8s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.5s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.1s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.8s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.5s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.4s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.0s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.5s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   3.1s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.4s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.4s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.8s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.6s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.0s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.8s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.6s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.0s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.5s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.0s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.6s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.8s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.5s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.0s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.9s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.3s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.8s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.3s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.3s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.4s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.8s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.4s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.9s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.3s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.6s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.0s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.4s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.9s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.1s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.6s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.0s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.5s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.4s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.6s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.2s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.4s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.8s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.4s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.0s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.0s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.4s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.1s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.5s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.0s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.1s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.5s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.9s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.4s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   3.0s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.4s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.5s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.4s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.8s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.7s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.0s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.8s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.6s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.0s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.4s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.1s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.3s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.4s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.9s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.4s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.9s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.4s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.3s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.4s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.8s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.5s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.9s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.5s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   3.3s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.3s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.4s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.4s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.8s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.7s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.1s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.5s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.0s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.3s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.4s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.8s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.7s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.2s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.5s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.8s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.5s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.0s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.9s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.4s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.8s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.5s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.3s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.9s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.3s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   3.0s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.5s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   3.1s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.5s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.4s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.8s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.7s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.0s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.8s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.6s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.2s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.5s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.0s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.3s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.9s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.6s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.9s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.3s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.4s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.4s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   0.9s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.4s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.2s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.6s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.9s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.6s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   3.3s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.5s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.8s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.1s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.8s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.6s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.1s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.4s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.1s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.3s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.9s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.4s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.7s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.0s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.2s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.4s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.8s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.3s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.4s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.8s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.4s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.9s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.6s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.0s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.9s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.5s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.9s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.1s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.6s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.0s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.4s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.6s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.0s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.4s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.3s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.8s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.6s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.0s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.3s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.9s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.4s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.4s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.8s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.8s\n",
      "[CV] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.7s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.0s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.0s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.4s\n",
      "[CV] END activation=tanh, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.9s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.0s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.8s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.7s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.1s\n",
      "[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.4s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.0s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.6s\n",
      "[CV] END activation=relu, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.2s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.5s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.9s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.5s\n",
      "[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.9s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   1.3s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=50, solver=adam; total time=   0.7s\n",
      "[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.5s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=100, solver=adam; total time=   0.9s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(50,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.8s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(75,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   2.4s\n",
      "[CV] END activation=logistic, alpha=0.01, hidden_layer_sizes=(100,), learning_rate=adaptive, max_iter=200, solver=adam; total time=   1.5s\n"
     ]
    }
   ],
   "source": [
    "end = time.perf_counter()\n",
    "print(\"Time taken: in min\", (end - start)/60)"
   ]
  }
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