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-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [PER57] - Perceptron Model 1957\n",
- "<!-- DESC --> Example of use of a Perceptron, with sklearn and IRIS dataset of 1936 !\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - Implement a historical linear classifier with a historical dataset !\n",
- " - The objective is to predict the type of Iris from the size of the leaves.\n",
- " - Identifying its limitations \n",
- "\n",
- "The [IRIS dataset](https://archive.ics.uci.edu/ml/datasets/Iris) is probably one of the oldest datasets, dating back to 1936 .\n",
- "\n",
- "## What we're going to do :\n",
- " - Retrieve the dataset, via scikit learn\n",
- " - training and classifying\n",
- "\n",
- "## Step 1 - Import and init"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
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- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
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- "text": [
- "Version : 2.0.17\n",
- "Notebook id : PER57\n",
- "Run time : Monday 01 March 2021, 18:41:10\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "from sklearn.datasets import load_iris\n",
- "from sklearn.linear_model import Perceptron\n",
- "import pandas as pd\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib\n",
- "\n",
- "import os,sys\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "datasets_dir = pwk.init('PER57')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Prepare IRIS Dataset\n",
- "\n",
- "Retrieve a dataset : http://scikit-learn.org/stable/modules/classes.html#module-sklearn.datasets \n",
- "About the datesets : http://scikit-learn.org/stable/datasets/index.html \n",
- "\n",
- "Data fields (X) :\n",
- "- 0 : sepal length in cm\n",
- "- 1 : sepal width in cm\n",
- "- 2 : petal length in cm\n",
- "- 3 : petal width in cm \n",
- "\n",
- "Class (y) :\n",
- "- 0 : class 0=Iris-Setosa, 1=Iris-Versicolour, 2=Iris-Virginica\n",
- "\n",
- "### 2.1 - Get dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:10.671294Z",
- "iopub.status.busy": "2021-03-01T17:41:10.670813Z",
- "iopub.status.idle": "2021-03-01T17:41:10.698183Z",
- "shell.execute_reply": "2021-03-01T17:41:10.698656Z"
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- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Length (x1)</th>\n",
- " <th>Width (x2)</th>\n",
- " <th>Setosa {0,1} (y)</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>1</td>\n",
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- " <th>1</th>\n",
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- " <th>2</th>\n",
- " <td>1.3</td>\n",
- " <td>0.2</td>\n",
- " <td>1</td>\n",
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- " <th>3</th>\n",
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- " <td>2.3</td>\n",
- " <td>0</td>\n",
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- " <th>149</th>\n",
- " <td>5.1</td>\n",
- " <td>1.8</td>\n",
- " <td>0</td>\n",
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- " </tbody>\n",
- "</table>\n",
- "<p>150 rows × 3 columns</p>\n",
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- ],
- "text/plain": [
- " Length (x1) Width (x2) Setosa {0,1} (y)\n",
- "0 1.4 0.2 1\n",
- "1 1.4 0.2 1\n",
- "2 1.3 0.2 1\n",
- "3 1.5 0.2 1\n",
- "4 1.4 0.2 1\n",
- ".. ... ... ...\n",
- "145 5.2 2.3 0\n",
- "146 5.0 1.9 0\n",
- "147 5.2 2.0 0\n",
- "148 5.4 2.3 0\n",
- "149 5.1 1.8 0\n",
- "\n",
- "[150 rows x 3 columns]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x shape : (150, 2)\n",
- "y shape : (150,)\n"
- ]
- }
- ],
- "source": [
- "x0,y0 = load_iris(return_X_y=True)\n",
- "\n",
- "x = x0[:, (2,3)] # We only keep fields 2 and 3\n",
- "y = y0.copy()\n",
- "\n",
- "y[ y0==0 ] = 1 # 1 = Iris setosa\n",
- "y[ y0>=1 ] = 0 # 0 = not iris setosa\n",
- "\n",
- "df=pd.DataFrame.from_dict({'Length (x1)':x[:,0], 'Width (x2)':x[:,1], 'Setosa {0,1} (y)':y})\n",
- "display(df)\n",
- "\n",
- "print(f'x shape : {x.shape}')\n",
- "print(f'y shape : {y.shape}')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.2 - Train and test sets"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:10.703015Z",
- "iopub.status.busy": "2021-03-01T17:41:10.702538Z",
- "iopub.status.idle": "2021-03-01T17:41:10.705040Z",
- "shell.execute_reply": "2021-03-01T17:41:10.705510Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x_train shape : (120, 2)\n",
- "y_train shape : (120,)\n",
- "x_test shape : (30, 2)\n",
- "y_test shape : (30,)\n"
- ]
- }
- ],
- "source": [
- "x,y = pwk.shuffle_np_dataset(x, y)\n",
- " \n",
- "n=int(len(x)*0.8)\n",
- "x_train = x[:n]\n",
- "y_train = y[:n]\n",
- "x_test = x[n:]\n",
- "y_test = y[n:]\n",
- "\n",
- "print(f'x_train shape : {x_train.shape}')\n",
- "print(f'y_train shape : {y_train.shape}')\n",
- "print(f'x_test shape : {x_test.shape}')\n",
- "print(f'y_test shape : {y_test.shape}')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Get a perceptron, and train it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:10.708911Z",
- "iopub.status.busy": "2021-03-01T17:41:10.708445Z",
- "iopub.status.idle": "2021-03-01T17:41:10.732586Z",
- "shell.execute_reply": "2021-03-01T17:41:10.733076Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "-- Epoch 1\n",
- "Norm: 1.44, NNZs: 2, Bias: 3.000000, T: 120, Avg. loss: 0.154667\n",
- "Total training time: 0.00 seconds.\n",
- "-- Epoch 2\n",
- "Norm: 1.44, NNZs: 2, Bias: 3.000000, T: 240, Avg. loss: 0.000000\n",
- "Total training time: 0.00 seconds.\n",
- "-- Epoch 3\n",
- "Norm: 1.44, NNZs: 2, Bias: 3.000000, T: 360, Avg. loss: 0.000000\n",
- "Total training time: 0.00 seconds.\n",
- "-- Epoch 4\n",
- "Norm: 1.44, NNZs: 2, Bias: 3.000000, T: 480, Avg. loss: 0.000000\n",
- "Total training time: 0.00 seconds.\n",
- "-- Epoch 5\n",
- "Norm: 1.44, NNZs: 2, Bias: 3.000000, T: 600, Avg. loss: 0.000000\n",
- "Total training time: 0.00 seconds.\n",
- "-- Epoch 6\n",
- "Norm: 1.44, NNZs: 2, Bias: 3.000000, T: 720, Avg. loss: 0.000000\n",
- "Total training time: 0.00 seconds.\n",
- "-- Epoch 7\n",
- "Norm: 1.44, NNZs: 2, Bias: 3.000000, T: 840, Avg. loss: 0.000000\n",
- "Total training time: 0.00 seconds.\n",
- "Convergence after 7 epochs took 0.00 seconds\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "Perceptron(max_iter=100, random_state=82, tol=0.01, verbose=1)"
- ]
- },
- "execution_count": 1,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "pct = Perceptron(max_iter=100, random_state=82, tol=0.01, verbose=1)\n",
- "pct.fit(x_train, y_train)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Prédictions"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:10.742618Z",
- "iopub.status.busy": "2021-03-01T17:41:10.742149Z",
- "iopub.status.idle": "2021-03-01T17:41:10.744592Z",
- "shell.execute_reply": "2021-03-01T17:41:10.745080Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Length (x1)</th>\n",
- " <th>Width (x2)</th>\n",
- " <th>y_test</th>\n",
- " <th>y_pred</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>5.0</td>\n",
- " <td>1.7</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>5.9</td>\n",
- " <td>2.3</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>1.4</td>\n",
- " <td>0.3</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>4.6</td>\n",
- " <td>1.5</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>1.5</td>\n",
- " <td>0.2</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>1.7</td>\n",
- " <td>0.4</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>5.0</td>\n",
- " <td>1.9</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>3.8</td>\n",
- " <td>1.1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>1.3</td>\n",
- " <td>0.2</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>4.5</td>\n",
- " <td>1.5</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>6.9</td>\n",
- " <td>2.3</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>1.4</td>\n",
- " <td>0.3</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>4.4</td>\n",
- " <td>1.3</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>4.9</td>\n",
- " <td>2.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14</th>\n",
- " <td>1.5</td>\n",
- " <td>0.2</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Length (x1) Width (x2) y_test y_pred\n",
- "0 5.0 1.7 0 0\n",
- "1 5.9 2.3 0 0\n",
- "2 1.4 0.3 1 1\n",
- "3 4.6 1.5 0 0\n",
- "4 1.5 0.2 1 1\n",
- "5 1.7 0.4 1 1\n",
- "6 5.0 1.9 0 0\n",
- "7 3.8 1.1 0 0\n",
- "8 1.3 0.2 1 1\n",
- "9 4.5 1.5 0 0\n",
- "10 6.9 2.3 0 0\n",
- "11 1.4 0.3 1 1\n",
- "12 4.4 1.3 0 0\n",
- "13 4.9 2.0 0 0\n",
- "14 1.5 0.2 1 1"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "y_pred = pct.predict(x_test) \n",
- "\n",
- "df=pd.DataFrame.from_dict({'Length (x1)':x_test[:,0], 'Width (x2)':x_test[:,1], 'y_test':y_test, 'y_pred':y_pred})\n",
- "display(df[:15])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Visualisation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:10.766633Z",
- "iopub.status.busy": "2021-03-01T17:41:10.766151Z",
- "iopub.status.idle": "2021-03-01T17:41:11.466713Z",
- "shell.execute_reply": "2021-03-01T17:41:11.467212Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/PER57-01-perceptron-iris</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
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OT7a/VvQvVbStwmXDlVJjlFLfFX195513Mnv2bCwWC0888QQTJkzwMHIhhBBVVeCqO9CuqoswuyMc69CGAn/UCQ7nWsQhnwgCLwHdgMcNw/jFxbHV7K+nK9h3qswxpRiGMdMwjPNLbrvpppv48MMPiYqKokGDBh6ELIQQwhui3CzZVtWhX3eEYx3aUOCPOsHhXIs4pBNBpdQU4G5gpmEYz7jxlhz7a2wF++LKHOOWkSNHsmPHDu6//35P3iaEEMILmlSPd1l7t9BmY+e+P5we44/6vcI3/FEnOJxrEYdsIqiUmoy5FuA7wJ1uvq1oaZiK/qWKtnk8wJ+SklL8+c6dO7n77rvJz8/3tBkhhBAeapWUgKtOQZvNYOvO3x3u91f9XuEb/qgTHM61iEMyEVRKPQE8AbwH3G64P1Njo/31wgr2dQP+BnZWNi6bzcawYcN47bXXGDlyJKdPVzQCLYQQwlsSY6LomlwLqyrfq6cAq4KGMdGczisMeP1e4Rv+qBMczrWIQ2odQQCl1OPAk8AcYLRhGBU++KGUaoi5WPQBwzBy7Nuigf2YC1GXXEfwHOAH4B3DMG53dn5XJeY2btzIoEGDOHHiBAMHDmTx4sVUq1bhY4dCCCG8JCuvgN2Z2RwoUVmkafV4UkpUFgmW+r3CN/zx7xPC3wNhs6D0XcB/gQPAJKBsEnjUMIzl9mPfBW4G+hiGsbpEGyMx1x0sqixSA3gAc/WA8wzDcDo07E6t4S1bttC/f3+OHTtGz549WbJkCdWrV/fgSoUQQgghvCY8ag3zzzqATYHZFexfAyx31oBhGB8rpXIxny+czj+1hh9xlQS6q2PHjqSnp9OvXz/S09MZMGAAX375JbVq1fJG80IIIYQQXuFRj6Cmaa2B/kBPzGSsDpAL/A5sBlYBK3VdP+WojVDnTo9gkT179tCvXz/27dvHvHnzuO6663wenxBCiPKy8grYlZnNwRJDx02qx9PKPnQMsOfYSb7ae4yYajFER1nIL7CRl5NH9xZ1aVnXvVGdjMxsFq7bw8qtGeTmFRAfE0XfDskM79Yy2IcOfUbuSVCo2tCwpmnXAhpwsYsGDeBP4F3gVV3X93kQZEjwJBEEOHjwIEuXLuX2250+eiiEEMJHjmSdYn3GCWxG6QoiCrAo6Jpci52H/+JwXj4Wi8Jq+WceZaHNhs1m0DAmmp6t6js9z8bdvzNlwQ8UFNpKVaGwWhRRVguTRnSmS4rjUnfhSO5J0KhcIqhpWh/gBeAczATvf8DXmLNvjwCZQDxQG2iDOfN2oP31NPAK8G9d1/+u+jUEB08TwbJ27NhBtWrVaNasmVfjEkIIUV5WXgFp+45R6KTPQ2EuOB3lZAmZ/IJCutSr6bBnMCMzmztnruV0fqHDNmKjrbw+pkfE9ILJPQkqlX5GMA1zNu01wCe6rudVcMxJ+8c+IBWYrGlaK8y1/e4GsoApnsccfoqGiqOjo0lLSyu1/qAQQgjv25WZjasqdDbDwGJ1vhihxaL4au9xh4ngwnV7KCh0Xr2koNDGovV7ufvS9s4DChNyT0KDq3UER+i6fr6u6x87SAIrpOv6Ll3XHwTOxMXkjUhSu3ZtWrRowYEDB+jZsyfbt28PdEhCCBHWDp7MxdUDUEopLMpV1QgLMdWiHe5fuTWj1NBnRQptBmlbvTInMSTIPQkNThNBXdcXVaVxXdeP6Lq+ripthJOaNWuydOlS+vbty+HDh+nVqxebNm0KdFhCCBG2Clx1B3rAWfWR3LwCt9rIPe3eceFA7kloCMnKIqEsMTGRJUuWcNlll3H8+HH69u3LunWSKwshhC9Euao/54H8AsfDnPEx7q3GFh8baqu2VZ7ck9BQ6buvaZoCGgAV9pXrun6gsm2Hu/j4eBYvXsx1113HokWLGDhwILt376ZePZk5JYQQ3tSkejz7/spxOjxsGAYGOB0eLrTZyMtxXEO+b4dkvtx00OlQqNWi6NeholL34UnuSWjwOBHUNG0k8C+gA2B1cJhRmbYjSUxMDPPnz2f06NF07dpVkkAhhPCBVkkJHPg7x+msYYtS5BfYsEQ5TgRtNoPuLeo43D+8W0uWbzlEoc3xDNkoq4VhXVu4FXc4kHsSGjxK1jRNuwtzSZgC4CvgkP1zUQlRUVHMmTMHVeKv0JycHKlNLIQQXpIYE0XX5FpeWUfQ2aLSyUkJTBrR2eWaeZG0TIrck9Dgaa/dA5hVRC7SdX2vD+KJOCWTwF9++YV+/frx/PPPSxUSIYTwkgaJcfRrXpfdmdkcKFFZpGn1eFLslUUatIqzVxY5Tky16BKVRfLp3qKOW5VFuqTU4/UxPVi0fi9pWw+Re7qA+Ngo+nVoxLCuLSIy4ZF7Evw8TQQbAW9KEugbn376KYcOHWLUqFHk5uZy6623BjokIYQIC4kxUXRqUJNODWo6PKZl3epul5JzJDkpgbsvbS/r4pUg9yS4eZoIHgRifRGIgIceeoi8vDwee+wxbrvtNnJycrj77rsDHZYQQgRU2pbf0JduI+vUP08iJcZFoQ1qR7+OjQHYvPc4+tJt7D+WVXxMs7qJaIPa0cnJs30lBUtNXHdqHnsj1mBpI5jOE4ncqjVcRNO0RzErhrTTdf2kz6IKYlUtMeeOl156iQceeACAZ599lkceecRn5xJCiGD23OJNpP2U4XB/v/bJNKqdwHtrdjk85qZerRjVs7XT8wRLTdz0XUddPqsYr1SVY/XG9frrngXLv02IczgTytN1BKdh1hleoWlaL03TqtaHLip0//3388Ybb6CU4l//+heTJ08OdEhCCOF3aVt+c5oEAqT9lOE0CQR4b80uNu897nB/RmY2Uxb8wOn8wnJLnRTaDE7nFzJlwQ9kZGa7H3wl7Dl2ksN5+URHWUslgWBWNomOsnI4L58Xv9hapVi9cb3+umfB8m8TzjxKBHVdLwReA1KAlcCfmqYVVvAhM4mraMyYMbz33ntYrVYaNmwY6HCEEMLv9KXb/NKWJzVxfemrvcewuFgA26IUbVo6H+p2Fas3rtdf9yxY/m3CmUeJoKZpQ4GlQC1gH/ANkF7Bx1qvRhmhbrjhBn7++WfGjh0b6FCEEMLvSj4TWFUlnx0sK1hq4sZUiynXE1iW1WqhdfPaTo9xFas3rtdf9yxY/m3CmaeTRSYDOcBgXde/8n44oqyUlJTiz7dt28bMmTP5z3/+Q1SUrNcthBDeECw1cZ3VMvb0OGexeuN6/XXPguXfJpx5mk2cBbwnSaD/FRQUMHToUH799VcOHz7M3LlziYmJCXRYQggR8uJjoshxI+HwdU3c/AIbMdGOCnaVPs4VZ7F643r9dc+C5d8mnHk6WeQ4kOeLQIRzUVFRvPvuu9SoUYOPP/6Y4cOHc+rUqUCHJYQQPpMY571f7s3qJjrc17dDMlYXz+b5oyZuXk4ehTbnSV5hoY2d+/5weoyrWL1xvf66Z8HybxPOPE0EFwIDNE2L9kUwwrnu3buTlpZGUlISS5Ys4fLLLyc7W2ZKCSHCkzaonV/aGt6tJVFW578O/VETt3uLuthcPA9nMwx+3uN4BjS4jtUb1+uvexYs/zbhzNNE8DHgBPCxpmnNvR+OcOX8889n9erV1KtXjxUrVnDJJZfw999/BzosIYTwun4dG9OvfbLzY9onc1OvVk6PualXK6eLShfVxI2NtpbrfbJaFLHRVr/UxG1ZtzoNY6LJLygs1zNYaLORX1BIw5hoHrisQ5Vi9cb1+uueBcu/TTjzdEHpPUA0UPQ/80/grwoONXRdP7PK0QUhfywo7Y6iusSHDh1i8eLFXHnllQGNRwghfMWflUWCoSauOzWPvRFrsLQRTOcJYw7H1z1NBPcBbr1B1/Ww7KcNlkQQYO/evaxdu5abbrop0KEIIYQQIng5TAQ9ehJX1/XmVQ5FeE2LFi1o0eKffHvr1q0kJSXRqJE8NCuE8L1Qqv+alVfArsxsDp7MpcBmEGVRNKkeT6ukBBJjZMapiFzy3R8mioaKq1evTlpaGs2bNw90SEKIMFZR/decvAK+3HSQ5VsOBVX91yNZp1ifcQKb8c+QVoHNYN9fORz4O4euybVokBgX0BiFCBRPJ4uIIFWnTh2aNWvGnj176NGjBzt37gx0SEKIMBVK9V+z8gpYn3GCQqP8c00GUGjA+owTZLm5cLEQ4cbTEnOPaZqWr2lahWOPmqYla5qWp2nav7wTnnBX7dq1WbFiBRdffDG//fYbPXv25Keffgp0WEKIMBRK9V93ZWbjYkUWbAbsDoKkVYhA8LRH8HJgta7rFRb103U9A1gFDK1qYMJzNWvWZOnSpfTr14+jR4/Su3dvfvjhh0CHJYQIM6FU//XgyVyXMxwN4MDJXH+EI0TQ8TQRTAG2uzhmu/04EQAJCQksWbKEwYMH88cff9C/f38yMzMDHZYQIoyEUv3XAlfdgR4eJ0S48XSySDUgx8Uxp4DqlQtHeENcXByLFi3ixhtvpH///iQlJQU6JCFEGAml+q9RFuVWkhflooyZEOHK0/+lB4FuLo7pBgR+PCDCxcTE8OGHH6LUPz/ccnJyqFatWgCjEkKEg74dkvly00Gnw8PBUv+1SfV49v2V43R4WAFNq8f7KyQhgoqnQ8OpQE9N066paKemadcCvYAvqxqYqLqSSeDWrVs588wzWbhwYQAjEkKEg1Cq/9oqKQFXnX0WBSlBtu6hEP7iaY/gNGAUMM+eDKZi9v41Ai4FrgAygWe9GaSoukWLFnHkyBGuueYaZs+ezahRowIdkhAiRBXVfy27jiCYPYFRVkvQ1H9NjImia3KtcusIgtkTaFHQNbmWLCotIpZHJeYANE07H/gYaEb5/1P7gJG6rn/vrQCDTTCVmPOEYRg8/vjjTJ06FaUUb7zxBnfccUegwxJChLBQqv+alVfA7sxsDpSoLNK0ejwpUllERAbv1BouomlaNOZSMt2AM4A/gXXAZ7qu51cqxBARqolgkWeeeYZHH30UgJdeeon77rsvwBEJIYQQwse8U2u4iD3ZW2T/ECFkwoQJVKtWjfvvv5/777+f3Nxc/vUvWf9bCCGEiETSHx6B7rvvPqpVq8a4ceNo2LBhoMMRQoSojMxsFq7bw8qtGeTmFRAfE0XfDskM79bS7aFhb7ThLcEUiz9E2vWKijkdGtY0bbiu65WeZqppWkOgua7r31a2jWAT6kPDJe3evZuUFFn7WwjhuY27f3c5WaRLSj2ft+EtwRSLP0Ta9QrHQ8Oulo/5WNO07zVNu0bTtFh3z6Zp2lmapr0I7Ab6u/s+4V8lk8AffviBBx98EJvNef1QIYTIyMxmyoIfOJ1fWG4twUKbwen8QqYs+IEMJ/V7vdGGtwRTLP4QadcrnHM1NNwPeBH4APhL07RPgK+B74DDwAkgDqgNtMGcPDIIOB/IA14BXvJF4MJ7Tp8+zZVXXsnBgwc5duwYb7/9NlFR8tSAEKJiC9ftoaDQ+R+NBYU2Fq3fy92XtvdZG94STLH4Q6Rdr3DOaY+gruurgHOBGzBrCN8EvI6ZCB7CLDeXCewCPgMeA1oBLwNtdF1/RNf1kz6LXnhFbGwss2fPJiEhgTlz5nDdddeRl5cX6LCEEEFq5dYMp1VFwOxZStvquMiUN9rwlmCKxR8i7XqFcy67fXRdN4B5mItIn4U51NsdaIrZE5gL/A5sAVYDK3Vdz/VVwMI3+vTpw7Jly7j00ktZsGABubm5LFiwgLi4uECHJoQIMrlu1BkGyD3t+DhvtOEtwRSLP0Ta9QrnPBr/03X9F+AX4DXfhCMC6aKLLmLVqlUMHDiQzz//nCFDhvDJJ5+QkCCzx4QQ/4iPiSLHjWQiPtbxrxhvtOEtwRSLP0Ta9QrnPK01LMJc586dWb16NfXr12flypWsXbs20CEJIYJM3w7JWF0U8LVaFP06NPJpG94STLH4Q6Rdr3BOEkFRTvv27UlPT2fu3LlccsklgQ5HCBFkhndrSZTV+a+PKKuFYV1b+LQNbwmmWPwh0q5XOCeJoKhQ69atuf7664u/3rRpE0ePHg1gREKIYJGclMCkEZ2JjbaW61myWhSx0VYmjejsdFFib7ThLcEUiz9E2vUK5ypVaziShdOC0u7aunUrvXr1ol69eqxYsYLGjRsHOiQhRBDIyMxm0fq9pG09RO7pAuJjo+jXoRHDurbwqLJIVdvwlmCKxR8i7XojnMNnASQR9FAkJoK///47AwcO5Mcff6RFixakpaXRooUMGQghhBAhwmEiGFJTgpRSE4DOwHlAC2C/YRjNPWxjNdDLwe4uhmF8V5UYw1G9evVYtWoVl1xyCRs2bKBHjx6kpaVx1llnBTo0IUQF/FVDdsH6Pfz8RxYpTZOIjrKQX2Bj94FM2tROZETXlgBk5RWwKzObgydzKbAZRFkUTarH0yopgcSYKJf73bV573H0pdvYfyyreFuzuolog9rRqUUdt+6J1N4VkSikegSVUgbmAtY/YCaDf1cyEWwHPFDB7i8Mw8h09v5I7BEs8vfffzNkyBDWrl1LvXr1WL58OR07dgx0WEKIEvxVQ/bpTzZzZkodLEphLTHxoLDQhs0w+HX3cW7t14b1GSewGVDyN40CLApaJyWyMzPL4f6uybVokOh6LdP303fy3ppdDvf3a5/MV78cdXpPAKm9K8JZeAwNK6VaGoaxx/75T0BiJRPB5p6+r0gkJ4IAOTk5XHnllSxfvpy6devy66+/Ur169UCHJYTA7NG6c+ZaTucXOjwmNtrK62N6VKmHa8H6PRQmxhAdZXV4TEGBDatVoZTzZUqcsSro17yu057BzXuP88jc9ZU+B0BMlJnI5hU4LrvmjfsmRAB5d2hY0zQrcBZQC6jwJ4Gu6+mVaduZoiTQG5RSFiAROGmEUjYcYNWqVePTTz/luuuuY/jw4ZIEChFE/FVD9uc/sjirem2nx1isCgMnv33cYDNgd2Y2nRrUdHiMvnRbFc5gyiuwuYxTau+KcOXx8jGapk0CjgFbgXRglYOPYNYIyAL+ArKUUouUUm0CHFPIiIuLY9GiRdxwww3F23JycgIYkRAC/FdDNqVpUqnh4IpYlMJShd5AMIeLD5x0XrG05DOBVT2XM1J7V4Qrj3oENU17GHgSM4GaAxwEQq0Y4V7ga8zayIVAV+BuoJ9SqrthGFsrepNSagwwZty4cX4LNJiVHO75/vvvGTx4MLNmzWLw4MEBjEqIyOavGrLRUf5bgrbARWLrT1J7V4QjT4eG7wAOAZ11XT/mg3h8zjCMW8psWqCU+hRYDbwADHDwvpnAzKJnBMU/Pv74Y44ePcpVV13FBx98wPDhwwMdkhARyV81ZPMLbMREO34+0JuiXJRC8yepvSvCkad/1jUB/heqSaAjhmGsxRzm7qOUig90PKHmmWee4cEHHyQ/P5+rr76aOXPmBDokISKSv2rI7j6QSaGLZxFthoGtio9fK6Bpdec/kpvVTazSOUqeyxmpvSvClaeJ4FFCbO1BD+zDnPhSK8BxhBylFM8//zyPP/44NpuNm2++mZkzZwY6LCEijr9qyLapnegyybMVGlWaKALmEjIpLmbpaoPaVfEs5qxhV8PdUntXhCtPE8GPgAGapsX6IpgAa4X5vKPTdQRFxZRSPPnkk0ybNg3DMBg7diwvv/xyoMMSIqL4q4bsiK4t+XX3cfILCsv1DBYW2sgvKGT37mNc3DgJqyrf26Ywl4Y5u3ai0/1dk2u5XFS6U4s63NSrldNj+rVPdnpPHh95Ho+PPE9q74qI5NE6gpqmxQPLgD+Be3Vd3+ujuFxytY6gUqohUBM4YBhGjn1bTSDLMIzCMscOBpYAXxqGcZmz80b6OoLu+O9//8v999/PBx98wMiRIwMdjhARx181ZBes38PPx7NIaVaissj+TNrUKV1ZZHdmNgdKVA5pWj2elBKVRZztd5c7lUVc3ROpvSvCWOUWlNY0raJ1+6KBZPvnf2EmhWUZuq6f6UGAblFK3Qg0s395DxAD/Mf+9X7DMOaUOPZd4Gagj2EYq+3brsScEPIZsAezB/AC4AbMnsCLDcPY6SwGSQTd8+uvv3LmmV7/FhBCCCGE5yq9oLSF8ssrFQAHXDTuq2let1G+TvAU++sazCVtnPkF+B4YAtTHTGp/A14HnjYMQxaJ8pKSSeD69etZvHgxzzzzTJWqDAgRDkKpnq2rXjZ3uLped84RSvfMX+SeCG8JqRJzwUB6BD2Tk5NDy5YtOXr0KLfffjuvv/46Vqt/lp0QItj4qw6wN7iq33tTr1aM6tnaaRuurrf7WfVJ+ynD6TlaJ58RMvfMX0Lp+0gEDYe9MP5bFVREpGrVqjF79mzi4uJ46623uOmmmygokEVZReTJyMxmyoIfOJ1fWK76R6HN4HR+IVMW/EBGZnaAIvzH5r3HnSaBAO+t2cXmvccd7nfnep0lgUXnePKj70LinvlLKH0fidDgUSKoadpKTdNucnHMDZqmraxaWCKcDBo0iNTUVBITE5k3bx7XXHMNeXl5gQ5LCL/ypA5woLlbv9fZce5crzvyC52PWgXLPfOXUPo+EqHB0x7B3kBzF8c0o/xzfCLC9erVi+XLl3PGGWewaNEirrzySnJzndcQFSKc+KsOsDe4W7/X2XHuXK83BMs985dQ+j4SocEXQ8PxhF79YeEH3bp1Y+XKldSpU4dly5axfv36QIckhN/4qw5wsHD3er1yrjC5Z+6ItO8j4XuVqRJS4Z8imqYpoClwGXCwKkGJ8HXuueeyZs0atm/fTu/evQMdjhB+4686wMHC3ev1yrnC5J65I9K+j4TvufxO0TTNRunkb7KmaZOdvEUBT1cxLhHG2rZtS9u2bYu/3rBhA2eeeSa1a9cOYFRC+FbfDsl8uemg02G9YKln26xuolvDw87q/Lpzvd4QLPfMX0Lp+0iEBneGhtNLfBiYawimV/CxClgA3Ak87otgRfj5/vvv6d+/P7179+bIkSOBDkcIn/FXHWBvcLd+r7Pj3Lled0Rbna89Giz3zF9C6ftIhAZPS8zZgMm6rj/lu5CCm6wj6F0ZGRn079+fHTt20KpVK9LS0mjSpEmgwxLCJ0Jp/TdZRzB4hdL3kQgalSsxV5amac2AP3Vd/8sbUYUiSQS979ixYwwcOJDNmzfTrFkzVq5cScuWLQMdlhA+EUr1bL1VWcTZ9bpbWSRU7pm/yD0RHvJOIigkEfSVEydOcOmll7J+/XqSk5NJS0ujTZs2gQ5LCCGECAeVqzWsaVpln/UzdF2f4vowIUy1atVi+fLlDBkyhPT0dPr378+uXbuIj48PdGhCCCFE2HI1a3hyBdtKdiGqCrYr++eSCAqPVK9enS+//JKRI0dy6623ShIo/CojM5uF6/awcmsGuXkFxMdE0bdDMsO7tfTrUFvalt/Ql24j69Q/S4QkxkWhDWpHv46NAddDtu4Mt7pzTFZeAbsyszl4MpcCm0GURdGkejytkhJIjIly654Fy30VQlTM6dCwpmkVVQh5AHOtwPeB1cARoAHQB7ge+Bx4Sdf1Nd4ONhjI0LDvGYaBUv/8jZGdnU1CgvzCEL4TLA/fP7d4k9PJE/3aJ9OodoLTSRznNEvix/2ZDvff1KsVgMuJIP06N2V9xglsRvm//i0KaikL0xducnrPgKC4r0II700WuQmYAfTQdf2HCvafj7mUzFhd1+dUItCgJ4mgf33zzTdcddVVzJ07lwEDBgQ6HBGGMjKzuXPmWk7nFzo8Jjbayutjevi0Bytty28898mPPmvfEzUSYrh+cPuKqwfY5RcUsmDpDv7OrrhueEyUucRJXoHjurj+uK9CCMBJIujpIk8PAPMrSgIBdF3/DvjIfpwQVfbRRx/x+++/M2TIED777LNAhyPC0MJ1eygodJysABQU2li0fq9P49CXbvNp+57oeFY9lwtBWyyKDq0d9+blFdjId5IEgn/uqxDCOU8TwbOAwy6OybAfJ0SVvfDCC9x9993k5eUxbNgwPvroo0CHJMLMyq0ZLpOeQptB2tZDPo2j5DOBgdaqWW0sFucLOVstFlo3d14NyNV4kz/uqxDCOU8Twb+Bi10c0x1wXZtICDdYLBZeeeUVHn74YQoKCrjuuuuYPXt2oMMSYSTXzXq4uaeDJ1Hztego9341uHucM5F0X4UIRp5Wpf4cGK1p2nTgSV3XTxbt0DStOuYs44uBd7wWoYh4SimeffZZEhISeOKJJxg9ejSnTp1i7NixgQ5NhIH4mChy3EgG42M9/XEZuvILbMREW906rqoi6b4KEYw8/R84AeiN+Qzg7ZqmbQaOAvWBTkANYA/wqNciFAIzGXz88cepVq0aEyZMoGHDhoEOSYSJvh2S+XLTQafDw1aLol+HRj6NIzEuKmiGh3ft/4OzW9Z1OjxcaLOxc98fTtspWkvMEX/cVyGEcx716+u6/jvQBZiFmUT2BEbaX6OAN4Gu9uOE8Lrx48fz888/c8UVVwQ6FBEmhndrSZTV+Y/CKKuFYV1b+DQObVA7n7bviS2//I7VxTOCNpvB1p2Of9THRFlcDh37474KIZzz+AEPXdczdV0fA5wBdAR62F/P0HV9rK7rzv9EFKKKzjzzzOLP09PTmTRpElIqUVRWclICk0Z0JjbaWi75sVoUsdFWJo3o7PMlTvp1bEy/9snOj2mfXLwOoCPnNEtyuv+mXq1ctnHl+c24sFEtrKr8mhMKsCpoGBPN6bxCh/fs8ZHn8fjI8wJ+X4UQzkmtYQ/JOoLB46+//qJFixacOHGCu+++m5dffhmLpeoPr4vIlJGZzaL1e0nbeojc0wXEx0bRr0MjhnVtEdGVRXZnZnOgRGWRptXjSSlRWcTVPQuW+ypEhPPOgtJCEsFg8+mnnzJy5Ejy8vK49dZbmTlzJlar64fchRBCiAjiMBF0OllE07SVmM/63qzr+m/2r91h6Lrez4MAhaiUK664gs8++4wrr7ySt99+m9zcXGbPnk10dHSgQxOiQq5q73qjNq+36vv6o06wq3rGQgjfcvW/rDdmIlitxNfukG5G4TcDBw4kNTWVwYMH88EHH5CTk8P8+fOJjY0NdGhClFJRTeOcvAK+3HSQ5VsOcc1FLZn/zR6H+92pzevqHO7W9/VWO84cyTpVrp5xgc1g3185HPg7h67JtWiQGFelcwghnJOhYQ/J0HDw2rBhA4MGDSI7O5s1a9Zw4YUXBjokIYq5U9PYFVe1eb1VN9kf9Zez8gpI23eMQie/gqwK+jWvKz2DQlSd12oNCxG0LrjgAlatWsWCBQskCRRBx52axq64qs3rrbrJ/qi/vCszGxeV/bAZsDszu9LnEEK45lEiqGlacx/FIYRXdOrUqdQag99++y1//vln4AISws6dmsauuKrN6626yf6ov3zwZK7LZ4gM4MDJ3EqfQwjhmqf97Xs0TdsDrALSgJWyeLQIVt988w0DBgzgrLPOYtmyZdSpUyfQIYkI5m5NY5ftOKnN6626yf6ov1zgZlLs7nFCiMrxNBFcAPQCbgNuBdA0bTuwEjMxXK3r+t9ejVCISmrSpAmNGjVi06ZN9OrVixUrVkhpOhEw7tY0dtmOk9q83qqb7I/6y1EW5VaSF+WiwokQomo8LTF3ta7rRXWFxwNfAk2Be4DFwB+apq3XNO1pbwcqhKeaNGlCeno67dq1Y/v27fTs2ZMDBw4EOiwRofp2SHZZts0VV7V53TmHO/V9vdWOM02qxzt+et1OAU2rx1f6HEII1yo1WUTX9S26rr+o6/oQIAm4GHgS+AOzFvEj3gtRiMpr0KABq1evpnPnzuzevZsePXqwe/fuQIclIpA7NY1dcVWb11t1k/1Rf7lVUgKu8mKLghSpPiKET1Xpp5KmaWcBY4GHgPuBokWldlUtLCG8p06dOqSlpXHhhRdy4MABBg4cSF5eXqDDEhHGnZrGN/VqVaXavN6qm+yP+suJMVF0TXZez7hrci1ZOkYIH/NoHUFN0xoB/Up8NMT8P/sb5nOCK4E0XdcrP5UsyMk6gqErKyuL4cOHc++99zJ48OBAhyMilKvau96ozeut+r7+qBPsqp6xEMIrvFNrWNO0otVF/wBW80/iFzE9gJIIhjbDMFDqn/8P2dnZJCTI0JMQQoiwVrlaw04aOgD8CuwGDlYyKCH8rmQSuHr1aq655ho++ugjevXqFcCoQpM/6tCGGnfq5kptXSFEMPH0p04/oK/99UHgYSBP07RvMZePSQM26LpeteXzhfCDefPm8fvvv3PppZeyePFiBg0aFOiQQoY/6tCGGnfq5gJSW1cIEVQqXWtY07REoDf/JIft7buygDW6rl/h4K0hTYaGw0dhYSFjx45l1qxZxMTE8NFHHzF06NBAhxX0/FGHNtS4UzfXAqBwWlZNausKIXzE+7WGdV3P0nV9ia7rDwCDMHsIjwHVAXkSXwQ9q9XKzJkzueeee8jLy2P48OF8+OGHgQ4r6PmjDm2ocatuLs6TQJDaukII/6vUn52aplXHrDBSNHu4nX2XAjIxJ5IIEfQsFgsvv/wyCQkJPPvss1x//fWcOnWK0aNHBzq0oOVJHdq7L23v9Lhw4U7dXHcU1dbt1KCmF1oTQgjXPEoENU2bgpn4nQ9YMRO/bGAZ9trDwCZd16U4pAgZSimefvppEhISmDJlCsnJyYEOKaj5ow5tqPFmPVyprSuE8CdPewQnAnlA0eSQlcA6Xdcj5ye+CEtKKR577DGuv/56WrZsGehwgpo/6tCGGnfr5rrblhBC+IunP6kvAdbqup7ri2CECLSSSeDy5ctZv349EydOLLXsTKTr2yGZLzcddDo8XNU6tKGmSfV49v2VU+XhYamtK4TwN48SQV3Xl/kqECGCyR9//MHw4cM5efIkf//9N9OmTZNk0G54t5Ys33KIQpvjWcNVrUMbalolJXDg75wqzxqW2rpCCH+rWgV0IcJU7dq1mTVrFlFRUTz//PPcfffd2GyyPCb4pw5tqHGnbm63RrXoJrV1hRBBptLrCEYqWUcwsixZsoQRI0Zw+vRpRo8ezVtvvYXVag10WEHBH3VoQ407dXOltq4QIgC8U2tYSCIYiVasWMHQoUPJycnhmmuuYc6cOURHRwc6LCGEEMJd3l9QOhCUUhOUUh8rpfYopQyl1L5KtnOZUuobpVS2UirT3mbkPNAkPNK/f3+WLl1K9erV+eSTT/jpp58CHZIQQgjhFaE2DvE05oLVPwBnVKYBpdQwYAHwI/AQUBO4H/haKXW+YRgZXolUhJXu3buzcuVKjh8/zrnnnhvocCLK5r3H0ZduY/+xrOJtzeomog1qR6cWdfzWRlZeAbsyszlYYki3SfV4Wnl5SDcjM5uF6/awcmsGuXkFxMdE0bdDMsO7tSwecnfnGCGEcEdIDQ0rpVoahrHH/vlPQKJhGM09eH80sA8oANoZhpFl394J+B6YZRjGGGdtyNCwKLJ27VrOOeccatSoEehQwtb76Tt5b80uh/tv6tWKUT1b+7yNI1mnWJ9xAptBqSViFOZM367JtWiQGOe0DXds3P07Uxb8QEGhrdTyPFaLIspqYdKIzgAuj+mSUq/KsQghwkp4DA0XJYFV0AtIBt4qSgLt7W7GLIt3jT1ZFMKp1atXM2DAAAYMGMCJEycCHU5Y2rz3uNMEDuC9NbvYvPe4T9vIyitgfcYJCsskgWB+XWjA+owTZLlZccWRjMxspiz4gdP5heXWaCy0GZzOL+Spj7/nqY+/d3rMlAU/kCH1ioUQbnI6nqFp2uOVbNfQdX1KJd/rS13sr99WsG8d0BdoDWzzW0QiJDVr1oyGDRuyYcMG+vTpw7Jly6hXT3phvElf6t5/Q33pNmbe2ctnbezKzHa69h+YawPuzsyuUo3ghev2UFDofImi/AKby0WrCwptLFq/N2LqPAshqsbVgy2TK9muAQRjIlhURPZQBfuKtjVCEkHhQosWLVi7di39+vXjxx9/pFevXqSlpUmdYi8q+TxfZY/zRhsHT+a6TL4M4MDJ3Colgiu3Zjit1lJ0HlcKbQZpWw9JIiiEcIurRLCPX6Lwn2r219MV7DtV5phSlFJjgDHjxo3zRVwiBDVu3Jj09HT69+/PTz/9RM+ePUlLS6NZs2aBDk14kbs1hKtaazi3ikPLpdo6LeXfhRDucZoI6rq+xl+B+EmO/TW2gn1xZY4pxTCMmcDMoskiQgDUr1+f1atXc8kll/Ddd99xySWXsHXrVqKiQm1CvnAkyqLcSvKiLFUrQRgfE0WOl5LB+Fj5/hNCuCekJot4QdHSMI0q2Fe0raJhYyEcql27NitWrKBfv368+uqrkgR6SbO6iVU+zhttNKke73i6nZ0CmlaPd+tcjvTtkFyuZF9F53HFalH061DRjzghhCgv0hLBjfbXCyvY1w34G9jpv3BEuKhZsybLly+nf//+xduys2XmZlVog9pV+ThvtNEqKQFXnX0WBSlVXL9veLeWRFmd/0iOjrIQE+X8mCirhWFdZX18IYR7PO660DStIfAYMAizFy2mgsMMXdcD2i2ilGqIuVj0AcMwioZ71wCHgduVUi+WWEfwHKA38I5hGPmBiFeEPqX+yRZSU1O5+eab+d///seFF1b0d4dwpVOLOtzUq5XLNQCdLQjtjTYSY6LomlzL5TqCVV1UOjkpgUkjOntlHUFZVFoI4S6PfnJpmtYI2ADUx5xZGwvsx5x80dLe3mbgL69GaaeUuhEoehK/LhCjlHrM/vV+wzDmlDj8GeBmzAkvqwEMw8hXSt0HzAfWKqXeBGoADwDHgCd8EbeIPO+//z6///47AwYMYMmSJfTu3TvQIYWkUT1b065JUpWqgnijjQaJcfRrXpfdmdkcKFFZpGn1eFK8WFmkS0o9Xh/Tg0Xr95K29RC5pwuIj42iX4dGDOvaojjBc+cYIYRwh0eVRTRNewO4HRik6/oKTdNswGRd15/SNK0x8CbQHLhI13Wvr7KrlFqNuSh0RdYYhtG7xLHvYk8EDcNYXaadIZi9mh0xk9g04BHDMH51FYNUFhHuKCgo4JZbbmHu3LnExcWxePFiLrnkkkCHJYQQIjJ5rbLIICBV1/UVZXfouv4bMBKIB570sF23GIbR2zAM5eCjd5ljR9u3r66gnSWGYXQzDKOaYRi1DMMY4U4SKIS7oqKimD17NnfccQenTp3iiiuu4H//+1+gwxJCCCFK8TQRbEDpxZYLMRM/AHRdzwKWA0OrHpoQoc1isfDGG29w3333kZ+fz4gRI/jwww8DHZYQQghRzNMHW/6m9OSQE5RfiuUvzOf3hIh4SilefPFFEhISePHFF2nYsGGgQxJCCCGKedojuB9oUuLrH4G+mqZVA9A0zQIMBH7zTnhChD6lFP/+97/56aef6NXL0SOuQgghhP95mgimAX00TYu2fz0bs37vN5qmPQ98DbTDnJUrhCihZcuWxZ9/9tlnPP/88wGMRgghhPB8aHgW5nBwHeCwrutzNU07D7gHcwYuwIfAv70XohDh5ciRI1xzzTXk5uZy8uRJnnzyyVJrEAohhBD+4tHyMY5omlYXcx3BfbquH61yg0FMlo8R3jB37lxGjx5NYWEh//d//8f06dMlGRRCCOErDn/BeGUVVF3Xj2EuyCyEcMMNN9xAfHw81113HS+88AI5OTm89tprWCyRVvVRCCFEIHn0W0fTtEJN0ya5OGaipmkFVQtLiPA3fPhw/ve//xEXF8frr7/OLbfcQkGB/NcRQgjhP552PyicdC+WOU4I4cJll13G559/TkJCAgsWLOCXX34JdEhCCCEiiHcKZJZWCzjlg3aFCEt9+/Zl2bJlnDp1inbt2gU6HCGEEBHEZSKoaVrPMpuaV7ANwAo0BUYB0q0hhAcuuuiiUl+vXr2aLl26kJCQEKCIhBBCRAJ3egRXA0VTiw3gZvtHRRRgAx6scmRCRKjU1FSuuOIKunbtyueff06NGjUCHZIQQogw5U4i+BRmAqiAxzETwzUVHFcI/AGs0nX9Z28FKESkadmyJfXr1+err76if//+pKamkpSUFOiwhBBChCGP1hHUNG0v8KKu66/4LqTgJusICn/Yt28fffv2Ze/evXTs2JHly5dTr169QIclhBAiNHlnHUFd11tUPRYhhCvNmzdn7dq19OvXjy1bttCzZ0/S0tJo1KhRoEMTQggRRio1a9hea7gfcDaQqOv6FPv2OKAGcFzXdZvXohQiAjVq1Ig1a9YwYMAAtm7dypAhQ/j+++9l0WkhhBBe4/FvFE3TLgH2AZ8D/wEml9jdCTgMXFP10IQQ9evXZ/Xq1fTq1YtXXnlFkkAhhBBe5WllkfOB/2FOHnkAmFdyv67r64C9wFVeik+IiJeUlMSqVavo0aNH8bbs7OwARiSEECJceNq9MAnIAc63TxjZVcExG4FzqhqYEOIfSv3znO8nn3zCmWeeyffffx/AiIQQQoQDTxPBi4H/6bp+xMkxB4GGlQ9JCOHMnDlzOHr0KH379uWbb74JdDhCCCFCmKeJYCJw3MUx1SrRrhDCTfPmzWPkyJH8/fffDBw4kJUrVwY6JCGEECHK04TtEOCqGGonYE+lohFCuBQTE8O8efO46aabyM7O5rLLLuOLL74IdFhCCCFCkKeJ4JfAIE3Tule0U9O0S4GLgCVVDUwI4VhUVBTvvPMOd955J6dPn+bKK69k8eLFgQ5LCCFEiPF0HcFngGuBZZqmvQo0B9A0bTDQE7gLc/mYF7wYoxCiAhaLBV3XqVatGm+88QYNG8qjuUJ4W35+Pr/99hunTp0KdChCVMhqtXLGGWdQp06dSi0x5lGJOQBN0zoDHwEtS2wuqkX8KzBM1/WtHkcSIqTEnAg2hmGwd+9eWrZs6fpgIYRH9u7dS/Xq1aldu3ap2ftCBAPDMMjPz+fo0aMYhkHTpk0dHerwm9fj1FHX9R+AszDXCpwGvIXZAzgSODuck0AhgpFSqlQS+PHHH/Pyyy8HMCIhwsepU6ckCRRBSylFTEwMjRo1qvT6sm4PDWua1hTogtn7t1HX9U+ATyp1ViGET+zfv59Ro0aRn59PdnY2jz76aKBDEiLkSRIogl1Vqk659U5N06ZjzgT+CPgY2Ktp2vOVPqsQwieaNWuGrusopZg4cSITJ07E08c/hBBCRA6XiaCmadcD/4c5vvwz8Iv98//TNO0634YnhPDU7bffzty5c7FarTz99NM88MADkgwKIYSokDtDw7cBBcAgXddXAWia1h9zKZnbgA98F54QojKuv/564uPjueaaa3j55ZfJyclhxowZWK3WQIcmRGT5PQOWLYJ1K+FULsTFQ7e+MHAY1EsOaGi9e/emffv2/Pe//w1oHCKw3Bka7ohZVm5V0QZd11dgPh/YyUdxCSGq6KqrruKTTz4hLi6O+fPns3fv3kCHJERk2boRJo+DtV/CqRzAMF/Xfmlu37rRZ6cePXo0Q4YMcXrMokWLeOaZZyp9jpycHB599FFSUlKIi4ujTp06XHzxxXzwgfv9Q/v27UMpxXfffVfpOETVuNMjWAtzOLisn4ErvRqNEMKrLr30Ur788kuio6NJSUkJdDhCRI7fM2DGVMg7XX5fYaH5MWMqTJ7h957BvLw8YmJiSEpKqlI7d955J19//TUvv/wy7du3JzMzk/Xr15OZmemlSIU/uNMjaAHyK9iej5N1aYQQwaF3795cfPHFxV+npaWRm5sbwIiEiADLFkFhgfNjCgtg+SKfh1LUOzht2jQaN25M48aNAfNnw91331183KJFi+jYsSPx8fEkJSXRq1cvjh496rDdTz/9lAkTJjBkyBCaN29O586dGTduHHfddVfxMYZh8Nxzz3HmmWcSHx9Phw4dmDt3bvH+Fi1aANClSxeUUvTu3RsAm83GlClTaNKkCbGxsXTo0IFPPim9UMlTTz1Fs2bNiI2NpUGDBtx0003F+1JTU+nRowe1atUiKSmJQYMGsWPHjsrfxDDm7nxjedJciDDwySefMGjQIIYMGUJWVlagwxEifK1bafb6OVNYCN+u9Es4a9asYcuWLaSmppKWllZu/5EjR7j22mu5+eab2bFjB+np6dx4441O22zQoAGpqan89ddfDo957LHHmDVrFq+99hrbt29nwoQJjB07ls8//xyADRs2AGbidvjwYRYtMhPjl19+meeff55p06axdetWrrrqKoYNG8bmzZsBWLhwIdOnT0fXdXbt2sWSJUu44IILis+bnZ3N/fffz4YNG1i9ejU1a9bk8ssvJy8vz6P7FgncXUdwsqZpkyvaoWlaRd/phq7rnpavE0L4WEpKCnXr1mXlypUMGjSIL774gpo1awY6LCHCzyk3e91P+6d3Pi4ujrfffpvY2NgK92dkZJCfn8+IESNo1qwZAO3bt3fa5syZMxk1ahR16tShQ4cOXHTRRQwdOpQBAwYAZjL2wgsvsGzZMnr06AGYPYAbNmzgtddeY/DgwdStWxeA2rVr06BBg+K2p0+fzvjx47n++usBs/cvPT2d6dOnM3fuXPbv30/Dhg0ZOHAg0dHRNG3alPPPP7/4/cOHDy8V6zvvvEONGjXYsGED3bt39+TWhT13ewSVhx+VX9lQCOEz7dq1Iz09nSZNmvDNN9/Qr18//vjjj0CHJUT4iYt377hYN4+rovbt2ztMAgHOOecc+vfvT/v27Rk+fDgzZszg2LFjABw4cIDExMTij6effhqAnj17smfPHlauXMnVV1/Nzp07GThwIGPHjgVg+/btnDp1iksuuaTU+2fMmMGvv/7qMJa///6bjIyMUo+0AHTv3p3t27cDMHLkSE6dOkWLFi247bbb+Pjjjzl9+p/nMX/99Veuv/56zjzzTGrUqEH9+vWx2WwcOHCgcjcwjLnstdN1XZI6UTlBvGxCJGvVqhVr166lX79+fP/99/Tu3ZsVK1ZQv379QIcmRPjo1tecHexseNhqhQv7+iWchIQEp/utVivLli1j3bp1LFu2jFmzZjFhwgTWrFlDu3btiodkgVKTTKKjo+nRowc9evTgX//6F1OnTmXSpElMmDABm80GwGeffVauBm50dLTLmCuq6FK0rUmTJvzyyy+kpaWxYsUKHnzwQZ588knWr19PQkICl19+OY0aNeKNN96gUaNGREVF0bZtWxkaroAkecI3ArhsgnCtWbNmpKenc/bZZ/PTTz9x5ZVXyqLTQnjTwGFgddHXYo2CAcP8E48blFJceOGFPPHEE2zcuJHk5GTmz59PVFQUKSkpxR/OZhu3bdsWgKysLNq2bUtsbCz79+8v9f6UlJTi4eeYmBgACkskzDVq1CA5OZmvvvqqVNtfffVVcftgDncPHjyYF198kY0bN7Jt2za+/vpr/vjjD3bs2MGjjz5K//79Ofvsszl58iQFBS4m70QoeY5PeF8QL5sg/pGcnMyaNWsYPnw406dPl3qqQnhTvWQY95j5s66woHTPoNVqJoHjHguan4Hr1q1jxYoVDBo0iPr167Np0yYOHjxYKvEqq3fv3lx33XWcf/751K5dm+3bt/Poo49y1llncfbZZ2O1Whk/fjzjx4/HMAx69uxJVlYW69atw2KxMGbMGOrVq0d8fDxLly6lefPmxMXFUbNmTR566CEef/xxWrVqxXnnncfcuXNZu3Yt33//PQDvvvsuBQUFdO3alcTERObPn090dDStWrWiVq1a1KlThzfffJMmTZpw6NAhHnroIaKiJOWpiNwV4X2eLJsw6m7nxwmfqlu3LmvWrCmVBGZnZ7scRhJCuKFDF/MP3uWLzNnBp3PNZwIv7Gv2BAZJEghQs2ZNvv76a1599VX+/PNPmjRpwqRJk7jhhhscvmfQoEHMmTOHiRMnkpWVRYMGDRgwYACPP/54cRWjKVOmUL9+faZPn864ceOoUaMGnTp14uGHHwYgKiqKV155haeeeoonn3ySHj16sHr1au69915OnjzJww8/zNGjRznrrLNYuHAhnTp1AuCMM85g2rRpjB8/nvz8fNq2bcuiRYuKl6OZP38+9957L+3btyclJYX//Oc/5SaQCJOS4SDPaJpmAOi6HuhQgtfdw+zDwS7EVYP/+n4NLeG+Dz/8kP/7v/8jNTWVjh07BjocIQJux44dnH322YEOQwiXXHyvOhzykWcEhfcF2bIJwj2GYTBnzhwOHz5M79692bhRnuMUQohwJ4mg8L4gWzZBuEcpxcKFC7n88ss5ceIE/fr1K/ewthBCiPAiiaDwvm59zYehnfHjsgnCfXFxcSxcuJCrr76akydPMmjQIFasWBHosIQQQviIJILC+0Jw2QTxj+joaObNm8fNN99MTk4OQ4YM4Ysvvgh0WEIIIXxAEkHhfUXLJsTElu8ZtFrN7UG0bIIoz2q18vbbbzNu3Dji4uJKlX4SQggRPiQRFL5RtGxCz0vN2cFKma89LzW3d+gS6AiFCxaLhddee41NmzbRuXPnQIcjhBDCB2QdQeE79ZLNdQJlrcCQpZQqXpcLYM6cOeTm5jJmzJgARiWEEMJbQioRVEpZgPuAsUBz4BjwEfC4YRjZbrx/NdDLwe4uhmF8551IhQg/u3bt4pZbbqGwsJDs7GweeOCBQIckhBCiikIqEQReBO4FFgP/Ac62f32uUqq/YRg2N9o4DlT0G2yP16IU7vk9w6xCsm6lufZgXLw543hgcK24L0ytWrXi5Zdf5u677+b//u//yMnJYeLEiYEOSwghRBWETCKolGoH3AMsMgxjeInte4FXgGuBeW40lW0YxlzfRCnctnVj+Rqcp3Jg7ZfwzXJzMok8Rxh07rrrLqpVq8btt9/OY489RnZ2Nv/+97+lTrEQDmRkZrNw3R5Wbs0gN6+A+Jgo+nZIZni3liQnSSlHEXihNFnkOswSKS+V2f4mkAM4LohYhlLKopSqoeS3V2D8nmEmgXmnSxdiB/PrvNPm/t8zAhOfcOqWW27h/fffx2q18swzz3D//fcjpSqFKG/j7t+5c+Zavtx0kJy8AgwgJ6+ALzcd5M6Za9m4+3efx7Bp0yasVisXX3yxx+/dt28fSim++06emgpnoZQIdgFswIaSGw3DOAVstu93RyMgC/gLyFJKLVJKtfFinMKVZYvMnkBnCgvMQu0iKF177bUsXLiQmJgY5syZw8GDBwMdkhBBJSMzmykLfuB0fiGFttJ/KBXaDE7nFzJlwQ9kZLp8vL1K3nzzTTRN46effmLHjh0+OUdeXp5P2hX+EUqJYDJw3DCM0xXsOwTUUUrFuGhjL/AccAswEtCBS4H1SqkO3gxWOLFuZfmewLIKC+Hblf6JR1TK0KFD+fTTT1m6dClNmzYNdDhCBJWF6/ZQUOj8sfWCQhuL1u/1WQy5ubnMmzePO+64gxEjRjBr1qzifY56+5RSLFiwAKB4xYAuXbqglKJ3794AjB49miFDhjBt2jQaN25M48aNATh06BDXXnsttWrVolatWgwePJhdu3YVt33w4EGGDh1KUlIS1apVo02bNnz44Yc+u37hnlBKBKsBFSWBAKdKHOOQYRi3GIYx0TCM+YZhLDAM4yFgIJAIvODsvUqpMUop6R/3hlO57h132s3jRMAMGjSILl3+6Yxfvnw5p087+m8qRORYuTWjXE9gWYU2g7Sth3wWw4IFC2jWrBkdO3bkxhtv5L333iM/P9/t92/YYA7ApaamcvjwYRYt+meUZs2aNWzZsoXU1FTS0tLIycmhT58+xMXFsWbNGr799lsaNmxI//79ycnJAUDTNHJycli1ahXbtm3jpZde4owzzvDqNQvPhVIimAPEOtgXV+IYjxiGsRZIB/oopeKdHDfTMIzzPW1fVCDO4W0uLdbN40RQmD9/PoMGDeLKK68kN1eSeBHZcvNcPP5SdNxp946rjLfeeosbb7wRgF69elGtWjU+/fRTt99ft25dAGrXrk2DBg1ISkoq3hcXF8fbb79N+/bt6dChAx9++CGGYfDOO+/QsWNH2rRpwxtvvEFWVhZLliwBYP/+/XTv3p1zzjmHFi1acMkll3DJJZd48YpFZYRSIpiBOfxbUTLYCHPYuLIPKuwDrECtSr5feKJb3/Kl58qyWuHCvv6JR3jFWWedRe3atUlNTWXw4MFkZWUFOiQhAiY+xr1FOeJjfbN4x+7du/n666+5/vrrAXPId9SoUbz11lteab99+/bExv7z6/j7779n7969VK9encTERBITE6lZsyYnTpzg119/BeC+++5j6tSpXHjhhTz22GN8//33XolFVE3ILB8DbMQcxr0AWFu0USkVB3TC7NWrrFZAAZBZhTaEuwYOM5eIcfacoDUKBgzzX0yiyjp16kR6ejr9+vVj1apVDBw4kC+++EKGfkRE6tshmS83HXQ6PGy1KPp1aOST87/11lsUFhaWen63aHb/wYMHsVgspbYBHg0bJySUXvrGZrPRqVOnCp/5K+pJvO222xg0aBBffPEFK1as4KKLLmLChAlMnjzZ7fMK7wulHsH5gAHcX2b7HZjPBr5ftEEp1VAp1UYpVa3EtppKqXLdUEqpwcDFwHL7DGTha/WSzXUCY2LL9wxareb2cY/JotIh6OyzzyY9PZ2mTZvy7bff0rdvX44fPx7osITwu+HdWhJldf4rNspqYVjXFk6PqYyCggJmz57NM888w+bNm4s/fvzxRzp27Mg777xTPOx7+PDh4vdt3ry5VDsxMeb8y0JXk/uAzp07s3v3burUqUNKSkqpj5JDyo0bN2bMmDF89NFHPPXUU8ycOdMLVyyqImQSQcMwtgKvAcPsS77crpT6D+YkjzWUXkz6GWAHZu9hkT7ALqXUy0qp+5RSdymlZgOfYlYbud8f1yHsOnSByTOg56UQVw2UMl97Xmpul8WkQ1ZKSgpr164lJSWFTZs2MXLkSFlnUESc5KQEJo3oTGy0Faul9JK1VosiNtrKpBGdfbKo9Oeff87x48e54447aN++famPa6+9lrfffpvY2Fi6devGtGnT2LZtG9988w3jx48v1U69evWIj49n6dKlHD16lL/++svhOUeNGkX9+vUZOnQoa9asYe/evaSnp/Pggw8Wzxy+7777SE1NZc+ePWzevJnU1FTatm3r9esXngmZRNDufmA80A4zKbwWeBUY4kZ5uV+A74EhwL8xE8juwOtAJ8MwdvooZuFIvWQYdTf8dxG8+aX5Oupu6QkMA02bNiU9PZ2LL76YF198USqPiIjUJaUer4/pwWWdm1ItNgoFVIuN4rLOTXl9TA+6pNTzyXlnzZpFnz59qF27drl9I0eOZP/+/axYsYK3337bjLNLF8aOHcvUqVNLHRsVFcUrr7zCW2+9RXJyMkOHDnV4zmrVqpGenk7Lli0ZOXIkbdq04eabb+bEiRPUqmU+fm+z2bjnnnto27YtAwYMoH79+syePduLVy4qQ8lf6p7RNM0A0HU90KFUnjdq/G76CtYugoQYsFqg0AbZedBjGJzb3Tzm7z9g+9ewZzPkn4boWGjZCdpeDDVqu97vz+sRPmEYRqkkMDs7u9yzRUIEsx07dnD22WcHOgwhXHLxverwr/FQmiwivMEbNX4Xvg5/7YPqsWB/4Jgoq/n1piWw5yfo2gfWfGCeo6izNv807NwIv/4A7XrCtnTH+3tdB43P8s/1CJ8pmQS+++67TJo0ieXLl9OmjRTzEUKIYBBqQ8OiKrxR43fTV2YSaLX+kwQWsVjM7X/th5VzoSD/nySviGEzt/+Y5nz/mg/MHkNfX4/wC5vNxpw5c/jtt9/o2bMnP/74Y6BDEkIIgSSCkcUbNX7XLjIndjhjUWBzPcvMeRyF5rCxM1KzOGRYLBY+++wzBg4cyLFjx+jdu3dx1QIhhBCBI4lgJPFGjd+EmPI9gWUp5TpZdMWwmc8OOiM1i0NKUVWDoUOH8ueff9K/f3/Wrl3r+o1CCCF8RhLBSOKNGr8u1sXyqnwXhWKkZnHIiY2N5eOPP+baa6/l5MmTDBo0iLS0tECHJYQQEUsmi0SSuHhzIoUrzmr8FtrMiSH+EB3jfL83rkf4XXR0NHPnzqVatWp88skn1K9fP9AhCSFExJIewUjijRq/2Xlgc7Fko2GYH1WhLOZSMs5IzeKQZbVaefPNN/nuu+9o3759oMMRQoiIJYlgJBk4zKzh64yrGr89hrlO8mwGWKrYa2i1musJOuON6xEBY7FYaN68efHXb775Ju+++27A4hFCiEgkiWAk8UaN33O7Q83m5iSMsj2DNpu5vWYz6HsDREWbPXslKYu5/Zx+zvf3us71otJSszhsbN26lbFjx3LLLbcwY8aMQIcjhBARQxLBSOONGr/D74Rzh8DJ01BQaPYQFhSaX587xNzf+Cy4/B5o3cWsGIIyX1t3Mbd36ut8vzuLSXvrekTAdejQgeeffx4ATdP4z3/+E+CIhPCSv/+AdZ/CvKdg9kTzdd2nrtdJ9YPevXtz9913BzqMoJKfn0/r1q1JT08PyPmPHz+OUorVq1cD5h/JjRo1Ijs722fnlBJzHgqLEnNCBKkZM2agaRoATz75JJMmTZI6xSKgqlRi7rdfyldYAnPkw2p1v4KSh0aPHs3x48dZsmSJ0+MyMzOJjo6mevXqlTpPTk4OU6dO5aOPPuK3334jMTGRs846i7vvvpvrrrvOrTb27dtHixYt2LhxI+eff36l4vCm1157jQULFrBq1aqAnP/48ePUrVuXVatW0bt3bwCGDx9Op06dmDRpktP3Sok54b69OyB9ERSeNNcEtNnAWh16DoMW9m+i7Rtg4xIwSizYrKKgyxBoe4F36vtKjWBRxrhx46hWrRq33norTzzxBNnZ2Tz77LOSDIrQ8/cfZhJYkF9+n2GDApu5//J7PKut7gV5eXnExMSQlJRUpXbuvPNOvv76a15++WXat29PZmYm69evJzMz00uR+t+rr77KY489VuV28vPziY6O9kJEcMsttzBmzBgmTJhAVJT30zYZGo40Xy2BVe+BLcv8i1Qp89WWZW7/agksnQMb/mcmgUWLQytlfr3hfzD/RZg8zqzneyoHMP6p7zt5nFn/15WtG6vehghLN998Mx988AFRUVG88847HDlyJNAhCeG57V+7t+C9qwpKXjB69GiGDBnCtGnTaNy4MY0bNwbKDw0vWrSIjh07Eh8fT1JSEr169eLo0aMO2/3000+ZMGECQ4YMoXnz5nTu3Jlx48Zx1113FR9jGAbPPfccZ555JvHx8XTo0IG5c+cW72/RogUAXbp0QSlV3Atms9mYMmUKTZo0ITY2lg4dOvDJJ5+UOv9TTz1Fs2bNiI2NpUGDBtx0003F+1JTU+nRowe1atUiKSmJQYMGsWPHDqf36bvvvmPnzp0MGTKkeNu+fftQSjFv3jy6d+9OXFwcbdq0YdmyZcXHrF69GqUUX3zxBRdccAExMTEsXbrU5bUDbNy4kfPOO4+4uDjOPfdc1q9fXy6ugQMHkpmZWTxc7G2SCEaSvTtg59fmotAV1gm2wK6v4fCOiquDFG3LPQbx0ZWv7ys1goULV199Nf/73/9YsWIFDRs2DHQ4Qnhuz+bytdTLcqeCkpesWbOGLVu2kJqaWuEi7keOHOHaa6/l5ptvZseOHaSnp3PjjTc6bbNBgwakpqby119/OTzmscceY9asWbz22mts376dCRMmMHbsWD7//HOA4lKTqampHD58mEWLzJKgL7/8Ms8//zzTpk1j69atXHXVVQwbNozNmzcDsHDhQqZPn46u6+zatYslS5ZwwQUXFJ83Ozub+++/nw0bNrB69Wpq1qzJ5ZdfTl6e40IFa9euJSUlhTPOOKPcvocffph7772XzZs3M2DAAIYOHcqhQ4dKHfPII48wdepUfv75Z7p27ery2rOzsxk8eDAtW7bku+++49lnn2X8+PHlzh0TE0OnTp1Ys2aNw9irQoaGI0n6IidPCdi5OwTXsglscvDXVVF931EOHkL2pEawozZE2Bs8eHCpr1NTU+nXr5/XhluE8Kn8024e56KCkpfExcXx9ttvExsbW+H+jIwM8vPzGTFiBM2aNQNwucbnzJkzGTVqFHXq1KFDhw5cdNFFDB06lAEDBgBmovPCCy+wbNkyevToAZg9gBs2bOC1115j8ODB1K1bF4DatWvToEGD4ranT5/O+PHjuf766wGz9y89PZ3p06czd+5c9u/fT8OGDRk4cCDR0dE0bdq01DOGw4cPLxXrO++8Q40aNdiwYQPdu3ev8HqK2qzIuHHjuPrqqwEzSV26dCkzZsxg6tSpxcdMnjyZgQMHun3t77//Pnl5ebzzzjskJibSvn17Jk6cWGECnpyczL59+xz8S1SN9AhGkqJnAp1xp06wUpDgrPqIi/q+UiNYeOjdd9/l0ksvZeTIkZw+7eYvWCECKbrihKv8cS4qKHlJ+/btHSaBAOeccw79+/enffv2DB8+nBkzZnDs2DEADhw4QGJiYvHH008/DUDPnj3Zs2cPK1eu5Oqrr2bnzp0MHDiQsWPHArB9+3ZOnTrFJZdcUur9M2bM4Ndff3UYy99//01GRgYXX1x6Ldnu3buzfft2AEaOHMmpU6do0aIFt912Gx9//HGpnw2//vor119/PWeeeSY1atSgfv362Gw2Dhw44PC8ubm5xMXFVbjvwgsvLP7cYrHQtWvX4liKlExE3bn2HTt20LFjRxITEys8T0nx8fHk5vqmXKr0CEYSV0mgNzmr7ys1goWH2rZtyxlnnMEnn3zC0KFDWbRoEdWqVQt0WEI41rIT7NzofHjYnQpKXpKQkOB0v9VqZdmyZaxbt45ly5Yxa9YsJkyYwJo1a2jXrl3xkCxQapJJdHQ0PXr0oEePHvzrX/9i6tSpTJo0iQkTJmCzrzX72Wef0bRp01Lnc6dnv6JJYkXbmjRpwi+//EJaWhorVqzgwQcf5Mknn2T9+vUkJCRw+eWX06hRI9544w0aNWpEVFQUbdu2dTo0XKdOHTZt2uQyLkdK3mN3rt2TVVsyMzNLLcDvTdIjGElclYbzJmf1fePcrP0rNYKF3QUXXMDq1aupW7cuS5cu5bLLLuPkyZOBDksIx9pe7F4JTFcVlPxIKcWFF17IE088wcaNG0lOTmb+/PlERUWRkpJS/OFstnHbtm0ByMrKom3btsTGxrJ///5S709JSSkefo6JMXtEC0uMEtWoUYPk5GS++uqrUm1/9dVXxe2DOdw9ePBgXnzxRTZu3Mi2bdv4+uuv+eOPP9ixYwePPvoo/fv35+yzz+bkyZMUFDh/JOncc8/ll19+KU7iSlq3bl3x54ZhsGHDBqfLCrlz7W3btmXr1q2l1ggseZ6SfvrpJzp37uw0/sqSHsFIYq1uzg521jNY9BeKs+Fhw4BsJ711rur7dutrzg52NjwsNYJFGeeccw5r1qyhf//+rFmzhoEDB/Lll19W+GC3EAFXo7a5TqCrdQT9vHSMI+vWrWPFihUMGjSI+vXrs2nTJg4ePFgq8Sqrd+/eXHfddZx//vnUrl2b7du38+ijj3LWWWdx9tlnY7VaGT9+POPHj8cwDHr27ElWVhbr1q3DYrEwZswY6tWrR3x8PEuXLqV58+bExcVRs2ZNHnroIR5//HFatWrFeeedx9y5c1m7di3ff/89YD4uUlBQQNeuXUlMTGT+/PlER0fTqlUratWqRZ06dXjzzTdp0qQJhw4d4qGHHnK59EqfPn04deoUW7ZsoVOnTqX2zZgxg9atW9OhQwd0XWf//v2MGzfOYVvVq1d3ee3XX389EydO5NZbb+Xxxx8nIyODf//73+Xa2rdvH4cOHSp+/tDbpEcwkvQcBq56ot3tqt5z0PE+V/V9pUawqKSzzz6b9PR0mjVrxrp16xg1alSgQxLCMVcVlnywmHRl1axZk6+//pohQ4bQqlUrHnzwQSZNmsQNN9zg8D2DBg1izpw5DBo0iDZt2qBpGj169GD58uVY7b2hU6ZMYfLkyUyfPp127doxYMAAFi5cWLxsTFRUFK+88gpvvfUWycnJDB06FIB7772Xhx56iIcffpj27duzePFiFi5cWJygnXHGGcyaNYsePXrQvn17Fi5cyKJFi2jRogUWi4X58+ezZcsW2rdvz1133cWUKVOcPiMJ5oSVYcOG8f7775fb9+yzz/LCCy9wzjnnkJqayuLFi4uX4XHE1bUnJiayZMkSdu3aRefOnRk/fjzTpk0r184HH3zAwIEDi3sSvU0qi3go5CuLfLXEXEJGUbpn0GYzk8TWF0P2CXMJGSjdM1j0vRJfF9asNmf2luzVs1rNBG7cY65Lu23daC4RU5U2RMQ6ePAg1113HTNnznTaYyFEVVWpsogIOdu2baNPnz7s3r2bGjVqBLzyyenTp2nVqhUffPBBuckzZUllEeGe7kOg0ZmwdhHknzTXDiy0QXR16OFBZZE+15jLu3y70pzUERtvDuUOcLMqSFGN4Kq0ISJWkyZNWLt2bamHybOyskrNvhNCCE+1a9eO6dOns3fvXs4555xAh8P+/fuZOHGiyySwKqRH0EMh3yMoRBiaMWMGzz77LGlpaaSkpAQ6HBFGpEcwsgW6R9ATle0RlGcEhRAhrbCwkPfff58DBw7Qs2fPcmt7CSFEZTVv3hzDMII+CawKGRoOJr9nmFU31q0019qLizdn2A70cKh0x2b4YAZk7P9nW3IzuG4cnN0J1i2FLSshpsTSBnmF0LEvdBtkfv3y47B1Q/m2O1wA9z0FS96G33eBpcQfGTYD6rWCIbeaXy98G778mNIzVBRcOhKG3+q96/VWOyIkWa1WUlNTueKKK1i1ahW9evVi2bJlnHvuuYEOTQghgp4MDXvIZ0PD3po88dn78Mkcx/vP7QQJ9o5gRxNBvvoKcrPLvbVY+9ZwRqLjNmxW2H8cDu113Ebt+nDyz6pfr0w6EXa5ubkMHz68eEmZ1NRUunbtGuiwRIiToWERKmRoOJT9nmEmM3mny6+tV1hobp8x1TzOmR2bnSeBNRPNJLCiMnJF23KPQYyTNQST65pJoLM2LIVgZDmP9Y+jVb9eb903ERbi4+NZvHgxV111FX/++Sf9+/dn7dq1gQ5LCCGCmiSCwWDZIrNHy5nCAnOGrTMfzHC+v2UT9+JxdlzzRu614e5xjrhzvd66byJsxMbG8tFHHzFq1CgSEhKoX79+oEMSQoigJolgMFi30nmVDTD3f7vS+TElnwmsSEK884ohYO5PcFLazWJxr42q1jV253q9dd9EWImKimL27NmsX7+e1q1bBzocIYQIapIIBoNTTsq1lXTazePChavrlfsmHLBaraVW4X/11Vf58MMPAxiREEIEJ0kEg0Gckx64kmLdPC5cuLpeuW/CDRs2bODee+/l+uuv5+233w50OCLCZOUVsOnIX3y66wiLfjnMp7uOsOnIX2TluXisRQCQn59P69atSU9PD8j5jx8/jlKK1atXA7B161YaNWpEdraTCZUhRhLBYNCtrznL1Rmr1ay64UyyizqE2bmuawkbhnmcIzabe23YbM6PccWd6/XWfRNh7YILLmDq1KkYhsFtt93Gf//730CHJCLEkaxTpO07xr6/ciiwmT83C2wG+/7KIW3fMY5knfLp+Tdt2oTVaq10VYp9+/ahlOK7777zcmTumzlzJo0aNaJnz54Bi6GkDh060K1bN1544YVAh+I1kggGg4HDzKVOnLFGmaXXnLlunPP9ew66F4+z4/Ydcq8Nd49zxJ3r9dZ9E2Fv4sSJxT+477nnHp577rkARyTCXVZeAeszTlBolF5JFcyvCw1Yn3HCpz2Db775Jpqm8dNPP7Fjxw6fnScvL89nbb/66qvcdtttVW4nPz/fC9GYbrnlFmbMmEFBQXj06koiGAzqJZvr3cXElu/hslrN7eMec7048tmdYOiNjvf/lQXZ9h69sr16Rdvi60Kekx6/jGPwZ5bzNmxWUC5qvtauX/Xr9dZ9ExHhgQce4PXXX0cpxSOPPMITTzyBrKMqfGVXZjY2F99eNgN2Z/pmiDE3N5d58+Zxxx13MGLECGbNmlVqv6PePqUUCxYsAKBFixYAdOnSBaUUvXv3BmD06NEMGTKEadOm0bhxYxo3bgzAoUOHuPbaa6lVqxa1atVi8ODB7Nq1q7jtgwcPMnToUJKSkqhWrRpt2rRx+uzud999x86dOxkyZEi5uOfNm0f37t2Ji4ujTZs2LFu2rPiY1atXo5Tiiy++4IILLiAmJoalS5diGAbPPfccZ555JvHx8XTo0IG5c+eWOufGjRs577zziIuL49xzz2X9+vXl4ho4cCCZmZnFw8WhThLBYNGhC0yeAT0vhbhq5szbuGrm15NnuL8o8uWj4MFnoVGZYeJGzcztdz0LbXqZlUSKEjfDML9u0wuueQBeXWhWEKkwzgvg/legTor5U6xkGzbD3H7rFHhyBlx6dcVrDV56NUyb7Z3r9dZ9ExFh7NixzJ49G4vFwltvvcUff/wR6JBEmDp4MrdcT2BZBnDgpG8msy1YsIBmzZrRsWNHbrzxRt577z2Pe8U2bDCrS6WmpnL48GEWLfpnKa41a9awZcsWUlNTSUtLIycnhz59+hAXF8eaNWv49ttvadiwIf379ycnJwcATdPIyclh1apVbNu2jZdeeokzzjjD4fnXrl1LSkpKhcc8/PDD3HvvvWzevJkBAwYwdOhQDh0qPRL1yCOPMHXqVH7++We6du3KY489xqxZs3jttdfYvn07EyZMYOzYsXz++ecAZGdnM3jwYFq2bMl3333Hs88+y/jx48udOyYmhk6dOrFmzRqP7mfQMgxDPjz4GDdunDFu3DhDCBG6Fi1aZOzYsSPQYYgQsH379kq9b+HPGW5/+ELPnj2N559/3jAMw7DZbEazZs2MBQsWFO/fu3evARgbN24s9T7A+Pjjj50ec/PNNxt16tQxTp06Vbxt1qxZRkpKimGz2Yq3FRQUGElJScb8+fMNwzCMDh06GJMnT3b7Gu677z6jZ8+epbYVxTR16tTibYWFhUarVq2MiRMnGoZhGKtWrTKAUteblZVlxMXFGenp6eXOcemllxqGYRhvvPGGUbNmTePkyZPF++fMmWMAxqpVq0q976qrrjJuuOEGt6/FH1x8rzrMa6TWcKjxV11dV/WK9+6A9EVQeNJcM9BmA2t16DkMWkg5JhHcrrrqqlJff/HFFwwaNAirq8lHQrgpyqKKJ4i4Os7bdu/ezddff80HH3wAmMO9o0aN4q233mL48OFeOUf79u2JjY0t/vr7779n7969VK9evdRxOTk5/PrrrwDcd9993HnnnaSmptKvXz+uuuoqzjvvPIfnyM3NJS4ursJ9F154YfHnFouFrl27sn379lLHnH/++cWfb9++nVOnTnHJJZegSoxU5efn07x5c8As0daxY0cSE/95tKnkeUqKj48nNzc8liaTRDCUVFRX91QOrP0Svlnuvbq6juoVZ+yH//wLLuwBKsusXFj0i9NqBVsWrHoPDl0M3YeUf78QQWjGjBlomsY111zDnDlziI6ODnRIIgw0qR7Pvr9ynA4PK6Bpde8vb/XWW29RWFhI06ZNi7cZ9udhDx48SJMmTbDYF/0v2g6eTahISEgo9bXNZqNTp04VPvOXlJQEwG233cagQYP44osvWLFiBRdddBETJkxg8uTJFZ6jTp06bNq0ye2YnMVos69k8dlnn5W6L0Dx//mS98KVzMzM4gQy1MkzgqHCX3V1XdUrjosBdRKslvLVQywWc/vOr80eQyFCQMeOHalRowbz589nxIgRnDrl2yU9RGRolZSAq84+i4KUpATnB3mooKCA2bNn88wzz7B58+bijx9//JGOHTvyzjvvAFC3bl0ADh8+XPzezZs3l2orJiYGgEJXFZyAzp07s3v3burUqUNKSkqpj6JEEKBx48aMGTOGjz76iKeeeoqZM2c6bPPcc8/ll19+KU7iSlq3bl3x54ZhsGHDBs4+2/FoVNu2bYmNjWX//v3l4itafL5t27Zs3bq11BqBJc9T0k8//UTnzp0d35AQIolgqPBXXV1X9Yob1XejxBywVur7itBw8cUXk5aWRlJSEp9++ilXXHFF8cPtQlRWYkwUXZNrYVXmj8SSFGBV0DW5Fokx3h2Y+/zzzzl+/Dh33HEH7du3L/Vx7bXX8vbbb2Oz2YiPj6dbt25MmzaNbdu28c0335SbGFGvXj3i4+NZunQpR48e5a+//nJ43lGjRlG/fn2GDh3KmjVr2Lt3L+np6Tz44IPFM4fvu+8+UlNT2bNnD5s3byY1NZW2bds6bLNPnz6cOnWKLVu2lNs3Y8YMFixYwC+//ML999/P/v37GTfO8RJq1atXZ/z48YwfP563336b3bt3s3nzZl5//fXiZPT6668nKiqKW2+9lW3btrF8+XL+/e9/l2tr3759HDp0iIEDBzo8XyiRRDBU+Kuurqt6xfVqu64jbLFA/smqxSGEH51//vmsXr2aevXqsXz5ci655BL+/vvvQIclQlyDxDj6Na9Li5rVip8FjLIoWtSsRr/mdWmQWPHzb1Uxa9Ys+vTpQ+3atcvtGzlyJPv372fFihUAxZV2unTpwtixY5k6dWqp46OionjllVd46623SE5OZujQoQ7PW61aNdLT02nZsiUjR46kTZs23HzzzZw4cYJatWoB5vDsPffcQ9u2bRkwYAD169dn9uzZDtusXbs2w4YN4/333y+379lnn+WFF17gnHPOITU1lcWLFxcvY+PIlClTmDx5MtOnT6ddu3YMGDCAhQsXFi+Tk5iYyJIlS9i1axedO3dm/PjxTJs2rVw7H3zwAQMHDixVxjKUKU/GxAVommYA6Lru3xPffinllyWtgFLw5pdVOM8lzvd37+y6RxDM5WRGP135OIQIgF9++YV+/fpx6NAhhg8fXryemohcO3bscDrkKHxr27Zt9OnTh927d1OjRg327dtHixYt2LhxY6nJIP5y+vRpWrVqxQcffFDpii2+4uJ71eEvbukRDBXBUle30M3Sce4eJ0QQOeuss1i7di0XXXQRzz77bKDDESLitWvXjunTp7N3795AhwLA/v37mThxYtAlgVUhs4ZDRbe+5uxgZ8PD3qirm9zM+fDw739AgzrOh4dtNoiu7ni/EEGsRYsWfPXVV6WWmMjKyiq1pIQQwn9uuummQIdQrHXr1rRu3TrQYXiV9AiGCn/V1XVVr/jQ0fKl5coygB5S31eErpJJYNFzSPv27QtcQEIIAJo3b45hGAEZFg5XkgiGCn/V1XVVr/hUHhjVzaHfslP6bTZze+uLZVFpERby8vL44IMP2LNnDz179mTnzp2BDkkIIbxKEsFQ4q+6uq7qFd82EfrcBNZEKLDXLC4oNL/uc5MsJi3CRkxMTPHCtwcPHqRnz5789NNPgQ5L+JlMqhTBrirfozJr2EMBmzUshAiY7OxsrrjiClauXEnt2rVZtmxZ2CwmK5zbuXMnzZs3L15cWYhglJOTQ0ZGBikpKY4OCY9Zw0opi1LqAaXUz0qpU0qpg0qp/yil3F6aXSl1mVLqG6VUtlIqUyn1sVKqhS/jFkKEtoSEBJYsWcJll13GH3/8QZ8+fRxWHBDh5YwzzuDo0aMVVrcQItAMwyAnJ4dDhw5Rr169SrURarOGXwTuBRYD/wHOtn99rlKqv2EYTv+nKqWGAQuAH4GHgJrA/cDXSqnzDcOoYn02IUS4io+PZ/HixVx//fWsX7+e+vXrBzok4Qd16tTht99+45dffgl0KEJUKDo6mvr161OjRo1KvT9kEkGlVDvgHmCRYRjDS2zfC7wCXAvMc/L+aOBV4CDQwzCMLPv2L4HvgcnAGF/FL4QIfTExMXz44YccPnyYJk2aBDoc4QcWi4WmTZsGOgwhfCaUhoavwxzjfqnM9jeBHOAGF+/vBSQDbxUlgQCGYWwGVgPX2JNFIYRwKCoqqlQS+Nxzz7F48eIARiSEEJUXSolgF8AGbCi50TCMU8Bm+35X7wf4toJ964AaQHitEimE8Kn09HQeeeQRRo4cybx5DgckhBAiaIVSIpgMHDcM43QF+w4BdZRSzqZ1JZc4tqL3AzRy9Gal1Bil1HduRSqEiAg9evRg4sSJFBYWcsMNN/DWW28FOiQhhPBIKCWC1YCKkkCAUyWOcfZ+HLTh8v2GYcw0DEOWMhdCFFNKMXXqVJ5++mkMw+COO+7glVdeCXRYQgjhtpCZLIL5HKCjudFxJY5x9n6A2Eq+vxRN09w9VAgRAcaNM8sz/vzzz/LzQQgRbAxd1ytcSzCUegQzMId/K0rkGmEOG+e5eH/RsRW9HyoeNhZCCCGECEuh1CO4ERgIXACsLdqolIoDOgHpbrwf4EJgRZl93YC/AZeFRB1l1N6mlPpOhqK9T+6rb8h99Q25r74h99U35L76hq/vayj1CM4HDMwFoEu6A/PZvveLNiilGiql2iilSj7ztwY4DNyulEoscew5QG/gY8Mw8n0TuhBCCCFE8AmZRNAwjK3Aa8AwpdQipdTtSqn/AC9gJnkl1254BtiB2XtY9P584D6gCbBWKaUppf4FLAOOAU/450qEEEIIIYJDKA0Ng9kbuA+zAshg4DhmtZDHXZWXAzAM42OlVC7wGDAdcwZxGvCIYRjB9nzgzEAHEKbkvvqG3FffkPvqG3JffUPuq2/49L4qwzB82b4QQgghhAhSITM0LIQQQgghvEsSQSGEEEKICCWJYBBRSk1QSn2slNqjlDKUUvsCHVOoU0q1Vko9pZRap5Q6ppQ6qZTarJSaqJRKCHR8oUopdZZS6n2l1A6l1F9KqRyl1M9KqReUUg0DHV+4UEpVU0rttf88+G+g4wll9ntY0UdWoGMLdUqpJKXUdKXUbqXUKfvP2lVKqR6Bji0UKaUmO/l+NZRSXl3hJNQmi4S7p4FM4AfgjMCGEjZuBe4CPsVcYigf6ANMBa5WSnUzDCM3gPGFqsZAQ2Ax8BtQAHTAnMh1rVKqk2EYvwcwvnDxFFAn0EGEkbWUf/Belg2rAqVUM2A1kAjMwlyPtybQkYoLOAjXFgG7K9jeEXgI+MybJ5NEMLicaRjGHgCl1E+Y/7FE1SwAnjEM468S215XSu0CJgK3AdLT4iHDMNIwZ9yXopRKBz4CRgPP+TmssKKU6oy5UsLDwH8CG03Y2GMYxtxABxFm5mLmEh0Nwzgc6GDCgWEYW4AtZbcrpd6wfzrLm+eToeEgUpQECu8xDOO7Mklgkfn21/b+jCcC7Le/1gpoFCFOKWUF3gRSMXsHhJcopWJKFhUQlaeU6gl0B54zDOOwUiq6TCEH4SX2+3otZincVG+2LYmgiFSN7a9HAxpFiFNKxSml6iilGiulBgJFf7F+Eci4wsADQBvg7kAHEmZGADnASaXU70qpV5VSNQMdVAi7zP56QCn1GZALZCuldiqlbghgXOHoaqAG8I5hGIXebFiGhkXEsfe2PI75XNs8F4cL527HXNS9yD7gBsMw1lZ8uHBFKdUCeBJ4yjCMfUqp5gEOKVxsAD7GfPaqBmYSczfQSyl1kWEYMmnEc2fZX98EdgE3A7HA/wFzlFLRhmG8E6jgwsxtmGV23/Z2w5IIikj0EtANeNQwjF8CHEuo+x/wM+bzrOcCVwB1AxlQGJgB7MUsnym8xDCMrmU2vaeU2gL8G7P86L/9H1XIq25/PQn0MQwjD0AptRjYAzytlJrtTuUv4ZhS6izMIfg0wzD2ert9GRoWEUUpNQWzF2CmYRjPBDqeUGcYxm+GYawwDON/hmE8gdkjME0pNSHQsYUi+3DaQOBOe3104VvPA3mYJUuF54pWXPigKAkEMAzjBOZKDQ34p9dQVN5t9te3fNG4JIIiYiilJmPWmX4HuDOw0YQn+2y3TYAW6FhCjVIqFrMX8AvgiFIqRSmVAjSzH1LTvu2MQMUYbuzJdgayRE9l/WZ/PVLBvqIZxDJxrAqUUlHATZhLyy32xTkkERQRQSn1BPAE8B5wuyFFtn0pHkgKdBAhKB5zWH0w5vNWRR+r7ftvsH99eyCCC0dKqTjMiWMyaaxyNthfG1ewr2ibrCdaNZcD9YE5hmGc9sUJ5BlBEfaUUo8Dk4E5wC3yvErVKaUaGIZRrhdAKdUHc0me1X4PKvRlAyMr2F4X0DGXjJhFBeuLCeeUUrUNw/ijgl1TMH8PenWB3gjyP+Bl4Aal1NSiCTf26kJXArsMw6hoYWThvqJhYa+uHViSko6R4KGUupF/hoHuAWL4ZyHZ/YZhzAlIYCFMKXUX5oLRB4BJQNkk8KhhGMv9HliIsz8M3hBYibl2YBxwHuY6VzlAb8MwNgcswDBinzW8F3jNMAxZTqYSlFIvYk4QW4X5syARc9ZwH2A95kQHqTBUCUqpMZjLRm3DnNEaA4zD/PkwxDCMZQEML6QppZIxv1+/r2Cyk9dIj2BwuQ3oVWbbFPvrGsweLeGZLvbXpsDsCvavASQR9NwHmBNDbsTssTIwE8I3gOcNwzgQwNiEKGs10Bbze7Y2UIg5zD4ReMEwjFOBCy20GYYxUyl1HLMCzhTMP7a/Ba43DOPrgAYX+kYDVnw0SaSI9AgKIYQQQkQomSwihBBCCBGhJBEUQgghhIhQkggKIYQQQkQoSQSFEEIIISKUJIJCCCGEEBFKEkEhhBBCiAgliaAQQgghRISSRFAIIYQQIkJJIiiEEEIIEaEkERRCCCGEiFCSCAohhBBCRChJBIUQQgghIpQkgkIIIYQQEUoSQSGEEEKICCWJoBBCCCFEhJJEUAghhBAiQkkiKIQQQggRoSQRFEIIIYSIUFGBDkAIIfxB07TewCrgSV3XJ7tx/GjgHeAWXdff9WVsvqZpWnNgLzBb1/XRXmz3KeBhoJWu6we91W4V4nkVuMEez/FAxyNEKJBEUAhRaZqmGWU22YATwBZglq7r71eh7cnAE0AfXddXV7adSGH/t1ij63pvP52vCTAemBkMSaDdv4HbgMnA3YENRYjQIEPDQghveNL+8SywGugJzNU07YVABiV8ahIQCzwf6ECK6Lp+BHgXGKtpWtMAhyNESJAeQSFElZUdatU0rR+wHLhf07RXdF3fF4i4hG9omlYTGAWkBVFvYJHZwDhgDPBYgGMRIuhJIiiE8Dpd19M0TfsZOBvoAuwD0DStMfAv4DKgEZAFfA1M0XV9Y9H7NU3bBzSzf7lK07SSbSv7Ma2BW4H+9mNrAEeApcBTuq7/5qvrc/c67MdOxj7EDdTBfKauPXAKWAY8qOv6oQrO0QVzqPNCwAA2YPbCDaLEkHmJZxkBepUZri/3PKT9ecFnMe9bIvATMFnX9SUe3ILrgGrAfEcHaJp2AfAg0N1+3ZnAVuAtXdc/KhHLXszkbQowDfM+xQDfAv+n6/pPmqbVtd+Ly4Fa9nYe1nV9Vdnz6rq+3v79c6umaZN0XS/7+IIQogQZGhZC+IqyvxoAmqZ1BjYDGvAL8CrwGeYw8leapl1W4r0vAWvsn8/mn6HnJ0scMwy4EzgIfGBvbztwO7BR07RG3r6gSlxHqbcCczGT4tcwE7BrgBWapsWWOUcPIB3oC3wB/BfIxZzsckGZdjfzz33ZT+l7tbrMsc0wE8rmwBzMRK498ImmaX1cXXsJ/e2vX1W0U9O0O4BvgCvtr/8BPgfqYd6HspoD64H6mEO7y+znWK1pWitgHeYfFPOBj4BzgC+dDP9+DTQE2nlwTUJEJOkRFEJ4naZp/YGzMJPAjZqmRWH+Ak/E7MlaU+LYZGAjMEvTtOa6rp/Wdf0lTdPOAHoB7zqYLDIHeFHX9dNlzj0Q+BJzWHCcl6/Lo+so8/ZLgC66rm8t8Z55mL1rQ+3tommaBXgbiAMu03X9yxLH3wnMKNmoruubgc2apj0B7HMxI7o3Zu9fcUJtjyEVeAgz0XRHd+AksLPsDk3T2gI68DfQQ9f1bWX2N66gvV7AY7qu/7vEcZOApzATxI8ATdd1m33fcuA94AH7R1kbMYeue2Im3EIIB6RHUAhRZZqmTbZ//FvTtAWYiYUCXtJ1fT8wGDgTeLVk8gSg63oG8BzQAOjn7jl1XT9UQbKFruvLgG2YQ6jeVpXreKVkEmj3pv21ZC/fRUAKsKpkEmg3kwqSLw/sB6aW3KDr+lLgAOV7GiukaVoMZs/dEQfDruMwOxmmlE0C7eeraMh+H+ZwdUmz7a+xwENFSaDdPKAA6OQgzCP2V5kwIoQL0iMohPCGJ+yvBvAnsBZz+Zi59u0X2l+b2Z+ZK6uV/fVszKFQlzRNU5i9PqMxhwprAdYSh+S5F7pHqnId31VwfNFEi1oltp1rfy037Krruk3TtG+A1m5FW95mXdcLHcRxYQXbK1Lb/nrCwf5u9teySayncWXYX3fqun6y5A5d1ws1TTsKVNS7CObziGA+myiEcEISQSFElRVN4HCiKHkY6eK4RA9O+wJwP3AYc4LIIczn6MBMDptV+K6qqcp1/FnBtgL7a8kEtqb99aiDth1td0dFMRTF4e4IUdE9jnOw/wz7a7kJME78VXaDrusF9klC5fbZFQDRDvbF219zHewXQthJIiiE8IeiX+ZDdV3/tKqNaZpWD7gX8/mvi8r2GGmadl1Vz+GAV6/Dgb/tr/Ud7He03S90Xf9T07Q8/kmKy/rT/toI+NkvQZVXFNvvATq/ECFDnhEUQvjDOvtrDw/eUzRUaK1gX0vMn1/LKkgCG9v3+0JlrsNTm+yv3cvusE8kucjB+2xUfK98YSvQUNO0GhXsK7pHl/oploq0sb9uDmAMQoQESQSFEP7wCfArcJej5VU0TbtQ07RqJTb9YX+t6IH/ffbX7pqmFSc/mqYlYk7A8NVoR2Wuw1Nf28/RR9O0ssnUGBw/H/gH0KQK5/XEaszfHxVNMJmBOWw7yT6DuBQHs4a9rRvmHxLpfjiXECFNhoaFED6n63q+pmnDMJ/l+9w+4WEzkIOZvHTB7MVraN8G5lImNuAZTdPaY5+coOv6VF3Xj2ia9iFwLebSKcswn60bgLlQ82Yczyj193V4eg6bpmm3Y868/lTTtIWYiWFHzOv7ErO3zVbmrWnAtZqmfQZ8j5mMpeu67otkaCHmYtGDgBVl4t+umQ/3vQ5s0jTtE2AX5nDt+ZjLzniyZqFH7FVPLsCseuLo+UIhhJ30CAoh/ELX9S2Ys3unYSZtt2AuNXIe5nDojcDxEsfvAG7GXApEw6w8MaVEk7cBT2NODLgLMylZgjl06rMEwNPrqOQ5VmOurbcac8maezGvsw+wx37Y32Xedh/mwtoXYFYgmYK5ILXX6br+Lea1jirZI1ti/5uYQ9tLMNcufAi4AvO+vOaLmEq4BnMiywxXBwohQBmGVN8RQohQoWna10BXoKau69kBjOM6zPX8hum6vjhQcZSladp3mLO22zlYKkcIUYL0CAohRJDRNK2avbJK2e2jMXs8lwUyCbT7ELPqx2T7mo4Bp2nalZg9s+MlCRTCPfKMoBBCBJ+mmM/XLQd2Y/6sPhdzuPVPzOfzAkrXdUPTtDGYNZ+T8WzdQF+JBx7QdX1JoAMRIlTI0LAQQgQZTdNqAc9jPifYALPM2hHMiRn/1nX91wCGJ4QII5IICiGEEEJEKHlGUAghhBAiQkkiKIQQQggRoSQRFEIIIYSIUJIICiGEEEJEKEkEhRBCCCEilCSCQgghhBAR6v8B3M+CnwmnL8AAAAAASUVORK5CYII=\n",
- "text/plain": [
- "<Figure size 720x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "def plot_perceptron(x_train,y_train,x_test,y_test):\n",
- " a = -pct.coef_[0][0] / pct.coef_[0][1]\n",
- " b = -pct.intercept_ / pct.coef_[0][1]\n",
- " box=[x.min(axis=0)[0],x.max(axis=0)[0],x.min(axis=0)[1],x.max(axis=0)[1]]\n",
- " mx=(box[1]-box[0])/20\n",
- " my=(box[3]-box[2])/20\n",
- " box=[box[0]-mx,box[1]+mx,box[2]-my,box[3]+my]\n",
- "\n",
- " fig, axs = plt.subplots(1, 1)\n",
- " fig.set_size_inches(10,6)\n",
- " \n",
- " axs.plot(x_train[y_train==1, 0], x_train[y_train==1, 1], \"o\", color='tomato', label=\"Iris-Setosa\")\n",
- " axs.plot(x_train[y_train==0, 0], x_train[y_train==0, 1], \"o\", color='steelblue',label=\"Autres\")\n",
- " \n",
- " axs.plot(x_test[y_pred==1, 0], x_test[y_pred==1, 1], \"o\", color='lightsalmon', label=\"Iris-Setosa (pred)\")\n",
- " axs.plot(x_test[y_pred==0, 0], x_test[y_pred==0, 1], \"o\", color='lightblue', label=\"Autres (pred)\")\n",
- " \n",
- " axs.plot([box[0], box[1]], [a*box[0]+b, a*box[1]+b], \"k--\", linewidth=2)\n",
- " axs.set_xlabel(\"Petal length (cm)\", labelpad=15) #, fontsize=14)\n",
- " axs.set_ylabel(\"Petal width (cm)\", labelpad=15) #, fontsize=14)\n",
- " axs.legend(loc=\"lower right\", fontsize=14)\n",
- " axs.set_xlim(box[0],box[1])\n",
- " axs.set_ylim(box[2],box[3])\n",
- " pwk.save_fig('01-perceptron-iris')\n",
- " plt.show()\n",
- " \n",
- "plot_perceptron(x_train,y_train, x_test,y_test)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:11.470616Z",
- "iopub.status.busy": "2021-03-01T17:41:11.470151Z",
- "iopub.status.idle": "2021-03-01T17:41:11.472470Z",
- "shell.execute_reply": "2021-03-01T17:41:11.472956Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Monday 01 March 2021, 18:41:11\n",
- "Duration is : 00:00:01 809ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
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- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
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- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
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- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
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diff --git a/LinearReg/01-Linear-Regression==done==.ipynb b/LinearReg/01-Linear-Regression==done==.ipynb
deleted file mode 100644
index 0c234cf23b6a29f3a07523d6ae3f9d1c196eab9a..0000000000000000000000000000000000000000
--- a/LinearReg/01-Linear-Regression==done==.ipynb
+++ /dev/null
@@ -1,410 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [LINR1] - Linear regression with direct resolution\n",
- "<!-- DESC --> Low-level implementation, using numpy, of a direct resolution for a linear regression\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - Just one, the illustration of a direct resolution :-)\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- "Equation : $ Y = X.\\theta + N$ \n",
- "Where N is a noise vector\n",
- "and $\\theta = (a,b)$ a vector as y = a.x + b"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 1 - Import and init"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:40:13.928691Z",
- "iopub.status.busy": "2021-03-01T17:40:13.928178Z",
- "iopub.status.idle": "2021-03-01T17:40:41.056739Z",
- "shell.execute_reply": "2021-03-01T17:40:41.057234Z"
- }
- },
- "outputs": [
- {
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- " font-weight: bold;\n",
- " font-size: 1.1em;;\n",
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- " border-left: 5px solid #92CC99;\n",
- " padding: 0.5em;\n",
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- "div.todo:before { 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- " font-size: 1.1em;\n",
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- "div.todo ul{\n",
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- " font-size:0.8em;\n",
- " color:#696969;\n",
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- "\n",
- "</style>\n",
- "\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 2.0.17\n",
- "Notebook id : LINR1\n",
- "Run time : Monday 01 March 2021, 18:40:41\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "import math\n",
- "import matplotlib\n",
- "import matplotlib.pyplot as plt\n",
- "import sys\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "datasets_dir = pwk.init('LINR1')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Retrieve a set of points"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:40:41.087469Z",
- "iopub.status.busy": "2021-03-01T17:40:41.080869Z",
- "iopub.status.idle": "2021-03-01T17:40:41.089210Z",
- "shell.execute_reply": "2021-03-01T17:40:41.089685Z"
- }
- },
- "outputs": [],
- "source": [
- "# ---- Paramètres\n",
- "nb = 100 # Nombre de points\n",
- "xmin = 0 # Distribution / x\n",
- "xmax = 10\n",
- "a = 4 # Distribution / y\n",
- "b = 2 # y= a.x + b (+ bruit)\n",
- "noise = 7 # bruit\n",
- "\n",
- "theta = np.array([[a],[b]])\n",
- "\n",
- "# ---- Vecteur X (1,x) x nb\n",
- "# la premiere colonne est a 1 afin que X.theta <=> 1.b + x.a\n",
- "\n",
- "Xc1 = np.ones((nb,1))\n",
- "Xc2 = np.random.uniform(xmin,xmax,(nb,1))\n",
- "X = np.c_[ Xc1, Xc2 ]\n",
- "\n",
- "# ---- Noise\n",
- "# N = np.random.uniform(-noise,noise,(nb,1))\n",
- "N = noise * np.random.normal(0,1,(nb,1))\n",
- "\n",
- "# ---- Vecteur Y\n",
- "Y = (X @ theta) + N\n",
- "\n",
- "# print(\"X:\\n\",X,\"\\nY:\\n \",Y)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Show it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:40:41.112696Z",
- "iopub.status.busy": "2021-03-01T17:40:41.107958Z",
- "iopub.status.idle": "2021-03-01T17:40:41.816600Z",
- "shell.execute_reply": "2021-03-01T17:40:41.817097Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/LINR1-01-set_of_points</div>"
- ],
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- ]
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\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "width = 12\n",
- "height = 6\n",
- "\n",
- "fig, ax = plt.subplots()\n",
- "fig.set_size_inches(width,height)\n",
- "ax.plot(X[:,1], Y, \".\")\n",
- "ax.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
- "ax.set_xlabel('x axis')\n",
- "ax.set_ylabel('y axis')\n",
- "pwk.save_fig('01-set_of_points')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Direct calculation of the normal equation\n",
- "\n",
- "\n",
- "We'll try to find an optimal value of $\\theta$, minimizing a cost function. \n",
- "The cost function, classically used in the case of linear regressions, is the **root mean square error** (racine carré de l'erreur quadratique moyenne): \n",
- "\n",
- "$RMSE(X,h_\\theta)=\\sqrt{\\frac1n\\sum_{i=1}^n\\left[h_\\theta(X^{(i)})-Y^{(i)}\\right]^2}$ \n",
- "\n",
- "With the simplified variant : $MSE(X,h_\\theta)=\\frac1n\\sum_{i=1}^n\\left[h_\\theta(X^{(i)})-Y^{(i)}\\right]^2$\n",
- "\n",
- "The optimal value of regression is : $ \\hat{ \\theta } =( X^{-T} .X)^{-1}.X^{-T}.Y$\n",
- "\n",
- "Démontstration : https://eli.thegreenplace.net/2014/derivation-of-the-normal-equation-for-linear-regression"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:40:41.821750Z",
- "iopub.status.busy": "2021-03-01T17:40:41.821285Z",
- "iopub.status.idle": "2021-03-01T17:40:41.824250Z",
- "shell.execute_reply": "2021-03-01T17:40:41.823754Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Theta :\n",
- " [[4]\n",
- " [2]] \n",
- "\n",
- "theta hat :\n",
- " [[3.25288542]\n",
- " [2.45807709]]\n"
- ]
- }
- ],
- "source": [
- "theta_hat = np.linalg.inv(X.T @ X) @ X.T @ Y\n",
- "\n",
- "print(\"Theta :\\n\",theta,\"\\n\\ntheta hat :\\n\",theta_hat)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Show it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:40:41.845596Z",
- "iopub.status.busy": "2021-03-01T17:40:41.840062Z",
- "iopub.status.idle": "2021-03-01T17:40:42.336579Z",
- "shell.execute_reply": "2021-03-01T17:40:42.336073Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/LINR1-02-regression-line</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "Xd = np.array([[1,xmin], [1,xmax]])\n",
- "Yd = Xd @ theta_hat\n",
- "\n",
- "fig, ax = plt.subplots()\n",
- "fig.set_size_inches(width,height)\n",
- "ax.plot(X[:,1], Y, \".\")\n",
- "ax.plot(Xd[:,1], Yd, \"-\")\n",
- "ax.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
- "ax.set_xlabel('x axis')\n",
- "ax.set_ylabel('y axis')\n",
- "pwk.save_fig('02-regression-line')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:40:42.345211Z",
- "iopub.status.busy": "2021-03-01T17:40:42.344733Z",
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- "shell.execute_reply": "2021-03-01T17:40:42.347702Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Monday 01 March 2021, 18:40:42\n",
- "Duration is : 00:00:01 288ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
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- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
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diff --git a/LinearReg/02-Gradient-descent==done==.ipynb b/LinearReg/02-Gradient-descent==done==.ipynb
deleted file mode 100644
index ebde719b798bef1118e42ae28e709fcc003d873b..0000000000000000000000000000000000000000
--- a/LinearReg/02-Gradient-descent==done==.ipynb
+++ /dev/null
@@ -1,722 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [GRAD1] - Linear regression with gradient descent\n",
- "<!-- DESC --> Low level implementation of a solution by gradient descent. Basic and stochastic approach.\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "\n",
- "## Objectives :\n",
- " - To illustrate the iterative approach of a gradient descent\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- "Equation : $ Y = X.\\Theta + N$ \n",
- "Where N is a noise vector\n",
- "and $\\Theta = (a,b)$ a vector as y = a.x + b\n",
- "\n",
- "We will calculate a loss function and its gradient. \n",
- "We will descend this gradient in order to find a minimum value of our loss function.\n",
- "\n",
- "$\n",
- "\\triangledown_\\theta MSE(\\Theta)=\\begin{bmatrix}\n",
- "\\frac{\\partial}{\\partial \\theta_0}MSE(\\Theta)\\\\\n",
- "\\frac{\\partial}{\\partial \\theta_1}MSE(\\Theta)\\\\\n",
- "\\vdots\\\\\n",
- "\\frac{\\partial}{\\partial \\theta_n}MSE(\\Theta)\n",
- "\\end{bmatrix}=\\frac2m X^T\\cdot(X\\cdot\\Theta-Y)\n",
- "$ \n",
- "\n",
- "and : \n",
- "\n",
- "$\\Theta \\leftarrow \\Theta - \\eta \\cdot \\triangledown_\\theta MSE(\\Theta)$\n",
- "\n",
- "where $\\eta$ is the learning rate\n",
- "\n",
- "## Step 1 - Import and init\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:40:43.738293Z",
- "iopub.status.busy": "2021-03-01T17:40:43.737818Z",
- "iopub.status.idle": "2021-03-01T17:40:46.600630Z",
- "shell.execute_reply": "2021-03-01T17:40:46.601116Z"
- }
- },
- "outputs": [
- {
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- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 2.0.17\n",
- "Notebook id : GRAD1\n",
- "Run time : Monday 01 March 2021, 18:40:46\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Regression Cooker**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 0.1\n",
- "Run time : Monday 01 March 2021, 18:40:46\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "import sys\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "from modules.RegressionCooker import RegressionCooker \n",
- "\n",
- "# ---- Init Fidle stuffs\n",
- "#\n",
- "datasets_dir = pwk.init('GRAD1')\n",
- "\n",
- "# ---- Instanciate a Regression Cooker\n",
- "#\n",
- "cooker = RegressionCooker(pwk)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Get a dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:40:46.604847Z",
- "iopub.status.busy": "2021-03-01T17:40:46.604384Z",
- "iopub.status.idle": "2021-03-01T17:40:47.288766Z",
- "shell.execute_reply": "2021-03-01T17:40:47.289264Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "### Dataset :"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "X shape : (1000000, 1) Y shape : (1000000, 1) plot : 1000 points\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/GRAD1-01-dataset</div>"
- ],
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- ]
- },
- "metadata": {},
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\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "X : mean= 5.004 std= 2.887 min= 0.000 max= 10.000\n",
- "Y : mean= -78.036 std= 49.192 min=-258.566 max= 95.948\n"
- ]
- }
- ],
- "source": [
- "X,Y = cooker.get_dataset(1000000)\n",
- "\n",
- "cooker.plot_dataset(X,Y)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 : Data normalization"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:40:47.293067Z",
- "iopub.status.busy": "2021-03-01T17:40:47.292594Z",
- "iopub.status.idle": "2021-03-01T17:40:47.318764Z",
- "shell.execute_reply": "2021-03-01T17:40:47.318257Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "X origine : mean= 5.004 std= 2.887 min= 0.000 max= 10.000\n",
- "X normalized : mean= -0.000 std= 1.000 min= -1.733 max= 1.731\n"
- ]
- }
- ],
- "source": [
- "X_norm = ( X - X.mean() ) / X.std()\n",
- "Y_norm = ( Y - Y.mean() ) / Y.std()\n",
- "\n",
- "cooker.vector_infos('X origine',X)\n",
- "cooker.vector_infos('X normalized',X_norm)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Basic descent"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:40:47.322604Z",
- "iopub.status.busy": "2021-03-01T17:40:47.322145Z",
- "iopub.status.idle": "2021-03-01T17:40:51.208552Z",
- "shell.execute_reply": "2021-03-01T17:40:51.209053Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "### Basic gradient descent :"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**With :** "
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "with :\n",
- " epochs = 200\n",
- " eta = 0.01\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "**epochs :** "
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " #i Loss Gradient Theta\n",
- " 0 +14.468 -7.340 +1.644 -3.596 -0.016\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " 20 +6.628 -4.900 +1.097 -2.401 -0.284\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " 40 +3.134 -3.271 +0.733 -1.603 -0.463\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " 60 +1.577 -2.184 +0.489 -1.070 -0.582\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " 80 +0.883 -1.458 +0.327 -0.714 -0.662\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " 100 +0.573 -0.973 +0.218 -0.477 -0.715\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " 120 +0.435 -0.650 +0.146 -0.318 -0.751\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " 140 +0.374 -0.434 +0.097 -0.213 -0.774\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " 160 +0.346 -0.290 +0.065 -0.142 -0.790\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " 180 +0.334 -0.193 +0.043 -0.095 -0.801\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " 200 +0.329 -0.129 +0.029 -0.063 -0.808\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Visualization :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/GRAD1-02-basic_descent</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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BmsqnP3ocgIs35msEzec+eYYvvXiDP7owSiN6WFcVhnta1131dBKt6ZzJ96/MbKoauNoDy3pFnGkJXr4+V/P6RnysGxWQ9cT96NxK8fsbHfbdyDZtpsq21oOKrOBJtgOZ5iKR7F52jRiGjQ1B1xO2zz19uO6NV1MVPvvsaZ6/cJOrk2GO7w3y7JOHij9fqEBkqjy6TlVWJ0FVz+PbyO9WY1oCSwjaAm6WVtJ2CoaucmwgyKPDXauKjueePsyrI/M1cXJO7N3TtCFPZ72udy9dmyMUv7RhAVTvuJ4c6ix+5nTORFcV9u5p4vd++Uncuuq4rsujC8yEEjWv9wb96/rMWcOqSCPxrkPk1RP3F67N8pVXbm1YNG5UeG6myrbWyIqs4Em2A5nmIpHsXnaVGOZbX4flBThwGA4cgT3ddibYKmykomxagt98/rWigLg+HaloUVzPelFgNQsG1Pf4Ktg+35xhNWQDKRc65TcAyxIgBK/fWl10aKpSEyfndWnomkLOsMgYVoWY3IxoLa+Cg10J34wAqndcq4WWYQnGFmJ85osX+P1PPeX4GW7NrTg+mDx5rGddovMzX7xQ8WCxHpHnJO4L3QIbsd3UY6PCczNVtrVGVmQFT7IdyDQXiWT3srvE8Gvfhzujpa+bW2HwUEkcDx4Cf1PNr2mqUnEjBlYVxGsJiHrVTrCbdPz02QN1LRiFbXCyRXS1evmVHz3B+EKs+LvgbHNw2s4ChiUYmYny4tXZNUWHW1f5/U89VSEqTw51blk76IJo/d2vv8V33ptZdVs2su7qkQKnLGmAyaV4XQHodDzdusr0coKLN+Yb9kZPLsVrXk83+BmdxP16ugPWY6PCczNVtrUeQGUFT7IdyImZEsnuZfeI4VwWJm9XvhaLwruv20uB7v6SOD5wGAYGMRVtXUPFawmIQgWi2jPsbcCCAXkxoKs1NgufW+fMoW4eP9IDrD3EvVqFOpMzQYia7nJOosNJVG5lxJSmKnzoRB+vjMw3LIA26r092NPi6N02LFFXAFZXlBTFrlx/570ZXhmZb8hWcGtuxdEmo6tKwyLP6ThsVjRuVHhutsq2mqVJVvAk24WMx5NIdie7RwwrCuwdgrkpMA0wTMBh5tf8tL1c/K79tctNsnMvj8ebaNX3cN3dzbxoWnWouJ6AGOxqLlZpP3ZqkI+dHuT2/Eq+OYPKcB3RVi3sHh7cQ1vAw3y0Mut3LpKq2CanCvV7EyG+9OINnnv68KoVareuMr4Yx7RKQlhR4Ehfq6Po2O58zvUIoM1MsDo13OXYVtujq3UFYHlF6ftXZnjp2lxFxnT5Pq/3/vUecDbqtS58ls2Kxo2uYzurbLKCJ5FIJJKtRBH3IJvo3LlzAuD8+fN3702NHPzmr4CZF35CgGXZwri4NJabG1Z9jLi78B05zsMffsy2V/gCxe9XT4Ty6CpHB4IgbPvBegSak7DTNYVkxqhJcVCAv/fMYX7hqUMAfPnFm/zh92/USH5Vgf2dzTx2pJtXR+aZDScrBLGuKjT5XCQzRoUX1q2r/PpPf6BYeV5tG8s/m1OM3KHe1g13jFtLAF28Mc/nv/pmxWfyujR+/ac/0FDFx+n4HRsINiSm6+1zTVHobPXy6Y8e50zeb1392coTRhqZuNcIq+2zRh9gGt3vEolEIpHsYOreuHZPZRjg7IdhdhISMXsinWGApgEe+/tClESxaSAMA0XUenODVoqz6Tvw9h14+6/skmnPAAwexho8xL+9lmU+4sawBLqq0Nce4Kce3c/vfP3thiYilYsUw7JqsofJOX+86uHrepVfS8DYQoyxhRgeXaWvPcATR3swTYvvvDdNOJElksjWrD9nWIwvxGrE8Goe6VNliRTV8XSNCswCjQ5hbnaClZMPej02C6d9bgrBXCTFZ//0BzywN8i/+sXHKta3XdXOevtsPdVzOXQskUgkkvczu0cM6y6ILMPyov11UwsEmsHttQVwLIIIL5NDxVAUdLcL3JBIZdGEiS4sdOylpk4nhC2yZydRX/02/wPwaUVn1LWH6+4ubqV7eOdNk0zWqEivcBJo1SJFc/CvOn68fNvk8uHrk0Od9Ab9jC/EnAwh9jYYFrPhJId6WwH42mvjdSPb6nlFVxOfgOMkvYyxuTSI1diKCVZrCcB6VdVTw10cyTckMR2OmyXgnYkwr47M8eSx3nW950Zx2lYZTyaRSCQSic3uEcOmCXPTpa8FEI8BsfyXgknTw7zlIiNUAqZBp0jhI4el6OQKGlYIvLqCXwfFNO3qsuWQCiEMHsjO8UB2DuLvwMI3+Luan+uuLq677WUi0FMj0JyivdZCVxV+7omDPPf0YcC2CdycjfLy9Tmml+N1hXCBgnAVgroT6ryreEVXE59Ondmq33erxdd2T7BaraoKNNQW7euvjdeI4e3AaVt7g36afa572jHvfmG7vfASiUQiuffsHjFsWfDRj8PsBMxMwtIc5abbRMYgmzVoFSUPQkpRierNxBQPQlHwW1m6rQTNbgVF10rrztsrhGmQy2TRhIXmIEHbzSSPm+M8nh63N2kJlP/8DRg6DIN2gsXtmcya7Z8LOb7ZsjzhghB2siSsRXnVtFrUaqrCvj1NPHaku6JxSDmrNbD4T98daeh9txIny8HJoc4tEzWrVVUBRmaijlXhcmLpOl6XLcZpW+s1SZHxZJXITncSiUSyO9g9YtjlgofPwMnH7a+zGTtZYmYCZieIvHsNS1R2ERNC4NcEHiuFZdkTvwyPD1ffHvD6QVgQX4FICBQXOVRiqgICFCHQse0VLsVCF1aNc1sFW5zPTsDL3wLg53U3D+l7uKp3MpKvIMd9rXzizCDzETs94omjPaiKwvhirMJbevHGfENCWMF2awhBTdW0IGrT+YgwIQTjCzFmw8mKxiHlrNbAwqkzG9ie4SN9rVhC8OUXb2551a3ccrAVoqa8Qji1HK9bVV2tul7O6eGuuvnPW8laDV4KeHT1fRFPtpWVXGklkUgkkt3B7hHDAN96wY5N6+rLL73wgcfg7IeZOjHP733lVbpTy+zPhRg0whwwwvS12ruoIKICHh0lk4FMWQe45lYINLGwkmPeWCFopmi10uTI2ys8Oi6XhrBMzGwOYRpowkSxagWyZmR5wJjhAUoNJqLuJia+0ccbop0rWif/57UeBvd21oi5eg0jChQSCs4c7uJQTxsTS7GaiVrlEWEXrs8V/cNOQsBJeFQ3sHDyHx/obGJ/Vwt3FmP8q6++WVHhXm/VrRHxs1lRUy2mXbpafJgosFp1vZomj861qRBfvzy+7RXH1eLzCqgK9LUH+Oyzp+9ZxXMjIrb6d04OdVZ0ftzsfpWd7iSSrUNajiQ7md0lhhdmYSVqL6PXSq8HOzjd2cePBGJcNn18y30Eze3maF8rn/uJQ2hzkzTNTNhV5LlJyBmV61VUSCZpM3OkRRZDUVnS/MRUD6gahzu8iPQKsWgcw1IQwoWiuNBdim25KHiPTdOuNlfRmo3zYOgGD+a/NpcUJmeCLEVfovuRh+3mIL176zaMKGAKu7XwbDi5ZnLArbkVclVCtlwINFJtdRJiigLT4SRji5Xd1jZSdWu04rtZUVMtprOG/RBTiI1zaWpF/nK1ZeRwbwsn9rVzbSrC8b1BDna3NJwsslnKLSz1BLElYDac5I3bi/dE5G2kcl/PC10eE7jZ/XqwpwV3Vfaze5W86e1ECgnJ/Yy0HEl2OrtHDGczdqSaE+Fl1PAyv6TDxzwZElkTra2b7r4h1Bk3dPfBkYfsGDbLhMX5vPc4vyzOgiUIeHS8bo101sQSgjaRxatpNKsuEqqbGcVPXHcjgCYrS6eVIIeG2+MuprtVZB8b+X+r0BAM5kLw7kv2AuDxcWb/QT6T8XHJaOO6u5tlzc4+Lgi3gkhuRCSslcjQSLW12kusayqmZdVNq1hNoG4mEcHps+iaymBns+N2VOMkpkXxP/mAkHxKSLVlZLCzGRQYm4/xiTMHODXcxR9dGL1rFcfy7RmdW+HCtVkmluI1nuZ7WfFcqzmM083S6XcKudDlbOZznRzqxFUlhl26ysmhzuLXd0OkblcGtURyt5CWI8lOZ/eIYbcHPvU/QWjRtkoszNoidnm+OJFOAYIBD8EAYMTg+tv2ArYQ3tNdsljsOwgPnQZVtVs9z02jzE7QN3WHyOhNRHi5ZKtAIWNYqMKiRaSLmxRSvAhfM51dQbsqHF+xW0S73PYCIAS5XI5MOosuTHTsyXk1t9tMCuXGe/wo8KP5lxa1ADc9XUw29/O6FWTE1UlGddk/voZIWCuRoZFqq6YqfPbZ0zx/4SZXJ8OYQvDenVDdQ+RxaeRMq8ZDXK+q8MC+9oZE5anhLo70tfLORKhobTAtixcujzs2wKhmtexgsCvF16fCxQt7obpenrFcvt0fOz246ei39VDun372yWG+9OIN/uTlWxXC8V5OnnM6lwxL8Mcv3+LVkXmeONpT06Sl3u9Uj4xs5nO9cXsRw6xqy22KYgX9blW7Lo8uVGSNG5Y9wvOZL17g9z/1lKysSXY80nIk2ensHjEMtqDt7LGXAkYOluZhfgYW8kskVBuPZZr2z8yXvLy4XLbvuKsPOnvhyIOop56mXVEgGbezh/PVY210FDWewSpbr6qq+DSRj3gDNB1a2sDnt3ORc1mIhtGBWNYiI/KHSwhcikWzW0UpVI8d4rw6zQSdyTFIjvHzgInCHVfQzj729XBC67Ur3apW87trNYFoJMvXtESFh1PXar22Bbwuexv++MIopqCiGYglRE3jkevTtuWgEVGpqQofP3OAa9ORYlXaEjDSYGWi+sFAVaBKI5ExLEbnohXrqlcN+ZiotVLcrclrmqrw3NOHuToZvifv70Tdh4286BtbiBWj/QpC0/H8y58zs+HklnyutW7gd6vadWtupaZNN8DkUlxW1iT3BVuR/S6RbCe7Sww7obvs7nE9A6XXshm7ajw/Y1eQF2ZgJVL7u7kcTE/YSwGv1xbG3f2lCXpP/gjNluDf/Mdvk50cpze1zJAV4TB2U4rleLpURVY1e3JeYYKe108Cjemsj5yw4906rCQGCjnNhdvjszc5myOTyaBbpeYgNZPzEAzlQgzlQpC4Dn/wPfhjH+wftn3HB47Y/7a22z+/ShOIRrJ8q8VCzrRQFNt3mctPmusN+nn8aA/fenuK+Wiq+LsZwyoKIScfdKEhSaOicmw+tqoHejWqrQZ/cXmcSLK2Q1+19aCemBpfjG1Lt7l6Q/ZOr2/H+2+Uwrn03kSort+9WmieHOpE15SKbowuXeVf//0neHt8aUs+11o38LtV7ao3F8CwhKysSe4Ltjv7XSLZLLtLDKeStl1Cq62EVuD2QP+gvZT/bqFyXBDIiXjt76bTMDlmLwUCTWhdvfzGg71cOfIw17I+mvs7+Xev3SZyZ5JeY4mD2QgPWHEe8WdRykunikLGBI+ZLdqKw4oXUMg2tdLT6oNEHHN5mSw62YKFUAg0BD4dPFgI0wCH9ArSKRh5114KBPdUiuP9w/Y+KaOR9sGOXlsBTx7tYe+epooItj++MFr3cDgJJI9LY7inlWefPNSQqNtsZaLwYAD1M4JnQ8kKi8dq71lupbg8usAfXRjdlOe03pD9Z589zW8+/5qj33SntFgunEtO9o1y0jmT71+ZKbYpz1adWznD4u3xpS37XKvlZ1+8Mc/UchyXrlZ44Lej2nVquIu9e5pq8qG9srImuU/YrnbzEslWsbvE8Gvfg+k7duWzowvaO+1/2zoqBLJjhc3nt0Xh/uHS+uIrRXFszU8TujVGOp4g4NFpC3hKwjMRh7GbqGM3eRB4EFi46ub4tMmE0sy03sobnn7+1OPlN37qAc605PL2ikmYncCTmUVVlJLFQlFQFYUmDFt8azpmcxtL0QwpdHRM2qw0fitLUqi4/T5ypoVhWriw0IVpJ1jUsVcQXrKXN16xv1ZV6N9vi+PBw7ZA7hmA/Ces13DNSQx6XRofOtFXE8HWaKe9QuORQlWhkRbGpiWwLEFbwE0onilWpTdSmbg1t1K3ocaF63MYplUhRFerhpiW4Ne/dJGrUxFypoVLUzk+0Mbnnzu77ptEvSH7L790o6LiulP9ptX2DafkC0Wx93HOsBzblGcNa9OV0uq//c8+e5o3bi9WNG8pWH8KWdxqnczurUJTFX7vl5/kM1+8UJwkuFpHSIlkJ7Jd7eYlkq1gd4nh5UWwLKzQEovjk6wks7T43XS2BVDbbIFsBjv5nRenuLRkkTJKwstxUkxTCzS1YA4esatymQ78VpzBVIyTao6fOehFXZy17RRVZMNhjqTiHMl/LVBY1gLoL03B2YfsCXonHwfdhTsW58/+w1/iXZxmIBtiyAjTqRsEPKXDF/C58SayKDl7+D6JTkZVSSsuprI6HsugzUygI9A1neaAD0WAEBapVAbFNNCFs70CyypVu1/8a3t7vX5ue7qYpJ2beid/E+ild19fxX46OdRJb9BfvIHXa7RxsKcFT9Ws/Wq8Lo1PnB5kPppCAE8fb6yVcXnFNJ0zcWkq3W0+Pv0jxzlzaO3Jc+XruTy6wORy3HHIWsG2gUBJiL5xe3HVasilG/MVk/pypsU7EyEu3Zjn8aM9rAenKnw6Z/KXr99xfNDYiX7TSjtKlAvX5or+X11TMcxSEkm90YLNVEqrq+vuMt96YQJf9UOHEHYyyVPHevjQib5tq3a5dZXf/9RTsrImkUgk28DuEcO5HKgaloDLo/OEE1m7q5yqEAyscGrYQg0vsRRN8cjYEg9YsKQFWNCaCKdaeecHzXzg5BF7klsVxRukYZHW/ITwc9XU2PvABzg73AmRZdt/vJi3VyzO4ffoFdVeBcEeM456412s7AxqvuRkBTv5q4ksqbiba+5evhU4Qv+eZn7vZx9AWZwqTtBTZidp8pa1clYUTDRcWGDaAjmieNERpBQ3e5uaaSFHLhwiLVSE4rYVnRDoiiCggy7yMW9WrUhV0kkOpcc5xDg/C7AM8zPNRBa+QcdDD2EOHua3Xl5mNpwszvLvDfoBhd/+2ls1Q/nHBoJcmQyRq56Zhi0EjvS3cXUqzMhMlEzO5NWR+YZm7jv5liOJLKqirEsIl4skpezXdFWhyecikqj0EJd7R+tVQ166NltTVRcCvnZpjLGF2LpsE05VeJemks46Zwtvpd90K+PFKpMvDpV82q+P1+xjcB4t2CiXbsxzZTJcfKgp960XKrFOCSaGabF3T9O2P1jIyppEIpFsD7tHDLtc8PHneP3KBH8w9jKtnijdRpwuMwaJDIsrKbpbfawkbZGsAJ1mnE4zjpKdo+l7c3CjzbZUFOwV7Z0Q7Fh7Ik17p70cexgAM2fw7//jN4it3KHPWKHfiNJtxFAQZAw7Y/XB/R0oliA6MUnvbJTuvGoyUFmMtjL1nTmGHjgKj5yFZ/42ABOvX+XPv/4iA9kQ+40wA7koKmVCVlEwUHBZBivRGGrAQ8bTRDibJqW5UYWgRaRpsrJkNRe6u5BeYdlpGoXkijr2im4jBjdehxuvowG/hcqYq53r+bbSo7lurruDZMzKvONCBfV3v/4W33lvpma9TxztprvNz5++cntdWcmwNZOcnKqBbl3lyaN2NdCyBL/9wlsVQlRTFXKmhWmJusKwnjHkvckw706E1hXV5eRvbQu4iy28q9kqv+l2xouV+7QT6dq8bbeu8nceG8KlqZuulJqW4AvfvFoUwtWsN8FEIpFIJPcPu0cM57m5nOG20oLwlm5eXivHp/bv4SeGAmhXR4ktXaUplyx+X1UUmn0uWwUV/LS38h3sFIWnDQ+L6RhTSoAFrZklzY/L7a57g7w8tsyrIZW0d1/xNV2Y9Bor9BlR9pordKtuukWKRMaoiGPTsejNhnFfewPmb9gv5iPeHu7s49vtbXwrtoeQ6kfHYsCIsD8Xzi8hukx70l8yY5DO2q2FharhEbbYSCkuDE2nszkAzQE7em4lAih28gaAEGRzBtl0Bk3UT6/QsTiUW+JQbomfTFwFIKa4uZEXxyPuLkbMrqIw/dCJPl4Zma+JyxpfiPPS1bkNNVTYikgfJ0GdM0rVQNMSHO1vq8mC/erFMa5OhusKw6eP9/L9KzNUj/gXjncjgr+8KvuTjw5ydCDC9WKnu1Z+5+tv1fhvXZrqWEXdSIX3bsSL3ZpbcRSp7U2eYlOOzVanL48uEIpnVv0ZO8FEvSez4mUHOolEItk+dp0YPtDdXDMDHI+XPYcPweFu9h99hH+XusSNsTk6jARdZoxuM44eNuhqtSfLVCAE/Wqax/QQ4cScXVVWVbRgB6eXXCAWMIOdvL5sMrqY5GBPCzdno7VDrYrGpCvIpCvIJSDw4GF+4cw+Qpff47t/dZGuTIR+I0KbZVf6QrEM/e1NtgDNR7yp0xP8j27Be4kQIUNlWmth3t3Ggj/IX6n7WbRc+K0se3Nh9hthBnNhhsww7XqWnGFhCYGqKLjcOgGvq+R1bmq1RXEhiSOTxhWLkDZcZC2RLxILXIrAr4EmLIRhoDq0lm4WWT6YmeKDmania+m/2AMjxzi9/zA/0pzju1EvcVOpaLHr5BFtpIvcVkT6rCWo66UhrCUMzxzq5sH9HVybCpM1LDRFKTbyKLBWV75q+4bAfma7Ph3hSF8rR/rbGMl/36WrtDd5+PRHj9c0G9lohfduxIsd7GnB62AB+fRHj6/alGU91Wmn9uPV2AkmLTz75PBd9e7KVrYSiUSyvewqMWxaghcujVVUmVQFjuTFUaH6Egy4yWpuphQXU642AL7r0mg5eZzTHardxS60CMsLEIuiKnBquJvFlVRpUl6LD/X2daxb13l9dJ5QIoep+LjobsEK7qHH1FhQA5hKbcxbcQjb7eHEYyc5/16C7+VjlfxWhj5jhf3WCm3NfvZZsYqIN1VReHB/B5FEhkOZNH7PEq3+FaLJLLfjgvfSHqb1Vm67OrjgGyKluPjls32cUGMkx2+zNxuiK7GIki3zZyqK3RFPCNsqoekore00B3LEhcZ8NIU3l0a1MqyYKrqmY2ouhFmqGhe65zk1j/VGl+DyS6iXX+IfA/9I1Qi39ZHbN8yop4f/mLSY0lupMOvSWBe5rYj0aURQa6qCrqrranOsqQqfL9u2nGnxZ6/erphIqGsqE0txLt6Yr9luJ/tGgXTOZGQmyq99/BHUM8qan32jFd67EaZfb/+fyW/XVlSnnT6Hgp1dXJ0+cre9u7KVrUQikWwvu0oMXx5dYGQmWiEaNFXl46cGASpSB6rJ5ExGQxlOnzhU26AjvIS6vEB3aJHuvEAusLiSKk7W6yBBRyqBmp7lqAALhWXNz4LWxILWzILeRNjVRG+wmZNDnfntU3jiaE8xYzSpehh1dzJKJ1dWfPzf//jDaMlYRf6xsjBDELu1dIFgwMMQGczYIkeyC8XXY7qfIwvH2Xf8MBx9xu6mp7sgtFDRQY/5aSgfqlZUFJcHJZMD0yKpuEirGhqCDC40xaJFTaMIyKGB4gIEzR4d1TIQhokmTBTLrLFXqJZJR2gSQpP0AE8CMcXDiLsz7z/uZsTdyYrma6iL3FaIl4+dGqS9aRaAp473OiZRrCYM6w1zl2+baYmKaDFFsQX/996bcZww6FSVLafQ4OMXnjq05mffSIXXtASWsCPrlmMZcqa1LZFfaz3QbEV12klwH+lr5eNnDjC+ELun6Q33spWttGdIJJLdwK4Sw043FcO0GF+MoapKXSEMq1S73B6721x3f+m1vEBmeYGxi++xTJw2ShOZCsVDO0EiwR4zwXHmUbAFcjQW4Gv/+3X+zk+cRevo4nBXAHe1tQNYjmVKQrCpBYaO2t8QAlbCpeYgeaHcFoAmr4t4OoclBAoK3WqWgfAEvDKZ3ygF2tptUdzVBw+fgR/6Kft7C7MlcTw7AUsLZHJm0eNqKfZ0PVVYCCCqeNAVC4FCTtHwq4J2l4GCBl61tK2FzOPC4pBe0SwyPJqZ4tEye8WM1sJ1dxf692fA9QQMDGFq+pbevLOGVZPvGopnOHOo8a58J4c6GxrmLhd9378yw4Xrc8Vj7lQNrNfGuIBLVxuu0K6nwmtagks35vnCN68Wc5tdukpPm8/RgrEVrPZAsxXV6dUE9+NH1hdztxbrFZj3qpWttGdIJJLdwq4Sw6vdVFarsq272lUukPU+/mThTcxshi4jTrcZp0/E2WPEaTGSFb8msAVyWy6ONpdg6dsrdLf6OIXC309GuJXzsqg1Ma83s6QFyJk4V4cUxW4s0toOh07Yr1kWSmSZQzNT/MkLL+GLLNKVi6LkRdbR/jaiySyJjEEgnqYtvIxS6EqnKtDelRfIvfDYR+x0jFyWmUtv851vXKQvvcR+I0ybmULJ2xkEYGDbQDQF2lsDKB4dcllw6aBqdge8RMzeZ4q3uK22MLZFsjCNyq58efrMFfpSK/D6KLz+AkLXmfZ2saR2MKV38qK/l6/t38fnNtDEAmwx8JkvXqjo/LXaEHU9QbWeYe6C6HPysFZXA9dqY9ze5Gn4nG3UW10QSOURZGA/NEQSWVS18ci6rWKrWr3eDfvDRgTmvWplK+0ZEolkt7CrxPCpzBTPuqa4mhLMCy8JTzP9fe3Fm0q1UC6Pz9pohbH8Rjaj6Cz7OzD725gUgttTy7Smo/SIBB25GN1GrDhBzrIEK8ks3a0+VASPd7vwjS1gZecBu0nHiivA6aUsjKRssRrsKCU+5KmuQll6L3+iHybdehBVWHSbMQZFnKORJE0ri3RkE2iKXUE+NhC0LQyWgKV5e7n6pr1iTYPOHk509vJqTzd/He1nxvKxR8vxREuWlsgcrZFZBrJh/Bj43BoBrw4opdbOAvD4wOUBI2v/i7DFMcJOyUCxhbBlIcwc2UwOzTLRHNIrFMNgX3yGfczwEwAhWJnxsjIzTPDEg/kW04chsPqkuwKXRxeYXKptuZ1ewwdcLag2MszdSDWwfOLeH798q8av7HWt0Xa8arsb8VYXBJJTusPdGrqv5n5q9boRgXm3P1/hmvG1S2M1ow736hjfa6RdRCJ5f7OrxLC2PM/PHfIzE0oQjkcJNqXp68iivhrnVHMbH25J8YMlkyU8qJpGMODmqWO9m24i4HQjAyomTn31on3jcQmDTiPBXiXBkaEguDIQi9LV6qe9KVb0H2sqHPTmGIrPYL02k5+8l8PbuYf+QwdQ93Rhtu3hN/96jCuzsYrs2cINzlJUZvVWZmnlsqpgtBzCJUx6jBUGifNce4BBEYPwcu0HM02Ym0adm+ZTCvysK0PMALW7j1fCKm+azYw19xHXfDzUKvitp7tR5qdse8XctP37BVQV4faQyBj2duo+Al4/imXYAtk0IbGCorhxuz3kDItYOocqzGLXPF1YaA7JvS1WGsbes5c8K817yAwM0/HQQ6hDR2BgsOIholwMOKZYqErNEPVqN8uDPS24qzrsudewMDRaDSy0MX51ZL6igg0wF0k1HMtW2Oa1KqOrjaDcy7zdRqu6d0PUOL0HsCmBudrn28rPVN2xsZq7fYx3ggh9P9tFdsL+lUh2ArtHDJsGxFdQFRjoCDDQEbBftwwILaIsL3IiNEVXPI0hIKL6CCV8fO35Cb4/0Mf/9PeeQfN4Vn+POtS7kTlNnMrkIORvp6t/iMFPnLEtCrksamiJR0/OM379NsnZGbqUDJ0tPqCqo958lJmxSU4Nd7O0kuLpsSWOKj57gp7WRDjbgk9rImWVLnjl7YVz+Yi3KYIM7T3M3ieG+cG1SRZHxzikJRjWkqiLM7BSmiSoYE/QCwLh5RkGZ6Psy9saUoqbhXgbd8YfYvih4/DED4PPb3fky0/Qs2YmmL5xm0zWKMa7ed0a/e0BFBTQVWhpByOHIgRZE+KZFVqERUbRSaOAAj6XipHLoVtmMcXCKb2iJbYE15bg2sX8DnDB3iE4cBhr8DC/+06SV5YgXSdqa++epgpRalqCX//SRa5O2RVTl6ZyfKCNz+ftGSeHOnFViWGXrhYnSdajkUl7UDvJssB6YtkavcEf6G62I9yqnhHcunN28U7ibogap/c43NtCLG0UfefVbEZgbvVnqq5cl7MdkyNXY6eI0PerXWSn7F+JZCewe8SwqsEzfwuiYXtyWTS/ZNMAzIQSLMXSmPnuc0EzSdBMQnYZfXSS2ecnGNjbbftwW9qgNWgvHl9N5Nd6WXMY1OWG7j607j4OnviA/VouC6Elrr4zwqXrWdqJEiSFaQnCiWwx5s1OsUjSYSY5xjxKCrxunTnhZVbxE/a0QkcXV+MqCbP0OTwujcHOZoeL5QCfe+5n0DLJmgl6IhFnOZauaBLiE1n2pxfwvnOR8MjrJDIGntZWOg4OoXb3wkOn+MHgY/zO8rt0pZaLzUEOGGG88QwdTR5bEBfi3YBYJElKcZFWdTzCwFA0hAKaaqGpJilUwFZsKgIXFh5VoJjO9gqMHIyNwNgIKvBrQET1MpJvDlJoEJLWvezb08Tv/fKTFTeLSzfmeWciVBSIOdPinYkQl27M8/jRHt64vYhR1WbaMAVv3F5sWKiWT9pzquYc6m2tyeJdTWSt9wZfeM8Xr8w4NR/kZ84c4BefObLjbqLl+8qwrG0XNU779d2JcN1ug5sVmFst1OpV/h8Z7OATZw7c1crhThGh9zLNYzvZKftXItkJ7B4xrCh24kJTC/Tvt18TAjIpiIa59uK73FZTtIskTVZlJyrTFITjGQaScUjG7aH+Am5vSRi35P8NNK9bIK978k5eIL/pSfBfvUmEl6LFotuM0eYPMBBIoc6vVHhJVVXh+EAbJxSFlWSGFn+EPc1pLi8vMpbWmFEChD2tNHf3o5i51S+W+4ftBTBNi3/5B99jRbtDty9CvxGlz4jiEzlURSEUSzMTspMn1FCCxdlFjg0EAQjORfn7CZUZvZVpVwvfDhxiVmshoJh8qNXkH55oQp2bgJlJe8JdHqEopJWSvSHl8uLx+EjGkuQUDRVBs5XGQMVSdXLCAiHKso8tXIqF6qDu2qw0Z9ITnEmXjvWcp53O3ofRLsRt7/HAIGg6L12brRGIQsBL12Z5/GjPum+mq92kTg13OVZz/r8/f4reoL9YffSsUaldzzatNXQO4HZp91wIVz8knBzq5Deff624r7SyEZACWy1qnPZrPSG8FQJzvefWWsPiTl51r0vjE2cOVKzvbgyv7xQReq/SPFZjK/b/Ttm/EslOYPeIYScUBbx+8PppfljntVsUfbtBM0W7mSRoJekmTVuz13kd2TQsztpLAU0vqx7nK8nNbfaksy2m/EKdU3RmXK2E/O18/IkP0Dvcxfk/fJnliWmC6Qh9JDnmy9HV5kcFult9xfWcHu7iwEqqKJA7fRlu/de3+NmlLPN6k52FrDezJJocL5aXby3yxkKWtN7Fe3pegAlBt5LmIV8Gd2ieLhGhz1jBLQzi6RzheIa5SJJ4OkdQCILZJCey9n4UKCxrAeZTbdw4eJKjH/4J6OiCWJTwa2/x8ncusy8XZq8RQRf2Bb3ZZwvjULLUwnpZ9eNRLNq8GrG0hSZMmq0MWXQyqoLXrYOwUE0TK5db1V7RkwnBpe/aC9gPJPuG+LARxEz6ue7uZl5rqnkQWu/NdLWbFFAjlK9NhfnV//hysVOfrir0tQf47LOn694g17NNqw2dQ1mTmHuIUzW90L2wsN1bbVFwYq24uwK6qtQIzK16v9Ui8dYaFm/Eq363htd3igi9V2ke9Vjv/q8nnHfK/pVIdgK7WwyXUX7BS+dgQW9mQW/Go6scGwjS93cfheRKyV5RsFqYRu3KTMPOGQ4vFV+yULiTUpnIuejo7+HYiSG0tvbi0P9WbHf1hVpTFT77i0/U2C9U04DwIiznO+mFFlBXInS3+ooC2RJgCYsOkSSYTXIUu1GHpio8cGse1DvQ3mm3mg5ZfO0HU7UCQFHo3tfPggJvp9rAB4qws5X7jQgf8SmklifpFNGayW92BnOcPck4zZcjcOsSqCp0dHF0Tw+vDOznK5GDzAov+0jwWEuW54Y9MDuBJ1HmP1ZVNLeL5mCAWChBOmuSRseLiapAzFDwWAKfsDBVF6LMXqFj4cpP0HO0V+SycOs6p4HT+ZfCqo/r7i5ueLp4NOCDVKLq3DLRVYXeoL+uZ3i9EYAZw6rwoxqWYDacrGvDADg51FlRSXbrKm0BN5YlMC1RcVPd0tjBbcKpml7Po6vnWzhvh6ip/nvUNRXTsqjejGrf+Va932qfqZFh8UaSK+7W8Pp2idD1VlV3WlrJevb/asJ5p4l8ieReoggnA+A2c+7cOQFw/vz5u/7eq1G4SI7OrWBaFpqqMNzTWv/CJ/IxYCsRiIbyIjliWy/KsAR8+50p25NsCjRNYU+zlx96aADVH8hnAgfz1eR28K7Ph1zY7k1dqHM5iCzB0gLW8gLf/t6bGOFQhf9XUxWCATenhrtRFftzXR6dJxTPsqT4WChUkLVmFvUmDFXDpdU2CynwoeO9vHh1FiUf8dZvROnPRekzV+g2YijYk+mGe1ppb6qcvCiASCJDPCfQunrpOXIQtdtuFGJ6A1z5wXvEb99ify5MX3oZJRpGIIqJFQIIxzMUzn9VWHiFgaXYNeGAlUXDwu6PZwtkl2Lh1xV0YdltqYXz56pAUaBnAHP/If5oWuViro1RLYjLpXNsIOhYzVntBnZ5dIHPf/XNCqGsOwz/K8Dfe+Ywv/DUoZpNKqz/2lS4OKlPye9Tr0OV6eKN+Zr3dGkqTx3bXOxg+fZsdsj3yy/e5A+/f6PGklC9bzy6ys88NoRLUxnsbAYFxuZjWzrUX/73ONjZzAuXxxkpexDam/edu3Wn8YfNvd9qf//19tGHT/Ty9Im+hveD03pWO982w5Zc26rWd79PGlvP/nf62/W6NH79pz9QnLy9U0S+RHIXqHtyy8pwGev27Zb7kPv2lV5PJyEaKVaPJ0YnWIqli5OoDFOwFEszE0owoACpBMxNln7f7amaqNcO/iZMlDVb+m4Ylws6e6Gzl9duzPNvdBOzNcseM06XEafHjPFou8KjvR4K18rFlRTheBZLCNpFkvayCrKiKIRVL3P5FIuCQM4pJavI9elIvgKKHfGmt/J63o1SiHgbMKN8EIO/3aajRkKlXU8pwYJsGN59HfI9QjSXi4e6emG4D7oespuFaBrK7CRNs5M0zUwwd/U6QqSL67MUlaRSqtKnVQ0XFl5McqhoCDxk0dyuvN1FKWsOYjcGwTBq/9KEgNlJtNlJPgl8EkgrOqOuPdwMdXOrbZnDT5zGbO3g8q3F4rH97LOneeP2Ys1NyqmaU20HAPtczplWTZUXSpWl8nSLwo3VqcpUr4L0zz72yKZvnFslThyr6bpKX3uA2XCyYt3PPX0YYNtEUfXf45nD3dsqOBr9+69n4fjelVm+f9X2vZfvB8DxenOguxlXVUfM7Rpe3+pGKO+HSWPrsTes5Qu+G41mJJL7ASmGt4O8D5nuPgBeTt7kj2400ZZPqGi3bD9yKJFjoMPh97OZGh+yoWg8//Yit1Iai4qPb3ma+Pq+Pv7lc49t28QVoWhFkfo28O2cwsNWC5/9sSG08CK3X32PBTVBh1nZSc8Wqm6Ip2gzU0WBDLaNYF5vZlFrYjHXzN7eHsZC6ZrKZiHibdIV5E00Ok9/gLP7W+19Up5iURbxVvrlHExP2EsBr8/untfVB2c+xOTRH+Lf/uWb9KSWigkW+4wILiyEECiqiu5y0dzkKYqlgFtDyeXs9Ak930kvbYDbg6L6bEWZbw6Sy2RRLRPNNl5U4BUGD2TneCA7B3/5NvzlF0i4AuDqIqt38pf+Hv7r4CH+P7/0dENd7goTxcorvYYl+OrFMa5MhPj4mQMVVb/VbA9Q21Sk8J6Xbs7z0tV81Nux3rq/Xw+nCvB6h3zrVZDrCfZ6DxWvXJ+r6KK3naLobmUEr0VhH1V3DxSUovIK++HSjXm+fnnccX++cGms4vdVBY7cJ8Pr74dJY+uxN0hfsETSGFIM3wUO9rSgut0s5DQWdLv7mdelcfqHHoZer22xWImUvMhGruL3LQF//fptSGQ5CBwESIEWu8bEn05wYHhf3mrRZidauD1shnoVJMMSXJmNczmqcfbowyhqD388E0QxjWIFucuM02XGaDINFEWh2oYTtFIEsyWBPBwfJ9XSxHenc8xrTcxrzSzqAXJK6dSsuFkNHLCXAqlETcQbidqucaRTMHHbXoBHgX+cSXPN8DGhNTMS6Kd5714+dqyd+O1RDpgR+jMh1KU5mjxln8HtKe1ftxd8AfvhRQjb05xOYhgKCcWFpbpr0its/7FDc5BcgrO5Mc4yBitgzUH65n/Gf+Q4HDhip1f07QdNcxRXhU50f/LyreKDRTpn8vadEO9Nhm2PbN7//rFTg6tO8lIUbAtBFV9/rSSOXhmZX1cltV4F+IF97Q2Jk7UqyKv5Oqv3lWkJvvDNqzVd9FbrLLgdbFVVvFFBXdhHv/P1t/juezN115fJmbx0bdbxIeX5CzcZmYlWpKdoqsrHTw3eF8Pr7wdxuB4Ps/QFSySNIcXwFuN0Y6qeQOXS8pOVFAWzuQ2tNVj6PSPKoVYNVzLK8tQs+z0GrniUaDJb+16mxfT4DPu9Fur0eOkbvkBl1Ftr0K5UN+hDLmzvexOhVaOoTg13sXdPE2MLsWIFuYAuTLpcCTqNWF4kx2oqyAAIwUNtGitzC8RSpQpyJF9BXtCaiHpbOdjhq/3dwmcti3gr+rgLwnh+2q4mp9MVv6YAJ7u9DCXSJDNx/J5F2pJ3UG622dXjrmN2Zb+13Z5kODNRbBJCmV0DVbP3bQG3l/hKgmQuYe9vBQIii4FGWrHbUStC4FJM3AjcigCz1l6hAv7lWXhlFl75dn7dHth3sCSOh45AcA9g3yB1Va1pyQwUX8sYFtemwnzs9GDF+ViNUvxPiY0ML1dn/JZXrgu/f3xvsCFx0ujkr0aGfC+PLhCKZ2ped2mrdwXcarZiyH69glpTFZ450cerI/N1H4Y8Lvtxzekh5epkuOZ1w7QYX4zx+NGeHd/R7NRwF0f627g2FSZrWLh19b6papfT6Lm+0yb/SSQ7FSmGt5DVbkyf++QZLt2Y5wvfvEoonmE+kuK3v/ZWceixkIeazpkFDZX38HloCwwQaWqh3crbLMwkQStFi5lmYilO6p0pezJe4fqWSuR9yFOljXN7SuK4MFEv0GxXM6soXECrK41QK1See/oQ/+av3mMlmcMsKxcZisaM1sKSp42Ooz1MWYI/vjLFHjNBtxGj04zTbcY5pKqoCvS0+YmlSpaHNitFWzbFMWWBdt3DqTeWYbQN2jvtiLX2TntxuZ1vwENHYeiovTIh7Ir7fL56nLdaKLmc7TsOlFXSVyL2MnrV/lpRoK3dFshDR+Gxj4A/YAvtcoGcTBR/3uX1kEqZxcmHCeHCjYlHmFiKQpOu4BM5NJeOomlkcyapdBZNFKrHzvYKshl7uwrbBva2DR6GA4f5gN7JX2gmYbN+hF/GsBhfiBVvkF+7NMZb45XttoWA8YUYjx/pKb62kTzb8r+Fehm/mqo0lLRR7/1H51aK329UfN2aWyHnMKmzo9lzV0XRVgzZb0RQnxru4khfK1cmwzXHpLD/nzzaUyOYPS6N43uDNQ9RhWvCfTM5TYhiZVuUe0Tep0hfsESyNlIMbyFr3ZhUVSGSyBYnnpQPPZb/nhCVE5qWYxlwuZkxXcyUVV9dwqTNTNJjZtind3CkSUAsYk/sqiabgaU5eymg6dDcWjlZr6UNNB1NVXju6cNlbaIrh9iqGzG4NBWfSyOeroyaK4gORQFT0ZjTW5jTbTGtKnDobz0MnTrG2yPcjLxBezZGu5lEye8Bu4ruYTGaolOAuhKB8ZvF9VvNrXztRpy3YyrTBPgzTwvD+zr5fPkNWFGgtR2zOchlOrllHeDg8SZOdWhoi2UWi6V5MKuqZUJAeNleRvIz9PIRb3T1womT8OGfsPfl/BTMTOCfmUB79xqkM7YgVhSy6ORUFz63Rkt7AMUS9jExcrjcHlKGhTBypBQXQnGjqwrNbgXFNO3kCtN0Tq+IhOCti/DWRY4D/xmFO3qw2DXvuruLO65gMSVDVxWGuluKk/FGZiI1IwBOldkDXc3omlphLVhPh7t6Gb/DPa383OPDfOaLF4pRaDOhBL/5/GsVIqre8PaFa7N85ZVb6xJfTuty6yqf/pHjd1W0bcWQ/UYFtQDHUYRCLN+fv36HI32tjMxEK/bts08eqntNuB8mp10eXWBkJlo8j3OmxchMdEdto0QiuftIMbyFrHVjqvd9p6HHcnKmRU+bj3A8U5EAkFM0FvVmlmjmB81DHHnqEFgmxFYqW047+JABOwkhsmwvRfIJGa1BtNYgn/uR/fxgqY/RUKZiiO3ijfmKG1/h5uKummWuKPDStbkafybkJ+6oOnT2cOQj3fzeLYWxhRiaMOk0E0V7xfRinK6FZdoDrmKsW4GFyVn8s0ucLbwQh2jEx5hvkuHjw7ZoDe7B1N31q1bHHs7vDxOW50se5PkZO4+5WjRYFizO2Qtv2q/pOuzphq4+1NNPs+/HfpZ3b88TuXWLlsgcweg8nZmwPQkPxX4S8PoAHwrQ4vWTTGXQ0mncKnjcOko6BZpiV/UVtSK9whbItRnXKoIDRogDRogfT14HIKXo3HB1MuLuItS5n1N7TlfEq5ULVafcYNMSvHB5HNOqPK5H+lrX1eEOnDN+L48uFBuGgF29biTRojfoZyaUqLFevDoyx635Fa5Ohjm+N8izTx6qiDGr56MstLu+W2yFn3Mjgvry6ALXpiJ1O+OlcyYj0xF+7ROPoCpKzfB6vWH3+2Fy2v2wjZKdyU63AEk2x+4Sw+ElW1AEmip9nlvEWjemet93Gnosx+vS+PRHj6OqCt+/MlMjLitufqpW8gnvzf+AEPYwfjRkC+NCLnJZa+MSAuJRe5keR8NuKHHaF4BIG9xcgNYgExMhMlmjwodcEO2RRJZMzsSl25VEJyFc2KzbC7bXUFMV9nc2M7YQq6kgA2jCpF9J09zXw3F/zvbxRkLMhpM1N/VWM0X82lVIlzzIszmdvaMJVPwsaM0siKbaqpWm5f3CfcAH8x8qZ1fTCx7khRm7SlyNYcDctL0AGvCIy2VXjwcPQfcz0NZh21cK1oqZieK6FEUl4PeBv8wb7WuyH2KyGfu4CAFmzm7U4sn/nGXmxbH9r7DMGnuFTxg8nJ3l4ewsxN+GX/9zMk1tfMxq55Cr024S4urE1N389NkDPPf04ZomCyPTkYpnApem8vEzB9bV4c7r0vjpsweK3tz1iCgnEXZzNsqXX7xZ8XvpnMm/+tqb5PIxhm+NL/Pnl8d5/lc/WhTEjfoot/vmt9p2NPreGxHUt+ZW6v5NFsjkTMYXYvzCU4ccE03Kh91NS3DxxjxTy/G7Frm2Ud4PE+gkd5/7xgIk2TC7SwxP3LIrpWAPaweabN9soNn+f3+znbe7Qda6MdX7fvnQY61nOF+1ymdCnhruIhSv/aNctZqkKPnP2lSZh5xJ11aQ4zFwqhkVfMjztth7fDlBKrnIAl7Cmp+Q6ifpbeLTP/wBVE3j9vwKk0txvrPKrHWAb7w1iWUJBILxhZW6P2cqGhME+IuVZt50NXHw0AOcOtDOpT95iVtXbxVTLDrKLBYVm7+0zFAqSlkOBVHVh3JhEYwHSh7k6iQOlwt699pL+X7bVMRbH/QMwMOnMQOtXLtyi/jYbfbnwvQkF1HK/Me43JVdCv3NdqRbNmNXlw1sQezxgOYHIUgk02Ca6MLChYXqsD888QiPE+Fx7HQNE8W2U1w+jKY/ak/S6x0AVXMUq7m897jcV1xOvYlK1UIbGhcoTt5Ht65WjJYARSFcIJ42+N2vv8X+zuYKYbmaj/Ju3fzqpV00+t4bmSB1sKcFV5XlpZpGBWK1XUrJD3qUX7t20uQ0ma4g2Qj3gwVIsjl2lxguj9wyjdJkqXLcHlscNzXbwiPQZE+YUutPSiqw1o1pte+Xv17ojDW+EFvXOtaNx1tstFHEyJX2S76rnrUSZWZphVA8Q3uTh772gL00hXHH4vRmYnZXPY+Xs7dyqK1tnG0J8q6wGFFSzFtuDMV5/81HU3z5pZuO36tGUeDC9TlyhlW8if3ko8d4YSxVnAOjCZNOK8n/62wvNJkQWoBIiGafqybqrZ00g6l5eKNMhDe31k7SqxbIHu/qEW+FiXoFUVtOOmU/lE3cQgAjU2EWchqTagsvedpw953kv/u7p9AiS6Xq8dwUZPNpIqpqv7/HW1qnadjfN7Ioiorfa5HN5kgZEEdBzUe66Vi4EaiitnqsIRjKhWD0or2ALdz3D/NM6wB3soJ3tT2EtIC9CxoRSw1OVKpOW1EV+zz/zrvTWJYoPgg6/V5fe4Cxhdjq2wG8dM1uLNFoB7h7efNb73uvd4LUqeEujg+08c5EqOKQKHkRu54W29XbKgToW9idcKuR6QqSjSDtNe9/dk87ZsuEsRu2IE7E7OpaoyiKHeFVqCT78/+us23y/YhpCf75l15lZnKOQDpOt5LmeAv8nRNByOWYCSUIxzME8yK5/J5SaEO9GMsQxk1ED7Ck+AhrPkKqn4y6dhW+sDpdUzEtq2Ko3uvS+LVPPMILl8a4OhUhZ1q4NJXjA218/rmzxRucmcvxv/6nbxO+M0mnEafTjLPHTLCnyc3pQ5UeZEcaEcjVFCLeCsK4kGJRFvEWTmS4ORutaHmtKgqHelsJ9tr+Y7r7oLMbFM22axQE8sKs80TJ/HsLI0covIKVyyGwrRICMFGxhC1+7exjs5h93MiZvKA1cdPTTaRrPz/20z+MduCQ475Yqw1sNVnD4r//v15ifLEyI1oBHtrfXnE8y/nD79/gSy829jBVzoGuZn7/U0/VFUH1Whd/5IE+fvUnH+aN24vbZp9otN3uZmwcpiW4dGOel67ZTVSeONqLqsD4Yu0D+FZs62aQXk3JvWa91zPJjqXuhWP3VIZVDQ4eK32dzdhiJRErCeRk3FlgCGF/LxnPT5rKU7BaFMRxQSyXD2vfJbbrhnF5dIFrMyukLbvZxG3gTUNj39AjnN3bxEA0zEC51SJdyhJWFfihhwaKgtkUgmuT08WJUknVXRTGIc1PWPMTV9ygKDwy2MHHTg0WK+QTS3G+V2W5SOdMXro6y7/8hTOOXcaKn2EsxMVlSHv6IK/bfJrgN394ELUNWF7Ie5CXnauXsai93BktvdbUCh2dlSK5XBSWt+o+6BzxNv2DK6SJ46Y0Ec4SgmTGIOgQ8Wa1dTChNDHuO0jw6Wd4oK/ZTsMoeJBDS8WfVVxu2rs6SGQMMjmTjBAkYwncwsjbJgS6sMioLrKahselYWSzKHl7hY6JU920y4zTlYzD+C34/33HrlT377dtFfmIN3oG1l1JeeP2ItOh2hxqAVydql8VPdTbineVBiL1mFyKr1rlrdd45sWrs7w2uoBhim2zTzRiG9msjUNTFR4/2sPjRyttLtVfb8W2bgbp1ZTsBKS9ZmPcTw+yu0cMV1PoJJZvWgDYQjidrBTHiRikHJpFwNpWi3JPsi9gT9DaBrbzhlFX1CzEOHukx34QKPPTmqkU77x7i8WJGQa9BsMBiwFFYaAjgCVgMZpiacVuv+y3svitLP2U/LZZRWNF9/NjXQ9zLJCC1jYeP9TJxdElx0YBL12bIxTP8LlPnqkrbJw+Q9pUGMl4+ODhsuqVadgT2kKL9rK8UCGQLQGLKylWklla/Ck6Y1HU9Qrk1nY75m34BP/mipfx9kPsMeP0G1EGjCh7rRiHfbXHTAjB9XdvEk/n8ApB5jWF6z4Pxx4+jNrdB8PH7RzpTMq2VcxMoszcoYk4TR4Xy/E0aUXPN/6wUYWFRxh4LRMjJ9AsUFHIqi5M4UZTBAGXiktYiHyKRc2WWRZMjtnLi39tv+b18xNd+3HHfLyn2xP0Ipp/VZG02qSunGnxtUtjADUXU6eb1OG+Vk4MBLk2HaHV7+bCtVmqbMQYllh1iLOw3qt5z3P575XHB26HfeLkUCe9QX8xas7JtrBTPIzbLRJ2yue8n27qkq1nI/aa3X7O3G8PsrtXDDuhqra48zdBZ1mFxDQrxfFaVotsxl7CS6XXttFqsdoNoxBdtdE/yPVUfkxL8Bt/+nbVyd/O537+R9ASK6jRMB/Zd5BLr48weuMOikMV3i1MDrkzHMnMw1vzWAKmIynIufhRxWDUVFkQttXCUDRyZm0M14Y/g5aPR9tTtp68QDaXF/nKX76KsRCmNRfHpUIw4K6MeiukcFQI5JZKcdzRBW5PMU5MKAqLejOLejNvMcCBrmZ+8r99HMILFR7k8OQU8XSuaKmwhCCRyhC9M0FwaR6uVEW89fTDgx+0z7NkgtjbV7h1+R0GsiE8whZzlqKSUtykwPZTq250bIHswcRAQVgC4dKJC5WccKEKgQsTlyJwYTkeQ9JJWieu8bPAz+ZfmtebmWsb4MHxNKiH7U56ZQ8Ka03qemt8matTYdqbPHz6o8eLPuK1blKmJZhYitf4ij366t3mCuv93a+/teYk0PW2cV7tJmlagt98/jVmQgkMSxSbYHz22dMVf7c7xcO43R7cnfA577ebumR7WI83X54zO+dBtlGkGG4ETbObUbS0Vb6ezeSF8comrBZapc2iMGlvLU9qGfVuGDdmI/yn747UVJjW8we5nspP3ZN/PGyf/ME9aMDYSjvPz7TTbKVpN5PFznodVooun8qPn9yHqpQ8x0uxNKYpCKoKj+kq6ayJUCCmegmpfiKaj4XRVtjfWjmxbAOfoYa8QL4cgudze0k39aEKiz1mggGSdDR3MOzO2BVkp+MeX7GXiVul15paUJYFD8SSzGtNLGhNRf/0k8d60Vx6TcTb33z3Gt/57g/oM6L055cOM2FbKso76FVFvAHgdrNvTw+v9Rzg/4keJI1G0Eyx34iw3wjRb0TRhIWiqhhCwVA0Etj+ZW+bD8PIkczE0TDzMSeQxm5D7dZV+2GxkHtsGo5Wk24jRvfSNfiza/YLqgYDg7at4sBhTu0/zIn+Vt6eDNdtCJY1LOYiKT731Tc5sTdYPI/rJTIUBOdzTx/mD78/wtSyLTA9usqxgWDN8XcSqR860ccrq7QuBvtZdrCzue73y9d9czbKy9fnmAklyJZNBC18lsLfUCEho9AE443bixWfbydFhG1nh7Od8Dnvt5u65N4jz5md8SC7HqQY3gxFq0VH6TUh8laLsgpyIm6nDThhmiVPavW6y8VxoZrsYLWo11HrW29PMx8tZQlv5A9yPZWfRk/+gz0tuN060ZyPqOZjjA50VeHnHh/ix0/1o8WjEA1z6/oYEwmBOz/GbVgCYVjFHNYWM02LmUa3FE4uCPjGmF1pL3TUawlCazuaP7Dp6lX5Z7MUlQW9mUWaea3jMMNPHbKPY2S55D8ueJDrCOTBdIqnMkvFLmArqpclvZnuKQNz2oO2p6tC2B/ob2fBv4eJXLD4Wqtm8S8+1Ee/txD1VifiLZtFnZng73gFRxdCpLIGScXNtN7CDzwD/Bf/MVyayiGfQUtkjv7MMj1mHK9bI+B1EYpbRNWS4LbtFSYa4FYUinlaujefuiLKxLHp2BwEyyymavD9v0ID/hePj8mmXkZ9PUw09XHN1cm74dr9VxgN+NKLN9BV1bG6Wl2VOdLfxi89c6TuBLF6lZzPPnu64kHKqa20UvyPM9XxY+WkcybXpsLFv8lG/4Z2i4dxJ3zO++2mLrn3yHNmZzzIrgcphreagh3CF4AyOzKmWWmzSOb/zWac11O0WlQ1eCi3WuT/PXWw07E71+RSvGa16x3ShcYrP42e/PU8kc996IgtUJqaoWeA1+a9fMWv4bZyBK0U7WaSoJVk0G2gp+JYpmVHujV76WsP5D9gCtLTxTxkAHQXWkuQs61Bzg4GocUNWNitMRpjzc+mabYFoqPsJl0QyAX/cZlA7mzxEQy4CcezmELQYqVpyaYx31ji8vUfMNjVzJLlxt/by/6jBznV3smDPX7enUsWj/GB/g5OPHaSijiMVMKeoLc4izU3zfKtMTIrKwQ8OgL7+NvpElmGc0sM55ZQFYUmr4sjg3uZ6e/njnKUdH8vB3pbUEILZK5cJ371Ok2G/WBlKSoZVUM0+8HjyjcDMe3zNZcpVYZ1vdQ9T1iV1WOHhwQ9k+JA5jYH8tnHAHN6C9fy3fOuu7sYde8hp+ikcyZ/8vKtim521dXV8qrMyHQE9YxSN+GgXiXnjduLFQ9ShYmc5XLYEnB7vn7mcvW6q8kYFqNzUc4e7l5X5vJGH/BMS3Dp5jwvXp1FAZ461ls3vu5esxOi0O63m7rk3iPPmZ3xILsepBi+W2iaHdHV3Fr5esFqkaxKtTDzVUhhxx0tRlN0tvoY7AQ1lYCl+bJVa3zueICRPj/jCUFPfxfXIxZ/4JC/qqvKtv1BNnLyN+qJhPILCsypLub0FrwujR/++CNowmRmYpZhn8nxVgU1FrEnMjpVIY2cnTccKnWkQ1Whuc2uILe22x37mtvqNl3Z0B92uUA+dCK/A2yBrIYWOTW8wFtvXGPm9iSKsIr7JxTPEElksYRAnZhl8b33ODXczf+swWynyjQBWgb6OPRAL1ouU2kN8QVg8BDmvmG7Gplqw2MlGEzFGVbjNOk6fUYUryi1525v8nCwpxUlGWcvcfYCIjpC5HKGZcWLq3eAd/ef5nrEQDWy7BNxHnIlONicgUzGfgDUdXsh/1AiRKl7Xi4DhWwKlzvvkwcMu720ZeTANBzTK3qMFXqMFT6csm0mOVRuuzqK4vi6u4tp0Vox4rFaVaaeh36tSk5huXhj3nEi54Vrszz75LCjSLs5G10z7aIwQrCR86zcVrLWpB3TEvz6ly/x7p3lYkzhd6/M8NC++vF195KdMAmp0eva3d7O7XrPnbDP73fuNyG4HWiqwmefPc3zF25ydTLM8b1Bnn3y0I49l6QYvtfUtVqkMOMr/Js/vcjyXARPNkWrskRv0McnTh+ozMY1TbTECsddcLwNSNyhLRxjPj1NWLiJqR5iqocV1cuezj3b9gfZSBWnUU8k1L+gFKtYx/orN8Cy7IeJ8qi3aDgvxqj92WjIXiYLlUjFrri3tuUtFkH7X69v6ypUZQJZPXSCa7kBvrR8nQ4zQbdpZyB3GzE6zAQa9o0pnMiyuJKiu9VHv8einxgsjsB3R+x1BporM5A7urh8J2pXIw2LtObjbXxc1bpROg6SzZm0W0n6jCiDIsZPD3pRzBXIZxKH4xnuLMXIGRaWiKDOzHPK6+Ing36SGQN9Txddhx5B6ey1hXgmZVfiZybsqrRpFrvnCZeLRMZrHz+fQkCxULJZWygrgKKQ09wkLftSZGcfl5qEVO9dFxZHcoscyS3yU4krAMQUDyPuTjzfuAmZxzjS0uZYlRnsauY3vnyJa1NhMoZV0YCj0UrOqeEueoP+mgl5M6FEXfuR1UCWu6aur1V01rD4zBcvFEdXXJpKR7MHn0tjJpx09CNDPipxKlyR1y0EXJkMV9hOTg51VmQpr/X1dgimantJ4TN++keOc+ZQd/HzbLdoa2SS5t2eLLVd7yknfm0NO2FE415TKHwVzqXr0xGuToZ37LkkxfBORFHA5+fyZIzvRL2k9T7Qba9mR9bgoKeXR7o8pUqyg9VisLOZfW0e3OEk7bkEmqoQbPLwd48G0H7wUmWiRaAJvH67WrpJ1rJUrMdLte4LiqqWqu/9g3aF4+Y8E5MLHG4SPNimosXyAjnv4baELWSK3fWEQE2s2MKugMcHrcGS1aI/Hym3BQ1XDnQ1I1SVRcVOlCh+FGEVBXKXGadH9dCtKrbXtppCXnbZJL2mkMEPhbPMa00s6k3Ma82kcdHT5iOSyBLOBUh6W9D72+j55BlAYC4v8sXnv0M2MkWvEPSIGBoCSwji6RwKMNAeACsBI+/aS2G/d3TB4CH44BP2pMNEHGt2kvcuvY0/toIlBIqioKsKna1+As06CopdGU4ksWIJVGGhCDAVlZyi4/W4cKv2zwjTQBgGqqi1VzSLDI9mpuDdKXj3mzwKfNEb5Iq2h6t6J7f9vbj2HQRBUQiD/SA2thDjM1+8wO/98pMNVXI0VeGJoz01YjhjWHz/yozj+amucZ54XRrDPSXR3Uir6M988ULFNuRMe3JhOU5zBG7NrVRExRUwLFG0nbh1FZeuVmQp66pCOmcWR3O8Lg3D2r6sZai1lxQ+4+e/+ibHBoIgBCMz0bsi2lY7JvdistR2vaec+LV1bOfE0vuB++1ckmL4LrOeIahq4WgpKkuWm6s5P48MlXkfc9ma5iFqIsYnTh+wLRYrKTpbfAx2NtsV5VQyn51csloUY+Wq85HXkWrRCOv1Um30glK/wpHvOpbNYEbC/LuvXGBlLkJzNkk7YTqbPfzQQwOVlfdMChZS9gS1ArqrZLFoacvbLFobattdgVIMaKjAUtRi5JrXpfGhH/oAHNxjV7LLPcjhZUeB3KnlOGwuczBXivdL6j5OHj5Grq2DW9kAnQf28cHj+/Lnn8KlZcFXV1oQgeOALch7zBh9OTsDudWlEFRzVJQVwa6yL85VpqToOnNqMy/RzWzzICrQYqbYb4Q5sBKhR0vQ3x5A0XS8Lc0sG3ZKiGVZeBSLJtWi2aWAZSEQpCyFnOLGREFX8tXjfPc8p0e4PekwHyLMh7gJURCLOssjffy3mRauu7sYcXcxo7WAojC5FK/xBq/24FWv0ceF63OE4pdqBNmh3lY8uloU4eWsp/VxgcujC47zAZxI50xG5+xJlbfmVjAsC5emkKsOXobixMCMYVVsa/XnNCxBPLO9WcuF7a1+cAa7Kn5tyk4dKUTxbeeNdq1r9r2YLNXIe27E7rCVn2Ur7RbSunH/cb9NIpRi+C6y3iGohoWjyw1tHfZSQAjUdIqhRIyhZFWqhdOwrWWVYsCq110ukP1N9v9rGzt17pb/bs2nUreHyxGFb8SaSLt94AZNmHSTpSe4jwfbFLuCvKoPOZ8aUUBV7cYbrWUWi5ZgXR8ywNh8zPFw6PnEjIr9oyolK8Tw8fzOMh0FcnGCXiKLZQlUVWGfz+JAdgl1cYmjAG+9AzeboN22WFx7cxmvmSWl2h0ULUVlRm9lRm/lPZfGB37iA3Cg3W4LXWgxvTBbO8kTwDCwQlN8MFESbVlFZ0Zv4fveAyRdAX7ukUGO+E2U2Un6PRMkVmLFcyLgyVeOgUQ6Szi0ghsDD0Zxf2UUF0lAoKBj4VXBhVn025ejGAZ7lif4eNlrUdXLiNtuCjLxvTh3xg+T0r3MRZJMLMWxhODModqJZYVz+MpkuCIXOWs4Z16fGu7i2ECwZoLrk8d6GOpqAQX+6MKoYypGPX9zdaLFaly4NsdXXrlNJmfi0tX8fm389xthO25y9boAAo7V7e3YhtVSRgo2EcOy7vpkqbXuDRu1O2zVxK+13n+trO3y750c6qwYbpfWjfuD+20SoRTDd5H1DhtsyoSft1rg8wNl67ZMSCYqJ+3FV+qnWuSydgJCpErweH2l6nEhAs63ttViu/13hQvp1y6N1dxEq2+W1U+upqIxK3y8S5AHH8xX3i3LntAYDZXEcTTkvL8sy/Yrr4Rhsuz1QHOlB7m1zbal4HzB8Lo0fvrsAVyaurY1RNNqBbJlokZCPLq8yO2royRmZugRSbqaPdSsJhG3l8nbnLyzRE80SUz1sKA1s6A1saA3sexqZri/oyTIe/dWdB0kk492y7eZZnEWVqIEPDqqohQ9s25hMJgLMZgLQQqafjAOfXugqw/liR+myeenKZd/yJidtH3IhkHGsEgpOqmq7nleYeARht38Q4CiqeAqS68oxLrVSa9otdKcTk9yOj0JP/gB/ACm9NZi5fiP3+jiL4YP8y9/8fGK/V84h3/n62/x3aqGHE6CrN45D9Q91+t977PPnsawSvGCa6EA0/lMYyiJSF1VaPa5eGSwg+6gnxcuja+7pXU5jd7k1vOgW++hA+zoyPLK8Hq2YT04XbOvTYX5zBcvMBu2k10KthIv2rqv0+t98C/Pq+4N+ovbUP2eGx2i3qqJX2s1glrPed8b9DMTShRHK3b6cLvE5n6bRCjF8F1kvcMG22LCVzW7K1pT1U0jl6tMtChUkp2qopCPMEvZ1cjiutVS5bg8H7mqEcZ2+e+qJxVVU32zbOjJVVVL+6t/0H4tP8GxYqLeStgWzU4U9udslQ+5pY1TzW18qC3HG8smS6aOx63bMXNPH17zONe9kaq2QNbaOzl0qCSQiYQqW01XWSwKN9dmK0OzlSlaLIZ7Wjjkj6C+FCpWkenoLAp6PF4YOGAvBVIJWuamGf3aS1hzM/TkIjRZpQcIVVHwe3R7PxbyhgsEmuyGI8cfAd1FaGKB1199l4HMMt1GDAWBpagkFTdJ3CiKgs+l0t/qgWzWflAxLfs4FePd8pYL08DI5tCE6WivGMi3xf7h5E0AsgsaybH9NB87kW8QcgT22NXiZ0701SRL1BNkTuf8xRvzdc91YFUR1ogQBrv+W88jnMqaPPNAP6eGu7g+FSnetKo9wy5ddVyHW1fJlU3UW+smt94H3cL179KNeb7wzauE4pni+x3pb3P0DG/1jdbxmm1YFdeYgkj7mceGGnuAzbPe/VH9825dpa89wJPHehmuEtIbHaLeqnvOau8Ptef2aue90/V8Jw+3S2zut0mEUgzfRTYybHDXTPguVz5irL3iZTOZ4O2rd5ibmmcwoHCkTUfLJJ2bSdS1WrgqxXEg3/Jar7UPbPQi7jSpqBwnb+aGn1zLq+7dZYkWuWxJGBf+ja3YVcpqMilYTKEtzvJP+mDGm2ApZdDc1cXgoQ60qdt2Jbmp1bHRyror6GpZBbmAZdrbmbdXdC0v0LaUYCWWxhQCTVFoa3JzqLfNjvObHLOXAv4mWxQ7CWRfAO3AYX7+nxyyxcw3rmCsROlKh9kvYjzgy2DqGaZCCQIenbaAp5QckYjD2A17AYaBsCfHFTp5yXcAgYJfZBnIRdlvhNnnNuhp89nD/7rb3i6wxXAuW8rsxq6U6l4vhoCsBcIywbCry5pDeoUbE/fsbZgtZR/T1AIHDnN68DA/6bf4TixAyNLXLchWO9eFYE0R1gguTUVR1rYVVN+0CmkRt+dXGOxq5muXxrg+HSFrWLh1laP9bXzizAHGF5ybmDixkbbxmqrw+NEezhzudqysb/eN1umarTs0XskaFi5NrZtjXU7hIfZ7V2Yqqt5rPfhX77+MYTEbTjLc01Lz85sZot6Ke85q77/e874wabN8n+/k4XZJiftpEuGmxfC5c+f2AE8BSeBb58+f3/hY2/uc+23YwLQEv/Fn79YKrmc/gpZJFSueZizG7bFpQkvRfBZyc+VwfC5XijErx+urmbR3sLtpQxfx1SYVPTLYwSfOHKi5WW75k6vLDXu67aWAaUIhAzkatvdBlQ9ZVWCgI8AAABm4cxPu5L+pqNDcUtVVL8jlsXBDFfRVh2FVDYJ77AU7CfjMRz/B22/fZGl8kgN6moPuDGpkGcswWVxJsZLM0uJ309nig0ScxdlFVpJvFV9TA00lYdzehdbeWSNmBruaeeHSGP/ndISAFWN/Ms4H1Sw/PeRFXZqzz5cyFOBUl4ve5RVmQnMIBAKFJS3Ae/69BM8epbe7xa7Mz0/bFotUyn5oKUQXFrBMlGwWVzaDK5fByFlkFYUUOmbeTVse76Y5eWvjK/Du66jvvs4/AP4BEG3pJDswTMfAQ6iTt+xW02v46tcSLI2IsLVw6wrDPW1cmw7XCOLy93K6aZV/feaQsxhVFdvDDKz5t1NPBI3ORYtiu96DXb2bqtNrWznZyumaXRhB2YjQXK0bIaz+4L+eQkH1drt1ld6gn5uzUSxLgGLPV1ht/2xmP651r1vPee8t2+f3w31Tcn/SsBg+d+7cPwL+G+DHz58/H8q/9kHgr4FCOfH1c+fOfeT8+fN1eg/vbu63YYO6lZzby/bFN9BcdnH3YGXbaJ81eGg5xz/58BBaKu9HNXLOb1CwWpRNQjuNws96ZrmeMglZLjIeP3093Wte+OpNKtJVhU+cOVD3yXSrnlzr3jg0rXZyY9GHHK60WmTTtSsWli2eVyKVn2s2xZlonGXNT1j1E9b8pLKumtnk6/Vfa7rOyQ8egw8eK302w+C3/9N3iC5OE8ys0LucYNgTR8Oym4PkJ+gFA25ODYOajMNUZQVZa+/kbEcnZ4e6uBxKMzITzWcgB1gmwFVTY+DBD3B2uBPCS/bEvIVpmJ+F5XkU02Sgo4lYKkc8ncMSgm4rwUGR5dBk1vZoFyLeHnkM/AH7QSS+AnOTMGf7j1E1+yHM67M/r7CILscQ2Qxuy8AjTLsfiKKSUXRUVSXg1lAK3mPDxGnyWevKIlxdhKuv2i+43LBvCAYP5+0Vh6GjuyKOr55gODnUyeujC7QF3BXWACcRthbprMmxvW381KlB/sO3rrIcy5AzrXWnWFT/nWzk3Kon/k1L1Fxnrk6F+d2vv8WHTvSt6xq51Tm5TtfsehO6GtmXa3UjXE1UH+xpwV2VSuLW1YqfL78Ofez0IB8TcHthhQvX5pgNJ/nSizdRCgk2grr7Z7P7cbV73cmhTnRNgbLbgq7Zr2uq4vg3UZiweD/cNyX3J+upDP88IApCOM/vAEHgP2LP0vrbwK8A/9uWbeH7jPtp2KCRSkTFxV3RmBMakbDGE+zh7MP5rmvpVFkr6rJUCwerhYrgF072lkXCwWBnEvXid6oSLfLV5LzV4mBPi2Pc1d49TVtWQXASvACXbs7zhW/UCo36loVyH/J++zUhbOtENFKqHq+E7f3lQI/b4oAVZW8uUto+3c3pZQOuxqG1nTcXc4xMhUlvcuLJ5dvLXFqySGtd4Lc/s1tV6LASBL0rdJlxuow4ZiJRbA5SQTJuL3mB3DYb5ZOLKeb1Zha1Jub1Jhat5tJ5Vejad+zh/AczYXkea26Gm3/1KuriLHtyDvulTsQbe7rhzDPg8diCOBqC2SlYmkOxVHr2tJDIGGRyJhkhSGUMNMugWbUIaBaKYdjHx+UGt1r0Hxcn5zmkV5DLwq3r9lKgubUojs3Bw7xhtfHAvnaO7w2iqSrDVTPnC40mutt8fPpHjvPowa4KEaY1UCk2BfzpK7d5YF87/+EfPVNXUKy3CrgRb3898a8qSs11JmtYfOe9GV4ZmW9IhG3UetAITtfsjRY16sXFwdoxeyeHOnFViWGXrnJyyLY+1ROwHzs1yFdeuV08VkKUHufq7Z+tyIitd6974/YiuapRipxhFRsv1du398t9U3J/sh4xfAj4L4Uv8vaIDwH/1/nz5z+df+0S8AtIMbzt3I3cxUZ8Zw0N3RUqcRV+VcsWxFX5yKRTqAoMdTUz1FVqQoGRK1VQy8lbLU75m3ii3eLtxRzLpo6mqcXuYlvVorT6RnOkrxUUhWtTlUPQG7oBK4rtt/X6obuv9HouWxLGhc8fi9LXHmBPs5elWBrTFGiaQk+TxpCahFvXAHDfCfGxUJiw5ies+ghpfsKmn7HZ8LpuKk7HOGsJZvEz6/FzNf+aKgT/cLiHTwwH8pP0FiG8WCMWW/xuWpQVArllhnJ2SommKjw0MgPmzQqbBT6/XV3v6uNyROP/FkukW4dwCZMeY4UDIkagvYn9IlY34o25aXsp4HZDZy8cfgB0HSWboSm8RGBmkumxSTKGhSUUkpZOVNXo7wygWMLuZFjwHwsrb8Pw2g83AtuDbRr2ezr5xGNRePcyvHsZDTgFdOtt3PR2s9I9yKmf+1Fev2nUNJqIJLKoioJbVyuEQtYwef7Crdr3qd4FluDKZJjXRxd4/GiPo63g1/Od+Qqe4GMDQT6/igB1OifSObNu4xGoXy28PLpQN0Ktkb+lzVgPNopTpfzijfk1r8dO11S3rvLk0Z41q+Bv3F7EqMqHNkxRFJH1BGx702xdAQ7O+2e147vZe45T45esYRW3QQpfyb1gPWK4AyiLDuCJ/L9fK3vtJWwrhWQbuVstMxvxOG94ooaqlqLZyjFyleK4IJbXsFpooUX+2RGV8fYciysxgp3tHDzYijY7Xqome33O62gApxvN1alIQ5OTNoXLXaqUFjBN1HiUjzwc5ub1MSIzc/TpWQZavRVe7fYmD15V0GnE6cT2U+uawpPjK8DtUg5ya9D2I9dpsOJ0jJ0mZrndOr0H9sLBbjiYt1lYli3gQwtFgdypLhJcWKnIQA4G3PR4sKvH5RYLXwDaOzHbO7ny9jJqJgWqh5yiMekKMkWQA3sPs/+pQ3bE28JsWQbyjD2BsZpsFqbv2EvxffxMN/Xw5x4fWV3gEzm6zTgHjDBtGYMmj8tOAPGUnUOmURLHubxAVjXwubEHosvi3erYK/YZEfbFIxAfgc//DR/UXHxO6+C6u6u4LIomR6HwyvW5mvXVI2dafOGbV0utzMu4dHOed+8sF3upZA2Ld+8sc+nmfNErXD0aUoh3q65M12s8UsBJ6JRfZzYiZjdjPdgKGrkerxWJ9s8+9simG2LU+z7U+nDLcesqOdPiyy/eLB5jp795RbGPb3mCyEbuOfdb/qxkd7AeMRwC9pR9/SHAAl4pe00AlTlaO4lEDFDA691w04idwN1qc9iIx3nLJwXqrlLTinIy6ZIwLkTAJeMVVouaivLSrL2Ur9sfqM1HXqUpRoGbs9Gam0l19mk523px1zRobUdrbefo/oP2a0LY+6dsol6fO8yeqXBF9XhPs5f+oL/MhzxWbEk9l1Vp7elm6PB+tLagPWnP63M8xoWq+Mhax11VIdhhL3mBrFoWj0ZCXHvnBtHJKQaUFHvVFKpTq+lUAmsqwevfu0RfPMs/EIKE4mZBb2JBaybibeFQW/5v2eOFvQdg74Gi+JiYmOeYO8UJTxp1MS+Ukw5TGlJJCC1ysqxJyIrq5S1XL6l9++hq9hJdDtNvrtCZXLatE5oOPt0W7IAQFslkGiudtvOPhUARwj7vXPl4N2GRy+ZQTAMNUZNeoZs5TphznMiWhG5Y8yFePQyxB+1ot8FD4PPXTU5Rlcqh8AKheMbxGvHi1dnapoICvnrxNi+8Nl5xjI/0tSKAa1MRR4tG1rAa8vtWj2wVPKHfvzLDS9fm1pUfvFHrwVaNrq2VklFto/KsEom2GmuJyHrff+p4L6F4priN5Z7hQozeVy+O1Xh0y//mdU3FtKziw+9m7jnruWfIznOSu8V6FOE14CfPnTv3zwET20N8+fz58+Wll0Gg8XLF3WZ20hZRYAsgj9ce5vT6bD+hx1fKJN3BbFebw3oXntWGrO7apECP115qrBbJ2nzkdMp5HUbOcTIaHm+lOA402aI531rZtAQvO1ThdFVBVZWayrBLUzf1QLChG4CilHzIffsAOx3iI08leffdURanZtnvNhj2maipkuCzBHz7namSYL41zfg7V0stqd0etJYgnzsZ5L2hTkaTKv37ejh1qAeojbYCOz/35mwUSwhUReFQb2tVioWK1r6HB54pe7YuNCxZXixVkUNLYBosrqTsKnK+eUdAZDmQCzGUC9GkuDj5ZgRuBIoRb2awk//523d4az5dVan7eTQF+xyZny6rIs9CJl3TJKTFStMmMnhHQizlTCwheEsPYHT08KOPHUYTln3eLUwjFuaYDiXtltJCRVU8eN1++jp8pJOpokDWLXufpBUXBrYo0RDoWLixbPFcRdBMwa237aVwrHsG+Gj7PqIpnXe1PYy72rEUOzl5f2czqYzBXLTy7yBXNhRdcerUOaXemwjb9uj8JqVzJlcmw5iWWLV/XcHv+9K1OTqaPXz6R45XdPIrVFKvTYXJGBa6qhQtTaeGuwjFa6usq/0tbcR6sFo1F1jX399aKRlXq2xUq0WircZqEy4Lf3NOVeczh7or0kAGO5tBgfGFGDnT4qsXx2qEfHl78tG5Fd4aX+LdO5VpQOW2mPXss9Wa0JRbTe5m5zkpuiXrEcP/B/ACMAUYgB/4tcI3z507pwFPUlkp3lmUdw3L5fIRTlXVFTXvA/QURHL+/z0+x7zXe8F2DDNtxnpxzzxeqpqfVNcEZRoZ06j0Iheqybk6VotM2l7KWysrii2I/c1cWcpiLMzjs3RSiqv4sDTQEaA14ClWzly6SnuTh09/9LjjcHQjbPlseL+fR848BGcewrQEr40uMDYd4kiz4OGgyujIHW4mZwlYtng2TMFSLM1MKMFARwArk2Fm5BaheIaOJg8PtgdQb92ExTZoCXK2NcjZB9qhpRUTHL2bnrwHddXPoKql1I2DR+3XLAszEuIbX3+FSZeLLjNOpxFHxxYWAkimDV6/tcCp4W7U1DhMj7MUTfHY2BInhCtfQW4ikm3l9Xe6EP5A/obXzqkzR+3tEQKiIVrmZ5n48wsoi7N0ZSN4sIqTMosC2UigLoyxcjlEMJB/cA52MLHvQf4mtgBqjlYrzT4jwp5skslQkpxhYQkdVXHh0lWafCpGKoVm5HBbOVSEHaHn8YKiIITAMuzJeapl1opVIWB2kq7ZSc7lX0orOqPuTmZa+/nwBz7C22oPv/WNcbJlHtN614injvXy3SszNW3BBbWd29cT7ZYzLeYiKT7/1Tc5nj/+AF968QbvToSKjUMMSzC2EOMzX7zA73/qqXU/XNcTiatZD+pVcy/dnOfrr41vaUpGPRvV6FwUYFUBVq+CXi/ZYrVGHNXX6MeP9PDlF2+uWlg5Ndxlx95NRRw/+0vX5liOXXQcJVo1taaBdJLq9JTyY1SI9NsK4Xq3bIeSnU3DYvj8+fN/fu7cuV8B/mH+pS+fP3/+S2U/8sPYFom/2cLt2zosy67+ZdL2rP16F3VLlCK/qidrufK+QW95Rdlr+zrvYjV5O/KK75b14q6g6SU/bDnZTGWiRcGX7NRARIhiq+LUzQUejM8jAFNRiakeYqqHZ/qO8LeeHOb1uTS3llNbUhXfruNQ74L/wL4D/LnPQPFatFppgmaSDjPJfuHDDGd448YMqayJZZVsFj/00ABqeMmOQCugqEwkoe3GMkPCa0e+aX6yik7GsDb0GUwUfuO/jHJ1zkXWP2y/jRAErSRdRpxuM0aXGSeXSPDO+BK97QE6W3ysJG0vcgC7gnwgF4I0JP/oKhFLJ6IE+JanlYu9vfz3v/gRtKZmaOtAa+vgE//0hC3E5iIc9ZvM37rN9Teu0mdE6TFiaFhYQpDMGLYYFgJCS2ihBA/lOxBaqMzpzbyr9iBUFbduEDST7DciBHIZMjnTFsYeD4rXhaqreDVQ8g1ClGwGTRH29Ub1kB/Utv3HhlHRNbCAVxg8kJnlgYVZ+OLrPAp82d3EVb2Tq3ont/29KAPDjteIM4e7eWhfO+9OhOpeFguUV84rX69/Sc3mj/+lG/N8/fI475UJ4XIml+LFc2Q9D9eNjE5Vi8qbs1FHEfjS1dl1NwZZT0pGAbeucuHaHF955faqPuN6Iq2wb6q7GK636rxWYaVwPapnCcuZVs3cidWuWfUqsE7XvXqd577wjatEEtktE647+d4nK9Z3j3UZZ8+fP//vgX9f53t/gx2ztjNRVdhv31CLnanSqZI4LlQH61UPoVRNru6wpqplFWRvpWDehmrydlgTtst6saMoNGAIVg3Pp5M1k/asZNKOd4umMIVA1+xJJpqwaDNTdJLhRHYe7b0kZ4Azbg9kmmE8XPIkl1ktGmUzx2G1C2e9C/7xvcH8zRA7dULzM62rLGUDTC3F8bpaCKopgmaSditJIpbinTvLPLS/o7KxirCILYTYm1rONw+xSahuO8VC9XPhuwZa5gQnj+9H05yaIVdS2ObyyppQFEJagJAW4Dr2/lCEIGgmGVhM8kjG4oneANZCws4GzmMLEwu3yDBIhsFcCO3WOEt/cJPu7o6KJiFnBzo5dbCTy7cW+YE7w4VWLX/sTbqNOIPE+KXDzSBi9miCEBUWCxWLXmOFPlaKdgJD0RjV24loPkDBKwy6rATHrSQeRQGXbj9UV3TPy5TaS+cyeZOnGxQVAWQyORTLrNscpCUb52w2zlnGYAXEvIoy8ael3OMDh6FvH5qq8fnnzvKlF2/wJy/fqhAgimLbfopZx20+7izFa4SvELY/N2dajkI3kzN56ZotNOtVlw1L1ORkr2fovZ6Arld1dBKBhW2t3va1GoOsJyXDpdnVW6eqZ7kAc/qbfW8ixJdevFFs177Z6/ZahZXV/NgFnISy0zasJu6d3sep85xLV4ve63r7bb1spuvpdgpVWbG+u9y/s8g2g1NnqgKmURLGmXRJMGfTq1ST897VVLL2e253SSAXfMlen33j2wRbbU3YtTN8VdUWIP4m6LR9sKYl+Bd/+ArTU0ncGZUONUeruwlfLoVq5NA1lZ6g3/beFSikClRUSxV7clV1PrLXV3ckYaPHYa0LZ70LvqaqdTts5SxBTvUSU71MuErPuX8VMTjjcfFPn9mPFovYIyiJGO1NHjRNqYh/ClhZAlaWvUTg5gyjt99gsa2ZH3v6IdRgvqtea7u9X9RKgVzvRlxdnSwKZALcyGocePQRLlhHmJuYoS0dpY8k+9QkvkQEndL6LEuwkszSnU5WpEtYAi6MRRhJuUgrAfbpzczrTcRxs+QLYjX1MPPIcfYe7kYzDViao2VumsW/voi2PEdbLo6aP74iv526MNlrRtlrRovvn1V0XjGCpBUvXV4fP3p0D2poARZn7anJ7vwDdXGDzeJ5ZqTTICyEopBGLzaSdikCjyIqHgQKKMKCmTv28vI37Rc9Xtg/jDZ4mOcGDzPV7eK1JbNiwtzHy9ounxzq5F88/xpXJkIVIkVgCxOFYh27Ao/LluurCStv2Xm+lULASVTOhBJFQVq+/qeO9fLKyHxDjUHKRdhaKRnVNqpb8yt8+cWbFdtZLcDqCcQ/efkWVyfDfO6TZzZ93V6rsOK0/mqckmWctmG1Cqzj59DVmmPUFnAzF6n0wpfvt40I1I3sw7shVHdyxfr9SF0xfO7cuX35/50+f/68Wfb1mpw/f35i01t2r9D0kjgqRwj7RlSoIqfLKspG7Y2nSDZrL9URT4VqcrUv2eNZdzVxK7jfWkVvJ5dHF7g6GyNtecDlYRL7Rv3TzxzAKwwOtag83Om1J6KtZbUoNJwobwShabXNQwLN4HJv+DhcHl0oTkgC+8J5bSpcvHAe6G7Gpalkq2bpD/e08OyTwxU3w5uz0ZobdTlxdF4OqTzNHs5+MN9YxcjRGwmzlLzAyuw8gWycoJVCrRpSN0xBNBJj5uZtBjrmS99QNWhpw2wJcjUquJlUMd2Bmo5b5a1Z6+XSfv3yOB87cwBOH2B80RZxlhD8r199E18mRqcRp9uM0y8SfKC5Nm5vcSVFaiVGvyXoJ5+DrChkXR5mCDAbC/DHfzzDN/b185t/70NovXvRevfyYw/bouLO9CK+yCLX3rhKZzpMvxGl1aqd1OkWBvuNMBigphWiWojgniAce8S+HpimncW9NG9PJlS1Yhb1ippm2UihY+ERBl5h4BEGimVhuTRUlx9DCIRpoWGhWhaKU1pHJg033oMb76EB/xzINLUxH9yLGDjMwKP9aINtPH6kp/grn//kGX7362/xnfdmalYnALem4vfqJDNGRQzX08d7ebVKaJYf1/LJYFvZPMMxJ9uwePJYD8M9rTWTuBq1PKxVPVxNaKqqsqYAqydEjbwwL1g3NnvdXq2w4rR+XVPIGXayxHqSZVarwP78E8MNdZ6zLMFvv/CW437bqEDdyD68G0J1V4zW7iBWqwyPY1/bjgE3yr5eC7HGeu9PFKUkWqsxjUpxXLReZGpnoBRYtZrssW0WHl+l/WKT1eTV2Kz14n71Njltd72LkEtT+fmnTtSuRIh8A5GqfGSnYwu2yHFKtXB70AJNfO6xVt5d8nErZtG/t5tTh3vX3Jc3Z6MVohFs/+DoXNSeBHPxdoUQhsoWqNU3w7WqQTUXZd2FtqeLc7/yieIMdMswGBubZnZ8mnYrSdC07RaKaRKOZxjoCJRWaJlY4WW+8/23i8kWuqbyk7qPJcXLAj4Snia6evr4fz/3RN0YLoC3xpe5Ph2puBGaluDIQJDr0wo3cgHuuPo42t/GLz57ChLRUpOQ0CLhhZtYVaNAphDouTR7RZq9LEMatCvvMf/FK/QND0L7HrSOLs72d3H2UBdffmmU77qTCPcgAH4rQ7+xwoARpc+I0m9ECFjZ0scv+JBTSZgar9zZTc1w4gP2379hQGIFbfwOSjyDIRQMRSNBfpRLCLp8GlYmg5rL4hZ2vrGhqLh9HhRhV6uNbBZNWPbkvSo88Xz28eS78NKf2ZP7+vcVu+dpB47woWM9NRXUAjnT4icfHWS4p2VNodnqd9PT5uPEvnZ+7vHhiu57a55zdTAtwaUb87x0bRYB9LT5HIXncE+rowhs1PLQSAW2ntBcTYBVZxLX884W9sVnnz3N8xducnUyzPG9QZ598tCWXXvrtaOu7mQItcky1duwWgV2tftP9US7evttowJ1I/e+uyFUd+1o7T1iNdH6/2AL22jV15JqNL2UalCOZeWryVVCOZ1ybuFaoDDkTrTydU2r9SUX4uHUtT2Ya36MDVov7oW3aSvEd932pacH13cRUpQaq4X9BmZl45BCBFx5qkk5+eOuhZd5BHjEAywuQOxWqXpc+Nfrr7BaOE1qKt9P16ajNd/L5Mxi96pyqpsgFMRkOfX2R/U5dPFGO59fNLhd2JdC0KEZPPDBQQgqpVzkdJKZUIKlWLposzBNiyaSnBrwoCkpgk0Wfe0m6vf+grMtbZw62oa2YPLGksmyqVXsD6dh7Lo3vNZ2ezlwBIDI/ln++Cuv0pqO0GXaVeROM45LVP7NWpYgEV0pWQ8KeH08ZngZy0aYJsCC3kxccTMZ6MHXe4TvToRACFqsdEkcmyu0mFn8iQxtAU9lgkQ8Zi9ltPT38XK6heVUDrcwaTNTDBhRvIqJobuJpAWWZudnq8LCi0WnruCxDKxMBgvbpmEJBUUBDQuPIpyznoVlC/SpcbjwDQDOeH38b54u3hTtXHF1MuLuIqQFiudGYQJXPaE5OrfChWuzzIQSLERTjMxEeXVkvm7Fv7DeRuxCv/6li7wzESrWIRQg4NXxohXtCm0BN5YlMC1RvG5kDatGVLp1+7raaPWw0etS4Xy8dHOel67aWehPHevFtERNOkRHodNk2d9geTW0/OevT0eKFoqtFMTVx9Lp2K7m2V6t4UhhHzZy/1nt73i9AtXpWDV677sbQlWO1t5d6orh8+fP/zerfS1pAFUttSKunlto5Gp9yZmUbamoV002TbthQHXTAEXJe5N9ldYLt7ehhhKb5W57m7ZKfNfb7o8J56HSdV+ENA2aW+2lnGymUhwXqslOD0iFqnNhuLx83WU2i0BqBbcwyCqVf9KaqnJrbsVxkkvOFI43iuobzmBnMy9cHl+7wYYDNRd0t87e/j08eOZhKmbgZdL84FtvcnlUJajak/WarXz2saLw4P720s/m94c2P83/2AszngTvzMa5EhX51tN+ljU/MeGt+HyFmf9gV3YK21d9zpw61MPX9vdxdcrHSKHaLgRBK0WXESsK5F6RoMVfO1pjpVIs3BznZCLDBwqbrLgw2/bwYMcRvjAWYkoJsKJ6uerxcdVjP0D9gRB0Z9I87jf5hw+32U1CluYcJ/WqsSg/3il4byJMKmdiARPudjxtbSRb/dy8M0+7maTXiIECSVTiLi+eJi+RWIpILIlXmHjy9gpVCCxVRfUUknHy6RWm6ZheoaRTDKfvMMwdfjb/2oLWxA1PF/PBvUTfhtdyx/ngsb01Xdhuza1gWhYzoUSFrcepAlrAozeW3X15dIGrU5GKS2jBq/x3Hhviu+/NsBzLMBdJ8dsvvFW8bpiW4Nl//U3iadvy9tb4Mn9+eZznf/WjuHW14cSK9V6XymPcXhmZpzfor9gvGcMimsyyb0+To4jc6b7S6n2yWvRbo9QTzesRqJu9h9wNoXrXMvy3iPt1dLjA+8/OcL+gu+yluh2xZdmT9ap9yZl0/WqyELYlI+NQcdQ0B1+y17ZibEE1Ge6+t2mrbgD1tnt8Mba9F6FiqkVH6TUh8qkWVfnI6aTzw5FpQixqL8DJRIz59ARJy45+W1E9ZN1+jrYoGF6/nQhQJYhVBbKGWVEdK1B9wzlzuHtD+6PhC7rHS/ehIW69W+r0pwuTHjXDww/thVYVoiH785aJM1WhaLeIRmbpyZQqqIqmcXY+BW+HoTWI2dTKb/6Xm1yZja16Ayxsc4UvVlGKaRsjdNse175WfvynjmKGFrh1ZZT49DS9pFCMLJFktmIYzSdyqJFZVi6HeFZAJJEljs6i1sS83syC1sSC3sS84uWFqMJDXQ/z+NM/an/W8HKpvfT8LCzP2y25FYUH93cQSWRIZgyOenTaAjkiiQXS5gqGULju3kNKdaOrCk/tawORxJOZQ6g6CaGRwBbzKtDb4rYnGBZGpoQAXQc1L/hFQSAbjudklxmnKxmH5G2Y/j7mtxTmAl30nnwEBg/zv19J81JIJ204t3I2LOEY0aYpCj/z2FAxQQHq33hXe/Cbj6SIJLKOPuSRmUhRCBeIpw2ev3CTX3rmSPG8WK1yud7r0nrixAricbPV0I2yXqFT+Plq7/dGG440gmOnzP42LEtUtJquF+W2nnvI3RKq9yzDf528H5IvGhbD586de+r8+fMvNfBz/8P58+f/j81t1i5GVYuTZKgqKJLL1fqS0+n6w+5g37zyebkVKEop3aLcl+z1rbtV9d32Nm3VDWAtD9tdvQgVkid8gcqm54VJVNX5yFXHfLCzmZ6gn7lwEreZoJsUPb4sJ6OjEIVnPTNcCeVYUb3FnOSE4uaPLtzi6lSEz69x0drM/mj0d6tvZrrbzZ7+Lo4/eaZURbYsO9owGrY71q2EIRqmrx32FIaTi62n3Qy6czAxCsDscoIHrs3Rj5uQ6iek+YmMR/nBtT4+eGxvzc3+Qyf6anyxLk3lqWOlzmYAv/EXI1yf1sjk+vHoKvu8BopvqVhF7jTjuIWJlRfBjwx2oCgKK8ksS7EkoXgpzzytuJjXmpj7XhT8Z6DD7qpHR5c9sQ5sMbq0AAszKIuzBOdnCOYj3gDaAh6avC7i6Rw9ZhzVUmjyuug2ddB1/MceYGI0RGQlhc9I02Ml6NRz+AI+4hmDjHDh8bcScKn57OOyeDewvcuqimEJcjkDXVhleRZlxx1Bf2IeXvobeOlv+KfAryhubrg7ue7uKi4RzQ/Y1V+fRyeSyFasxxICl6Y2lL97sKfF8cGvYHeod924OlmVKZ+n3utOrPe61Gic2Gq2k7tx7V2v0Cn/+fV6vzdTXawZ0epq5oVLY/z2C281FOW23ntIvevadlRId3rVdaePUDTCelTPd86dO/db58+f/5dO3zx37lwQ+E/AT2B3q5NsNS6XvTRVXegsM18ZrvIlZ9LOKQeQr0Tmq8/V6HqtL3mVVtV329u0VTeA+8KTpWmlNsvl5LIV4lhNxvnEY8OMz0VYXEnR2eJjsLO5qCGffbSfV2/M8froYrFiaSkqcdVN6uYsV1518dDx/fZIhVPk4F2gno+yAlW149ha2oAD9mtCoKYSfOTRMCNXbxOemaPflWNfQK1wYoTiGSzTooU0LWaawVwIJQ3e7y7wV39hMRJXWBBeXvE08dd7e/mN556u29kMcKx8pQ2LsZSO4u9hxOgqbl+blbLtFUacLpeXk23QncsST1daILwix34jTP9iBl7MCzGPtySKC0tXL2Znr32D1FcYftjHo60m2uIsysIsR1tniM7MkswY+D16yYdsGKjLC3w4CBG3RTKj4vV30dbfy4s3F1lJ2QK5LxGj1SXobw+geMoSN0yjWDk2U3ZaiIVCKi+HFUWgCVHMPq6+WgRElg9kpvlAZrr42pzWxIi7ixFPN65DR/jzhE6SUqJO9d/2ajfeU8NdHB9oq/AMqwocGwjWjU0b6m4hZ1q8Nb5MNcf62ypaBK8mQtZ7XXL6+fK0lEauSXfjGrbZinc1bt1+WKmu1jqJ7kK039h8rCERWC5QL96YZ2Qm6rjd2/UQsR0V0vuh6vp+SL5YjxgeBX7r3LlzzwDPnT9/vpgVde7cuceB54G9wNe2dAsla6Nq4PPbSzW5bK0vOZO2qz31MAwwHKrJqlLbqtrtRfN676q3aatuADvJk7XuJ3+Xu9S+OI8qBEPpFEPliRaJOKQSqAh0tTI7QBUWLaYtDG++8hpNs7dtAe12V07W8+cnh65zxGCjVPso17zw5ycwav4mjvftLb2ezZQqyNEwzUkVdTKMWVY11DQFS0AsHKXLFHQBZEC/PsrcH93h80MDjHS5GMv46N7bxyMP2gJ8tcpXzrToafMRimfs7FVFIaL5iWh+JgK9/PCHPwCHuiAWQXtzhLf+5jKdRqxYQVaAnmDZ33ImDTMT9pLHcnv5L7cTvJfQmSbAX3lb6d/bzeeeO2tHdwHBTJrgwmzJYrEwU4x4VIBgwGN30QPCExP4Q1G8eQU5r/i5pfhRO3voa3bZ1fjlBUAHnw6+ADlvjrlQAi3vPS7Eu6lYGIpGRtjHRlNAtSx0TJyMWT1mnJ5UnA+lbsPlV/lFVMZc7Vx3dzHq62betxfLLNl51rrxfv65s8U0CYCnjvdy5pB9Qy6fGOrS8hPphODnHh/mzy+PV1glmjw6V6bCfO218YZEyHqvS/V+vjpObK1mI9XVUAT80YXRmuvIRquLTvs7nbMbkTRa8S7g0VVcuspXL47V7FMn0f3ORIhr05GKiL5GRWC982R0LspQVwttATeheKZi3RudEFlgOyqk90PV9f2QfLGeu9sHgX8L/CLw5rlz5/7e+fPnv3nu3LlfB34LMIH/7vz58+e3YTslG8XlthfHarKDL3m1avIqrao1l4uzHi9nD3jBIyAeLTUX2eJW1VspYneCJ2vLnvwVpeyhqOzzWPbES497jMmJN/Dl0jRbaTyidOOfXIozF07SE/TzidMHUHPLEKmqlPn8tfnIXv+mveflNxzDsmou/Fcmw/zO19/imbw1oeF94vbY6R75hI99jzzGv0+/wvzkLIFMnC4lzfFmAarANCt9mqYpiEbj7AstcAw4pgBT0zDzBrdTKi03ltiPl5DqJ6z5MJRSJdPr0vj0R4+DAl/4xtVixyxv+Q1XUaAlyImnzvClCbg4FSabM+lUM5xuV/jR091YoQXmb08Qj9kT9TpbfP//9v47PJL0PO+Ff29V5wCgkdPkPLuzu7Pk7Aw3kBQpKtiWGBUYJAdZ1ndaOvSxrWNZtPxJNv2R9pEt+5OstmlbRzoSKVKUGWRaEkWK5O5yuGmWGyfnGWCQgQbQuSu854/q3NVIg5kBBu/vuvrCTAHdXV1d4X6fut/7qVS6p6eT+GZmeNiWPAyQAXPex8gXr7Lz4J5qBXl4J2zbVf1g2TRUBPI4TN6CXJZMwaxLJAlhEDIWkBMWFEsReL2DzncvNCjmCS/OE8jcJF8UpKVORgSQUiKkxCdNAlilyXkmICk4iccIIdGkREe62is82OwzZthnzEDmLMxA+re/wJW2Ifa+7Rhv8ffyda3IlF2dvFh74dU1weMH+3n8YD+NfOqjx3n+wgS/85enSeUMJuZz/Jsvv8qh4Rif/Yc/yBefu1xJk9jT18Zv/tnrKxYhqz0vrSRObCVirPz3x/b2tjyPAMueY1q9157+tqa8b4CT5yb4ycf38srV6brnuAkjn0fjiYN92DZ87/xExQZSu03dxKuUy7d5dltvANNu9qbXtsEuD4j6OoL8wg8d5vi+vhVNiCwPVty+kztRId2IVdfGbf7o7p6Nf5d1GVYshhOJRBb42/F4/FvA7wJ/GY/HzwIP4OQQ/1QikXjjzqymYt3R9KpHtZZyq+raKnJZMK+oVXV9BBSaaPAlr0+r6o0gYteLOz7y1xyrxZHHHkK/mOXl0i1krzSJ2oXSI0/ULmDN551GFb3R5tep5GLXNsvQqpXjUEkgR9pWbLVovOC4TawyLJvvnB7j+ZVUiZdA1wT/+mceb86QvTTBX/2PFwjnU8TsLJ1Wll7yxCIun8G2SE9MsyM3S7kLkRSQ0hxhnPFH6Ozr4/iOdvRgkOP7lp54qGuCT7sIIoBf/dyLnE8GCBQyDGczPFKUfPSBDrTkNIvZhaYsZK9VxBi5Aea8s6oSJnMWtwgTHRpk7wP70Lt7nbb0O/eVVl5CepH5l0/zwl+foreQZNBcJCANNCEI+WsuEQ1JNgIYfPAwk3mnk18+ncaYnSVq5ykILwWqSTaarG0OYhEQJlIK8ugle4VzZ8ODjcclwTNiF9k/fw3+8hoHgT8CJjxtnPf2cDnYT2F4D8d2dKxoP/jss5fqfMkF0+bcaJLXr89UJssBfO7ZS2tqtLFcxFijiFpNG+mlBKzbQLJ8HgGWPMcs9V7H9vYy2Bnm2lT9uX1sLsPHf+9kk6Xjkx9+zHUi21yqwJmRZMvM5JV0u8s3bH9Xa8VQB0jJhbGFeu+1p7kNtmHZzGeKaEI0nVPczsvnRpOun7n8ndyJCulGq7ouN0i413dZ18qq73smEok/jMfjUeB3gAeBaeDtiURier1XTnEPqG1V3RgJZlnuvuQlW1XL1s1FvN5mX3K5ucg6V5M3Mndr5K9rou4W8rWpFNemUpV8WAAhJf2dO9j9QB9kUljpFFeu3GJueo7etkCdDxmoTmhLO7ffbQnXp1NMpA06+7rZt3cYPVJjuWiwWjRecFpFa8H6DBLcxMexff0Mbh/g/K0g18sn98F2PvbBI1BuN12yWpDLNLWdFhLarDy9usGJHX6GuubR/vorEAiit8U40R7jxJ4YtIdoKoO2WKcXLk4628W0yetB5glyuaCzb+dRTrynl4XXL/OtP3uejvyCk+JgpQgKuxL1Zks4dXmSZKaIbUtmLl5i/qXnOba3D80fgM5ux4dc8iIfevvb+KNRne/cmqdQNBnQChzvMHnrQx1Oe+iZCaRhMJ8pkCmYhEs+ZC2Toh/wGwUmclmyms41vZOC8CCQtGHQZWbw2QY54SOHD00I+toDzC5k8FlGJd7Ni42FRhEBQiCQ6KXJea72CnORfnORd+auwNz34B/9sVMJ37XfyYzetd+pZtecS05dnmJkJt30WgXTbjre1lOErOXuz3KD5JUMJMvnESmbJw/mDYtnzowtK6RP7O/jiYP9TWK4YNp16Rfl57xydbqp4l3uHOd2fJe3aWO+uRteXVvWP35uNOnUdGrsUB7NSSTRhFi2DXYZ1/Nyi8/84qVJNCFcs5QHYiEujTupP2sRiBttbkur/bKcWb9ZC1SrEsPxeFwDPgn8CpAG3gAeB56Ox+M/nUgk3lz/VVRsGMrZtm6tqo1isy+5kF9hNXmZVtW+8r/996RV9Z1mvS+6S91Wrb2F/MLFST795Vfr39fnYfuwI5KsWE/pYqtjFDvo1C0emrP5J+/ejV5OuChUJ2DaEr7y0jUmkllMy8ZzcZwzp684tovyKgSCJYuFI5BHbkxRLJpNg5/GGfVl7tQgoeXt7XAE+oerf1wsMDCfZGT2O+RnZ4hZOdrtHEKCadoIIarBF7kcY7dmmEsX6Iz4GewMo3m90BaD9tKjLQbRtqb9erkB0tGH9vKnb87ycvkC6dF4S6+P97xrGyRnuHnhKpPZcfTSNrRsSTJTZHoxR187MDHqPMrbwBfg0/3dXOr0cdUI07PzIEeP7EbTHRlqmSa/+ft/TXZ6hJ58ku2pFHvmFzg82Mb5W/Ms5qqV1nbp7BOacFIsbF+EG4ZOQQp8QjLgl0QiknDQx+R8ltlc6RwhJf6StSKIhc82EECxVD1GgF/X0KXlHjNpmXDjsvN4+i+cZaGIUwUviePRcd11v/Jooul4W08Rspa7P8vtAysZSNaeRxrPMULAyfMTGC2i7mrfa99AO4GG57sdo7XPqRVGblX28muUt2ntcfiVF6+5Tmj0e7W6ZimtWm03YtmyIqRXeq51Oy+3+syf+cZZ5jPFuizlJw72873zE4zNZfjcs5fWbH/bSHNbYGPaNtaD1USrbcOZJPc48DqOLeJijWf4xXg8/svKM7wFqa0mN9KqVXWxsEQ1ealW1b76KnKgppq8SVmvi+5qq0/LvW/dxVZoTNsa35vTeLsV48SDB50XMYxK85Bz529wfhH8tsCDU5mZSGbrbRdlz/msc+v2LQspJgsjJG1vJfKt4Avy7uOHGE2ZnDw/UXdxu1O3B1dsu/H50Xv7GXjrW/hsqcKkS4sOO+80ufDHGI4FsReSfOvVGw1RbwHe/dAw2twUzE1VX1PTINrhJGS0x6C9k73d7i2Ea32xLS+Qu/bzvXQPf3SpgzY7X0qxcGLedhbr3ORVinm0yVEOAAcATp+Fi6UKcmcPp1M6b0xkmdUHIDIIQNgDH9vZzhtTp+mz5xk0nSq1QCIQDMRCDHc5A+ehUhZyyO91ki00DRGKkPZluHFrlja7QIedo4CgIDx4ogFSRZNiwcBrmwQwCQkLTdhIKbB1LzbOwEMTEmFZ7nMdsmk4+6rzAD4EPOFp45y3Gu121dfNcHfz8baeImQtAmK5QXKriWqeUjJD4/FcF1uoa1i2XTm2lhPSbueK9pCP2VS+KQZuNcLyJ5/YU5cfXT4OAdcKcTpv1jVL2dUXRYj62GtReh239VrNudbtb8tV37q4RY9WmRcA1SxlW0qnQryM53klbCRb4EazbawXq6kMv4bTRu0/A/84kUgUABKJxKfj8fgzOEL5d+Lx+A8mEokPrPuaKjYnrVpVS1lqVd3gSy7knTSLVhRLuaephmqyW6vqcvLFOjUXuVOs10V3tdWn5d53uQu4ZUtOXZurVKEv6X1817eI9EHANojaedrsAgdlmN3hKHYmzfWpRaYXcvS0O9FvO3uiDHQEEcksbWYej67RHwjxfu8t6PXQd2Oa8/MWc7aXgi/E0EDfhpiUUVsps4TOrB4mE2gj8JajsL+Ply6M84evvUDIn6LTytFpZSmk84zNZRjsDDM2l6mvGC/MOU1FRpzXPybh74lZzheduLeMP0JP/2DdZ1/qArmnvw2/z8OCEWRBD3LJ10PAq/Pw33gEBoPOYGRuuvpwyyov5mFiFHt8lLkzt/ho0aIgPNUGIXqUN6eDPO8dBp+T4uGVJgNmiiFzgR/v97ItbMD8HB2l1IpMwTm2O8J+RHqRbrtAkiK2gCk9TFZzhHJnb5QhmSczv1ARIiG/B6RkYjaFLBbw2SYB2ySAja7rCE9Nt027VD12aQ4yYC4yYC7yrpyTP20IHd2zG+2L50sWi/3QM+AkYaxBhLjdnWklIK5PpfiVP3qhqf0zLD9YbeWx7YoG+KFHtjV1eKs91m/OpHm63FCmhlZCuvZcUW6lfWu2vkFIYJXC8uBQR50QbvX3jZ+vzgstaXaYlxrxTMznmrbbas61bn/76O6euvbXfq9OR9jHxHyu7rkFw+LsSPK+rKBuNNvGerEaMawBH0okEl9u/EUikXguHo8/DPw+8N71WjnFfYwQVfHayHq2qganYh2oqSaXBfNdaFW9UtZj5L+W6tOygqpFBcCtCj0QC1X+Pq95yWteUt4Owg8fxdrbyz//7AuMjOTwFQ26tCyHYkX+7mNDvP+xXVyfTjXnI1smP3u0t+Z3Fjt7UmgvP1tJtLCCEV6ZzHNx3mDPQMddu3243AXhymSaaduH9HZxvbSbCSkJbtvOzcsjJMdyRIoWvcyxI5x0KsY1q60J+LFDXbxlLkMynScWkQx2jsI3v8KlvM6Ngoee4QEePLLX8WQ32Exart++PufFo+31k+gyKUcg14rkkkCeWsiSKzr7gF+abDPn2WbOownB7plbPLyYY0ILM+WJMKU73fQmIp38yA+8Bfb3YWWz/M7/800K6VF680m2Lywy4M1yaDhW1yDEK21iMk/E66XTYzOfhUVfG8HuKKFoAFHMszg5RcYEW/hAr3bPG2zzEqLUvbNYBGk6A/HSQFhKibQshG012ba90oIbl5zHd0qbJBxF7NoPO0vieOe+5kQeF5aaXNT4fRRMi2dKedqN7Z9h+cHqsb29DMRCTV7e2VSe3b3RyoC1MSe5nMH7fGPmskfjg2/bXbETuFmsyueJP33uCsWaBBaPJvjAiV0txe3tJG24WSZqvdCNlwQp4anDg+zujfLs2TFmUgU6wn5evDRZSYxY6bnW7W9b+aEbz5OHt8WaxPxmraA2DvA++eHHePnKVOs8+E3IasTw0UQicb3VLxOJxDzw/ng8/ku3u1KKLc6SraoLzb7kfK51q2qotpdloX55batqX43lYh1bVd9N1vv21VKCz60KXa56ujUMOHV5inNjC+RtL3jaGQMuZXUe6DzEid2d7M6km/ORTQNNwO7eaH26RclqYc9O89WSR9mwJaPeIG/0dvFzP34MPdrmCOZA0P3D3SbLXdxdvwufh7zu52RSJ+/pr5x92zRJ98Aujnbq1a56qUU0bIa7wpV207aEb526XLFeTH9fMPXtAO9+dBdaewe0d1asFnq0feXiQ4hqY5cde51lNQL5ub94kSmPSa+Vrovj8+qCvf3tJNMFfGlHIINzmzraHuGxG0VI93I2rfP9OZsp7w7w7QSgSzP51Yf6OBIocHDHGLNXrlJcTBHye2gP+Th/a5503sCWEm1mkZmAl0PDMRall3EtQl54EEBIFum0suSkTigSrp4zbNvpmFcsIIsFjFwOYUssNCQCTRP4NIGw3e0VIpOC0993HmV6B0riuDQ5b9su5zyFIxZevDTJl164ypmbyUq10m1S2eWJRb526rqrBaC2/XN5P2sl3HRNuE5sM23JZ755lrfu7W2qYtYmRKymUluL26C77MltFdP26O4e1ziypeY4LGWZqD2vNXqZvbrGzp4IX33pWl3zlWfPjnFkR9ey3TaXo/E7sWzpui0//OQ+zo4kN30FdbnEjhXnwW9wVhOtdn2Ff/ef1rw2CsVSaJojbgJBHMdODa1aVRvLVJNbtar2+Zp9yf5A5eK3EVnv21dLCb5WE1eePNTP3v72Ff19XdW6PKGslnyuRhxXG4iUxcv16ZQjhEtePX8xizGe58YpvSqePd6G5iGlZIt1+B6XEiqtvgtNiKbtkLIE5/I+ju7ZV11oWU6SxeK8Y59YSDJ6dZSZVL6SYmFakplUnrGpeYYtw6nmltE09Ei7k2QxHIM24fj3ternbiVEGpdf6DvE0zMdIGXVg2yleLxHQw/4eWxfH1ML2YYZ9AGYuAWTtwiML/Dh2QXyJYvFtB5hyhPlYmEbR546jiYEPaWIN6bGuPzGeU6PvEEv8wRwBHE67yRZhANe/CKLV1ZtHSk9SHdXD3RGS3eI0s5r+YPgD5IpGEwYWYTd0BzEshAIpObB2YWk05ymRXqFk888Di894/zf44Ftu7F37udPbun8dSrILa2tqUpfGwd2bG8vF8bmWci6Nz06c3NuxR3vwLHruLWfnksX+PzJS0vaptZqzVpu0O0mnjy6wDAdj3JttbyVWK9dj+XOawcG2+tEr2XbfPbZS4zOZupO/baEc6PJdW9WsdR5ciNNfFsrK0ns2IiNQFbL3WkppdjS3JW+6i1bVZdunbo1GGlVTZYSp721i4+ysVV1uaLcolX13eROnHxbCb5WF8S9/e2r+vslq9blgU9nT3VZeXJlNsWF2bOMiVmiWp6A7SQSmJbN9GKuKoZNw4lEa2gSgz9QFcblfORQeN3SSlp9F6cuT61sO+h6TYfBPQB817rAn119kw47R5eVJWZl6bSzJNOFSvW4bjuVq8wjNcvDUWiLYUU7+PfP3OSVGZN5U8Pv87QUKP0dQQQghWBRD7KoB7kievjBH30LHOhDy6TomZniG1/5HsWpCTozi0wu5IiFUxzb20dbyIemCfx21WKhG4KjF2dg7vvO91tuN90zwIudOn8U9SGlpMvOMmguMGwuEAloHPIViCSz1aqxEIQDXroCWtUipXucCnkpy3x2IkmGIiEhyQofWUqTbaXEi0VAWvg1RyDr0sLpoSfw6BoCHGuFbG4OgmnCtYto1y7yEeAjwLwWcFpLlybnXfD1UvAG66xFp2/OuSQpO0zM5/j0l19dcfzasb29dEX9TZ5Vw7SX9aw2NvcoRy5K4O2lrn23PfEWRyhREypUFk7LifUyy53X3nd8F+duzVcmA9oSbkynXOdnF10i9NaDVufJjTTxba2sNLFjs/uhVxutJnAm5P4wMAS4JevLRCLx7nVYN8V9wD3vq65pTpe0QAgaYpPXv1W1373ByF1qYwx37+S7Xq1nV1211rTKhMzYAxqnLxjkDQuPtIjYBbo1g3fv2gHtXiflolW0X3lA1FBNJRiqiuPaLntrwDXTeIXbwe02sykh4w2xYAe54e0EIODRePgdh6DXWxX9i0mnOupGqco+fvoc266MM2BJ8prHaRiSi/CX/6vArZtJ8pYHhCBvWEzM59jRE+HWnFOF9+oah4c7OL6/r2KxODWW40uFPvKhbghKonaBbSJLR2c3+/qLhGfyZFJpbFuiaYJY2Omoh1F0uuBN3qqs4rsyJvlsjjEcH/JNT4xL4WFO/OijiL3dHJid5uKr50iN3GSbtUC/mULUWB0kMJ81yCRzhP0eoh7BpO4jb3soaB4sBH5p0WHnQAoM4SFVupQJKZ3cYywC0sQnTbwSp5G0puHTNYSUzgQ9F3tFh53neP4mx/PV1tnjvhh9J89x49VBzKtFEB0g3AddC9niquLXdE3wCz90mE9/+dWm1BU3z6rXo7G9O1pXfX50dw///I9f5M0bVZH+ndNj7OqN8ts/92TdpL7ye6524m0jq51gttR57dpkCqNBnLUKKvJ5tHXx7N6VAs8GoVVHwaYsZ11jZ49Ls6ZNwmqi1fzAXwDvxLGFSeoj5GXNcoUC2OB91ZdsVV3AtcHIkq2qS9XnpvfxNvuSy97kTdpcZD1bz66VemEJ+UCUyFAHh95xnMpstEK+arHIln+m3b9H267aZmpbCFUSUaL11eQ1TMBcyXZoHED6PBpej4Zh2q6z9x99YIfzefuGqm9kFKvCuGy1SC2CdD73XLpQaUEdsE0G7UWEuUjodJIfmc9hCI2kHmJOCzFfDPIDjzzI0Dv3cXU647rOdQJICFJ6gHME+H7bXg48tY/H3/VjvHr6GtPXbrLLW2Cfv4g2N+3ctWmgL+ThQV+Wocw8dsERzxFCPHY9D6le9K5eDp04Cu95p3P8WCbMTMHUGPbkGC+cfBXPwiK2bVeyjsuT9HyyekFfFH40ATnhXAbDmLRZOfLCSx5vZYaBXu6eh0W7JvFahtOS2qODcGwltmWVmk03M1BMwotPswv49zi5yZd93ZVot/O+Xib1KLommnzEK6m2vXVPLz6PVieGPbrgJx/fy5mRJG/emK2IQ8Oy+a2vvYZpyYplYaAjyPXpdNOF+9pUio//3kl+9+efatkCeqUTbxtZzwlmK3k/cA6RQ8Ox2/bsuh2f5VzhfQPt950wdhvAHxjqQNo2b44k6+wpXz11neP73e8obHRWU7L6FeAHcJpu/DbO5eI3gP+KI5D/DfAc8DPruoaKTc2mDOjWdKdCGGyoCN5uq2pcWlWX498CDRXl22hVfbdYbRV6vavWKxLYpW1qdXQ7lZx0kD19uzg2FKk2Dik/8jn3N7JMR1Auztcv9/mr4rgslFdgtVhuOzQOIAumXckqLbPc7H28Pujucx6Vz2FBegEWkvi815gbf5NoMY2nJJB1XdDTHmQmlUdaNj1mmh7SeHTB8QmDYfMyJ6JtkInB9WS1eYjXt6wNRtc13vrwHnh4T3V9ypP0yukVs1MwO41WzHNsbx/TizkWs0XaQk4VWZsag6maODCvr5SDXOqkt2MPL/mG+bcBH5ZWqES87bQX+ZFhDwOZBWZTeWZTBSQShMAG/FhOV7xYhEIxQDKdJ4/TQS8kDdrsfMleASIcoCvsd/aJ0sRcrVhA2haWFBhopaqQRC89Gr8dHxaHi5McLlbbmie1IBd8PVz095UykHvIav4VicNXrk43VUYN0+b16zO879hOzo4ksUsVPCkhU6h+R3nD4sZMpmUFa2QmverChZt4cvMMr9cEs5V0rtvVG+Vn37m/pfVjNbgdn+VunoGaO5/lv93s1eNW59kXL01yfuzVOnvKhY1S6FoDqxHDPwG8kkgkfh0gHo8DkEgkJoAvxOPxl3CyiP8PnAGwQnF/BXQv16q66OJLLuTq7tlZUnJpfIGJZJb+WIh9A+3o+Vyzp7W2VXVtRXmLtapejuWEZdkL+ZlvnmUuXcAw7eZKzqG9zkXKMquV43I1OZNyBkBulFNKkjPVZUJAMNw8aS8QXPH3tpLbzI2z91eCJTROTRpcmbDZtfNBRqcjXBhN4itk6dMK7A1aDEd0fOECpLNOl69Ss5DBzrBTVS4PCkavVV84FOGxaAc/Glnk9aTNpO3H8gWWb0Nbm2KxvSSSSwJZm5umr/Rgdqqu02EFowiTYzA5hi1hejGHOZ3lb2adiXqTnijnfL28oO1gcmiInW1eDnhzXHj5NNbELYaMedrsPAKIBLwMd0W4NZfBShfw4mz/otCZ04JIITDR6e5oA82EvF3pximAhVSOdCpTN0FPlza20DABZL1AbiRm5ziRv8mJGnvFiLeDydg2Hr1lQ+AADO1wtVxdmVhs8nAWTJunT98imSk2Ta5rxG41wRgnmWK1hYtW+byvXJ2+IxPMal/nmTNjTY16Al6dv/MDB9ZNoC11fFbaM1+c5M9OXb939sB1otEO8lNP7K2sv5s9ZcMXupZgNWJ4D/Dfav4vgcp9wkQicTUej/858HdQYlhR4n4N6G5C1x0RFGyYyFTTqtrK5fjtr5xiYmIW3SgQ0mG4M8zPvHM/eqNQWq5VtVuDkU1QTb6blG9nnhlJ1gmCVpUcvTzxqq2j/oWKhfpEi3IEnJvVQkrnd9k0TE9Ul+t6c6JFOOraOXGlt5lXM6B0jUcabOdXPnCUq5MpTp4b59tzGb4+auP3dbJzp4d3bQ+yN2RzKApaat753G5kHWvJ3wynebDkVZ7M2dy46eHszSAv+SP81fAAv/az70D31O+jrt5LN4GcTddnINcIZFvCqcuTJDNFLFsyBAyZC1Ca/2oIndkXo1wWYU4F2mkb2sa7fuSH+d6FSfxGjnf1aRz05xHT4/jNq2hzmTqBaAvH/BD1acTCfqA0IBaaM6HWNPAVTcyMh4L0UD5iNWkTwHKag+AIZAkUEGiahpQSIWVLe8U2Y55tU/PwhTedBV6fs0121cS7dfawp78Nn0drunvwnTPjy+4X0Lr1OTjZw2spXLgNUu/kBLPy6xzb28tcunmOynpeb5Y7PguGxXfPjd8Re6Db8QJ3pgK93Hyf+6rQxerEsAHUDs9TQE/D39wAfvx2V0px/3C/xMusmZpq8qmJAk/P+8jrvaCX2vhmJA9ZbTw6FK36kov59WlVXbZfbOJW1bdD+XbmUpWxFV2kyncDYt3VZVI6MW+14jiTcv9ewLlzsEKrxbFdXXUDyLJn2LTkmi/wbt79C2MLaEKwt7+NP33uSl3b2OuL0Ld/Lw/UbhPDqCZUlCfrpRewLZtvvTFayT/WNIEtJT0SenBEqefCFW798XW27x52rBWlRItf+18XOTu22LJ61nTxf2i387sagXzp9GXeOJshJhcJUm9X0oVASIv+4jz9zEPhFnrqHHvMMzy5ezt09ThWi64eiL6DTluS+IPvMH/9OoPGAkPmAoPmIp1eyYPbO6uWh3KF1rZB04l0dzOStkkbEks4E/Ta7Dw5vGR1b2Wf8eD4j2NeCAoLu1DAtgW2pqNrAoF0XtNtoGUU4co551GmrYPjO/fz94o+ni+2c9HXS1Zrfbx7dQ0hwLYlpi0JlAZFEifWraaPBn6Pti4e2/ViJZPW7sb1Zjlbht/r1P7X2x7YakCLEFxYhwp04/a1bbmkoL/fCl2rEcOjOAkSZS4Cb2v4m6PA3O2ulOL+4n6Il1kPGm+vWUJnzobzWQ+P9g9X/3C9W1WXq8mNvmS/f92ixO4lrS6SK7EbwBovUkJUbpPT01+zMla9OF6D1UIXgk8fDHG+38+NDPQP9fLAwR28Mpbm6lRqTRf4pbz7Uq7wwu31Ot7crpqLnW3x6utX+PNXLcJ6mk6RJWblnK5uNViWZGEx63zO0mcdn81w5NwEg/iZ00MkC0Hmri/y8rkhjj+wbfkkmtLg4eXrNl8KmcigJCKL9Jop+qw0R9tteuwM09P1FiTLlkxOz9MX9jg+5Mrn86HHuvm1h3o4s/sQz06ajPgi7Dw8wAO9XrSZCcezPHnLqfrXHItCCNrawizOZRA49ooZLYRfWhhCAwQBaRKxC2Q1P+1tYW5lC+R1E49mEpQWIWER0WyENJzjstz4p5xe4WZnWJxHvPES7wPeB9jAiCdWNznvurcTW2hEAh6Kpo1h2ng9Gv1tAX7hPYc5XopW+/zJS5wZmaMj5Ge4O8L+DTQZrHZfyBsWHk2wrTvSlHZxN1IeyoL7xYuT/JdvnGFqIV8xvmgCDgx18PbDA80d/m6zauo2oD07Oo8Q1aiztVag3Y61jrBvyQmd91uhazVi+HvAD9b8/6vAv47H478HfBlnEt0PAn+8XiunUNxPrPi20lKtqi3T3ZdcKLRuLrJkNdmtVXVg01STlxJMK51lvq639nR9CatFTaJFWTC7ZV1LiZbLcNgPh/1AbgReHeGErnMiEgEKMJ6rRsD53BIu63HbFromMCybff3ta7/dqemcTwnOa53IYGdl/aOyQMzK0WlliNk5+sgRi9Sv51y6gG3ZdJCjw3ImL4o8hL4zBePbuJrV0C9P0S0DzOlBskWf64W+9rOlhZ+0z8+Yt48fef9RbCn53P94gfb8An1Wmh4rTZ+ZYmI+R+HyJMf29lXbYBtFmBpDnxrjIeAhALxwswcyPU4O8pFj8MR7HP/03IwjjqfGYWqMUO4mWjJbtVgIQUFUL7FZ4SUnPLR7YV7zM2laBIVElzYLmoeUEIiOEBGv7uwvpQ56TvfM0rQ8ITAtu2KvaJQdGrDDTLLDTPLD2QsA5IWHS75uLvn7OONxso+nZZj5TBGt9OEbs6UXskU++tS+JmGzVrF5uyK1UQiatmxKu7ibMZ665nQxXMgadQ5wXdN437GdHN/Xt+5VU7cBrdtdr7UM7t2E9ly60NTQpfG8cD8VulYjhv8Y2BaPx3eWutH9R+C9wN/F8QkL4DLwz9Z3FRWK+4N1ua1UifmK1C+/E62qG33J5Xi4DdSqeqnovsbbmV5dozPiI+T3uraMvqNUrBZd1WVSQj5bX0Eud9lzw7IgteA8Gl+7tnlI2Ztc4yEvb4tzo8mKHcK0JV9+4RoHhjo4MNheaa262m3SJLSFIKMFyHtDjFixyuv97AcfgvR8JfItmgF9ZK7SUQ+cRItYxA+5DLmbcxzOzHKo9LuC5iGphci+akL4QSfNIhxt+p49mmAgFuLR3T3ommDb9n7OjPi5anVXtntEFhkmS0dXDwcCBsxNuaeJmEZJ8NZUkD1eRxh39jj2iqMnINpBu2Xx9O9/k/zoCD35JNvtRXqtNFJKbCkRCIJ+D7aucWu+gETD0PxkhBeftDCETlj4iGi2U4UPBICSJcSywChQzGYxsjl8mIDARuC8sjMxT3NJrwhIkyOFCY4UJvhAadmsFuK8r5fAt65z4eYhbowskLec/aVVdXGtYnM9ROqViUXXQW1t2sXtxHiuRay7iVPTsrk+neLxg/3rXjV1G9CWbS+NGdMrHdyXP/dXXrzWtH0N06avI8h8pnhf2CCWYzXtmJ8Gnq75fzYejz+BI4j3AteBryUSiRamOYVia3NHbyst1araNJrzkgs5x1KxVKvqbKba1atMq1bVvsCacndvl+Wi+9y2N7Axbu2VkyeCYaixI1daCjfmIxddOiJCjdVitn55TaqFHo7wqQ88yGdfGuWLz12tTJjKGxYXbs3zK+9/BE2INW2TVjmk7zu2k+vTDbaOUAh6BwHYflTy3wrfY3JknHAhTR95DkUlg91RkDadET+6Lipi2W+bDIsU+3Pj8ErJCqR70Ns6+NcPtvOb04ucL8CMDDA2l+FffP4lPvXR43zqo8f5zT97je+cHqts97TwcwE/34/u5sBT+0oe5Expgp4T8cbctDNYaaSFQNZj3fzy0R5O7zvCsxMmb/rDPLWvF//8JGdfPkM0Nc1u0uTnZp1ot/LXLTRyQkMTgkAoCCEf0jRIpXJkLEnYoxEVJsLvJ23pzBa9ICU+nPbSfuk0CBHSwoaKHBZQEciNdNlZnshfh9euw2t/wecQ3CzZK8od9K6Nz9eJyLWKzfXImt/T3+Y60a827WKtMZ5rFevLxgmuompaK8Z39UZBOGkNtcLc9Thr4RleiWBttJ404vfq/MIPHV7zeWGzcVutsRKJhAl8aZ3WRaG477knt5U8XucRbugOtN6tqnXdxZdcai5yh6rJa70greY7uOvdpnTdie5rjO8zis2JFplU6+8ql3EeM06erQ7svz7DiUySRREgpflJaX7Stp/rUyk+8tS+Ne2XSw3yHj/Yv+TzPvkzTzQ9T8OG1CID83NkF18gMzlFtJghoNnVmLcylgnJGSYv32DX5DjbLIkUsKgFSGUjnHvG4sEje/iB/d1NHs6yTcSyZcmDXLrjsm2X8wflSZKzKxTI0+MwNU7u8iR7M0UKUuPSS1HGtTATWoQpz3aSWgh/zGTQrE7OGzbnabcLRAJeOsJ+JHBuMlPXcjri93CoN4SHArmsxGOb+G2bjOYnVRa/pe55nX6BX5qIYgGk7UhhTWBZNkj37GMdyS5zjl3mHD+aPe9s2j/9Gry8D3buh137GZvyrUlsrkfW/LG9vWzrjnBtqj7RpDbtwrVy6tEYmUnzwsXJyj652oliS63TelghGkWpEKXuZZImYe4WWffy5Sm+G3GSQ55aoo12I42DlFrKKTvl17ofbBDLcff6xG4EkjPOyVP3OlUs3eNE4+ieDXXrV6G4KyzZqtpo9iXn862rk1CaPObSqloIZ7Jeoy85ELztVtV3ekbzPW8nXovXBx1dzqOMlE61v3HSXi7jWvXvi/rppEjUrAYDeXWNx2YkvDHfnI+8wri+tQ7y3J+nQ3sMvT3G3/ml3c7Ff2KBvR0eHu3SnZi3cle9krWhtqOekNBu5enI5ZFnXoX0DY5J+PvMcD4PUwRJ6kFm9RBffv4qZ0eS7t9n7SRJV4FcI5JLAnl6MVeJePNg0VOcp4f5kv/YiXmb0cNM6lGm9Ahn/P2kPGE+8tgQP7U3hJgZZ+TsRaaK8wRK358tJemCybwp6Ii1483YpPMGaeEjKJ30DIkggAlCQ0QieAMlf7ZllibWFtCNPLJQQFoWUtMQQjiiy7ZBNvuPdaMAF087D+ADwNv1cN3kvJFQ/7K35NcjgkvXBL/9c0/yv//373JzJo0tnUi42mO98VwghOOp/fbpMZ67MMnBoQ4++eHHmvzRy00UW2qd1uNOX6MolbLaxteZJJfkxUuTPH6gv+54cTs3zaULHN9Xb21pNZBvNcH4kZ1dvP/4rvu6CuzG1hLDuWzrWd267lTPdE+pklYjlD1eJZYVWwtvacC4Hq2q5RKtqj2eZl+yP7jiVtV3ekbzhm4nDiWrRblbYkO3uboOe45Q3tkD/bEQE8kspmXj0TX6YyF2dwZhftZ51BIMNecjB0IrOh8WTZvPn7zE2ZEkh7fF+PCT++pm/q8Ed7G8o/rPQh4Wk+jhK9yafJ1IMU3UziNkjf8YZ5b/3zzczbYbs5y5OVZJLiwKD6l0mIt/neXQ4V2ODznS1vrzuQlkcAYic9OcfeY1rugWvTJFSDZ3pfRKiwFzkUHTsXlomiAaCfE2O4822wvdfZzqD/PfbvQTlYVS9XiBYXOBPssmhtNSeD5TIFMwmEsVyBsWtpQUhA/p19kWrTl28pbTNjrQjhAxhJROFbtYcK6FxQLCKDqBFVDyH4OQziS9RrqtDE/mrvFkzmm6YiMQX/hmKfu4lH88MFyXUrNeA1ZdE7SH/XiSWYqm7Uz+E/URfA9u7+TwthjjcxlOnp+sTP4qH7efP3lpTRPFllont0FgrmjxH772OhfG5jkw2ME/+rGHCfrcB5bLpd4UTZvPfONsU8XX7dx0+uYcn332Ih97+36AVecEB0ptsq9MOPtnbTX9xYuTfPfcOBJ4+yoq0JuFrSWGzSVa5lrW0hONNL0qkD01VeWycL4PIqoUimVp1aoaqq2qa33JhbxTmWqFaYLpUk12a1XtCziCuaGafCetJ/eqnfhtWzN0vdrdrQbNKPK+I4ucPn+TqbEptgdhX7uOZreyWpRTSCZrXkRrbh4SjtalWhRNmw//h2+SzjvxY69dn+V/nrrO5//Re1YtiJfEH4CeAQ6+s58/vOXhuVvzWMUifVqBRzp1Bh4exF6c59b1WyRTOdI5oy7C2ydNugsLGBfPMjp2hbl0gVhbkKGdw2jtpXbT7TEnHWSpuxihCFYgzJ+kR7gWeRCAsF2g10zTa6XotdL0mmnC0jkWYmE/3W2BaqvpmQmYcZq0PLGQw5dOMiFCTOkRRj0dnI3sYNcH3saOPj9iaozY9DixyVsMTU0wv5ghWzAJ+T10hP31Fd5AyDnGinmnSYi0wDKcOzXhCM4EPRthFB1bRbHgHK+W4QxiRTnezT37WEPCrevO4+Q3St9JEHburYhjfdf+daugXrg1X5ksVjRtLrTo9tYR9jUlLRQMi7MjyeZEhoaJYj6Ptnz3xCXIFS0++H99vZLZPDGf4+S5cb70T3/EVRCvJPVmNlVoGoC7TuCzJV/83hXOjiR572M7V50T7NEFX3r+al3b7E9++DF+7Y9f5I2bc5WbTc+cGePB7Z28//iuJm/zZmVrieHegdLF13B+WqV/Wy0yHGuxLSharW8Ta5pzsvQ2CuXS/1V3MMX9jtfnPFyryW6tqpeoJtul2/8tW1U3+JLLzUXWuVX1veiydEetGV4femc3Dz/e0EBkFVYLbNvpjNjYHdHrrSRa/PnZWfRMCl3zYQnn3JfOm3z+5CX+9jsP3N5ncKHVHQKAX/3ci1wcs/EXMvSIPG3+EDErS8zK4pMWui64MZ3i9M25in+45+Ys735ouBq7Rql1dFkYt3c6/64ZAJy6PMXYXHXCaUbzc83n5xpVW0tZIA/KNA/ZNj/eF0bLZyvtpBezRaJBL90hHU8mxaC5iKYJYrqPY6/OYMe6uVL0cdUI0L3rcR75sV3EFpPEaiLemJ2qFnaEqN7lKROKlmLbis51yzSd7zkQKh27AmmZ5LNZ7HwBnzTxmMXqtDwhAOkco9Ll+C3k4MKbzqOEFuvmxK79nNh1AMR+MDsq226lA79WA1O3bm9z6ULThDufR+PwtliTT7Z2otjliQVOnptgPJnlc89eWtOx9x++9npd8xIAS8Jv/c/X+OcfekvT3x/b28uBwfY6sdmIYdk8c2asbhu16jxoljzQsbPjSw7kG48Zw7L50vNX65rvlKvpZ0fn69bNlvDmjTmnqVGNcF5qO931uRerZGuJ4YBLNQtK0TVmVSjX/rssmpcVyzbYxdY2DCFq7Beln3qDHUOhuB/RlmlVXVtFLgtmY4m7OJVW1Q3tgTXR4Eu+/VbV96LL0l23ZrSyWtilRJHGfOSCi90FnO9kYQ4W5rAvXOPxnFPtz2m+ymS9iYsaHBty3svFirDaC6bb39duoxcuTjrb0pRk9RBJQmjBTnRNwzQtOnWTfWGb3PQMnXqWGDlCdpHJhRy3ZtNs6y5HGEpILziPWzUrEAhVqsfTFxfxFHMUROtBWa1A/r7U6X/oKMeGwvz2H32b9OQYnYUF+mWaoYDj3UzljEr1GNPk1POvk8wUsW3J9POCFyNBTpx4EK2rB7bthkeOO1X6cgby5C1nYt/cTPUaJkp3XXw1OeaRNqd6bBSRQmNsNgWFAgWhYwsfAX+EoXY/ouQ/rtgsypdFIZx/S9v1WinKzVZeec5ZoGkwtAN7536+POHhmWyUy7QhNEFPW5DP/H/e0VRFbTUwdev2Vm4sUnsbwOvR+MnH93J2JNl0PNfe8v/T567e1rF3YWzedfkLl6aqkzVr0DXB+47v4lxN1bsRTcB3z01gWlXh+Rs/dQyviximtD1EafssNZCvvav2uWcvNb1WvlRNd8szlqy82ceGmnvRAqXAoEaoeoGg+99YpnOyLwvluv8by4vl8oV/ObHcaL+orTYrFPcTNa2qm5ITLMvdl7xkq2rZurmI19vsSy43F1mimnwvuizdK2tGE5q71QLDqBfH5UqyVe3INhALMTLjiOGgXSRoF+klxWOaBt8/WWO1qOYjW8EIn/gfr6/4grmSC6zbtpQSnjrUz7buCLv72vjO6Vu8kKpWeP22QaedBRnmY0M9jsBPp8Aloox81nlM3uKRmTQfSI9TQCeph5yuelqIOT3IghZENuxnecPimTNjPH0GTs55MbzbwLsNgE7d5FePDvNQ1HYqvXPTTE7MVCbnlT//YjrH9JVr9JUsFgDoOnZHN1cMP1eNIN3738kjh3egJ6dLArkUC1d7x6VmMu18psAtO4DQvQSlgUfaZAxJrmAS8ujQ1u7sG5KK77jSJMQoYiNKlVFZiXdr+vZsG0auoY1c4yeAnwAywsdFXw/n53v5rU+e5//8Rz+Nr7Oz8pRWA1O3bm9ej9Z0STYtyevXZ5Y8ntfj2Dsw2MHEfHNmtWnZLcXitckURgsh7PM4fuZG//MXn7tcl9Fdi9+r88TBfl66PEVth3KPLnh0d4/rc3b1Rp0xTc1LCgEHhzs4fXOuKdaukaW204afe8EqxHA8Hu9NJBJTd3JlNjT6MtXbSjXZdPxWtZXl9RLLbkK59t/rfItYobhn6Hp1olIt5eOk0ZdcyK+wmrxMq2pf+d/VVtV3Ow7vXlgzVoXXW7IJdNYvz2crAvlYZx/PjTyHXshVJmIFvDqPlSvqLlaLG1MpBs7fIiC9pDQ/i0aA8ZtZXr4wxvFDQ02rsZILbKtt+Y4HBit/8/SZsbrXLWhexrV2bsUG4dGjzkLTcJqdLCQrTUNYnHeq5w34pEWfmaLPrN65sITGvBaoE8lJPcjJ8xOu1cCk5eG03cZDD+2rLHv6W2/ynclX6TWrnfQidpHFbJG+9moRxzYtXnr+DaeCLCVTzwleiAR424kH0bp7Ye8heOwdzv4+O1kjkMchvUimYDpd9IRGWlQHCWF/iFB7wDn2bKuU/2U7ueOhsHO82DaphRSFTBa/NAlIE2dqntOyWhMCaZdbhdQTlkWOFm5xtHALUsCvfh3Z2YsoTc7Td+3nUz/xCKduLjRZYRpFckfY1yRIa8Vaq+N5PY69f/RjD/Ps2fGmoZMtaSkWWzXUeOqQE0347dMN+2gL/zNUUzY0TTQJbMO0eeXqtPu5rBzpVrtMwp6+drqifiYXWtwRKrHUdtowA/wlWE25cSQej38V+Ewikfj2HVqfzUtZLLfqjGpZ1Spyrf2iXF1u5Z0sU54FbBqAS6ckIRoSMVzsGEosKzY7tdXkRlq1qi4WlqgmL9Wq2tfsS74LrarvhTVjXSjH9HX14gU+fvBhvvDsBa5en+ChHh8/dqgLTz7j+JJdur1NL+QQpkEMg5jlfB+iADyfhtSOpkl7l8aSrpFYl0sz4csNDA4MdSzZlODthwd45sxY3S6iCSeztYyleTg1bXFlAvb07+DYkWNg27x6+iqTN8bYHTSZQ1LAU+oOV48ubbqsLF1WzX5WykOe00oCWQ8yp4UoaF5XYbFjWy9jwR6umVUPcodm8onHhyFqO1Fvs1NMjU8zly5URI0tJQupHFNXrtE/WzMRUtch1gOd3XDgCLztXeDxkXz1LN/7qxfpKc4zZC4Qtp0CzVy6SCjgI1vUCfv9zmS9aEd1cColIPHpggXdxyIBJwINmwAWXX6BbhaRhUJFCJe752lI3KZVirkpJ77u+yedVdZ0jg/tZGfnMC+/3sn//eIQD7z1QT754cd4+coU3z3r5O32dgT5ygvX6m77r0TUNh57Xo9GR9iHadk8d2FiRZPFgj6dT3zwKJ/+8qt1+5RX17jZIvP40vgCA7EQY3OZuslrv/zeRzh1eYrnGirf/lLqQ6P/2aMJfvKJPXzs7fv5wsnLTQOtomm3FKDXJlNNp0kJ/N63zvEP3nOYT3/51abqsKf0GcoNQGwp+dyzl5q20YYf4LM6MXwR527Gh+Lx+BXgM8AfJBKJ2aWfpgCcE4+uu1/EwRlpN03uq/l/q9neZaSsVqZbUakme1TWsuL+o1WrailLraobfMmF/NLHS7HoPFIN1WS3VtXl5It1OIbuhTXjTuDzaPzsuw5BpaFyDabR1DykO5bFo0/X+RM9uub4ZcuTKeemgdLY5nuXeTIrK37klObH8Ic5eW6cP33uSl2Xrl95/yNcn0q5bsvj+/o4sqOLc6NJiqaNz6NxaDhWyWt1s2O4df4aiG1jLBbDY+TptLLErBydtjNRrywoy5SbKrRZedqsPDuNucrvil4/oe4ejpkDMF504t5CYR7d3dPkETV9AQ4dP4qlCSf6KjnOFWMCIjP0maUUCytNxC4wkczS31Ezb8aynASLBovFofZuvhvw8qoY5q+CBzCFzqC1yJC1wPbZRfqKaYJYRAJeDg3HEA2D00B7J8bIFORyCCHREPh0DV9blLm8zayVr+meZxKQFj5pUv5UEtDAtbU0toUYuUL/yBX+VmlR+ns+rrcNkm0bolhs57TWTT4QcTzDUCculxtQlo+9Fy9N8plvnGU2VWBiPse//tIrLRthuPHEwQEe2nGzrpGGZds8fXqM55fIPB7sDPPkoX729rcv2Xnu4FAHH35yX5P/+cBgO/sG2vnCycuYtt00wW4pAbqnv60pYg5gcj4HCB7c3lnXzh2can9fR4Cf/8FD/M+Xb/Bvv/Kaq2VpMwzwhVzu9n0N8Xj8ceAf4IjiIFAAvoxTLX52Fa8jARKJxKpWdktj2zWV5cbJfsbSsXArpVXWcnmZEsuK+421tKpeCp+/5EtumMh3D1pVbzYsW/Ibf/hdxkYn8ReydGoGBzt0PvKWwaaWwlenUvzlKzebLtxtQR+LRZsk5Ul7AYq+EPEPHne1WtS+d6vBxwsXJ/n0l19t6l4npVN1LRPw6hWvdGMFzW8bxOycI5LtLJ1WlrZSHnLta27vjrCzN8pgZ5hanWV7vDw3XuAbV9NMiyBzeohFLYDP5+VX3v8IX33x2pJpBCG7yN/aFeTnjnZVG4Zk001/Z0s4dXmSuXShUiW00JjWw0x5nEYhU1oYIWAHGX7uSBu7RRqmJ+oGlraU3JrLkMoZRINehtoDaMU86cU0U/M5PNJElzZFoWMKHUrd88rWCr808ZTksSaEsy2kXPExOa5HuRToI7D/MPO9O2g/eIi3Hhxa8YDS7TuvJeDV+dUPHF22zfOpy1M8c2asyQ4T8Op84MQuvvzCtaacX7fXbbV/1i7f2RPlq6eu1w3QPLpjlSiuIPHBsiUf+Y9/zXym2ar5rgcH+eX3PsJnn73IF793pW7/XulnWeoYu4u0fMNVzcpKJBLPAc/F4/F/CPwsjjD+MPDT8Xj8IvBfgD9MJBLJJV5GsRY0DTRf61u0FbFca7+o8SyvRCyvOGvZW19lVlnLis3Kkq2qC82+5Hxu6WOkPJmIhfrlta2qfTWWizvYqnqzoWuC3/jZp1zaM8tKAxErneLipVFevbHgOsO9PeQllcsQw1yR1YJgGDRtSV+4m9/RcrHdFAyLJw8NYNl2k2CwvT6mbC8TnpqZ/NKiw3aqyH0iz6EoPH441pT5bEv41vevMbWQY5+EsovYFoIFLcj400nMsQLdOJ5kUzSfh/O6j0PHH4ED/ZX1//7Zm0xdvcFeX5H9/iJacprp8emS17jme8Gm30rRb9V4oNGY0cOMZyPsfvw4VnsXb1ydYu76DXaR5tb5S3jn82BbpPMGqZzBoeEY4VCEvJkknTfQbJOwNPFJA4FTCTY0D1n8GEJDlzZBLLoCAr9tQrGAbZpOaAWOqhEt7BUDVoqBTApevews+KYOw7tqmoPsh97Blsfeco0wGu04btaJ8j51ZWKxybvbyvPbykfbav+sXf7CxUkuNHjoA+h88G278erasgJU1wSP7Ozi6TPjLX/v0bSmfX+ln+Vuz71YLWuKKEgkEgvA7wC/U6oW/zzwk8BvAZ+Ox+NfBP5TIpF4ed3WVLE0y4nlso2icXKfylpWKJrRNEewBoJArP53rVpVG0tUk5dqVe3zNfuS/YFSus3Wwv2CKSAcddIm/uom50aLFMwBPOFeInaBqF2gzc7TKYr0dbcxMZ9bkdUCcL7nYKiSaFHXZa/ESpoigHMLem9JFDXevnab0GUJnVk9zLY923lPqf2thnSsI4vJymS90Ss3mUnlm/ycmpR02VmiUyMczTivLQWktABJLVjyIYdI+yLs2t67pO3j4FAnn/roj/L0d07z7cnX6DHT9JWahUTsWo9v6XvCZlCm2ZsawX5+hpcvTzKbMZkUIV7xRZnQBpmJ7sErLfqtNDvlIp2al36ZrXTQyxZMckWT6VRpYpaU+LAI2QY+aaIL8GsCXyjkXNc0jVw2z+J8qlJFrrVXlPaUlvYKbl52Hs/8hbMsFIadNZ3zdu2HSBuWLTFtG70hp7jxu26047Squrbyy7bKPF6rj7bVJDWvrvGRp/a1eFY97zg8yDNnx5sSJZ46NHBXP8u9YD3yumaBJJDHsU74cKrGPxOPx78G/L1EIjG3xPMVdwO38PVa7nnWsleJZcXmoGWratuJfnNrMNKqmixlqb21ywCzsVV1uaK8wlbV9xvl9IiyZ9EUOvN6iHndEa67eqM89rNP8vXPnmR8dBJfIUeXZnCgQ2dnbxu2lFyfTjG9kKOnPcjOnihYNtevjTO9cLWyTBPU+M+jHAtFONbj4dUpi3QLi3l5Bn+58tbo+bZtyae+/GpTNdvn0Xj/8V014l84MYPRdhjaCcB3ixf50pU2x4NcY7MI204FdyaVr6QAiBof8i4zyY6eqGO76LPQTj0DbTFOz9uM3pwgbzmTqmtTOHZs62Ui1MN1o5oUEsXgo0c6SN4YpTA5QWdxkXaKxMJO/vH0Yo5kpoi0Jb2k6M2lOFx6roVgRo8w5YnwRt9h+p98EGEUiM1MEpscY/LSFebSN0vpFYIiHkyPl86In1g0QHvQw+JCGjObJSBsgh6NgkcjY/lIihC2dLoIBmo8yF5plbckmiacNBO3a1c2A2dfdR4lcu09vEonBa2bvZ4ervi6MUqVdk1UPcPlyW6NDSoao8IsW2Lbko6wj7l0oa5BhZvn93Z8tOsxSe34/j4e2t7J2dF5DMvGq2scHu7geOkzrca/vNE8wcuxJjEcj8e9wAeBXwDejrPfXQQ+CfwB8AjwT4EfB34Xx0qh2MisJWu5sbq8nlnLjZP7VNayYqNTk9VKQ2zy+req9rs3GLkPjpFWjTeWu3X95KEBfB6Nf+litZC2zSf/6FnGRxfwFy06RZL97Qv4bYOJZBbTsvHoGv2xEO9/bBeaZTrRaYvz6MAn9sL19ixnJrN8fyLHnPSR0gKkND8Fj58PPbGXj719f6Uq2FjhtmzJ4eGOOl+vJuDQcGxZwbBnoB3LH2LU8DPq7ags90nHCtJpZemys3RYOdrtPEJKhIDe9iCPH+x3xH0hB1M5mBpD3JjjR5OzFIVeSbCYL4YYuz7Ke9/1cJPY2TPUxY//+HGAynbtjPl4SwdoyWluvHiGBRaJ0Dyg05H0WSkGZZqHJ0z45iXHThfrgs4eun/oAH/ovcbY+BzdhXm2yxSHvDn29DkT8s6NOpYKW2poQicS8HJwTw/FqSTFhRQ+LHQhsaVgUQtQLPmPg1h0+wV+aZbSZBr2mxbpTcGFaR5nmse5AICBxlVfF/b2feh7D3LB10vP3t1cmljkc89eqnturS3AsiUvXpzkM988y1y6QNF0hGVfR5Bf+KHDlSYftYOmnb1RkPCFk5fZ1RsFwapaHR/b28uBoY66iaAHVilIdU3w6Y+daOntXWpy72af9LuqM2c8Ht+L4xP+O0AXYAFfBRKJROJbNX/6NPB0PB7/H8CPrMeKKjYAq8larp3ct5asZbdIQ5W1rNisLNmquoBrg5ElW1WXqs9N7+Nt9iWXvcl38dhYa+vVpRppLGVXCJQsCuButXjh8gyvThbIEwGfkzbyckHDIy18vjxtdp6oXSCZMnh4LsfervqCgCZgd2+UnT1RitlrTCSTmEZJQIdDvD/cjnYh59gsyr7kQLCyzcsi48WLk3z3nOPJfOrwQF3ns1Y0VuPKt++LwsOkp41JTxsCeOcDg+SlRaiY4cmhAA+2a2ipUh5yTUOUzogfXRd4LYteM00vaTy64LGxIvpfXeLT29u51KVxveCnZ9sgDx/ZW1nHJgvL8A5s/zb+eOZVRCFHr5V2LBZ2mn47TcTMO+2kS1VkwNnnZ6dgdgod+CchmOotMCFChAcfZPehvYhwmDcvjfHNiZfp8SQZMhcIySLpvMFC3qJvoIdzlofZvIEtJR5pEZIGQdvAIyQBDXxBP3ijznWhPAeg9kGpYiwEslQ9bvwmvNgcKE7D5Wm4/Bz7AcJRDvbswJMOcFrv4YKvl5QeqFRhy/vwmYbObYZlM7OY59L4QsWyUt5Xj+3trez35fQJgXOo6wK624L84EPD7B9sX/pYkrJymXWS7pqvua2OzcblP/XEXtf3WYl/eTOymqYbfw38AM53NIZTBf6viURibImnfR94/22toWLzoLKWFYrVoek1rZBruN1W1dS3qrYQXJjJcWPRZKC/kyP7h9CDQaeivM72pNtpvbpUI41y5euN67N12RKaYNkKmFtV2bBsDAQ5PciCXhW/O7v2s/fE9vrueqUIOM22ef9juxy7xWKOnraStSKXcSb5TdfGlJWsFiVxrIcjPL6nk8cP9q98Y9JcjTMsu2nmvt+r884HB92FiG07yRELc7CQZLA7SdtYmoX5VKU9cHc0wGBnGGwbbTHJAeCABxifgvHXHHHfHnNi3tqc9tOOn75WrMNNw8dkqAdjsJ23Ht/F6K0ZDgSKPBix0ZKlFItM/b6pCehv89OPBalReGkUW0Lx5hyxAtzytvOaf5Cc8BKSBh/sC/G2DpuDPh8LyRTZgknQ5wGB0yXP76Ej5EMYxaqf37ach98P4QhS95DL5rELeXy2iTAKCNM5rsp7aKvJeWRSdGRO193uHvO0MxEb5pERgzPjPVwencXtJoZpS774vSucHUnyqY9Wq+3PnBnjbKmiC6XgjNJzLAmTCzk+991LBJY4lk5dnuLCWHViqWHZXBhbqLNutDo23WLeystfuTq97KB2rYPfjcRqKsPvAr4DJICvJhKJlWR5fQ1HOCsUa8xaromSW++s5drJfSprWbGRWK5VddHFl1zIuTYXsaTkj56+wOhcBsO08Hp0XnsxzM+8cz96eS5Boy95Ba2qW3E7rVeX61T1vmM7OdtQcdM1jfcd27nkxdetqlyuhjVi2XZ128e6q7+wbchn0TJpdmdS7C7lI7s2bAHn/FWyWtTh89cnWoSj1Q5uLaitulm2XJ0/U9OqbbWHdiJtyTOXglyXU0QKafpEngdDwhHtuea4NaA6MBi7WV3mD0JbB3p7J5/6gQFene3h4oLF7pqM3HJ6RR3lSYzlx+xUnUCuxrsV6ZWSXqu6TkLTGdL3wsButMNHiXl9xPI5Jyt5aqw+4q1y/JSeLCUU8shCjunpJKZh4pEWi8KD9LRREDZe28QvrUq8m47teI9LldpWdzcHzQUGpxfgi2c4AnwBjcu+bs77erng6+W8r5dxvQ2EU9U/f2ueFy9O8menrjdNPFuKpY6llXR5a3Vsfv7kpabl50aTfPz3TjKezC45qL2dwe9GYjVi+FAikbiwmhdPJBKngdOrWyXFlkXTwbeUWLYbJvetIWu5/LwWgRgqa1mx4dF1JxIsGK5f3qJV9cUr44zOZSiazvFRNC1G5zJcGl/g4GDH8q2qG33J/sCSwu12Wq8uNwno2lQKs2ESmmHZ/Nmp62ilcH+3C7DbxJ/2kNe1xWzLC7imVVuE99SIPMt0qsdlcVyuJLeaG1G+TZ+cqS4Twvk+ywLZxWpRu363488sVxDztocFbwe3gLNFnT3bj3JiV6zabrr8M7XotF1upJCD6RxMj6MDbwXeqntgOgaFUvW4PQaR9vq7D4EgDG53HpXXyjuieG6a6+eucDM3TahBeOpCEAt7GCaHffEM04s5FrNFouEAvTuG0YZ2woNvdd4rn3OE8fS487qW5WzHQJB5S+M6JrZHokmbgDQJWwYRzUZKiUSQ0gIsaDqd0QAhYSOKheqdmrK9ojxt0UUge7E5VJziUHGqsmxeC3ChJI4v+Hp56Y02zt9aXLEQrmwql2OpVQJG4wS6VsemazSaaddlZrcS4rcz+N1IrFgMr1YIKxTrjqaVhPISYvleZC3XTvZTWcuKe0WLVtWv3PLwvCeNXzcJ2gZBaRCUJqNpi4OaWJ9W1eVYOK/vtma1L9epqpVv+LXrs5y/Nd+yIuWa8CAl/+bLr9Z36PI4OarllrKP7u5Z/jax7oG2DudRS7HgarVwtYNJ6fwum26wWuj14rgklnWvb83+zGUHK92lRxnLgtS8U+FeSDp2iwYfcvVvzWrFt4ymOYK4PeZso/ZO52dtDKg/UBHIz8+18UfRMD7boNcqRbyZaY51wrFBx0506vKkk4dsSzRtkdjkPMf29lUblWgadHTB9j3w0DHn2Mg5FeTp185hSycH3BYaWeEjiw9/V5iw30suV6ANk4hw7BOYpnNuDwZB7y5FjBbBKA1qyoOekiiW0rFYNNJh5zmev8nxfKm6/q2/4EOeds6XKsfnfX1c83a65kTX0ngs1VZm64SwR2u6Y7CaaDSPS7ScmxC/ncHvRmLzTz1WKMqsNWu5XF1er6xlt8l9KmtZcY/Y09+Gx+cjY+hkNEcoB7w6kYePwr7e9W1VrWkc8wV4V3ue8zN5FiwN2xdg5+DyqQmwfNWzViw3CuLlKlJuCQ+HhmN1wtujC770/NXKbHyvR8O05Npu/7pZLaSEfLZeIC9ptbCWsFpE6vORQ5EVnV9WPVjRdUdYdnRVl1V8yMm6TGSKLpM6bdv5m8WGXlyhSFUYl6vI/iC7+qIIAQXNy4gWY8QbQxNw7H1vQdvZwWuvnOcvL71ETF+glzRtdp5kpsj0Yo6+9mD1Pd1EeUcX2t5D/PliF4bpTLobsBbZbqc44PcQC/uJhRuKLeW5KuWcatNwrgM+f2mbe5xlRhGKBUSxgLTMygQ2Ueqe57bHDJsLDJsL/GDWSaYoonPV381Zb29FJE/q0crdgYCLJaaxMguOkP3g23bXJZzA6qLRBmIhxpPZZfeTVvuTYdmVQeVm8BArMazYOqw4a7nWs7yGrOXKbGUXyo1JVNay4i6xZLVViGpltxHLdPclFwqtjwPbRs9n+aW3DXBpfIHJ+Sx9HQH2DejoF95waVUdaBq8LjUrvVYsf+XFa7x2fbbu96upSLlNTPvS81crleKCaddVjdfl9m/ZDhEMQ41GxrLqbRbZ0s9W55GK1cL5/LaE69MpxrKSrv4e9u8bRo+W/MiBUJ3VYrnq+4qo8yHvcJZJ6ewfZWG8OO9UkV3aPgPVSvh4jQ/ZF6AnI3gkP8WsFiKpBVnUAkghnBll/gBnjDAveIeQXqe9tt826LPS+Lvb6RvwOgK40fIDFYG8W8LfEpMki0UMGxZ8EZJ9e2n7oaOAdAYmMxMwNe68jhDVNJiSf19KyeJ8CiObIVzM4xc2RtHEwIsWDRP0e9FMs/I9yWIe265JrGghkH1YHCxMcrAwWVmW1IJc9PVQ2LaXnoceZv/jD9QJy1YdEr26tqI7JK2i0R7d3eM6qa5xP3Hbn2oHlZvFQ6zEsEJRpi5r2QUpnYrA7WQtr7QxiWsnv2Wi7RQKF9bsMa00nojUL19Bq2pdCA4Odjie5DJLtap28yX7Aq4e/bJYBm6761Wt8P7cs5cqs/lbccdu/+p6tdlGLcVCvTguWy1qrFy2hK+8dK2al3x+hLOvn3fykkXptWtsFnooyqd+4hFO3VxY30xYIao5231D1eVGsSqMy1aL1IK7D7mYJzc2x+F8dZBjCo2kHqL4mgWhBzgYsQl6BFnTOdcWNC9T/m6CR49C+Xsp5Osn6M1NY6cWKz7jHT1RdvRAKmfQFvLR01ZAe/0F57ma5lSsDz7kbDfbqhPIMpfl3K35mvzjAJoQSNvCbxuEiwbt2QJdQd0Zg4QjiPaYI3wNo3ocGAWntXQpOkK4dc4DYnbOsVZcugmXvg1fAvqHK22lj2jdhDyQqbmRs9RxoJe89eAIaaDy/ddO0jx1eYoHt3dyeFsMXdMq3RXdBPYnP/wYnz95ibMjSdpDPp6/MEHRWtprvNFQV1aFYqWUc443RNZyzeQ+lbW8YdkokUPrmgG6VKtq02jOSy7kHEvFUq2qsxnnUcsyrarXpcJZw0raLt/1FrMVq0WNRUFKZ9uWhPGF8ze5umDiLYkPw7KZSGa5Pp1id2+05PldcB4ldOCE18eJ9ijoJkwZVcG83nenvD7o6nUeZSwL0gs1VeTST8ukM+LHowvM0ufxSJsBmWFvfhJeT3NUwv9m3+JKTjBNgJQvQld/P8d2dFRf3x+AgW3OA+c4/PU/PMnc7CixwgIDySyHQkV+YEc7TYejbTuTGxsnOHZ0weGjXEgW+Z8Tl/H58wxYKQbNBfy2CQiymuM/nhUCPdZOLFCazFcoWyxMZ/u2OZMKhS0RhlHxH9uGgZSy4jkW4CqQmRh1Hs9/m4eAL2geLnu7Oeft5UqwH6t/H8f29Lh+HculP7T6/YefrOYO157XdvVF+eqL17gwtlCXg13LZvAQKzGsUKwnK81arp3ct+asZRdU1vKG4X6JHFoV5Tsr4Wj98vVuVa3r6IEgn3p7D29MhLm2UGR4sJu3HBxe87Yti+vGZgllvHrzhKQ7wbIDKCFqsqn7eOW6zbf8WYTPJmIXiNoF2uwCD5h+dvv8ra0WRhHmZ51HLcFQ86S9YHjJ88ZS6+z6O113qq/t1bbPSAmZFAPzSVKp50hPThEtZogIs5qDjBNz9oNHhjg8lyGZLhCLGAx2zqF940vOOpdzkMu5yIEgpy5P8eZEhrxog4AzmPm6phF+5DAnevRK9ZjZaUekNyJlRSDr4ws8lF3ARjCjh3nJv52c5sErJSFZYMBMMWgtki2Yjv+4HJ9Xfp2y/7iQAzPvnPe9PmQwTM60SaXzeGwTv23glyZapWIsK+K4yV5hmxwuTHC4MAHpN2D6G3Dxc071eOd+2HUAdu6DYGjZ9IdTl6c4N5qsazV9bjRZ+X3jec3r0TAsuzLWbRTCcA8GkWtAiWGF4m6yobOWlVheT+6XyKF1YclW1UZFHFu5HOeujDExOcdw1Mu+gXYnC7kRy4JMGp00RwNwNABYk3B2ymms0OhLDgTr7ui0Em+f+uhxfvPPXuM7p5vj8Z861M8vv/eROzqQWcsAqlrRhkU9yKIeZNarE3i0ZBswis2JFplUy0GInc1y/cYk0ws5etpLTUV0rSnRgnAUfP4l1xmo66zm0QTbuiP89s89ic/TYIERAiJt6JE2fu4Xt1dsPXtiPt7S7UFLzVcqyFomxXBXmOGuhnjBsg95YqS6zOfHHi9yOLXIXKn9dEoLYFg2//npKxz7xR9A7x+u/n2xAHPTWDNT3Dh/hcz4OL1akZ62IJqAtpAPTRNIW9JjpempyUGWJYH8ff9Oeh/eA33RakLI7JQzKGzwHyMlslhg9NY0FAoEbCffOK/7WJABJAIdG780CUiTABZ6yXNcfr4ri/Pw+kvOo7x9+4fpCfbz7mSAc75erns7sYVWV7m9NL5Q55UHxz9/eWKhIpZrz2utrEWeUpXZ59EYiIW4NO4MMjbqZDolhhWKjcSqs5YbRPN6Zi3XTe5TWcur5X6JHLrjlCa1WqEon/iLFzl/a4GCAUGPyZGJPL/+3gfQjcLKWlXLJVpVezzgD2D5Avy7r1/gzHSOBdOJazw4HKsIznc+MMjzFybr7BIBr847Hhi84xfxtQyglrWLeH3NaRANVouyWLazGb7y0tWq/1jX6I+FHP9xerF5YprXx6V5E67cosf2sqgFSEtfZZ2h3tdt2pJrUyk+/nsn+d2ff6rl9nS19fTX+pANJ+6t1FWPxXnn/277RbHAdjIcMSaxCo5wtIRGUg+yWIxw9nsRjjy4x0m1KBUqrN4hfvWbo5wbjVA09xDVLI57NP7xE4P0zE3jTb4G88lStJtAE6XpIFLSLzMc8hrsXRxxGkEKAR2dMLTT2QdNo9StcLJixZg3BeO2H9vjTCYVUhLCJGQbBOwiPmkhgIzuR4uECHp155xfdBIsMOvj3VwFspQwPsIeRvh4aVFeeLjk7eZysJ8HFgTMdWC3OLbKDWrczmuNeHWNg8MddEf9XJtMMZ7M8rlnL23ou2NKDCsUm4nVZC1XhPIas5ZbiWWVtbwibidvdyvSKASzpuT1yRynZixO7B+q/+NyA4SGBiMUW0xMhdIxkObSpVGM0RvsLDUhkQWBfXmMsycFR/YPcazLzyN9Ad6cyJA1aelFvhN+8LUMoNY0QbLBalHmpfPjfKWYxefJEtUcy0VhoVj1HzdiFFkYm2IwN8NAzeJczkf2VZOCL0RHLklK85MVPicVAhiZSd/eHRKvFzp7nEcZ23Ki/mqj3haTYBoMdoYJ+nTSeeeOmS5tus0MPWbGqZzOXwbhJGRY0Q6+fjXF5OU50EMgPKRsnW9PwxPeQR5/+6O888kf4vvnRpi8NsIuX4F9foPxK9eZvTWBBAZiNe3VpXRSP5I1dhQhsNs7uebrYjJVYMHKMuadYsBapNPKIoUggxczEGDWtLGlxIuk02vTE/Y6+71hONeDaJtzzi1bMMoT9Gxr2epxQJocKU5wpDgBX3sNvvZf+IlglL2yq9I976Kvh5zmQy8VQdzOa5pwOkEalo0QThfH0zfmmiwUG/numBLDCsX9xO1mLS9lryhzO1nLW0gsr/cEr3vB3ZwAuCohWL7VHGkYWNiWuy+5ppo8kcximNX3EUg8Rp65W+PQLtCB/+/RIJfGi4wtFuntjXJoTx/67GTFcmHpXj7xx82xU0tVvFayLdc6gFqvCZJXptLM2D5kzflDAIPdu9j9cH99NnImDZZJT3sQj67V+azbhMlOPQdWjrcWb2HZEltoZISPlOYnpQWYuHoDdrS7xvqtab/T9KpXeFtpWamZibaQZF9wkK8//TptRoag7cy50HVBLFIqLEgbe3Geb588TXIhxw+WBFxG8zGnh0hqIc685OPx7VH0QIjHHtgOD2yvrO9/uvkiV/Uh2vMLDE1lOJKz+cC+CJqLB9m2JadOna00DhFC0C9hWo9w0duNRCOk2fzogRiBxVmM+XlCfg8dYX/VL1wWv+XJefm883+vDxkIkbdsTNPCj43XMpz8Y6QzQU9WJ+g1EsqleIIUT+SvO+sK3PR2Enr9MIiHObZjP4cG2zg3tljZ9w8MdfC+Yzv57rlxTp6fqFgn3CwUG/XumBLDCsVWYi1Zy7VRciprecXcbtvce83dngC4LpV0bZlW1YU87cUQycsZtGKeoDTxShOvR6evo1rNq0bDlRYkp6GmZ8Sl8QX0yzcZtnXywkvO8nJzpMCpi+OcqDypykq35b0eQLX6DnYOdrlPeCvk2JFKYU4/z+zEDIFilnZh0h9zvMYAsYifmcU8mrSJyjxROw8scGQxAC/OORaZYIRzSZNraUn/UC9femOKcxPpJbfVigSzEBV/86H+bfzRRIDv3ppHFPL0aQUeiWkMPDDgWCwyKcbm0syk8nWnuLBdJGwX2cY8u8ZT8NfTzt25thhWtIPTC5JnRnOcG1mkYAkWvR2M0MFrls7w4aMc297Om69dYPbGCDv1PLu8eaZHJ0hmihXrgSxlDvfZWXqsDJomiAV8DHsiaHt2libgCedcnE3BzKQT91YaFMpIO/OZApm801Z6MbmIKBQJ2EVsbHIeD9FYF8l0Ect09vmAtPBJp/ixVHqFBuw05uD0STh9Eh34tM9PqncHI21DTHVuY7a7C00TDHaGMZaJJ9yod8eUGFYoFFVWkrVcK4xV1vKSrGuk2R2ilai42xMA76gQrGlV/dBb2gieT1feJ+gRPNQfYt9jDzgRV2XrRTHfslX1xFwGr5Gnq/YtilB4VYK9oz4v2R/k5ZuLnB9Nkq+Zoe+2Le/1AGpV30EpV1gPhPilX3hvdZ17IhwbDKHlMpBNcfSozv/67hn8djUBR6tRXXbR4KsnX6n4lG/oGgO2TXu5ilz0k7w2zxf/2o/tD7FnoKNlQ4ilBmrLblvD4NRfv8rzVzzErCydVpYOO4dWrqIK2NFTytwuFrCnJ/j2t15mJpUnZkk+UPIhJ7WQU0m2QlwZm+MrL14rrafA741wcGiYhw8e5VuTr9FrpekttZvusHP0dwQJB7yl7OMgGu4WC3oHK/nfdrHIMy9dxDs/h8cynMxj6UWWzuFl//GgJchbBYIl/3Fa82ESQCDRpCxN0DPQkWgChARwryCLYoG20Ys8wEUeKC2b0cP4ooP8tOjmtKebS94e8pq3YqEwLXtD3x3bPFcVhUJx71lOLMO9yVqujZJTWcsrZqmK5d2eAHi3hOCK36e8Hzb6kgt5+mMhvB6dYo3dolJdNgznUTPZLHdmjIdS42Q1j1NJFl7ylpebI1Oc2NtdZx263QGU2+AGWJHtYK3fges6tzlpCa+NaHwnVMQjrUr0W9TOM2J42ePxcn1szrGulGwWZulnUBYJ2kV6SUERZr4ziiVh1Bvk+fZ28mlJVHpBC5A3WNFArdW2tWzJqWtzXMx7uRbq50Jp0KJJmzY7T4/M8UinxtD+fqeKbBQZm8swk8pX8pDLPuRunLxsTcC2l0ZJz1uECTgi2QxxdcTg8LYYM6EuRo2OyjpEdcmvvWsb+9plNeqtsQ03OPvlwpzzAKbms+QWFhjROsh4fGhS4sMiaufpt9J4sMjgZVL4WPR4KusalAZB2yAkDXyYSCFYEAEsNKIBDxGvKE3QyzvvWXpI3O0V3VaG7vlLPIrTWtpGcMPbyVTnNroeeogL/l669u3j2P7+DXl3TIlhhUKxvqwla7m2urwuWcu1DUlU1nIrlqr+3osJgHerkr6i96mpJjeyb5+BOeHl1q0ZtGKBNt1iqNPPvtqOezX0x0L4PQJhFgnj3BHxeXQeKo7Dm7lSc5FAc4ORVt7/FrgNbg4MdYCUlaYIy1VRlxSMa/CP7+lvw+fRKJiQ1EMkdceOkk1FeerEU5z6zhle8FpE9LJQLhCRhYqvtfb9AfzFLPpsjl22ZFfpd6bQSWt+Um8KiByqRsAtNWh32WZ5w0IIR8hKCR6vh0Cki4+85zDH9/ehlX+RzfDqt1/jNY8kpmWJWTlCdv2dLlvCyHSKkIRdZNmFI15FBg5emMTrE5zJwaT0k/VHGR7uJdvRx+emUuzpf5hjj/eiW0ZTJz2ne5+svMe50SRSSrpkli47W3l/ieCaJ0ZeePHqGg8PRZi5MU6nkQYBaeEnowUQAmzbxotN0DYIY+DVhXN+9XggEKP0R0jLpJDN4bGMkqViKXuFZJcxy67JWfjma+wFZ5/euc/JP/4bP+XeBv4eocSwQqG4u9yVrOUlxDKorOUSS1V/f+qJvZt+AuCdQvd6+Rd/+53NFVTK3uRc3US+fdt0hi9MMjqXwTAtvB6d4c4w+wZKWbPFovNINUSXubWq9pX+7xJx6Da4ccQSlcrrWuwut+MfP7a3l8HOMNemUnXLx5NZTl2eYse2HhaDHUzV7IdCSiKyKo6jdp6oXSBQslpYtqx0SwPwSIsumWe3tQCXz1bfJBBszkcOhuu2XeM2kxI8usZTh/p5xwOD7k1PwhF6Duzj/Jl05Xl+26Bb5ui0s7QbWTrtLFE73yQSdV3Q75c8OhBibC5DMj1PRzjLm6NXeOnCi0wR4HlvhM+2d/CRH32U4/sH0RvbW8/NwNwU185eYcKeoY0sjXgEdNk5NC1PzO/jofYohbCXKxlHIAskAWnSJQrEzCyGLbA8Hgi04RuOle6MFEpRhU6TENMwyKFT1LwgQJcSr7TwSwNPpSlIta10055RyMOFN+HyOfjhD7nuL/cKJYYVCsXGYtmsZcvVs2wZBmeuTnJrepHh7giHh2OtL9Rrylqu8SzfJ1nLS1V/77V/daPjXkEVVfFa+7fAz+w/wivnRrk1PsOudi8P9oXQi/m1taoG5/gI1FST/QGu3ZprGtysx4z+2/GP65rgiYP9TWK41aDLaecLKREgpdVvR4+0iNoFujWD7SFJNjlPxCrgkRZS2rx2Y5ZdfW3VFsv5UtLC3HT1RTTNEcQlcTx+ZQZRyINWrSKbls227siSn+3R3T0MxEKMzKSdrms+P1akjdPzOaS3ur4xK0e3zNFuZuglz96wZLAzjCaoNA4Znc2wsJCmy5J0sQCFSUjDld9/jfHuLt73nkfROzpLXfU6oG8Q+gZ5YTrMH0UDeKRJj5mhz0rRY6XZ48nzlgE/qWyRtpCP7miAmVQev0cjJrNIu+o/s4Wg0LeNrCXobQuwvz+MyKVgccEZePkCznsCk7NpZmeShEoWCw8GGpDR/FgIBM6EO5808UsDHUrLGvbvcqv1DYQSwwqFYnNRFss1PozaylWxaBL2ZDk8kOPXP/Awum3dmaxlb4NPeRNmLS83YWozTADcLOg+H8ce3s2xh3fX/6KcvtLgSyafW3pfrSS2VKO7jubnuWDcYtHWHF+y5sX0+MkJL2lbq+T8rtbucrv+8X0D7QRWOOgyLJsvv3Ct7m/LmEInF4gSHurg8bfu5N9+9VWKpk3ANojaebqzJoeMAIdjXqexhZvlyrar8XCM83AmxXsKN8nZgrTmZ1HzU/SFOBAp3WFysVpYtuRffP4lxpNZTFviKSUpfOzt+/jNP3u92mRE6CwG2njP2x7Bq2vOgHJ3F1o2XclBtueTXL54puI9rn8ji+LMNOOvvVHTbU9AJArtnTxsSZ4lzbj0M+ZtZ8zbjs+j8bYPHKV/dyc9s9Ocf/MiX3v+NKF0lnazuYJsS0l6egYpYTIpKM74OLavD21oh3M+k6X9M7VAOFNgTPeTkz5nhIfjqw5Kg5BtOD+lgRSCRQJITaMt4MGv4byGYQByQ06C3nhrpFAoFKukrnIlBCkLp2HDZKH5Yl1pWe0yuW81WcuFJYTKJslaVtXfDYCmObfzA0EgVv+7mlbVFbGcz7eMLdw30M7OzmDJjlHAK3SGomFAMjqXJW1pWD4/A7FOjnUKRxD6A8t6a2/XP76aQZdlS56/MNlUSfbqgqcODVSsC184ebkS45XXHOE/A7zmH+LwW/Y5ojeXdaLIavOR87m6193ZE6U/FmIimcVrZekhT3/Q4OjiVXjuqrN9yjaLkPPz1GimqbPeeDKLpgnXz/mxt++vP6baOqCtA8veySc+9yJnC3vxRvN0llIsYnaOTitLyC5iWZJkulAjhqUzOTO9yGEJHxKjzGTzpKSXRV+EaEcPxyNFrEKBT3zjJmdv5SmK3RAFb6mC3GulnCQLM02nna2Epli2JJkpMr2Qo2mI4w/QvmcPi8YUycUcXrNAzM4TwCAj/OT0AJoQ2FKi2SZhadKhW/j8wtl3Pd7SPq5Bd5/T5GQDocSwQqHY9KyqcrWRs5bL1eW7mLWsqr8bmPJ+2tRcxHZm+Tc0GNELeX7mnfu5NL7A5HyWvo5QxZfcuEy/db36eqVW1Y2RcPj8IMRtx96tZtDVylZhWrLOurCsQNe0khUiAj21L2SUWlA7AlnLpHn/416uj88xvZijp83JSK6sWnkgUmu1uDzNYwtJFksNRFKan7Tt5/rk4qoGl+VBfNGSFDU/Gc3PiLc6IPLbBv1agUceGIZ24VST04uUI880Ae9+aLjkPS4Qi/gZ7LTRXjnJ6GyGB85P00Og0jRkTg8y4Yky5nX2CY8mEJZJj5Wm13QEcp+ZZjFn0NcerF9ZKdHyWd61I8L0os5i1kc00InlDzCft+gIeukPCOYnp8hn8/VNQuqSWXKlhAqbSnl5A6DEsEKh2PSsa/LBqrOWayb3rXfWcmNs3CbMWlbcATQNAiHn0V7/K90ocnBvnoMNkXBOE5EO99crtaomk254HydRQ/cH+dS7Bnl9vJ0r80W2D3Xz1gODq7qDsJpB11K2ijJrFugeL7TFnEf5YwK7C3l2lzvrlavJ2bSr1aK3LUCHMAibBQZwJj16dY3jMxL9jTlOhKOc2BOFsNc5J9T4kWtTOUZn0652EL1UYRX+ALGhfg694zgVdW6ZTuRaqeW0tphkWNcZ7qp/nbl0Ac006MOgz6wOLCyhkfaGGNgxiL+7h6+cyzBBhDGPsyMFvDr7/saD0K1hzUxx/fxVMmNj9Im8k30soK89WCeWh0LlKq+kq7/HGUTpumPzyeccAS/KySwd0NO/oqSPu4k6oyoUik3PXe3ctaqsZbdEjFVmLbdaB5W1rHBjyVbVhWZfck2r6iZsWUoTyKMDj3rh0R6geAvOTzkVZF9NFJw/UKkm3w4rOZ7X3eJTXv/OmjKybUM+W7VZZJ2fO3uoWCtMy8aja/THQuzqCjlCtTEf2OeHcBQrGOG3vn2F16eLzFkedK8HIepPB36PxgfftrvqMW78TLoHYt3Oo3Y904uwmCwJ5Tk6Foro+lyTF9knJAdCFu8ekMAUfkaZzhRI4iPti9AW6+WtXRpWNMYn/uom52+FKBi7CHvgMa/GLz85iJ6chrkp573czmW1d8Q0zbGFeL2OPcyyHDFs2xtqIrKQy52U7wDxeFwCJBKJu/7eCoXi/qRccdkU3tfbzVpejsas5cZOfkosK8rUtKpujIRzJjytEk1U499qRbI/uCr7z4Y+ni0TK5XizXM3mBqfYnsQ9rfraC5xjraE69MpphdyWFLy6tWZUsydIKP5SOt+snqQeXwU/CF2DPfwqY+duO3Palk2/+oPn2V6ZJxIIU03OYY9RZ7a2cZQV6RSaLYlDTYLJ+lidDbDNy7MMkWAOc3Jh874I3z8Qyc4caDfebJhwPyMk4E8W8pDXkwuP9gXAn76H9yLO1wtN6oSwwqFQrHRaMxarp3ct5Ks5ZXgNrlvC2YtK5bAshx/Z40vuSKaW7SqXhKvtz4vuba5yAbY39baWKRCsVCdqJdJY6VTfOXbbzCZzGBYNppo3mwCODDUQSzid/zK/R1okWhzPvIqG7DUfp66AYVZdARryWbB4rzTyKMh/uyNG3O8cX22bqkAHtzTxyMP7XVi3tpLdpNoW3VScFkgz007Anl2qlkgx7rhb/7Uqj/POtDyy1Q2CYVCodhorDFruVJtXolYrmQtu/W8ZstkLSuWQNedTN5guH75Eq2ql6wmu7SqBpx9qVJBbpjId5eSV26nsUiFcsfCkoXhxfMT/H4hid/f3ECk3LHOo2vsH+xgd2/UeQ3bWtJqURHH5QYiS1TbXX3aPj909zuPyoc3nVzhskheTNKRzDfZLHRd0B3UHYvE3JSzuhJuJXOMmV46BvrYe3Cnk4m8+xAceMh5YqNAjt65LpZrRYlhhUKh2Gy4ZC3XYdsuQvkOZy3XRcltnPg4xR1giVbVWGZNBbnGl1zMt64ml2PQcs05uE2tqsv2izVUSpfidhqLuGHZks988yymBLOUFDFR83tN2nRpBo/0+Njx8G4nFzmTaj1PoJxOk5ypLhPCmUQZbqgiB0Krq7TrHoh1OY8SQycsLv3h08yMThAupOmRWQY9BmdHk8yk8hzZ3okQgm+9McpMKo9lSfSL17n5yuu8+6FhNCGcdWnvgPZOxze8Y19VIG8wlBhWKBSK+w1NA83XWjDYtiN067KWjfpq83Ism7WsVyf3rSBr+bZvUd9BbmfdNvLnuiPoHucWfyhSv1zKUnORBl9yIb/0/rZUq2qfv9mX7PevaSB2u41FGjl1eYq5dKuRJKBpvOfJB+oyiC1b8vK5UW6NTLInKniwy4eeK6VauA1gpXREdC4DM5PV5bpeb7Mo5SO3vNPkgu7R+ed/512cujzFxfF5vvT8NfJFk5BVpHM6x2CywEcf6uZGRuIvVY9NSzKTyjM2l2GwM8zYzXHm0tfprPEiEwg61oq3PrmhUnE2zpooFAqF4u6gac5jqazl2mrymrKWLShaK8patjQPn/qzNzg3kSZjgubzsX+4c3W3qNcBN+EKrPn2+brcer9fEO6tqi1b8vKFMW6OzrAn5uPhgTC6URbNhaVbVbesJje3qq54k1vgFs/o0TVuzqR54eLkkoMYt/3mysRipSmIG7Z04thqhbD7vnICXVBKtahtIJJ2lrltH8tyfMCphfrlPn+9OA6XvMnLTGx848ZcpaFRVvjJan5GgfxCJ2+GdLzSJFZqGNJp59hjejj75i1mF3NOxVgXdEcDTsU4X/Kbb7A7R0oMKxQKhaKeFTcmcclaLlebV5G1fPbmHMnRMWKm7fRgM6FwdZbTL/h4eG9/8+S+O5C13EqMvPexnWu+fb7et95X8hk2UxV62cHCOraqBhzR5+ZL9gXq4tzyhoUQYNk2T58e4/kLky0HMS33m2M7m8R1LYGG3ORl95Wyd7smUQ3LqsS91eUjtxqAVqwWs/XLg6GqMA5HIRLF8gX5xB+/VLdOjaTyRukzwqSnjUlPGwGvzo69u/jq9BUCwXSlq15vLs+B+TzbYwFn4t0GmDBZixLDCoVCoVgda81aNmpsGTVieXQmTbGhimaaFmNT8zw81GKyTXkdKp7l28tabiVGYmfH13z7fL1vvS/FRqxCLyfOlxWAS7WqNo1mX3Ih51gqavYtS0oujS8wkczSHyt15Muk65cNdqD7/XzqiRinJ/1879o83706z6KtYwp9yUFMq8/w3seoF9dU8xoCLrnJbvtK3rC4PLHEvqLrEG13HrUYxfoqclkwtxpAVCrsVavF9ekMHRcn2G/7SGn+yqNQ00DkxP5ezo3ON+1zmhDkTElWDzOnO5MvBdC1cy8feXRgZXMW7jJKDCsUCoVi/dFLgrThtniFsmfZNOndKcmdmcU2DDzSxiMtgh7BcFfE/bmwisYkK8tabiVcBay5u+G6dkZsQVlwPn1mjDMjyVKG7Z2vQq9kvZYT57c1WCgPxsLR+uU1raqtXI7/9JVTTEzOoRcLBDyCoc4QILg1l8EwLbweneHOMD/zzv3ohQIPhyDJApPZMSROx7ac8JIvepm6fBX6fNXmIprW8jNcn0rVNQXZ2RMFAdenUq65yW77CsDJc+N8+Mm9qxvQeH3Q0eU8ykjpDBpqxXEm7fiNXe7izMxnCBWzBKm3oRjCQ0rzYwZCfGTfEfSjfbw8nuXKTLbyuU5dnnLf7/s7mpvBbBCUGFYoFArF3UfXS7et4ZGH99J9erZOOB0abOPwsYfAbrBflP+9XGMSKasTA90QorQOjjg+GLHp1oqkTTCFjoGG3+fhqUMDzKULa+pueKc7I9YKTrdb2XeqCr0SVmIRuSODhZpW1acmJ/n2QoC83gdB8EqLaMoiiIEHCGgGQctgdC7DpfGFSrvq/lgIr0enaFro0iYiC3R6TPaLFFy/5LyPEOD38xBZ9sgFFiydvOYhK7x4fT5297W5Rps9Xm5Y0cCxvb0MxEJcm0rVLR+by6zPgEYIxw4RDAE1r2VZ1SSLmmpyT3sQj65VBlfgRLV1Rzw80Rfmsb29eK6eBeA4cDwYclp631zkWHuER/oCvD6RI2/ad7Yj6DqhxLBCoVAo7ilraq1bm7XcOLlvJVnL5UmCpazlR2Iaj3U6lbuiYePzaGzr6OB4m8HxH97JayPzXJ/Lsb0/xqP7B9CRLJHhv/bPtQoaBWcj612FXg0rqfre6cFC4zoYQmcOHfCBt5qdrEub/b4BDu4YhEKefe2ddF+ZZ2oqiWWalerxvoEaO4J0WlUfbhMca7MYnVvEKDiV5oGONo6JWRjJVn3J/uCSrap1TfDEwf4mMVw07Ts7oNF1p1rbULHdUSiQnH+WybEp/MUsncJgX7vOB4/twHX3rbFa6MCv74ZrbRlu5QXd/T0c2NeHvjDrVPJXkWpxt1BiWKFQKBT3HNcGAUtRm7UcdPl9JWu5ZnLfElnLuib4xR95kLOjSUZn0wx3RTg8HHOSDYwCb+n28JbuKGDCxEh1HZbJWl7151oFboKzjJsv9W6ykqrvSgYLtzMp0G0dvLqGENR51L0+L9u39VZsBTrw8/E9nLo8xfWxWfZ2+Dg6FEGvjYYrOvYcXQh+5p37uTS+wOR8lr4Ox5esZ0uRaLW0bFUdAN3DvoF2ArdRKV/PCZS638+v/r0frP9u9vSgGfn6RItMytVqoQnY0xNhDwBZuH6h+kuvD46/Y0MlSigxrFAoFIomNlsyQRNryFrWTYMj+4Ic2X0HspY9LqL5NsWAm9jzeTSePNjPOx4YvKff2UqrvksNFm53UqDbOhwYbAchuHCb6/XihXFefPMGPqvI23a08/DBXRw0SskXrSw8dsm3m885nd5q8Xo55vXzzo4i56ZzLFg6ts/P7sHOFQ1o7sQEStdtoDsWFLpq1sm2IJupT7TIpJbu5+VH7AAAFsdJREFUbrmBhDBsNTGcXnR2Uk1zblUIAaLm35oGCGdIU/s7hUKh2EJsxGSCdWe1WcuNTUnWM2u5trqse6v/Xyb/tZXg/OX3PnLPv6f1sIjcbjRdq3Uov/Za1suyJb/6uRd588ZspaHe166keWh7J5/+WCkT2CjWx8GVG4ws06paNww+fqKvpsrsZ9+gB/3ymeZIOF+gbv948eLkvZtAqblbLTCMenFcriY3TnrcAGwtMWzbzslpOS9ZLU2iWWsWy27LFAqFYpNyt/NxNyR3OWu55TrUdu5ryFrWdc8d9STfLrdrEVmPaLpW67DW9Tp1eYpzo8m6ztJSwtnRmuOj3Kq6MfLMsprzkhtaVetCcHCwozKZD2Tr5iJeL/iDWD4/X/rz1wgVLXKalyI6CEF+HSdQrulOkdfrtGJu72x4sRXcdbnLbC0xvNyJqdVzpASWmblcy0oFtKCmGq2tft0UCoXiDnA383E3LeuctezKCuLjdI+XE+0eTnS1O+uSS685a3mjcTei6VrRSvxdmVhsysQGMKwVTHQrt0l2a1VtFOvzksuCeZlqMobBpbF52ucnCJeqwjYaOc2Dqfs57M3C/Oxttape9ztFG6gNc5mNt0Z3kmh7SdzaVZFr20svWwtrEdBQFcpuNo5WyxQKhWKduZci5L5iFVnLTXaMdRLLq8la3mjc6bSJViwl/vb0t+HzaE2C2Ktraz8+hKhWk2vW4dTlKa5OJdnX6ePRwUipTXVZLFdbVU8ks1g1EWgaNmG7SCwoOBLIw40r1ffy+ap2i9qJfEu0qt4Kd4q2lhiuiMdVjIzcxHLt/+3a39X8zVqQttOiZlU2jlUK6PJDoVAoWnCvRMiWoyZr2RXLarBg3Nms5fpEjNLPe3i9uNPRdK1YSvwd29vLoeFYnWdYCDg8fGfyo1tWYm275E3O0V4IMn8pjWYUCEoDj7Tw6Bo/cnQbeuP3Vyw6j9Ri/fIlWlVvhTtFW0sMr4W1COjlxPI9F9DKB61QKFpzr0SIooGyWG6Vy9oqa7ksnleZtdxyHWoFct1kP8+a7lCuxn96J6PpWnFpfKEpu7lW/H36o8d58dIk3z07DsBThwY4vr/vjuVHu1ZiNa0iXh96aweBC5mKeA574EhfiP2PPVDpxufWqhrc21U3CuhHChlOFSfJCi+50qOoe9nZu/Emwq0VJYbvBEKAKInnlWroJcUy997GoQS0QrGluBciRLFK1pK1XBMl15i17IpllSZ+tfh9WSy7Zi17m8Ty3UgquZ1YQMuWfO/8RNPyWpuQrgkeP9Dfspvc7bLaSuyKB6+27SSblMSxlcvxX7/2CpNTSaRp1Lemrrl+a8Ui7XaONpmrLBMCOq6FQJ+rryjXtKreTCgxvFFYi4CG5T3P91pAUxNbp3zQCoVCcfdYUdZyYyJGQ4TccpTFcst1qM9afvVGkpGbk9gWCDTyBuvqP71dsX3q8hRjc5mm5QOx0F2zCbk2C/FoS1ZiVzR41TTHIxwIAjFOXZzk6+koeW8Ir8ciYBtcT9kczXp5eCDsRMIVC0wu5JpuXksJ08l0KTatobmIECVvcrC5wchSE07vIUoMb3bWywddXkatcL5NG0f59ZQPWqFQKDYey4nlJbOWS5XlVWYtT18boTOfJFb+NQLL0Jm6fBW6tZrqshdL6Jy6NruqCu/tTvZqlRbx5KH+u2YTOra3lwNDHXW+ZMOy+eqL1zi+b/3sGLUVaEPoGLpOGjhjRXl41z7nj2ybkD7CzasWouh4koPSIIyJaUssKZt9yVI6E/wKLrcTPKUJpbsPbqjilxLDW5H70gddjqrTq/9XNg6FQqFYO6vJWq6b3Nc6a3m4O4LPo1EoCU4NSVC32RXVIbVQ+TvLlvzu109zdSpN1hI86/Xynf4O/umHjqH7fNVqc0NM1+1O9nKryga8Onv725d4VnWd16Nro64J3ndsJ2dHktillAgp4cLYwromOKwoNUbTeMvh7fS9Os650WTlewM4e0NyspDh137ssGuraldME0RhQwlhUGJYsVLW3Qddswwc4Vv2Rq+FioBWPmiFQqG4K9RlLbuZlmma1Hc43Eb/uRluTswjDQO/R7CzN8rh4Vjd086OJrk+lcIwbbwARZPpsQJnT1/myPaaJg7ldSh5lg9GJV2aQdoEQ2hYQl9VLOBak1TW2wt9bSqFadVfz9Y7wWE1LbM/9dHjfPbZi3zxe1cwS+XqlCV4ZarIqaTgxP4dlb+3TJNXzo0ycmuG3R1ejvSH0csT+WzbsU9sMJQYVtw51iygXSwaG8kH3aqNt/JBKxSK+5Q1Vz0bspZ14J/8/R+tTvbqCXNsRwzdtursGNfnxymY9VXlomkzOpuuF8MNWcuPdAiOd0quT6UoGjZej862jnaORQ2YnaqZ3OeetbzWJBU3e8aZkSQvXpzk8YOrn2h3N7K+V/NZdU3g0TQsu/47aRToli35xOdfdh8UCJzvaTVFq7uEEsOKjUWd/3edfNCtvNF3qyMhqIYqCoVi07LeVc+VTPbqOaAzeiZDsWjglRYebCIe6BsegFC4Zdayrgl+8Uce5OxoktHZNMNdEQ4Px5zKZNElPq6ctVxTXdY9Xk5sj3JiV+eKs5avTCw2xbEZls1nvnl2TbFrdyvru/G7sGzJCxcnXQc9KxHoy3q2W8UE3mOUGFbcH9y3Pmg3sVzzO2XjUCgUd5h70YGsXgxqCK/OwFAHDz960DnnlXHJWtZNkyN7AxzZucqs5VasIGt5T38bXl3DaLA2zKULa9pO9yLre7lBz0oE+mZt0KHEsGLrcid90KqhikKhuE+4FwJnxWJwg2QtH4t4OOjPM5YyMNEwhIYpdExDX/N2uttZ38sNelbynWzWVu5KDCsUq2FL+KDdBLSycSgUW5V7JXDWRQyuNmvZMKre5VVkLeuWxc8cG+T3v32hrjrs92g8KBZgfKTep1zboERrfTFZr4SKlbzeSgY9y30nm7WVuxLDCsWd5m75oO+mgAblg1YotgibVeCsiHXMWn5wWye7+6LO5D3TxldqlPHAULuTs1xsUVrWNEcge+uFsqV5+LUvvsLZscV18WoXTZuP/95JRmbSmLbE79E4NByrvN56DHo2ayt3JYYVio3KbTdUYQPaOFRDFYVis7FZBc66sIqsZd00+MWf7uT1K5PcmpxneyzAg4NtTorCUtg22NVEjDJnb85RuHaZIVNiCB3T1Ji5keK117t4y/6Buqzl5SrIli35+O+d5NpUqrKsYNqcG01WbBDrNejZjK3clRhWKO4n7suJhMoHrVDcazajwLkrNGQt68Cjb+3i0dq/qZnc51pdbnE+HZ1JUzRtBOCTJj4JolBk6sYIdNYIXQT//788y8XpDFkThNfH1wdj/IufPo7u94Hu4dTlKUZm0k3vUTDtig1iKw96lBhWKLY66zqRcBlv9Fq4Yz5oJaAVCsVdoJy13Irazn01dozB/hgezy0Ms1o88Hk0hrsidU8/e3OW0ckkumkTBSjkWRxJc/b7YSeTWQhmzt+ir5B0KsxCw0DHEDroOrt7o9VV3aKDHiWGFQrF6lmLgIaN7YMWgmWbpygftEKhWG8qjUnqFz/YPYD/coEbo3PYxSJhj+BQf5jDh3aCtCrV5nIFuZa6BiVSsiMWoEO3KJhG3d8NtYU45k/Drevuk/t074qzljczSgwrFIq7x237oF2ap6yXjaPSjAXlg1YoFPecldoWYpkgk+eyWEYRj7TxSouwBwb6Op2kCtvi8HCMnb3O5L6CaePRBH0dQf7P9z7ieJqXy1qu7dzXImt5M6PEsEKh2Njclz7oFTRPUTYOhWLLsxLbwrH9/eze1s35W/NkSxPfhoc6OHL8YeccYtvolskv/uwAr16aYGRynp2xAA8Nt6Pb5sqylstieYms5Wpl2akmW5rOyzfmuTydYc9Ax4b2HysxrFAo7j/uZEMVWAcbR0lAKx+0QqG4TZatIJfi43Svj7c+spe3Ups3nGdPX4RjOzvRbas5a3m1jUnK/7Ulv/v105WYuZc8Hr7X38H/8b63oPu8EOu+MxtjjSgxrFAoFHB/N1RB+aAVivuZ1Ux8W67tchOryFouc3Y0WbFkAJimya2JOc5eHOHIzh4lhhUKheK+4W41VOE2fNDl591JH7QS0Pc9690JTXHvWK7tchOryFout7q+cmaWBUtHFwKvdN6nMqlv3+Cd+mhrRolhhUKhuNvctz7oGrFcqUbX/E7ZODYlq64kKjY0K2m7vCoaspYB+vfuZOaNZEVw69Ii6hF079oJbR239wHuAEoMKxQKxWbgTvqgVUMVxRKsupKo2NCsR9vl5WjsZuf1+dg51MGjR3Y5x/MGQ4lhhUKhuF+5n33QbmJ5KW+0Ys2seyVRcdvcjm1lvdouL8Vm62anxLBCoVAoqtwtH/TdFNCgfNC3wd2oJCpWzu3aVu6WUN1M3eyUGFYoFArF7bPWhiqUBTIb0MaxAgHt5o2+z7gblUTFylkP28qdFKqbcbKlEsMKhUKhuDeUBbR2P00kvP980Jvtlvf9zka2rWzWyZZKDCsUCoVi87AeEwmptWtsABvHagU0d9/GsZlued/vbGTbymadbKnEsEKhUCjub9YioEH5oBUbko1sW9nIVeulUGJYoVAoFAo31uqDdhPL5cYp99zGsUoBXTehUrER2Mi2lY1ctV4KJYYVCoVCoVgv7suGKvefD3qzs1FtKxu5ar0USgwrFAqFQnEvuVMNVbiHNg4loLckG7lqvRRKDCsUCoVCsdm4H33Q5cmByge9qdmoVeulUGJYoVAoFIqtwnr6oFstY402jspzubM+aCWgFQ0oMaxQKBQKhaI196UPukEYKxvHlkaJYYVCoVAoFOvLnfJBq4mEijuAEsMKhUKhUCjuPWsW0Ms0T7nXPmg3sbycN1pxV1FiWKFQKBQKxeakLgf5DvqgVUOV+xolhhUKhUKhUGwt1jqRcKnmKffcxrGC5inKxuGKEsMKhUKhUCgUy1FbfVU+6PsKJYYVCoVCoVAo7gS3NZGQhmr0BrFxrFRACzaNjUOJYYVCoVAoFIqNQp2Avk990JG2tb33HUKJYYVCoVAoFIrNzno2VFnKG327Ng7W+Pw7iBLDCoVCoVAoFFuRe9FQRWw8y4QSwwqFQqFQKBSKlXG7DVU2IEoMKxQKhUKhUCjuHLUCegOy8WrVCoVCoVAoFArFXUKJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlkWJYYVCoVAoFArFlsVzr1fgrjM/e6/XQKFQKBQKhWLr0tF1r9egDlUZVigUCoVCoVBsWZQYVigUCoVCoVBsWbaeTWKDleYVCoVCoVAoFPcOVRlWKBQKhUKhUGxZlBhWKBQKhUKhUGxZlBhWKBQKhUKhUGxZlBhWKBQKhUKhUGxZlBhWKBQKhUKhUGxZlBhWKBQKhUKhUGxZlBhWKBQKhUKhUGxZlBhWKBQKhUKhUGxZlBhWKBQKhUKhUGxZlBhWKBQKhUKhUGxZlBhWKBQKhUKhUGxZPPfyzePx+L18e4VCoVAoFArF1kAmEgnh9gtVGVYoFAqFQqFQbFmElPJer4NCoVAoFAqFQnFPUJVhhUKhUCgUCsWWRYlhhUKhUCgUCsWW5Z5OoFMoFArF3SMej/8B8LeBXYlE4vq9XRuFQqHYGKjKsEKhUCgUCoViy6LEsEKhUGwdfhU4BNy61yuiUCgUGwWVJqFQKBQKhUKh2LIoz7BCoVDcBvF4/KvAe4GPJxKJ32n43SeBXwN+L5FI/P0VvNYPAB8GngSGAS9wBfhT4N8mEol8zd/uAl4FbOBoIpG4UfO7MPAysB94VyKReKa0/A9w8QzH4/EfB/4hcBjoBGaBS8CfJBKJxMq3hkKhUGw+lE1CoVAobo+/B9wEfjMejx8tL4zH4+8GPgGcBT6+wtf6FeCHgNeAzwD/HSgCvwH8ZTwe18t/mEgkrgF/H4gBn4/H47XFjQRwEPhXZSHcing8/g+AP8MRwl8D/j3wF0AQ+LsrXG+FQqHYtKjKsEKhUNwGiURiLh6Pfxh4BviTeDz+KBACPgsUgJ9MJBLZFb5cHLiWSCTq/Gs1FeYPAX9S897/Ix6P/2fgfwM+CfxqPB7/WeBngadLy5bjF3AE98OJRGKq4X27V7jeCoVCsWlRlWGFQqG4TRKJxHPAvwD24VR0Pwv041gnzqzida42CuES/7H084ddfvePgdeBX4nH47+EUxWeBj6aSCTsFb61CRgu6zOzwucrFArFpkVVhhUKhWJ9+LfAO4GPlP7/+UQi8d9X8wIlr+8/BN6P4/eNAqLmT4Yan5NIJPLxePyncDzCvwNI4EOJRGJshW/7ORxrxJl4PP4nOBXu7yUSienVrLtCoVBsVlRlWKFQKNaBUkX3KzWL/uNqnh+Px73At4H/HxDAsUN8GviXpQeAv8XTLwJvlP59FvjGSt83kUj8Fs6kups43uavAJPxePw78Xj8rav5DAqFQrEZUWJYoVAo1oF4PL4P+HdAEifh4b/H4/HAKl7ivcBjwP+TSCSOJBKJf5BIJP55IpH4DRzrxVL8M+BxYAZ4ACdPeMUkEok/TCQSJ4Au4G8Cvwe8HfireDzeu5rXUigUis2GEsMKhUJxm8TjcT9OJTcM/DRORfcIq6sO7y39/JLL796xxHs/Dvwr4ALwYOnnv4zH40+u4r0BSCQS84lE4i8SicTPA3+AE7P21GpfR6FQKDYTSgwrFArF7fPvgKPA/5VIJL4B/DrwPeAX4vH4T67wNa6Xfr6zdmE8Ht+N40duIh6Px4DPAxbw04lEYhL4KZwJcZ+Px+Ndy71pPB7/kYZYtjLlivBKkzAUCoViU6Im0CkUCsVtEI/H3wf8EvAiTvwZiUTCKsWtvQb8t3g8/nIikbi6zEt9DbgM/ON4PH4Ep6HGduBvAX9e+ncj/3dp+ccTicRrpfd+PR6P/xPgPwG/D/z4Mu/7BSAfj8dP4ghygVMNPgZ8H/jrZZ6vUCgUmxpVGVYoFIo1Eo/Ht+MI0gXgw4lEwiz/LpFIjOA05GgDvhCPx31LvVYikcgA7wL+GMf3+3HgIZys4I+5vPf/DrwP+J+Nne8SicTv4kyE+7F4PP6PlvkY/wx4HngUJ+f47+J0vvsV4AcSiURT5JpCoVDcTwgp3SItFQqFQqFQKBSK+x9VGVYoFAqFQqFQbFmUGFYoFAqFQqFQbFmUGFYoFAqFQqFQbFmUGFYoFAqFQqFQbFmUGFYoFAqFQqFQbFmUGFYoFAqFQqFQbFmUGFYoFAqFQqFQbFmUGFYoFAqFQqFQbFmUGFYoFAqFQqFQbFmUGFYoFAqFQqFQbFn+X1X4X0Hu/9KlAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Loss :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/GRAD1-03-basic_descent_loss</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
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- {
- "data": {
- "image/png": "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\n",
- "text/plain": [
- "<Figure size 576x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "theta = cooker.basic_descent(X_norm, Y_norm, epochs=200, eta=0.01)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Minibatch descent"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:40:51.212779Z",
- "iopub.status.busy": "2021-03-01T17:40:51.212315Z",
- "iopub.status.idle": "2021-03-01T17:40:52.100462Z",
- "shell.execute_reply": "2021-03-01T17:40:52.100958Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "### Mini batch gradient descent :"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**With :** "
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "with :\n",
- " epochs = 10\n",
- " batchs = 20\n",
- " batch size = 10\n",
- " eta = 0.01\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "**epochs :** "
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " #i Loss Gradient Theta\n",
- " 0 +0.414 -3.223 -1.331 -0.003 -0.758\n",
- " 1 +0.509 -1.265 -5.226 +0.024 -0.815\n",
- " 2 +0.403 -0.509 +1.738 +0.025 -0.824\n",
- " 3 +0.223 -4.433 -2.696 +0.003 -0.814\n",
- " 4 +0.300 -5.126 +0.511 +0.057 -0.784\n",
- " 5 +0.342 +5.415 +4.915 +0.001 -0.803\n",
- " 6 +0.459 -0.127 -6.173 -0.015 -0.824\n",
- " 7 +0.343 +1.987 +4.441 -0.001 -0.825\n",
- " 8 +0.217 -0.572 -6.593 +0.015 -0.817\n",
- " 9 +0.501 -4.101 -4.573 +0.006 -0.818\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Visualization :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/GRAD1-04-minibatch_descent</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
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- "data": {
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\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Loss :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/GRAD1-05-minibatch_descent_loss</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
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- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 576x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "theta = cooker.minibatch_descent(X_norm, Y_norm, epochs=10, batchs=20, batch_size=10, eta=0.01)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:40:52.104349Z",
- "iopub.status.busy": "2021-03-01T17:40:52.103880Z",
- "iopub.status.idle": "2021-03-01T17:40:52.106318Z",
- "shell.execute_reply": "2021-03-01T17:40:52.106791Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Monday 01 March 2021, 18:40:52\n",
- "Duration is : 00:00:06 509ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/LinearReg/03-Polynomial-Regression==done==.ipynb b/LinearReg/03-Polynomial-Regression==done==.ipynb
deleted file mode 100644
index 72c774f2609e0189bdbf73cfdd99e49bb5fb6e8a..0000000000000000000000000000000000000000
--- a/LinearReg/03-Polynomial-Regression==done==.ipynb
+++ /dev/null
@@ -1,586 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [POLR1] - Complexity Syndrome\n",
- "<!-- DESC --> Illustration of the problem of complexity with the polynomial regression\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - Visualizing and understanding under and overfitting\n",
- " \n",
- "## What we're going to do :\n",
- "\n",
- "We are looking for a polynomial function to approximate the observed series : \n",
- "$ y = a_n\\cdot x^n + \\dots + a_i\\cdot x^i + \\dots + a_1\\cdot x + b $ \n",
- "\n",
- "\n",
- "## Step 1 - Import and init"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:40:53.462704Z",
- "iopub.status.busy": "2021-03-01T17:40:53.462236Z",
- "iopub.status.idle": "2021-03-01T17:40:56.097893Z",
- "shell.execute_reply": "2021-03-01T17:40:56.098383Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>\n",
- "\n",
- "div.warn { \n",
- " background-color: #fcf2f2;\n",
- " border-color: #dFb5b4;\n",
- " border-left: 5px solid #dfb5b4;\n",
- " padding: 0.5em;\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "div.nota { \n",
- " background-color: #DAFFDE;\n",
- " border-left: 5px solid #92CC99;\n",
- " padding: 0.5em;\n",
- " }\n",
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- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
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- "text/plain": [
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- ]
- },
- "metadata": {},
- "output_type": "display_data"
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- {
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- "text": [
- "Version : 2.0.17\n",
- "Notebook id : POLR1\n",
- "Run time : Monday 01 March 2021, 18:40:56\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "import math\n",
- "import random\n",
- "import matplotlib\n",
- "import matplotlib.pyplot as plt\n",
- "import sys\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "datasets_dir = pwk.init('POLR1')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Dataset generation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:40:56.108648Z",
- "iopub.status.busy": "2021-03-01T17:40:56.105684Z",
- "iopub.status.idle": "2021-03-01T17:40:56.600983Z",
- "shell.execute_reply": "2021-03-01T17:40:56.601474Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "#### Generator :"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Nomber of points=100 deg=7 bruit=2000\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "#### Datasets :"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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- "output_type": "stream",
- "text": [
- "100 points visibles sur 100)\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/POLR1-01-dataset</div>"
- ],
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\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "#### Before normalization :"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "X : mean= +0.4134 std= +2.9894 min= -4.9259 max= +4.9196\n",
- "Y : mean= +1871.2910 std= +3598.1973 min= -5219.7615 max= +15570.0449\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "#### After normalization :"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "X_norm : mean= -0.0000 std= +1.0000 min= -1.7861 max= +1.5074\n",
- "Y_norm : mean= +0.0000 std= +1.0000 min= -1.9707 max= +3.8071\n"
- ]
- }
- ],
- "source": [
- "# ---- Parameters\n",
- "\n",
- "n = 100\n",
- "\n",
- "xob_min = -5\n",
- "xob_max = 5\n",
- "\n",
- "deg = 7\n",
- "a_min = -2\n",
- "a_max = 2\n",
- "\n",
- "noise = 2000\n",
- "\n",
- "# ---- Train data\n",
- "# X,Y : data\n",
- "# X_norm,Y_norm : normalized data\n",
- "\n",
- "X = np.random.uniform(xob_min,xob_max,(n,1))\n",
- "# N = np.random.uniform(-noise,noise,(n,1))\n",
- "N = noise * np.random.normal(0,1,(n,1))\n",
- "\n",
- "a = np.random.uniform(a_min,a_max, (deg,))\n",
- "fy = np.poly1d( a )\n",
- "\n",
- "Y = fy(X) + N\n",
- "\n",
- "# ---- Data normalization\n",
- "#\n",
- "X_norm = (X - X.mean(axis=0)) / X.std(axis=0)\n",
- "Y_norm = (Y - Y.mean(axis=0)) / Y.std(axis=0)\n",
- "\n",
- "# ---- Data visualization\n",
- "\n",
- "width = 12\n",
- "height = 6\n",
- "nb_viz = min(2000,n)\n",
- "\n",
- "def vector_infos(name,V):\n",
- " m=V.mean(axis=0).item()\n",
- " s=V.std(axis=0).item()\n",
- " print(\"{:8} : mean={:+12.4f} std={:+12.4f} min={:+12.4f} max={:+12.4f}\".format(name,m,s,V.min(),V.max()))\n",
- "\n",
- "\n",
- "pwk.display_md('#### Generator :')\n",
- "print(f\"Nomber of points={n} deg={deg} bruit={noise}\")\n",
- "\n",
- "pwk.display_md('#### Datasets :')\n",
- "print(f\"{nb_viz} points visibles sur {n})\")\n",
- "plt.figure(figsize=(width, height))\n",
- "plt.plot(X[:nb_viz], Y[:nb_viz], '.')\n",
- "plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
- "plt.xlabel('x axis')\n",
- "plt.ylabel('y axis')\n",
- "pwk.save_fig(\"01-dataset\")\n",
- "plt.show()\n",
- "\n",
- "pwk.display_md('#### Before normalization :')\n",
- "vector_infos('X',X)\n",
- "vector_infos('Y',Y)\n",
- "\n",
- "pwk.display_md('#### After normalization :') \n",
- "vector_infos('X_norm',X_norm)\n",
- "vector_infos('Y_norm',Y_norm)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Polynomial regression with NumPy\n",
- "### 3.1 - Underfitting"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:40:56.606573Z",
- "iopub.status.busy": "2021-03-01T17:40:56.606106Z",
- "iopub.status.idle": "2021-03-01T17:40:56.607795Z",
- "shell.execute_reply": "2021-03-01T17:40:56.608271Z"
- }
- },
- "outputs": [],
- "source": [
- "def draw_reg(X_norm, Y_norm, x_hat,fy_hat, size, save_as):\n",
- " plt.figure(figsize=size)\n",
- " plt.plot(X_norm, Y_norm, '.')\n",
- "\n",
- " x_hat = np.linspace(X_norm.min(), X_norm.max(), 100)\n",
- "\n",
- " plt.plot(x_hat, fy_hat(x_hat))\n",
- " plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
- " plt.xlabel('x axis')\n",
- " plt.ylabel('y axis')\n",
- " pwk.save_fig(save_as)\n",
- " plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:40:56.626596Z",
- "iopub.status.busy": "2021-03-01T17:40:56.624635Z",
- "iopub.status.idle": "2021-03-01T17:40:57.104602Z",
- "shell.execute_reply": "2021-03-01T17:40:57.105097Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Nombre de degrés : 1\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/POLR1-02-underfitting</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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K+i1JxyS9O8LxoUuu3u6UjYIAAGyM82Wg7Rlha+1tkm6re3/BGPOzkq6VtEvS/ZK+ZK1diHaIQPvW2yiY5j6HAABEifNlINQtlq21K5L+KqKxAKHVNgrW3/ucjYIAAKzG+TLQSWkEkHi1jYKlYkE5SSXHNgoCANAOzpeBUDPCQNK4vFFwPewMBgDU43wZIAgjc7Jw7/MosTMYQFZxkR8O50uCMJB59TuDpdU7g10++AFINy7yEQVqhIGM45bbALKI9l+IAkEYyDhuuQ0gi7jIRxTaDsLGGLe2EQIZwc5gAFnERT6i0EmN8AljzCFJH7HWfqNH4wEQMXYGA8ii2kV+Y40wF/noRCdBeFLSr0h6jTHmmKSPSPqktfbhnowMQGTYGQwga7jIRxRyvu+3/cnGmKskvU1BIB6WtCzpVgWzxLd38Dy+JFlrOxosAAAAkiFl7euaDqyjIFxjjDlf0psUhOJnS/IVzBj/V0mfttY+ssHXE4QBAABSKoXt66ILwvWqs8S/Lum1kkoKZon/UtKfW2u/2+JrCMIAAAApdXhyRjfdeseTPeqlYDP2Da++MqlleE2DcBTt0x6W9Iikpeo3GVQwW3zEGHPIGHNhBN8DAACkkFfxdXhyRgdvv1eHJ2fkVcJNwCEZstK+rqs7yxljipJ+WdLbJb1QQQCelPR+SZ+U9NOS/oOkV0j6sKQ3hB8qAABIkxQun6NNtfZ19TPCaWxf11EQNsbsUlAX/GZJF0nyJB2SZK21X6/71Nsk3WaM+bykX4hioAAAIF24xXt2ZaV9XdtB2BjzNUkvUTD7e1LB7O9HrbUn1/myf5D0qlAjBDIiZbtrASC09ZbPCcL9FfU5KCvt6zqZEb5G0t9JspIOWWu9DT5fkr6kIDQnw6c+KM09LJVGpNJwk3+HpeGRc+8PjZx7rDgo5dL1y0VysDzoHi58gOwsn6dR/THosrHNOnTkPt1z8tFIz0FZ6FHfSRB+prX2nk6e3Fr7Q0k/7GxIPXTPndJsl7k8n18dmEsjTd7f6GO1x0tSvrDx90RmsDzoFi58gEBWls/TpvEYVBzIq+xVVGsUxjnonLaDcKchOJGWFrv/2kpFWng8eIvCUKmN0NzOv8PBbDUSjeVBt3DhgzSLcjUjK8vnadN4DDq7UlnzOZyDAl11jUitd7w3CLJLC9W3xeBtsf79ho/VHlspRzuW5aXg7dF17z3SnsJAtaRjWBqqhuTh+nB93uqgPTxS/bzGspDzpMEhSkB6gOVBt3Dhg7TqxWpGFpbP06bZMagR56CAW0H48md3/7Ur5bqgvNgiNC+sDtbLzR5fDB6PkrciPT4fvIWVywelG6W6sDzcbNa6xQx14+dSAiIpm8uD1MC2xoWPe7LyemA1IxuaHYPyOamQz2vFq2TiHBQVt4JwGANFaVNR2hTBiaxSkc4unQvGiwvS8sLaoF0/U73cJFDXwrbXzr7FNvmV4PkXF6J5vsGhIFAPNynpqIXtph9rmMWulYCkdLY6a8uD1MCuL4sXPmgtS68HVjOyodkx6Bnbt+iV45fq/tOPpf4cFCWCcBye3Hg3Ev65fD+YrV5snJ1+QlpaVGVxQSdOzOrRM4/q4kFf24al/HJ9mF5cHbbPLocfU72zy8HbY3Phn6tQaFH6sdHmxIbHh0ekwVLwe+ijLC0PMmu0vqxd+ISRlZnS9WTp9cBqRjasdwy6as+2uIeXKAThtMvlgpnS4qA0umXVh87NUuS1XH5KMEtx3hYdeOs6sxSe11Av3aIMpH4We7Hx8+uCtR/hrTQ9L+INi8NaUztdq7MebpyRHmnxudV/B9x6KTFrtLEsXfh0K0szpevJ0uuB1Yzs4BjUHrfO3o7papaiUJDO2xy8hVWpBLPBzWqp62ekm5WF1D7nyTrrxR5sWKyG9UcjeK6B4tqZ6vo+1K06fzQG6tJwKjYsMmuEdmRppnQ9WXo9sJoB1xCEMyz2WYp8/ly400Xhn698tvkMdTu11PUfW1yIvgRkpSw9/mjwFlazntXr1Vmvu5GxNz2rmTVKviSUJMR+DOqTrL0ekjyTmIS/a2QLQTjDsjRLIelcCcjm87v68lUH0K2bNP70zSqcrW+T98Q6QbtFoK59vr+2R2PX+t6zulUnkCZfU+1ZnbRZI06OqyWlJCFzx6AWkvZ6yKqk/F0jWwjCGZa1WYowenoA9f1qCUirlnqtelZXy0IaNy4mvWd1tdyjMDSsfaUR7auF5G83hul1bgJTGgkCegQlIJwc10pKSYJLx6Akz6JmRVL+rmu4AM8GgnCGMUtxTk8PoLlcEOqGStL5F4QfbMue1c3+bQzbjRsae9Cz+onHgrewmvWs7qLO+nvTj2vywTNaWgk2Zob93Wbh5JaUkgSOQYhSUv6upXgvwLNwjEoSgnDGMUsRSMIBtO2DVy96VjebjW4sBVlsKP1onMVOaM/qcUlfkLSUG9BirqjF/KAW8oPa/Kn/JY1d2FHPam9wWP/x0A/1wx8/ruWVSmpnl5NUksAxCFFJ0t91XLPTrIBFjyAMJ8R9AI3t4FXfs3pLyA2LLXpWe4tP6Pj9M3r49BltG87pkk2FoFd1baa6Tz2rS/6KSv6KLqhUZ8FnTksznT1HQdIfSlpRXov5ohZyg1p8YFAL996szReMblBn3eQmMDH1rHapJCHrmP07J0l/13FNriStPCQLCMIZxcFztbgPoJk4eDXpWX0u4A+e61XdbsBvt2d1q7KQaumHv7ggf2lBUUbNAVW0ubKszVqWPEknH5ZOhnjC9XpW14fpVv2rh0c66llNSUI2MPu3WpL+ruOaXEnC6mbWEIQziIPnWnEfQLN68AoV8CPqWZ2TVFnx9J2jD+rB6dPacX5RV4wNq9BY4rEqSDfvWV1+4glpcVFFRVgCIvWmZ3V97XR9H+rhIEAXSsPaVwo2NGphWLp3Zm3ATkHPapdl4gI6YkkptYlrciXu1c0sIghnEAfP5uI8gGb14JWUgF8YKOj5z/lJPf85PxnqefLVi8ipBx9SYXlJ5xc8PeviIV13zU4VarXWq2720qIjSP3Hlpci+imr+tGzui5Qt38r8970rHZZUl5fWCuuyZW4VzeziCCcQRw8k6f+4LVU9jSQz+mpF4zouTueEvfQQslawO/Jya3iSUtL67TNW7/0Y80mxyT3rB4cOlf60Vag3rhntcuy9vrKmjgmV+Je3cwignAGcfBMnkI+p/e/4fm67uN/rxMPPa6Viq+TZ57Q79zynVSXrGRxdiLyk1u+II2cF7yFtWHP6g7qrBd70LP67HLwNh9tz+o1tdStbk8+3CxkR9ezut+y+PpCeEkpD8kKgnAGcfBMpu8dP60fP7KglUrQ73Z5pZL6khVmJ/os8p7VK81b6XXas7r2fpTi6lndcha7bta60J8SkG5eX2yUBjpDEM4gwkkyZbVkhdmJFBsYkDZtDt7Cqu9ZvaZtXjZ6Vj+pOLg6HA83hOaWPaubzFgXB9edre7k9cVG6XO4IEC7CMIZRThJHkpWkGn1PasVUc/q+nrpdWerW8xa1+qsI+5ZrfLZ4O2xufDPVf//rbH0o2WddZOe1aVhTTwwz0ZpcUGAzhCEkRppv8KnZAVoU33P6s1bwj9f2J7VjZ/r++HHVBPhhsV9km6p3V2x+u9irqiLbvm69LSLG9rutbhteYc9q5OIzknoRDr/yuGcLFzhU7ICxCSintWSzm1YXGysq26zZ3VjwI54w+KIX9aIV149J39iWjrRxZNt1LO6VVlIs7Ddw57VjZMk9/740UyWofVK2ieZwiIIIxWycoVPyQqQcvUbFqOwUm5eO72qbV6Lm8CkqWd1Lt9B6UerOuu6z6n2rG42SfLUC0YoQ2tTFiaZwiIIIxXCbjRz/Yo36/j9IrUGitKmorQpgpBW8eQtLOoH95zQyZMP6ZLNBR3+wX166PQjKpaXNJrzdMnmvF6252Ll173zYvXfSoQ9q/1KMIu++EQ0z1ftWX02P6i3LVb0uAa1mC9qMTeo5dlB5UfO00Nnc3rMH9DZYkkXjmzR+OKF0r2n6VldJyuTTGEQhJEKYTaaccXbX/0Opfx+gap8QYVNm/Tc5z1Tz32edHhyRl8+PK+loS3SUPAppUJBF1xx5cYhZ1XP6lY3gWlV/tEkYJfPRvuzVntWD0va2ezj8w3vz0r60eebP1etZ/V6d1Csr53OUM/qrHYz6gRBGKkQZqMZV7z9E0co5fcLNBcq5PSqZ/Vyw01duulZvRzxhsVIe1bn1tZLd1pn3cee1XQzIggjJcJsNOOKt3/iCKX8foHm2g05fVnF6VHPam9hQR8+9B3NnjqjgfKSRvMrumx0QNdeMaZ8LXTX2ug1q7OOvGe134Oe1U1mqmsz0uveyrzh65r0rKabEUEYKdLtRjOuePsnjlDK7xdJkMQ69XZCTipLi+p6Lxe2XKR/8xtPXzNJkm937I09q+tv7JKontURbFhs0rO6UBrRHw0N66FSTmcGCtq0ZVRPvfBi5b95uvVNYErDQQDP58OPKQEIwsg8rnj7J45Qyu8XcUtqmGxnJS0LpUWhuvH0omf1mq4fndy6vOFjfoQbFlv0rM5Jekr1rSNPln6sdwfFxvKPEWlkk7RjT0Q/VHgEYWQe/Xv7J45Qyu8XcUtymNwoJFJaFLFCIQh6I5vCP9eTGxbr77DYqsVek1uZN/53xD2rtVwdR6eT1edfIH3glmjHEgJBGE5oZ8YgiUubaRNXKKU/c7R4LXQmzWGS0qIEW7VhMYLnq/WsbgzUzYJ2/Ux11D2rSyMR/DDRIQgDSu7SZhoRStON10Ln0hwmk1RalJYLsLSMc42Ie1ZraWl1cF7eIFDX3t9yYfjvHyGCMKBkL20ikNqTT8q49lqI4u8qSWGyU0kpLUrLBVhaxtlz+YI0cl7wlnIEYWRGmBNampc2XcDJp39cei1E9XeVlDDZrSSs4qTlAiwt40T7CMIZ58osWtgTWpqXNl3Ayad/XHotRPl3lYQw2Q+9Oqek5QIsLeNE+wjCGebSLFrYE1qalzZdwMmnf1x6LfB31ZlenlPScgGWlnGifQThDEvrLFo3Mw5hT2hpX9rMumYnn0I+p7JXkVfx+T1FyKXXAqGmPbVj8m13ndRdJx5R2Qt620Z5TknLBVhaxon2EYQzLI2zHd3OOERxQnNlaTONaiefHz34iJZXgpPwSsXXrYfv090nHulqRsqVsqFuuPJaINRsrP6YvNRwPpGiO6ek5QIs6nFyHIofQTjD0jjb0e0sNie0bKudfG6+fVJ/+a1jWqn4krqfkXKpbAitpSV8xanxmNwoynNKWi7Aohonx6FkIAhnWBrDYbez2JzQsq+Qz2kgn5dXDcE13cxIpbVsCNFLS/iKS7Njck0pBeeUJOM4lAwE4QxLYzgMM4vNCS37olrlSGPZEBCHZq+5wYG8XrBnm1707Kcl/pzSD92WN3AcSgaCcMalLRx2OotNfZVbolrlSGPZEBCHVq+53772pznWKlx5A8ehZCAII1E6mcWmvso9Ua1ypLFsCIhDGlcW+ylMeQPHoWQgCCNx2p3Fpr7KTVGscnByRz9kZcWq3ddcVn7eToQpb+A4lAwEYaQW9VUII21lQ0gX11asXPt5a8KWN3Acil8+7gEA3aodgOpRXwUgCepXrHytXrHKItd+3ppaeUOpWFBOdNJII2aEkVrUVwFIKtdWrFz7eWsob0g/gjBSiwMQgKRyrSOAaz9vPcob0o0gjFTjAAQgiVxbsXLt50V2EIQBrOHi7m8gSq6tWGXx5+U46AaCMIBVXN39DTQKG4RcW7HK0s/LcdAdBGEAq9CfGSAIuY7joDtonwZEwKv4Ojw5o4O336vDkzPyKn5qx7Pe7u9+SNr/S7jJ1XZgCMR9HET/MCMMhJS0maOw44lz93fS/l+6KMl1kf0cW9bbgSX595wELnfBcA1BGAgpaUtoYccT5+7vpP2/dE2SL0T6PbYsB6Ek/56Tgi4Y7iAIAyElbeYo7Hji3P2dtP+XrknyhUi/x5blIJTk33NSZLELBpojCAMhJW3mKIrxxLX7O2n/L12T5AuRfo8ty0Eoyb/nJMlSFwy0xmY5IKSk3Ws+aePpRJrHngW1C5F6SbkQiWNstSD0xqsv177dY5kIwVKyf89AvzEjDISUtJmjpI2nE2keexYkuRwgyWNLG/5fAufkfL//rYmMMb4kWWv7/r0BAK3Vugkk8UIkyWNLG/5fNkc3jUxr+oskCAMAEoEQgjjRTSPzmv4SKY0AAMSOEIK40U3DTWyWAwDE7sjkjO468Qh3cksYl+70yN3k3MSMMIBQWM5GWF7F10f+z90qe5VVjy/R0itWrs3S077RTQRhAF1z7USJ3piYmtWZx5fXPF4s5AkhMXKtVIBuGm4iCAPommsnSvTGsVPzKq9U1jx+0eYhQkiMXLvxBu0b3UQQRuawVN8/rp0o0RvNlqQHB/J6+88/i9dujJr9XgYKeV36lM0xjqq3uJucewjCyBSW6vuLmjpEodWS9N7LCSNxGt+1Vc942vm684EzqnVa9SoVHZq4X3szdKc9uI0gjExhqb6/qKmDFH4VhiXpZCrkc3rl3sv0o+k5na2WrlR86R6OqcgQgjAyhaX6/iLAIKpVGJakk+m+mcfW1G9n/ZhKeZ1bCMLIFJbq+48A4zZWYbLNtWMq5XXu4YYayJTaUn2pWFBOUomleqCnuAlBtrl2TK2/sOPGLm5gRhiZwlI90F+uzRi6xrVjKuV17iEII3NYqgf6hw2T2efSMZULO/cQhAGEwsYSt7k2Y4hs48LOPQRhAF1jYwkkt2YMkW1c2LmHIAyga3QMAJKD1ZlocGHnFoIwgK6xsQRIBlZngO7QPg1A12obS+qxsQToP9p+Ad0hCAPomms9RoGkop8z0B1KIwB0jY0lQDLQ9gvoDkEYQChsLIkWG57QDdp+Ad0hCANAQrDhCd1idaY3uDDNPoIwnMDBDGlAOzqE0avVGVePn1yYuoEgjMzjYIa0oB0dksbl4ycXpm6gawQyj7ZCSAva0SFpoj5+ehVfhydndPD2e3V4ckZexY92wBGiE4cbmBFG5jHLhrRgwxOSJsrjZ9pml+nE4QaCMDKPgxnSgg1PSJooj59pKzXgwtQNBGFkHgczpAnt6JAkUR4/07Y6x4WpGwjCyDwOZgDQnSiPn2lcnePCNPsIwnACBzMA6E5Ux09W55BEBGEAANBzrM4hiQjCAACgL1idQ9LQRxgAAABOIggDAADASQRhAAAAOIkgDAAAACcRhAEAAOAkgjAAAACcRBAGAACAkwjCAAAAcBJBGAAAAE4iCAMAAMBJBGEAAAA4iSAMAAAAJxGEAQAA4CSCMAAAAJxEEAYAAICTCMIAAABwEkEYAAAATiIIAwAAwEkEYQAAADiJIAwAAAAnEYQBAADgpIG4BwDAHV7F18TUrI6dmtfObaMa37VVhXwu7mEBABxFEAbQF17F140Hj+jo9JyWy56GigXt2b5FB/bvJQwDAGJBaQSAvpiYmtXR6TktlT35kpbKno5Oz2liajbuoQEAHEUQBtAXx07Na7nsrXpsuezp+Mx8TCMCALiOIAygL3ZuG9VQsbDqsaFiQTvGRmMaEQDAdQRhAH0xvmur9mzfolKxoJykUrVGeHzX1riHBgBwFJvlAPRFIZ/Tgf17NTE1q+Mz89oxRtcIAEC8CMIA+qaQz2nf7jHt2z0W91AAAKA0AgAAAG4iCAMAAMBJBGEAAAA4iSAMAAAAJxGEAQAA4CSCMAAAAJxEEAYAAICTCMIAAABwEkEYAAAATor1znLGmDi/PQAAANzgW2tzjQ8yIwwAAAAn5Xzfj3sMAAAAQN8xIwwAAAAnEYQBAADgpFg3ywEA+scY80lJvybpMmvt/fGOBgDix4wwAAAAnEQQBgB33CDpmZKm4x4IACQBXSMAAADgJGqEASAEY8whSddKus5a+6GGj71f0nslfdxa+9Y2nuslkt4g6QWSni6pKOmYpP8p6Y+ttUt1n3uZpDskVSRdaa39p7qPnSfpu5J2S7rGWvvN6uOfVJMaYWPMKyS9S9KzJF0o6WFJ90r6nLXWtv9/AwDShdIIAAjnX0t6QNKfGGOurD1ojPk5STdKulvSdW0+1/WSfl7S9yV9RNLHJJ2V9D5Jf2OMKdQ+0Vp7n6S3SrpA0i3GmPqJDStpj6Tfr4XgVowxb5P01wpC8JckfUDSVyQNS3pLm+MGgFRiRhgAQrDWnjHGvEHSNyV9zhjzXEkjkm6WtCzptdbahTafzki6z1q7qmatbmb5NZI+V/e9P2+M+QtJ75D0fkk3GGPeJOlNkm6rPraRtysI2z9lrZ1t+L4XtzluAEglZoQBICRr7bcl/Y6kyxXM5N4saZuCcom7Onie440huOqD1X9f1uRjvynpB5KuN8a8U8Fs8GlJ+621lTa/9YqkcpPxPNTm1wNAKjEjDADR+GNJL5b0xur7t1hrP9bJE1Rre98l6VUK6ns3S8rVfcr2xq+x1i4ZY16noCb4Q5J8Sa+x1p5s89seVFAOcZcx5nMKZra/Za093cnYASCNmBEGgAhUZ3K/UPfQBzv5emNMUdI3JP2hpJKCEoibJP1e9U2Shlp8+aSkO6v/fbekv233+1pr/0zBBroHFNQyf0HSjDHm74wxP9PJzwAAaUMQBoAIGGMul/Snkh5R0MnhY8aYUgdPca2k50v6lLX2Cmvt26y177HWvk9BucV63i3pKkkPSXq2gn7BbbPWftpau0/SRZJ+UdLHJb1Q0leNMVs7eS4ASBOCMACEZIwZUjCDe56k1yuYyb1Cnc0K76r++1dNPvaidb73VZJ+X9I9kp5T/ff3jDEv6OB7S5KstXPW2q9Ya39d0icVtFK7utPnAYC0IAgDQHh/KulKSf/JWvu3kn5X0rckvd0Y89o2n+P+6r8vrn/QGLNDQf3xGsaYCyTdIsmT9Hpr7Yyk1ynY/HaLMeaijb6pMeYXGlqv1dRmgtvteAEAqcNmOQAIwRjzSknvlHREQYszWWu9aku170v6b8aY71prj2/wVF+SNCXpN40xVyi4WcYlkn5J0per/93ov1cfv85a+/3q9/6BMea3JP25pE9IesUG3/ezkpaMMX+vIIznFMwCj0v6B0lf2+DrASC1mBEGgC4ZYy5REEYflfQGa+1K7WPW2hMKbrYxKumzxpjB9Z7LWvuEpGskfUZBne91kv6Zgl7Av9rke/9bSa+U9MXGO9pZaz+sYNPby40x/36DH+Pdkv6fpOcq6GP8FgV3tLte0kustWvaqgFAVuR8v1nLSgAAACDbmBEGAACAkwjCAAAAcBJBGAAAAE4iCAMAAMBJBGEAAAA4iSAMAAAAJxGEAQAA4CSCMAAAAJxEEAYAAICTCMIAAABw0v8HP3dgVgcLABkAAAAASUVORK5CYII=\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "reg_deg=1\n",
- "\n",
- "a_hat = np.polyfit(X_norm.reshape(-1,), Y_norm.reshape(-1,), reg_deg)\n",
- "fy_hat = np.poly1d( a_hat )\n",
- "\n",
- "print(f'Nombre de degrés : {reg_deg}')\n",
- "draw_reg(X_norm[:nb_viz],Y_norm[:nb_viz], X_norm,fy_hat, (width,height), save_as='02-underfitting')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 3.2 - Good fitting"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:40:57.120204Z",
- "iopub.status.busy": "2021-03-01T17:40:57.108935Z",
- "iopub.status.idle": "2021-03-01T17:40:57.646782Z",
- "shell.execute_reply": "2021-03-01T17:40:57.647282Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Nombre de degrés : 5\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/POLR1-03-good_fitting</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "reg_deg=5\n",
- "\n",
- "a_hat = np.polyfit(X_norm.reshape(-1,), Y_norm.reshape(-1,), reg_deg)\n",
- "fy_hat = np.poly1d( a_hat )\n",
- "\n",
- "print(f'Nombre de degrés : {reg_deg}')\n",
- "draw_reg(X_norm[:nb_viz],Y_norm[:nb_viz], X_norm,fy_hat, (width,height), save_as='03-good_fitting')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 3.3 - Overfitting"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:40:57.651619Z",
- "iopub.status.busy": "2021-03-01T17:40:57.651146Z",
- "iopub.status.idle": "2021-03-01T17:40:58.195012Z",
- "shell.execute_reply": "2021-03-01T17:40:58.195501Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Nombre de degrés : 24\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/POLR1-04-over_fitting</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "reg_deg=24\n",
- "\n",
- "a_hat = np.polyfit(X_norm.reshape(-1,), Y_norm.reshape(-1,), reg_deg)\n",
- "fy_hat = np.poly1d( a_hat )\n",
- "\n",
- "print(f'Nombre de degrés : {reg_deg}')\n",
- "draw_reg(X_norm[:nb_viz],Y_norm[:nb_viz], X_norm,fy_hat, (width,height), save_as='04-over_fitting')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:40:58.198952Z",
- "iopub.status.busy": "2021-03-01T17:40:58.198481Z",
- "iopub.status.idle": "2021-03-01T17:40:58.201335Z",
- "shell.execute_reply": "2021-03-01T17:40:58.200830Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Monday 01 March 2021, 18:40:58\n",
- "Duration is : 00:00:02 105ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/LinearReg/04-Logistic-Regression==done==.ipynb b/LinearReg/04-Logistic-Regression==done==.ipynb
deleted file mode 100644
index 3afac4f3474f00db0eeeb9ccaf6c83443e3c834a..0000000000000000000000000000000000000000
--- a/LinearReg/04-Logistic-Regression==done==.ipynb
+++ /dev/null
@@ -1,829 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [LOGR1] - Logistic regression\n",
- "<!-- DESC --> Simple example of logistic regression with a sklearn solution\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - A logistic regression has the objective of providing a probability of belonging to a class. \n",
- " - Découvrir une implémentation 100% Tensorflow ..et apprendre à aimer Keras\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- "X contains characteristics \n",
- "y contains the probability of membership (1 or 0) \n",
- "\n",
- "We'll look for a value of $\\theta$ such that the linear regression $\\theta^{T}X$ can be used to calculate our probability: \n",
- "\n",
- "$\\hat{p} = h_\\theta(X) = \\sigma(\\theta^T{X})$ \n",
- "\n",
- "Where $\\sigma$ is the logit function, typically a sigmoid (S) function: \n",
- "\n",
- "$\n",
- "\\sigma(t) = \\dfrac{1}{1 + \\exp(-t)}\n",
- "$ \n",
- "\n",
- "The predicted value $\\hat{y}$ will then be calculated as follows:\n",
- "\n",
- "$\n",
- "\\hat{y} =\n",
- "\\begin{cases}\n",
- " 0 & \\text{if } \\hat{p} < 0.5 \\\\\n",
- " 1 & \\text{if } \\hat{p} \\geq 0.5\n",
- "\\end{cases}\n",
- "$\n",
- "\n",
- "**Calculation of the cost of the regression:** \n",
- "For a training observation x, the cost can be calculated as follows: \n",
- "\n",
- "$\n",
- "c(\\theta) =\n",
- "\\begin{cases}\n",
- " -\\log(\\hat{p}) & \\text{if } y = 1 \\\\\n",
- " -\\log(1 - \\hat{p}) & \\text{if } y = 0\n",
- "\\end{cases}\n",
- "$\n",
- "\n",
- "The regression cost function (log loss) over the whole training set can be written as follows: \n",
- "\n",
- "$\n",
- "J(\\theta) = -\\dfrac{1}{m} \\sum_{i=1}^{m}{\\left[ y^{(i)} log\\left(\\hat{p}^{(i)}\\right) + (1 - y^{(i)}) log\\left(1 - \\hat{p}^{(i)}\\right)\\right]}\n",
- "$\n",
- "## Step 1 - Import and init"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:00.509961Z",
- "iopub.status.busy": "2021-03-01T17:41:00.509487Z",
- "iopub.status.idle": "2021-03-01T17:41:03.753641Z",
- "shell.execute_reply": "2021-03-01T17:41:03.754137Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>\n",
- "\n",
- "div.warn { \n",
- " background-color: #fcf2f2;\n",
- " border-color: #dFb5b4;\n",
- " border-left: 5px solid #dfb5b4;\n",
- " padding: 0.5em;\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "div.nota { \n",
- " background-color: #DAFFDE;\n",
- " border-left: 5px solid #92CC99;\n",
- " padding: 0.5em;\n",
- " }\n",
- "\n",
- "div.todo:before { 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- " margin-top:-20px;\n",
- " margin-bottom:20px;\n",
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- " font-size: 1.1em;\n",
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- " font-size:0.8em;\n",
- " color:#696969;\n",
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- "\n",
- "\n",
- "\n",
- "</style>\n",
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- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 2.0.17\n",
- "Notebook id : LOGR1\n",
- "Run time : Monday 01 March 2021, 18:41:03\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "from sklearn import metrics\n",
- "from sklearn.linear_model import LogisticRegression\n",
- "\n",
- "import matplotlib\n",
- "import matplotlib.pyplot as plt\n",
- "# import math\n",
- "import random\n",
- "# import os\n",
- "import sys\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "datasets_dir = pwk.init('LOGR1')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.1 - Usefull stuff"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:03.774249Z",
- "iopub.status.busy": "2021-03-01T17:41:03.756507Z",
- "iopub.status.idle": "2021-03-01T17:41:03.780178Z",
- "shell.execute_reply": "2021-03-01T17:41:03.779679Z"
- },
- "jupyter": {
- "source_hidden": true
- }
- },
- "outputs": [],
- "source": [
- "def vector_infos(name,V):\n",
- " '''Displaying some information about a vector'''\n",
- " with np.printoptions(precision=4, suppress=True):\n",
- " print(\"{:16} : ndim={} shape={:10} Mean = {} Std = {}\".format( name,V.ndim, str(V.shape), V.mean(axis=0), V.std(axis=0)))\n",
- "\n",
- " \n",
- "def do_i_have_it(hours_of_work, hours_of_sleep):\n",
- " '''Returns the exam result based on work and sleep hours'''\n",
- " hours_of_sleep_min = 5\n",
- " hours_of_work_min = 4\n",
- " hours_of_game_max = 3\n",
- " # ---- Have to sleep and work\n",
- " if hours_of_sleep < hours_of_sleep_min: return 0\n",
- " if hours_of_work < hours_of_work_min: return 0\n",
- " # ---- Gameboy is not good for you\n",
- " hours_of_game = 24 - 10 - hours_of_sleep - hours_of_work + random.gauss(0,0.4)\n",
- " if hours_of_game > hours_of_game_max: return 0\n",
- " # ---- Fine, you got it\n",
- " return 1\n",
- "\n",
- "\n",
- "def make_students_dataset(size, noise):\n",
- " '''Fabrique un dataset pour <size> étudiants'''\n",
- " x = []\n",
- " y = []\n",
- " for i in range(size):\n",
- " w = random.gauss(5,1)\n",
- " s = random.gauss(7,1.5)\n",
- " r = do_i_have_it(w,s)\n",
- " x.append([w,s])\n",
- " y.append(r)\n",
- " return (np.array(x), np.array(y))\n",
- "\n",
- "\n",
- "def plot_data(x,y, colors=('green','red'), legend=True):\n",
- " '''Affiche un dataset'''\n",
- " fig, ax = plt.subplots(1, 1)\n",
- " fig.set_size_inches(10,8)\n",
- " ax.plot(x[y==1, 0], x[y==1, 1], 'o', color=colors[0], markersize=4, label=\"y=1 (positive)\")\n",
- " ax.plot(x[y==0, 0], x[y==0, 1], 'o', color=colors[1], markersize=4, label=\"y=0 (negative)\")\n",
- " if legend : ax.legend()\n",
- " plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
- " plt.xlabel('Hours of work')\n",
- " plt.ylabel('Hours of sleep')\n",
- " plt.show()\n",
- "\n",
- "\n",
- "def plot_results(x_test,y_test, y_pred):\n",
- " '''Affiche un resultat'''\n",
- "\n",
- " precision = metrics.precision_score(y_test, y_pred)\n",
- " recall = metrics.recall_score(y_test, y_pred)\n",
- "\n",
- " print(\"Accuracy = {:5.3f} Recall = {:5.3f}\".format(precision, recall))\n",
- "\n",
- " x_pred_positives = x_test[ y_pred == 1 ] # items prédits positifs\n",
- " x_real_positives = x_test[ y_test == 1 ] # items réellement positifs\n",
- " x_pred_negatives = x_test[ y_pred == 0 ] # items prédits négatifs\n",
- " x_real_negatives = x_test[ y_test == 0 ] # items réellement négatifs\n",
- "\n",
- " fig, axs = plt.subplots(2, 2)\n",
- " fig.subplots_adjust(wspace=.1,hspace=0.2)\n",
- " fig.set_size_inches(14,10)\n",
- " \n",
- " axs[0,0].plot(x_pred_positives[:,0], x_pred_positives[:,1], 'o',color='lightgreen', markersize=10, label=\"Prédits positifs\")\n",
- " axs[0,0].plot(x_real_positives[:,0], x_real_positives[:,1], 'o',color='green', markersize=4, label=\"Réels positifs\")\n",
- " axs[0,0].legend()\n",
- " axs[0,0].tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
- " axs[0,0].set_xlabel('$x_1$')\n",
- " axs[0,0].set_ylabel('$x_2$')\n",
- "\n",
- "\n",
- " axs[0,1].plot(x_pred_negatives[:,0], x_pred_negatives[:,1], 'o',color='lightsalmon', markersize=10, label=\"Prédits négatifs\")\n",
- " axs[0,1].plot(x_real_negatives[:,0], x_real_negatives[:,1], 'o',color='red', markersize=4, label=\"Réels négatifs\")\n",
- " axs[0,1].legend()\n",
- " axs[0,1].tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
- " axs[0,1].set_xlabel('$x_1$')\n",
- " axs[0,1].set_ylabel('$x_2$')\n",
- " \n",
- " axs[1,0].plot(x_pred_positives[:,0], x_pred_positives[:,1], 'o',color='lightgreen', markersize=10, label=\"Prédits positifs\")\n",
- " axs[1,0].plot(x_pred_negatives[:,0], x_pred_negatives[:,1], 'o',color='lightsalmon', markersize=10, label=\"Prédits négatifs\")\n",
- " axs[1,0].plot(x_real_positives[:,0], x_real_positives[:,1], 'o',color='green', markersize=4, label=\"Réels positifs\")\n",
- " axs[1,0].plot(x_real_negatives[:,0], x_real_negatives[:,1], 'o',color='red', markersize=4, label=\"Réels négatifs\")\n",
- " axs[1,0].tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
- " axs[1,0].set_xlabel('$x_1$')\n",
- " axs[1,0].set_ylabel('$x_2$')\n",
- "\n",
- " axs[1,1].pie([precision,1-precision], explode=[0,0.1], labels=[\"\",\"Errors\"], \n",
- " autopct='%1.1f%%', shadow=False, startangle=70, colors=[\"lightsteelblue\",\"coral\"])\n",
- " axs[1,1].axis('equal')\n",
- "\n",
- " plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.2 - Parameters"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:03.783331Z",
- "iopub.status.busy": "2021-03-01T17:41:03.782858Z",
- "iopub.status.idle": "2021-03-01T17:41:03.784472Z",
- "shell.execute_reply": "2021-03-01T17:41:03.784947Z"
- }
- },
- "outputs": [],
- "source": [
- "data_size = 1000 # Number of observations\n",
- "data_cols = 2 # observation size\n",
- "data_noise = 0.2\n",
- "random_seed = 123"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Data preparation\n",
- "### 2.1 - Get some data\n",
- "The data here are totally fabricated and represent the **examination results** (passed or failed) based on the students' **working** and **sleeping hours** . \n",
- "X=(working hours, sleeping hours) y={result} where result=0 (failed) or 1 (passed)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:03.791545Z",
- "iopub.status.busy": "2021-03-01T17:41:03.791083Z",
- "iopub.status.idle": "2021-03-01T17:41:03.792755Z",
- "shell.execute_reply": "2021-03-01T17:41:03.793226Z"
- }
- },
- "outputs": [],
- "source": [
- "x_data,y_data=make_students_dataset(data_size,data_noise)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.2 - Show it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:03.807390Z",
- "iopub.status.busy": "2021-03-01T17:41:03.803795Z",
- "iopub.status.idle": "2021-03-01T17:41:03.956843Z",
- "shell.execute_reply": "2021-03-01T17:41:03.957329Z"
- }
- },
- "outputs": [
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset X : ndim=2 shape=(1000, 2) Mean = [4.9764 6.9711] Std = [0.9792 1.4549]\n",
- "Dataset y : ndim=1 shape=(1000,) Mean = 0.652 Std = 0.47633601585435464\n"
- ]
- }
- ],
- "source": [
- "plot_data(x_data, y_data)\n",
- "vector_infos('Dataset X',x_data)\n",
- "vector_infos('Dataset y',y_data)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.3 - Preparation of data\n",
- "\n",
- "We're going to:\n",
- "- split the data to have : :\n",
- " - a training set\n",
- " - a test set\n",
- "- normalize the data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:03.962409Z",
- "iopub.status.busy": "2021-03-01T17:41:03.961937Z",
- "iopub.status.idle": "2021-03-01T17:41:03.965802Z",
- "shell.execute_reply": "2021-03-01T17:41:03.966283Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "X_train : ndim=2 shape=(800, 2) Mean = [-0. -0.] Std = [1. 1.]\n",
- "y_train : ndim=1 shape=(800,) Mean = 0.64875 Std = 0.47736090906147727\n",
- "X_test : ndim=2 shape=(200, 2) Mean = [0.002 0.0961] Std = [1.0283 0.942 ]\n",
- "y_test : ndim=1 shape=(200,) Mean = 0.665 Std = 0.4719904660054057\n"
- ]
- }
- ],
- "source": [
- "# ---- Split data\n",
- "\n",
- "n = int(data_size * 0.8)\n",
- "x_train = x_data[:n]\n",
- "y_train = y_data[:n]\n",
- "x_test = x_data[n:]\n",
- "y_test = y_data[n:]\n",
- "\n",
- "# ---- Normalization\n",
- "\n",
- "mean = np.mean(x_train, axis=0)\n",
- "std = np.std(x_train, axis=0)\n",
- "\n",
- "x_train = (x_train-mean)/std\n",
- "x_test = (x_test-mean)/std\n",
- "\n",
- "# ---- About it\n",
- "\n",
- "vector_infos('X_train',x_train)\n",
- "vector_infos('y_train',y_train)\n",
- "vector_infos('X_test',x_test)\n",
- "vector_infos('y_test',y_test)\n",
- "\n",
- "y_train_h = y_train.reshape(-1,) # nécessaire pour la visu."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.4 - Have a look"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:03.969870Z",
- "iopub.status.busy": "2021-03-01T17:41:03.969409Z",
- "iopub.status.idle": "2021-03-01T17:41:04.236685Z",
- "shell.execute_reply": "2021-03-01T17:41:04.237172Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "**This is what we know :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**This is what we want to classify :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "pwk.display_md('**This is what we know :**')\n",
- "plot_data(x_train, y_train)\n",
- "pwk.display_md('**This is what we want to classify :**')\n",
- "plot_data(x_test, y_test, colors=(\"gray\",\"gray\"), legend=False)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Logistic model #1\n",
- "### 3.1 - Here is the classifier"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:04.240904Z",
- "iopub.status.busy": "2021-03-01T17:41:04.240432Z",
- "iopub.status.idle": "2021-03-01T17:41:04.245830Z",
- "shell.execute_reply": "2021-03-01T17:41:04.246307Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "convergence after 15 epochs took 0 seconds\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
- "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s finished\n"
- ]
- }
- ],
- "source": [
- "# ---- Create an instance\n",
- "# Use SAGA solver (Stochastic Average Gradient descent solver)\n",
- "#\n",
- "logreg = LogisticRegression(C=1e5, verbose=1, solver='saga')\n",
- "\n",
- "# ---- Fit the data.\n",
- "#\n",
- "logreg.fit(x_train, y_train)\n",
- "\n",
- "# ---- Do a prediction\n",
- "#\n",
- "y_pred = logreg.predict(x_test)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "### 3.3 - Evaluation\n",
- "\n",
- "Accuracy = Ability to avoid false positives = $\\frac{Tp}{Tp+Fp}$ \n",
- "Recall = Ability to find the right positives = $\\frac{Tp}{Tp+Fn}$ \n",
- "Avec : \n",
- "$T_p$ (true positive) Correct positive answer \n",
- "$F_p$ (false positive) False positive answer \n",
- "$T_n$ (true negative) Correct negative answer \n",
- "$F_n$ (false negative) Wrong negative answer "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:04.249373Z",
- "iopub.status.busy": "2021-03-01T17:41:04.248912Z",
- "iopub.status.idle": "2021-03-01T17:41:04.920033Z",
- "shell.execute_reply": "2021-03-01T17:41:04.920538Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy = 0.934 Recall = 0.955\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1008x720 with 4 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_results(x_test,y_test, y_pred)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Bending the space to a model #2 ;-)\n",
- "\n",
- "We're going to increase the characteristics of our observations, with : ${x_1}^2$, ${x_2}^2$, ${x_1}^3$ et ${x_2}^3$ \n",
- "\n",
- "$\n",
- "X=\n",
- "\\begin{bmatrix}1 & x_{11} & x_{12} \\\\\n",
- "\\vdots & \\dots\\\\\n",
- "1 & x_{m1} & x_{m2} \\end{bmatrix}\n",
- "\\text{et }\n",
- "X_{ng}=\\begin{bmatrix}1 & x_{11} & x_{12} & x_{11}^2 & x_{12}^2& x_{11}^3 & x_{12}^3 \\\\\n",
- "\\vdots & & & \\dots \\\\\n",
- "1 & x_{m1} & x_{m2} & x_{m1}^2 & x_{m2}^2& x_{m1}^3 & x_{m2}^3 \\end{bmatrix}\n",
- "$\n",
- "\n",
- "Note : `sklearn.preprocessing.PolynomialFeatures` can do that for us, but we'll do it ourselves:\n",
- "### 4.1 - Extend data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:04.924773Z",
- "iopub.status.busy": "2021-03-01T17:41:04.924305Z",
- "iopub.status.idle": "2021-03-01T17:41:04.926497Z",
- "shell.execute_reply": "2021-03-01T17:41:04.926971Z"
- }
- },
- "outputs": [],
- "source": [
- "x_train_enhanced = np.c_[x_train,\n",
- " x_train[:, 0] ** 2,\n",
- " x_train[:, 1] ** 2,\n",
- " x_train[:, 0] ** 3,\n",
- " x_train[:, 1] ** 3]\n",
- "x_test_enhanced = np.c_[x_test,\n",
- " x_test[:, 0] ** 2,\n",
- " x_test[:, 1] ** 2,\n",
- " x_test[:, 0] ** 3,\n",
- " x_test[:, 1] ** 3]\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 4.2 - Run the classifier\n",
- "...and with Tensorboard tracking and checkpoint recording."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:04.930562Z",
- "iopub.status.busy": "2021-03-01T17:41:04.930098Z",
- "iopub.status.idle": "2021-03-01T17:41:05.439322Z",
- "shell.execute_reply": "2021-03-01T17:41:05.439795Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "convergence after 3794 epochs took 1 seconds\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.5s finished\n"
- ]
- }
- ],
- "source": [
- "# ---- Create an instance\n",
- "# Use SAGA solver (Stochastic Average Gradient descent solver)\n",
- "#\n",
- "logreg = LogisticRegression(C=1e5, verbose=1, solver='saga', max_iter=5000)\n",
- "\n",
- "# ---- Fit the data.\n",
- "#\n",
- "logreg.fit(x_train_enhanced, y_train)\n",
- "\n",
- "# ---- Do a prediction\n",
- "#\n",
- "y_pred = logreg.predict(x_test_enhanced)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 4.3 - Evaluation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:05.442868Z",
- "iopub.status.busy": "2021-03-01T17:41:05.442409Z",
- "iopub.status.idle": "2021-03-01T17:41:05.992581Z",
- "shell.execute_reply": "2021-03-01T17:41:05.993096Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy = 0.949 Recall = 0.977\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1008x720 with 4 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_results(x_test_enhanced, y_test, y_pred)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-03-01T17:41:05.996382Z",
- "iopub.status.busy": "2021-03-01T17:41:05.995914Z",
- "iopub.status.idle": "2021-03-01T17:41:05.998732Z",
- "shell.execute_reply": "2021-03-01T17:41:05.998240Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Monday 01 March 2021, 18:41:05\n",
- "Duration is : 00:00:02 247ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/README.ipynb b/README.ipynb
index 977fcdbeebed859b428c566eb96a267665cf976f..fb29089f2dc2e8fbc2411cbd6e968d230aef0b5b 100644
--- a/README.ipynb
+++ b/README.ipynb
@@ -3,13 +3,13 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "7b719e4f",
+ "id": "b1c7f34f",
"metadata": {
"execution": {
- "iopub.execute_input": "2021-11-03T10:54:20.206711Z",
- "iopub.status.busy": "2021-11-03T10:54:20.206233Z",
- "iopub.status.idle": "2021-11-03T10:54:20.217397Z",
- "shell.execute_reply": "2021-11-03T10:54:20.216947Z"
+ "iopub.execute_input": "2021-11-05T17:13:16.992221Z",
+ "iopub.status.busy": "2021-11-05T17:13:16.991863Z",
+ "iopub.status.idle": "2021-11-05T17:13:16.999547Z",
+ "shell.execute_reply": "2021-11-05T17:13:16.999868Z"
},
"jupyter": {
"source_hidden": true
@@ -41,24 +41,25 @@
" - Apprehend the **academic computing environments** Tier-2 or Tier-1 with powerfull GPU\n",
"\n",
"For more information, see **https://fidle.cnrs.fr** :\n",
+ "- **[Fidle site](https://fidle.cnrs.fr)**\n",
"- **[Presentation of the training](https://fidle.cnrs.fr/presentation)**\n",
"- **[Program 2021/2022](https://fidle.cnrs.fr/programme)**\n",
"- [Subscribe to the list](https://fidle.cnrs.fr/listeinfo), to stay informed !\n",
"- [Find us on youtube](https://fidle.cnrs.fr/youtube)\n",
"\n",
- "For more information, you can contact us at : \n",
+ "For more information, you can contact us at : \n",
"[<img width=\"200px\" style=\"vertical-align:middle\" src=\"fidle/img/00-Mail_contact.svg\"></img>](#top)\n",
"\n",
"Current Version : <!-- VERSION_BEGIN -->\n",
- "**2.0.28**\n",
+ "**2.0.29**\n",
"<!-- VERSION_END -->\n",
"\n",
"\n",
"## Course materials\n",
"\n",
- "| | | |\n",
- "|:--:|:--:|:--:|\n",
- "| **[<img width=\"50px\" src=\"fidle/img/00-Fidle-pdf.svg\"></img><br>Course slides](https://fidle.cnrs.fr/supports)**<br>The course in pdf format<br>(12 Mo)| **[<img width=\"50px\" src=\"fidle/img/00-Notebooks.svg\"></img><br>Notebooks](https://fidle.cnrs.fr/notebooks)**<br> Get a Zip or clone this repository <br>(40 Mo)| **[<img width=\"50px\" src=\"fidle/img/00-Datasets-tar.svg\"></img><br>Datasets](https://fidle.cnrs.fr/fidle-datasets.tar)**<br>All the needed datasets<br>(1.2 Go)|\n",
+ "| | | | |\n",
+ "|:--:|:--:|:--:|:--:|\n",
+ "| **[<img width=\"50px\" src=\"fidle/img/00-Fidle-pdf.svg\"></img><br>Course slides](https://fidle.cnrs.fr/supports)**<br>The course in pdf format<br>(12 Mo)| **[<img width=\"50px\" src=\"fidle/img/00-Notebooks.svg\"></img><br>Notebooks](https://fidle.cnrs.fr/notebooks)**<br> Get a Zip or clone this repository <br>(40 Mo)| **[<img width=\"50px\" src=\"fidle/img/00-Datasets-tar.svg\"></img><br>Datasets](https://fidle.cnrs.fr/fidle-datasets.tar)**<br>All the needed datasets<br>(1.2 Go)|**[<img width=\"50px\" src=\"fidle/img/00-Videos.svg\"></img><br>Videos](https://fidle.cnrs.fr/youtube)**<br> Our Youtube channel <br> |\n",
"\n",
"Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)** these notebooks and datasets.\n",
"\n",
@@ -217,7 +218,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
diff --git a/README.md b/README.md
index c742514961799e9f05c092c10af4e098d8285a45..88d31a02c5155aa4e9cc3660780bb13913895f85 100644
--- a/README.md
+++ b/README.md
@@ -20,24 +20,25 @@ The objectives of this training are :
- Apprehend the **academic computing environments** Tier-2 or Tier-1 with powerfull GPU
For more information, see **https://fidle.cnrs.fr** :
+- **[Fidle site](https://fidle.cnrs.fr)**
- **[Presentation of the training](https://fidle.cnrs.fr/presentation)**
- **[Program 2021/2022](https://fidle.cnrs.fr/programme)**
- [Subscribe to the list](https://fidle.cnrs.fr/listeinfo), to stay informed !
- [Find us on youtube](https://fidle.cnrs.fr/youtube)
-For more information, you can contact us at :
+For more information, you can contact us at :
[<img width="200px" style="vertical-align:middle" src="fidle/img/00-Mail_contact.svg"></img>](#top)
Current Version : <!-- VERSION_BEGIN -->
-**2.0.28**
+**2.0.29**
<!-- VERSION_END -->
## Course materials
-| | | |
-|:--:|:--:|:--:|
-| **[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img><br>Course slides](https://fidle.cnrs.fr/supports)**<br>The course in pdf format<br>(12 Mo)| **[<img width="50px" src="fidle/img/00-Notebooks.svg"></img><br>Notebooks](https://fidle.cnrs.fr/notebooks)**<br> Get a Zip or clone this repository <br>(40 Mo)| **[<img width="50px" src="fidle/img/00-Datasets-tar.svg"></img><br>Datasets](https://fidle.cnrs.fr/fidle-datasets.tar)**<br>All the needed datasets<br>(1.2 Go)|
+| | | | |
+|:--:|:--:|:--:|:--:|
+| **[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img><br>Course slides](https://fidle.cnrs.fr/supports)**<br>The course in pdf format<br>(12 Mo)| **[<img width="50px" src="fidle/img/00-Notebooks.svg"></img><br>Notebooks](https://fidle.cnrs.fr/notebooks)**<br> Get a Zip or clone this repository <br>(40 Mo)| **[<img width="50px" src="fidle/img/00-Datasets-tar.svg"></img><br>Datasets](https://fidle.cnrs.fr/fidle-datasets.tar)**<br>All the needed datasets<br>(1.2 Go)|**[<img width="50px" src="fidle/img/00-Videos.svg"></img><br>Videos](https://fidle.cnrs.fr/youtube)**<br> Our Youtube channel <br> |
Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)** these notebooks and datasets.
diff --git a/fidle/02-running-ci-tests.ipynb b/fidle/02-running-ci-tests.ipynb
index b0679d93cf158f85ee9f97fe33ff2dd08c0fddb4..128f2b91353e9c064a2bd984d3d918b2c35757d8 100644
--- a/fidle/02-running-ci-tests.ipynb
+++ b/fidle/02-running-ci-tests.ipynb
@@ -47,8 +47,8 @@
"outputs": [],
"source": [
"profile_name = './ci/small_cpu.yml'\n",
- "reset = False\n",
- "filter = 'Nb_LINR1'\n",
+ "reset = True\n",
+ "filter = '.*'\n",
"\n",
"pwk.override('profile_name', 'reset', 'filter')"
]
diff --git a/fidle/config.py b/fidle/config.py
index 43bf23d762ca35460e2ec726d9587818927d3ec0..2b4dd5db84502818471d7f9d59125dcbbde848f3 100644
--- a/fidle/config.py
+++ b/fidle/config.py
@@ -14,7 +14,7 @@
# ---- Version -----------------------------------------------------
#
-VERSION = '2.0.28'
+VERSION = '2.0.29'
# ---- Default notebook name ---------------------------------------
#
diff --git a/fidle/img/00-Videos.svg b/fidle/img/00-Videos.svg
new file mode 100644
index 0000000000000000000000000000000000000000..67a37e4c9306238b238c172af00ae6021fd2a578
--- /dev/null
+++ b/fidle/img/00-Videos.svg
@@ -0,0 +1 @@
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diff --git a/fidle/img/00-fidle-header-02.svg b/fidle/img/00-fidle-header-02.svg
deleted file mode 100755
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