diff --git a/Classification.ipynb b/Classification.ipynb new file mode 100644 index 0000000..3f241e2 --- /dev/null +++ b/Classification.ipynb @@ -0,0 +1,369 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "mount_file_id": "1P2LN0ebFShjhx-JorOk5KhdbMi6TmAVw", + "authorship_tag": "ABX9TyMINrdcgPZ9gt/BexhjZbVw", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "059090a31e5840dbb92f25f852179e26": { + "model_module": "jupyter-matplotlib", + "model_name": "MPLCanvasModel", + "model_module_version": "^0.11", + "state": { + "_cursor": "default", + "_data_url": "data:image/png;base64,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", + "_dom_classes": [], + "_figure_label": "Figure 25", + "_image_mode": "full", + "_message": "", + "_model_module": "jupyter-matplotlib", + "_model_module_version": "^0.11", + "_model_name": "MPLCanvasModel", + "_rubberband_height": 0, + "_rubberband_width": 0, + "_rubberband_x": 0, + "_rubberband_y": 0, + "_size": [ + 800, + 300 + ], + "_view_count": null, + "_view_module": "jupyter-matplotlib", + "_view_module_version": "^0.11", + "_view_name": "MPLCanvasView", + "capture_scroll": false, + "footer_visible": false, + "header_visible": false, + "layout": "IPY_MODEL_56570081049a4a348800c0ca56b9eab4", + "pan_zoom_throttle": 33, + "resizable": true, + "toolbar": "IPY_MODEL_a2c0823342e842848f31094bd4f2a01e", + "toolbar_position": "left", + "toolbar_visible": false + } + }, + "56570081049a4a348800c0ca56b9eab4": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "a2c0823342e842848f31094bd4f2a01e": { + "model_module": "jupyter-matplotlib", + "model_name": "ToolbarModel", + "model_module_version": "^0.11", + "state": { + "_current_action": "", + "_dom_classes": [], + "_model_module": "jupyter-matplotlib", + "_model_module_version": "^0.11", + "_model_name": "ToolbarModel", + "_view_count": null, + "_view_module": "jupyter-matplotlib", + "_view_module_version": "^0.11", + "_view_name": "ToolbarView", + "button_style": "", + "collapsed": true, + "layout": "IPY_MODEL_79cbc2b0e676467a854ad9bca0a7e8ac", + "orientation": "vertical", + "toolitems": [ + [ + "Home", + "Reset original view", + "home", + "home" + ], + [ + "Back", + "Back to previous view", + "arrow-left", + "back" + ], + [ + "Forward", + "Forward to next view", + "arrow-right", + "forward" + ], + [ + "Pan", + "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", + "arrows", + "pan" + ], + [ + "Zoom", + "Zoom to rectangle\nx/y fixes axis", + "square-o", + "zoom" + ], + [ + "Download", + "Download plot", + "floppy-o", + "save_figure" + ] + ] + } + }, + "79cbc2b0e676467a854ad9bca0a7e8ac": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + } + } + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "id": "eadHC_J3gQMT" + }, + "outputs": [], + "source": [ + "new_directory = '/content/drive/MyDrive/Harsh/Supervised Machine Learning/Week 3'\n", + "os.chdir(new_directory)\n", + "import numpy as np\n", + "%matplotlib widget\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "\n", + "from lab_utils_common import dlc, plot_data\n", + "from plt_one_addpt_onclick import plt_one_addpt_onclick\n", + "plt.style.use('./deeplearning.mplstyle')" + ] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import output\n", + "output.enable_custom_widget_manager()" + ], + "metadata": { + "id": "u0E9pBBv5yAU" + }, + "execution_count": 62, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "x_train = np.array([0., 1,2,3,4,5])\n", + "y_train = np.array([0,0,0,1,1,1])\n", + "X_train2 = np.array([[0.5, 1.5], [1,1], [1.5, 0.5], [3, 0.5], [2, 2], [1, 2.5]])\n", + "y_train2 = np.array([0, 0, 0, 1, 1, 1])" + ], + "metadata": { + "id": "pgmMpXvbhJRw" + }, + "execution_count": 63, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "pos = y_train == 1\n", + "neg = y_train == 0\n", + "\n", + "fig,ax = plt.subplots(1,2,figsize=(8,3))\n", + "\n", + "#Plot-1\n", + "ax[0].scatter(x_train[pos], y_train[pos], marker ='x', s = 80, c = 'red', label = \"y=1\")\n", + "ax[0].scatter(x_train[neg], y_train[neg], marker='o', s=100, label=\"y=0\", facecolors='none',\n", + " edgecolors=dlc[\"dlblue\"],lw=3)\n", + "\n", + "ax[0].set_ylim(-0.08,1.1)\n", + "ax[0].set_ylabel('y', fontsize = 12)\n", + "ax[0].set_xlabel('x', fontsize = 12)\n", + "ax[0].set_title('one variable plot')\n", + "ax[0].legend()\n", + "\n", + "#Plot-2\n", + "plot_data(X_train2, y_train2, ax[1])\n", + "ax[1].axis([0,4,0,4])\n", + "ax[1].set_ylabel('$x_1$', fontsize=12)\n", + "ax[1].set_xlabel('$x_0$', fontsize=12)\n", + "ax[1].set_title('two variable plot')\n", + "ax[1].legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 325, + "referenced_widgets": [ + "059090a31e5840dbb92f25f852179e26", + "56570081049a4a348800c0ca56b9eab4", + "a2c0823342e842848f31094bd4f2a01e", + "79cbc2b0e676467a854ad9bca0a7e8ac" + ] + }, + "id": "_spJPP0U3gt6", + "outputId": "74912218-b74d-4883-c659-75712330215c" + }, + "execution_count": 80, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyAAAAEsCAYAAAA7Ldc6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABS0ElEQVR4nO3deVxWZf7/8deNuAuigggCLplrkhtj7qK5b7librhMmjQoNdXk9J1qyrJcCkuzmlzLcp0SN9TKyqVpcc+0zCUQFTElcEFZ7t8f9w/yhpsbUDj3Dbyfj4eP6ZxznXM+5+bMfZ3PfS3HlJiYaEZERERERMQALo4OQERERERESg8lICIiIiIiYhglICIiIiIiYhglICIiIiIiYhglICIiIiIiYhglICIiIiIiYhglICIiIiIiYhglICIiIiIiYhglICIiIiIiYhglICIiIiIiYhglICIiIiIiYhglICIGWLlyJR4eHvz2228F3tfDw4OZM2fmWa5fv37069fvTsK7Kx4eHsyaNavA+7399ttERUUVQUQiUtxs2rSJBQsWODoMw/z22294eHiwcuXKAu/br18/evfunWe5WbNm4eHhcQfR3Z07rYtK2z1Q2ikBETFAr1692LFjB7Vq1XJ0KE5j0aJFbNq0ydFhiIgT2Lx5M2+//bajwzBMrVq12LFjB7169XJ0KE6jtN0DpZ2rowMQKclSU1NxdXXF09MTT09PR4cjIiIOZDabSU1NpXz58gQFBTk6HBGHUQuIOMRnn31Gjx49qFWrFgEBAYwaNYoTJ05YlclsZv7yyy/p3LkzPj4+tGvXjo0bN+Y43pEjRxg5ciR16tShVq1a9OrVi71799qN4dNPP8XDw4Mff/wxx7bhw4fToUOHrOX33nuPHj16ULduXQICAnjwwQfZtm2b1T6ZTervv/8+zz33HI0bN6ZmzZr88ccfNrtgrV+/ngEDBnDPPfdQu3ZtOnXqxEcffWQzVrPZzNy5c2natCm1atWiT58+HD582O71AVy6dInHH3+cJk2aULNmTYKCgli2bFme++3atQsPDw82bNjA1KlTqVOnDv7+/jzyyCNcvnw5z/3z+vs2b96c2NhY1qxZg4eHBx4eHkydOjXP44pIyTN16lQ+/vhjzp07l/V90Lx5c9LT0wkICGDOnDlZZY8ePYqHh0eOLkhNmzblX//6V9byhQsXmDJlCvXr16dmzZq0b9+e1atX240jPj6eGjVq8M477+TYNn/+fDw9Pbl06RIAX3zxBcOHD6dRo0ZZddNbb71Fenq61X7Nmzdn8uTJfPDBBwQFBeHl5cW2bdtsdsHav38/48aNy/qeb9OmDS+++CI3btywGe/mzZtp165d1nf7J598Yvf6ANLS0nj99dcJCgqiZs2aNG7cmGeffZaUlJQ89/Xw8OCll166o7roxIkTjB49moCAAGrVqsWDDz7IZ599lrU9t3tASi61gIjhPvvsM0aMGEHnzp1ZsmQJ165d45VXXqF3797s2rULX1/frLKnT5/mmWee4fHHH6dGjRosWLCA8ePH8/3331O/fn0ADh48SN++fQkMDOTNN9+kYsWKLFmyhIceeojt27fTokULm3H07t0bd3d31qxZw3333Ze1/uLFi3zxxRe88MILWetiYmIYO3YsderUIS0tjejoaEJCQli3bh0PPvig1XHnzZtHy5YtiYyMJD09nfLly9s8/5kzZxg0aBCPP/44Li4u7Nmzh2nTppGSksLEiROtyq5atQo/Pz9mz57NrVu3eOWVVxg0aBD79++nWrVqNo+flJRE7969SUlJ4ZlnnqFOnTp8/vnnPPHEE9y8eZMpU6bk+jfK9M9//pMuXbqwePFiTp48yUsvvcT58+ftdp3Kz9/3ww8/ZMSIEdx3330888wzAGohEimlnn76aX7//Xf279/Pxx9/DEC5cuUoU6YM7du35+uvv+app54C4Ouvv6ZixYrs27ePa9euUblyZU6cOMG5c+fo3LkzANeuXaNfv34kJiby3HPPUbt2bdasWcOUKVO4ceMG48ePtxmHt7c3Xbt2Zc2aNTz66KNW21avXk337t2zvqfOnDlD586dmTx5MuXLl+fgwYO89tpr/P7771Z1B1h+0Dly5Aj/+Mc/8PLyIiAgwOb5Y2Njad68OaNGjaJKlSocP36c2bNnc+bMGZYsWWJV9tSpU/zjH//gmWeewcvLiyVLljBx4kRq1KiR9TnYMnnyZKKjo5k+fTpt27bl559/5uWXXyYmJoYPPvgg1/0y3UlddP78eXr37k2VKlWYM2cO7u7uvP/++4wYMYLVq1fTo0ePXO8BKbmUgIjhZs6cSd26dVm3bh2urpZbMCgoiDZt2rBgwQJeeeWVrLK///47W7Zs4Z577gHg/vvvp1GjRnzyySf8/e9/B+C5557Dz8+PqKiorC+s7t27065dO2bPnp1rq0KFChV46KGHWLduHS+88AIuLpYGwXXr1gGWVpDbY86UkZFBly5d+PXXX1m8eHGOBMTLy4uVK1diMpnsfg6Z8Wces2PHjsTHx7N48eIcCciNGzf473//S+XKlQFo3bo1rVu3ZuHChfzf//2fzeO/8847xMbGsnfv3qzPr2vXrvzxxx+89tprTJo0Kevzz03jxo2z+uQ++OCDVKtWjcmTJ/PVV1/RpUsXm/vk5+97//33U65cOWrUqKFuCCKlXL169ahRowblypXL8X3QqVMnXnrpJW7evEn58uXZtWsXDz/8MKtWreJ///sf3bt3Z9euXbi6utKuXTvAMunHyZMn2bhxI506dQKgR48eXLx4kZkzZzJ27FjKlCljM5aQkBAmT57MiRMnuPfeewE4fPgwP/30U1YSBFh9R5vNZtq3b8+tW7d46623eO6557LqE4DExES+/PJLvL29s9bZmpBk0KBBVsd84IEHcHNz49FHH2Xu3LlUr149a/vFixfZsWNH1uf14IMP8sADDzBr1qxcE5C9e/fy3//+l0WLFvHwww8Dljoh83v98OHDBAYG2tw3053URQsXLiQxMZEdO3Zk/XDYs2dP2rZty0svvUSPHj3s3gNSMqkLlhjq2rVrHDp0iCFDhlg9/NatW5e2bduyZ88eq/L33HNP1sMzWB7uvby8OHv2LGD5MtyzZw+DBg3CxcWFtLQ00tLSMJvNdOnSJc9uWCNHjuTcuXN8/fXXWetWr15Nly5drAaMHzx4kJCQEO69915q1KiBp6cnO3fu5Ndff81xzH79+uWZfACcPHmSSZMm0aRJk6wxIitWrLB5zB49emR94QPUqVOHoKAgvv/++1yP//nnn9O6deusVpvMf927d+fy5cscP348zxgfeuihHMsuLi589913NssX9O8rImJP586dSUlJ4dtvvyUjI4M9e/bQrVs3Hnjggazv7a+//pqWLVtSpUoVwPKg7evrm5V8ZBoxYgSXLl2y+93Xv39/qlSpYtVda/Xq1bi7u9OnT5+sdRcuXCAiIoL77rsPLy8vPD09mTlzJn/88QcJCQlWx2zTpo1V8pGbpKQknn/+eVq0aEHNmjXx9PRkypQpmM1mTp48aVXWz8/P6kG9TJkyDBo0iH379pGRkWHz+J9//jnlypVj0KBBVnVCt27dAPKsL+HO6qK9e/cSFBSUlXxkxjt06FCOHDlCUlJSnueVkkctIGKoxMREzGazzS9jb29vYmNjrdbZatItV65cVn/VK1eukJ6ezpw5c6z6Cd8uIyPD6teo27Vr146AgABWrVpF165d+fnnnzl06BDvvfdeVpmzZ88ycOBAGjduzOzZs/Hz88PV1ZWXX36Zn3/+Occx8zPT1dWrV3nooYeoVKkSzz//PPXq1aNcuXIsXryYDz/8MEf5mjVr5ljn5eVltyJNSEjg1KlTuXZtys9YjuznLVeuHB4eHpw/f95m+YL+fUVE7LnvvvuoXr06u3btwt3dneTkZDp27MiJEyfYuHEjZrOZ3bt3ExoamrXPlStXcv0Oytyem0qVKjFgwADWrFnDs88+S0ZGBuvXr+ehhx6iQoUKgKVOefjhh7lw4QLPPPMM9957LxUrVmTz5s3MnTs3x3iK/M5++Nhjj/HVV18xY8YMmjdvTuXKldm3bx9PPvlkjmN6eXnl2L9mzZrcunWLS5cu2awzEhISuHXrllU359vdSZ2QGYu9uujKlSs2W1a8vb0xm80kJibi7u6e57mlZFECIoby8PDAZDIRHx+fY1t8fHyufUhzU7VqVVxcXPjrX/+a1aScXW7JB4DJZGLEiBG88847XL9+ndWrV1OlShX69++fVebzzz8nKSmJpUuXUrt27az1169fz/WYefn++++JjY1l69atWd0GwDJA0JaLFy/mWJeQkICPj0+u56hevTpeXl68+uqrNrc3aNAgzzizn/fWrVskJibmet7C/vuKSOlmMpno0KEDu3btws3NjebNm+Ph4UHnzp2ZOXMm//vf/7h06ZJVa0e1atVstiRnfi/l9T00cuRIPv74Y7755htSUlK4cOECISEhWdtPnz7NgQMHePfdd63Wb926NddryEtKSgpbtmzhmWeesZqQ4+jRozbLZ29lAcv3dbly5XL90al69epUqFAh1zjzkyjdSV1UrVo1m/vFx8djMpkc8q4ScTx1wRJDVa5cmRYtWrBhwwar2UJiYmL47rvv6NixY4GP165dO3788Ufuv/9+WrZsmeNfXkaOHMnVq1fZuHEja9asoX///lSqVClre2aiUbZs2ax1v/76K99++22BYr2drWMmJiayZcsWm+V37NjBtWvXspZ/++03vv/+e7t9Zbt3784vv/yCn5+fzc/Fzc0tzzg//fTTHMsZGRn85S9/sVm+IH/f8uXL5zq7i4iULva+Dzp37sy+ffuIjo7OGt/QokULKleuzKuvvkq5cuVo27ZtVvkOHToQFxfH//73P6vjrFu3Di8vLxo3bmw3lk6dOlG7dm1Wr17NqlWrCAgIoH379lnbbX1/p6amsnbt2oJd9G1u3rxJenq61TGBXMcwnj171qrbU3p6Ohs2bKB169a5/ujWvXt3UlJSSEpKslkn2EsiMt1JXdShQwe+//57q3Ev6enpfPLJJwQGBma1fqhOKF3UAiKGe/bZZxkxYgQhISFMmjSJa9euMWvWLNzd3fnb3/5W4OO9/PLL9OvXjyFDhjB27Fi8vb35/fffOXz4MOnp6TlmJMmuQYMGtGnThn//+9+cO3cuR0tK165dcXV15dFHH+Vvf/sbFy5cYNasWfj5+eXa1zYvbdu2xd3dnSeffJIZM2Zw/fp15syZQ40aNWz2h61YsSJDhgwhPDycW7duMWvWLNzc3HjsscdyPUdYWBiffPIJffr0ISwsjAYNGnD9+nVOnDjB3r17s2Yasef48eOEhYUxdOhQfv31V2bOnEnHjh1zHYAO+f/7NmrUiG+++Ybo6Gi8vb2pXr06derUyTMmESl5GjVqxJUrV1i8eDEtW7akfPnyNGvWDLAkBKmpqezdu5eIiAjAMoagXbt2bNu2jfbt21OxYsWsY40aNYp33nmHsWPH8q9//QtfX1/WrFnDzp07iYyMzHUAeiYXFxeGDx/O0qVLSUtLY+rUqVatGI0aNcLf35+XXnqJMmXK4Orqetcv0KtatSpBQUEsWLAAb29vatSowYcffphrd9eaNWsyYcIEZsyYgaenJ0uWLOHXX39l3rx5uZ6jU6dODBs2jHHjxvHYY49lJSsxMTFs376df//733m2jN9pXfTRRx8xePBgZsyYgZubG4sXL+bXX39lzZo1WeXs3QNS8qgFRAz34IMPsmbNGv744w8mTJjAE088QcOGDYmOjs7XLzDZtWjRgi+++ILq1avzj3/8gyFDhjBjxgyOHj1q9auVPSEhIZw7d87mwMUmTZrwn//8h9jYWB5++GHefPNNXnjhhXwf2xZPT08++OAD0tPTCQ0N5d///jfjxo1jxIgRNsuPHDmSnj178vTTTzN16lQ8PT3ZsGGD3a4EVatWZfv27fTo0YPIyEiGDh3K3/72N7Zs2ZLjGnMza9YszGYzEyZM4KWXXqJXr14sX77c7j75/fs+//zz3HvvvUyYMIHg4OBcu4qJSMk3btw4hg4dyosvvki3bt0YOXJk1rbMdyq5urpafe9mtoZk/z6rXLkymzdvJjg4mBdeeIFRo0bx448/8u677+Y6BW92ISEh/PHHH1y7ds0qFrCMhVu5ciXe3t48+uijPPXUU7Rv357HH3/8Dq/e4v3336dFixY89dRThIWF4e3tnev3Yv369Zk9ezYLFixg7NixnDx5ksWLF9udghcs77R65pln2LBhA6NGjSI0NJT33nuPe+65x+a4kuzupC7y8fEhOjqaxo0b8/e//53Q0FCuXLnCmjVrrGaRtHcPSMljSkxMNDs6CBFxLrt27WLAgAF8+umndO3a1dHhiIiIg3l4ePDkk0/mOt2uSEGoBURERERERAyjBERERERERAyjLlgiIiIiImIYtYCIiEix8eGHH+Lh4cGmTZtsbo+OjiYoKIhWrVoxZswYvWVZRMQJKQEREZFi4bfffmPFihW5vnPg6tWrhIeHs3LlSvbv34+Pjw9z5swxOEoREcmLEhAREXF6GRkZTJs2jdmzZ1OuXDmbZT777DMCAwNp2LAhAJMmTWL9+vVGhikiIvmgBERERJzewoULadu2LS1atMi1TGxsLP7+/lnLAQEBXLhwgbS0NAMiFBGR/NKb0EVExKn99NNPREVFsWXLFkeHIiIihUAJiIiIOLVvvvmGmJgYWrVqBcDFixeJiIggPj6eSZMmZZXz9/fnyy+/zFqOiYmhVq1auLqqqhMRcSalbhreypUr4+KinmciIrfLyMjg2rVrjg4jX/r168fUqVPp37+/1frk5GRatmzJli1baNiwIU899RTly5dn5syZdo+nekFExFpR1wml7mchFxcXVTQiIiXEyy+/jI+PDxMnTsTNzY0333yT0aNHk5aWRpMmTVi0aFGex1C9ICJirFLXAuLm5qaKRkQkm4yMDJKTkx0dhkOoXhARsVbUdYK+cUVERERExDClrgtWdhkZGcTHx5e6aRpdXV3x9vbWr34iIiIitymtz4ZVq1bF3d3dkHOV+gQkPj4eNzc3qlSpkldBiI6G0NDcyyxfDr17g7d34QZZBK5evUp8fDw+Pj6ODkVERETEaZTGZ0Oz2cylS5e4ePEiNWvWLPLzlfqfv9PS0vJ3gwUHw/jxMH++7TLz51u2Bwdbyju5KlWqlLrMXkRERCQvpfHZ0GQy4eXlxc2bNw05X6lPQPKUeYMdO2ZZjojIeaPNn29ZD5ZyxeBGExEREZE7oGfDu6YExJ7sN1im22+022+wTA680aZNm0bdunUxmUwcPHjQ8POLiIiIlFjF7NnQWZ8LlYDYEx2d8wbLFBEB9evnvMEyHTtm2d9gw4YNY/fu3dSpU8fwc4uTio+39EG1Z/nykvPLjK43p5J0vSIijlTMng2d9blQCYg9oaEQGZn79tOnc98WGWl/UFIu5s6dy+TJk7OWExMT8fT05PLly/nav3Pnzvj5+RX4vFJClbA+qnnS9eZUkq5XRMTRDH42LKnPhaV+Fqw8TZ9u+d/csllbIiP/3K+A/vrXv9KwYUNmz56Nh4cHS5cuZdCgQSQkJNCtWzeb+7Rs2ZKlS5fe0fmkBLPVRxWs701bfVR37nT62Tps0vVa/rekXq+IiLMw8NmwpD4XKgHJj4LcaHeRfAB4eHgwbNgwlixZwuOPP86iRYtYvXo1jRo1cqq+e+Lk7PVRBcs9aq+PanF7SNX1WpTU6xURcTYGPRuW1OdCp+yC9fTTT9O8eXM8PDw4fPhwruVWrFhBq1ataNGiBdOmTSM1NbXogpo+HerVs1+mXr27Sj4yTZs2jXfeeYfo6Gi8vLxo2bIlP//8My1atLD5b8KECXd9Tilhilkf1bum6/1TSbxeERFnZNCzYUl8LnTKFpBBgwYxffp0evfunWuZM2fO8Morr/DVV19Rs2ZNHn74YZYtW8YjjzxSNEHNn2+/Xx9Yts+ff9c3WuPGjalfvz6TJ09m9uzZAMU+0xWDhYZCYmLuD6FFMH7JoXS91kra9YqIOCODng1L4nOhU7aAdOjQgdq1a9stExUVRZ8+ffD29sZkMjFx4kTWr19fNAHZ6sqQG1tzQd+BRx55hLS0NIYNG1ag/aZMmYKfnx9nz56lV69eNGjQ4K5jkWJq+nT7A+VsucsuhA6l681bcb5eERFnYvCzYUl7LnTKFpD8iI2Nxd/fP2s5ICCAs2fPFv6JCnKDZbI1GLSAdu7cSVhYGGXLli3Qfu++++4dn1NKIAPHLzkFXW/uSsL1iog4Awc8G5a050KnbAFxGsuX27/B7PX7i4jIe25+G86dO0fjxo3Zv38/EQW9uUVsMXD8klPQ9eZUkq5XRMSRDH42LKnPhcU2AfH39yc2NjZrOSYmpvDnOe7dG5o0sb0tMhJOncq9C0STJpb9C8jX15fjx4+zd+9e3NzcCry/SA4F6aNaEuh6cypJ1ysi4kgGPxuW1OfCYpuADBw4kK1btxIfH4/ZbGbJkiUMGTKkcE/i7W2ZrjL7jRYZSVr4dK6lgnmajX7YTZpomktxDg4Yv+RQut7clYTrFRFxNDvPhlktzbbG6OnZ0IpTJiARERE0bdqUc+fOMXToUFq2bAlAeHg4W7ZsAaBu3brMmDGDXr160bJlSzw9PYtm2rHbbrRfat7LE2/to3a16ZR9F6q8D+Xfgw4B01n+5jZulK2gG0ycx532US2uD6m63rwV5+sVEXEW2ZIQc2QkP46ZzpbfYOMZ+DYebv3ttiREz4Y5mBITE82ODsJIbm5uuLj8mXdlH8xuy8XrMHl7ChvOV7BbrjopzGp5i8kPuBdKrEUtP9cuxdTy5TB+fO7b69Wz321n2bLiNVWrrtfaHVxvRkYGycnJhRJeURg8eDDx8fG4uLjg5ubGq6++yv33329VZteuXQwfPtxqlpcdO3ZQsWJFu8fOXi+ISOmW3+ejq3EXWbb1NG9XacuxK9bbvCrApCYQdmwt/n07F5vkI/Pai7pO0DduHs4kwQP/Jc/kA+AyFZhywJ2n9oK5VKV14nQcMH7JoXS9fyqJ1wssXbqUvXv3snv3bsLCwggLC7NZrkGDBuzevTvrX17Jh4jInTjyO9y3sybhqTmTD4CEFHj1ADRKG87q5OKRfBhJCYgdiTeh92Y4XcAEcO4hmHeoaGISyZfS1kdV12tRUq8X8PDwyPrvpKQkTCaT44IRkVLtp8vQZQP8lo/nwxtpMHIHfHyi6OMqTpSA2PHKfvg50Xqdezl4qgV8OwROjILVPaC7jXcmzvgWYhzQm+HEiRO0b9+ehg0bEhQUxNGjR40PQpxD9odUW++BmF6C+qjqekv29WJ5oVazZs145ZVXcp3b/syZM3Tu3Jng4GDef/99gyMUkZIuNR2GbIMrN3Nuq1YefCvb3m/8F3AisUhDs8lZnws1BiSXfn430qD2CusbrFk12DEAfGzcXMuOw4Sd1uuebQUz2xZW5PnTrVs3xo0bx/jx41m3bh2vvfYa33//vc2yGgNSSsTHQ3S0/TEOy5dbuuUU44fTLLrenPJxvc4+BuR2H330EZ988glr1661Wp+UlITZbKZq1arExcUxfPhwnnrqKQYPHmz3eBoDIiK3s/d8tPYkjNhuva6VJ8xuB91qg8lkaSF5cR+s/tW63GP3wYJORRR0LgryXAjGjQFRApLLTbb8OIzPllD8GALNqud+7Ek7YcnxP5e9K0LsWChbJv/xzZ07l19++YX33nsPgMTERBo0aMAvv/xC9ep2Tg5cvHiRBg0acPnyZVxdXTGbzfj4+LB7926rQZmZlICISKbilIAA1KpVi59++snu9+Lrr7/O+fPnmTNnjt1jKQERkdvZez4K3gBfnvtzuUk1+GEoVMr2gnKz2dLqseKXP9e5lYW4ceBWLv+xGPlcCBqE7nA7zlov9/Czn3wAPB5ovRx/A45cLth5//rXv/Lpp5+SmJgIWAZeDho0iISEBFq0aGHzX+b0w7Gxsfj4+ODq6gqAyWQiICCAmJiYggUhIuJEEhMTOX/+fNbypk2bqF69OtWqVbMqd+HCBTIyMgBITk5m27ZtBAZm+2IWEblDSbeskw+w9HbJnnyApSXkxb/A7aPVklPhq3M5y9pTUp8LXR0dgLO6eMN6uZuNcR7Z3VfDMu1aQkrux8mLh4cHw4YNY8mSJTz++OMsWrSI1atX06hRIw4ePFiwg4mIlABJSUmMHz+elJQUTCYTnp6erFq1CpPJRHh4OH369KFv375ERUWxZMkSypQpQ3p6OoMGDWLMmDGODl9ESoj46znXDaibe/k6bhBYAw79/ue6CzaOYU9JfS5UApKL7BOsZOSzo1p6tnJ3Mk/LtGnTGDhwIE2aNMHLy4uWLVvy888/ExISYrN8y5YtWbp0Kf7+/pw/f560tLSspraYmBgCAgLuIAoREecQEBDAF198YXPbW2+9lfXfkydPZvLkyUaFJSKljK3J9/J6Psy+/U4m8CuJz4VKQHLhnW3q+B1n4Z+t7e+zPwEuZ5sVwbtSwc/duHFj6tevz+TJk5k9ezZAvjLdmjVr0qpVKz788EPGjx/P+vXr8fPzy7Wfn4iIiIjkT61Klh+Wb88pPjkNExrbLn/yj5xd8X31XAhoDEiuemcbe/TlOdiXYH+f17O9+6N2Zbgvj3EjuXnkkUdIS0tj2LBhBdrv3Xff5d1336Vhw4a8+uqrLF269M4CEBEREZEsVcpCj2zPhzP3wR82puQ1my2vZLidRzno4ntn5y5pz4VqAcnF0HsgYo/1eI6RO2B7f6jnnrP8m4dhZbaXzExuCq53mOLt3LmTsLAwypa1MbLJjkaNGvHNN9/c2UlFREREJFdhzWB77J/Lp5Kg7X/h5b/AoHpQxgTfxlum4d2abaz3hMa2B6znR0l7LlQCkovyZWBKM0tmm+nXP6DJKghtBMPqQ40KllaR936CHxJy7v/XbC8pzo9z587RrVs3qlevzrZt2+7uIkRERESk0PSvYxlYfvi2geU/J8Kw7ZYfncu7wLW0nPtVcoXpdzApX0l9LlQCYsc/WsKm3+DgpT/X3Uy3JBzv/WR/3zfa5/42THt8fX05fvx43gVFRERExFBlXOCT3tD+v5bXLdwuLcPyL8c+Jvj4QcusWAVVUp8LNQbEjiplYXNfyxvQC+Klv8DU+4omJhERERFxnPrusHswNPbIu2zVchDVBwbWK/KwihUlIHnwrWy5ycY2tGSw9vhVtmS4/5fHbFkiIiIiUnw1qAoHR8CKbvCAd87tdd3g1Qfg11HQt47x8Tk7U2JiYj7fcFEyuLm54eLyZ951/vx53NzcqFKlSp77nrsG//kJVp+EmGRISYdq5SGoJkxpCv3q3Pmgc6NdvXqV5ORkfHx8HB2KiDiBjIwMkpOTHR2GQ2SvF0SkdCvIs2GmmGSIvQppZqhZERp5gMudvAzOQcxmM5cuXcJsNlOzZs0irxNKfQKSkZFBfHw8aWk2RgyVYK6urnh7e6vSFRFACYi+C0UkU2l9NqxatSru7papXou6Tij1g9BdXFzUCiAiIiIigJ4NjaCffERERERExDBKQERERERExDBKQERERERExDBKQERERERExDBKQERERERExDBKQERERERExDBKQERERERExDBKQERERERExDBKQERERERExDBKQERExOkNHjyY9u3b07FjR/r06cOhQ4dslluxYgWtWrWiRYsWTJs2jdTUVIMjFRGRvDhlAnLy5El69uxJ69atCQ4O5tixYznKZGRk8M9//pO2bdvSvn17+vfvz6lTpxwQrYiIFLWlS5eyd+9edu/eTVhYGGFhYTnKnDlzhldeeYWtW7dy4MABLl68yLJly4wPVkRE7HLKBCQiIoLQ0FD27dtHRESEzYpmy5YtfPvtt+zevZu9e/fSpUsXXnzxRQdEKyIiRc3DwyPrv5OSkjCZTDnKREVF0adPH7y9vTGZTEycOJH169cbGKWIiOSHq6MDyC4hIYGDBw/yySefADBw4ECeeuopTp06Rf369bPKmUwmbt68SUpKCq6uriQnJ+Pr6+uosEVEpIhNmTKF3bt3A7BmzZoc22NjY/H3989aDggI4OzZs4bFJyIi+eN0CUhcXBze3t64ulpCM5lM+Pn5ERsba5WA9OnTh127dtGoUSOqVKmCj48PmzdvdlTYIiJSxN59910APvroI1544QXWrl3r4IhEROROOGUXrPw4cOAAx44d46effuL48eN06dKFJ554wtFhiYhIERs1ahS7du3i8uXLVuv9/f2JjY3NWo6JicHPz8/o8EREJA9Ol4DUrl2b+Ph40tLSADCbzZw9e9aqWR1g1apVdO7cGQ8PD1xcXHj44YfZtWuXI0IWEZEilJiYyPnz57OWN23aRPXq1alWrZpVuYEDB7J161bi4+Mxm80sWbKEIUOGGB2uiIjkwekSEC8vLwIDA1m9ejVgGVTo6+tr1f0KoE6dOnz99dfcunULgG3bttGkSRPD4xURkaKVlJTE6NGjad++PR06dOA///kPq1atwmQyER4ezpYtWwCoW7cuM2bMoFevXrRs2RJPT08mTJjg4OhFRCQ7U2JiotnRQWR34sQJwsLCuHz5Mm5ubixcuJBmzZoRHh5Onz596Nu3Lzdv3uSpp57if//7H66urnh7e/PGG29Qt25du8d2c3PDxcXp8i4REYfKyMggOTnZ0WE4hOoFERFrRV0nOGUCUpRU0YiI5KQERPWCiEimoq4T9I0rIiIiIiKGUQIiIiIiIiKGUQIiIiIiIiKGUQIiIiIiIiKGUQIiIiIiIiKGUQIiIiIiIiKGUQIiIiIiIiKGUQIiIiIiIiKGUQIiIiIiIiKGUQIiIiIiIiKGUQIiIiIiIiKGUQIiIiIiIiKGUQIiIiIiIiKGUQIiIiIi+RcfD8uX2y+zfLmlnIiIDUpAREREJH/i4yE4GMaPh/nzbZeZP9+yPThYSYiI2KQERERERPKWmXwcO2ZZjojImYTMn29ZD5ZySkJExAYlICIiImJf9uQj0+1JyO3JRyYlISJigxIQERFxaikpKYwaNYrWrVvToUMHHnroIU6dOpWj3G+//Ub16tXp2LFj1r/Tp087IOISKDo6Z/KRKSIC6tfPmXxkOnbMsr+IyP/n6ugARERE8jJ+/Hh69OiByWTivffeIzw8nM2bN+coV6VKFXbv3u2ACEu40FBITMw9ybCX6EVGWvYXEfn/1AIiIiJOrUKFCvTs2ROTyQRAUFAQMTExDo6qFJo+3ZJMFERkpGU/EZHbKAEREZFiZdGiRfTt29fmtuvXrxMcHEznzp157bXXSE9PNzi6Eq4gSYiSDxHJhSkxMdHs6CCM5ObmhouL8i4RkdtlZGSQnJzs6DDyNG/ePKKjo9mwYQOVKlWy2nbz5k2SkpLw8vLiypUrTJgwgeDgYKbn8RCseuEO1K9vv9tVvXpgY5yOiBQPRV0n6BtXRESKhbfeeouNGzeydu3aHMkHQPny5fHy8gKgWrVqjBkzhr179xodZsk3f7795AMs23N7T4iIlHpKQERExOktWLCAdevW8emnn+Lh4WGzTEJCAqmpqYClNWTjxo0EBgYaGGUpYGuq3dzYek+IiAjqgiUiIjh3F6y4uDiaNWtG3bp1qVKlCmBp7fj88895+eWX8fHxYeLEiURFRTFr1ixcXFxIT0+nU6dOzJw5k/Lly9s9vuqFfCpI8nE7jQURKXaKuk5QAiIiIk6dgBQ11Qv5sHw5jB+f+/Z69ex3y1q2TFPxihQjGgMiIiIijtW7NzRpYntbZKRlwHlus2M1aWLZX0Tk/3PKBOTkyZP07NmT1q1bExwczLFc3r569OhR+vXrx1/+8hf+8pe/EBUVZXCkIiIipYC3N+zcmTMJub17la0peps0sezn7W1ElCJSTDhlF6wBAwYwcuRIRo8ezYYNG4iMjGTnzp1WZa5fv067du145513aNeuHenp6Vy5cgVPT0+7x1ZTu4hITuqCpXohX+LjITgYjh3LfWxH5lgRJR8ixVapGwOSkJBAq1atOH36NK6urpjNZho1akR0dDT169fPKrdixQq+/vpr3n///QIdXxWNiEhOSkBUL+RbfDxER9sf07F8uaXblZIPkWKp1I0BiYuLw9vbG1dXVwBMJhN+fn7ExsZalTt+/DjlypUjJCSEjh07MmXKFC5duuSIkEVEREoPb++8B5SHhir5EJFcOV0Ckl9paWl89dVXvPHGG+zatQtfX1+eeOIJR4clIiIiIiJ2OF0CUrt2beLj40lLSwPAbDZz9uxZ/P39rcr5+/vTsWNHfH19MZlMjBgxgh9++MERIYuIiIiISD45XQLi5eVFYGAgq1evBiAqKgpfX1+r8R8ADz30EAcOHCApKQmAHTt2cN999xker4iIiIiI5J/TDUIHOHHiBGFhYVy+fBk3NzcWLlxIs2bNCA8Pp0+fPvTt2xeAVatWMX/+fEwmE76+vkRGRuLn52f32BpsKCKSkwahq14QEclU6mbBKmqqaEREclIConpBRCRTqZsFS0RERERESi4lICIiIiIiYhglICIiIiIiYhglICIiIiIiYhglICIiYriMjAw+/vhjR4ch4jjx8bB8uf0yy5dbyomUMEpARETEcKmpqTz22GOODkPEMeLjITgYxo+H+fNtl5k/37I9OFhJiJQ4ro4OQERESqbXXnst122pqakGRiLiRDKTj2PHLMsREZb/nT79zzLz5/+5/tgxS/mdO8Hb28hIRYqMEhARESkSc+fOZdCgQbi7u+fYlp6e7oCIRBwse/KR6fYk5PbkI5OSEClh7vpFhD/88ANt2rQprHiKnF44JSKSU1G8dKpLly7MmDGD3r1759iWkpKCj48PV65cyfM4KSkpTJw4kZ9//pkKFSrg5eXF66+/Tv369XOUjY6O5l//+hfp6ek0bdqUt99+22YCdDvVC2KY5cst3apyU68enD6d+/ZlyyA0tLCjEsnB6V9E2KNHD1q3bs3s2bM5c+ZMIYQkIiIlQWhoKBkZGTa3lS1bln/84x/5Ptb48eP54Ycf2LNnD3379iU8PDxHmatXrxIeHs7KlSvZv38/Pj4+zJkz547jFyl0oaEQGZn7dnvJR2Skkg8pMe66BWTt2rWsXbuWnTt3kp6eTlBQECEhIQwePJhq1aoVVpyFRr90iYjkVNS/dhWmAwcOMG7cOI4cOWK1/tNPP+WDDz5g/fr1ABw/fpwhQ4bw008/2T2e6gUxnK1uVvZERlqPEREpYk7fAjJ8+HDWrFnD8ePHefXVVzGbzfz973+ncePGjBo1ig0bNnDr1q3CiFVERJzYs88+a8h5Fi1aRN++fXOsj42Nxd/fP2s5ICCACxcukJaWZkhcIvk2fbr9lpDbKfmQEqjQfvKpUaMGkydPZvv27ezfv5+///3vnDhxggkTJtCwYUOmT5/ON998U1inExERJ/Pee+8xevRobty4kWuZmJiYuzrHvHnzOH36NM8///xdHUfE4aZPt4z5sKdePSUfUiIVSZtzhQoVqFixIuXLl8dsNmMymdiyZQv9+vUjODiY48ePF8VpRUTEgdauXcuePXvo06cP8dneWxATE8P06dMJCgq64+O/9dZbbNy4kbVr11KpUqUc2/39/YmNjbU6Z61atXB11YSP4oTmz7c/5gMs23N7T4hIMVZoCUhycjIffvghgwYNonnz5rz00ksEBASwfPlyfvnlF44fP87SpUtJSEjQy6dEREqgrl27sn37dv744w+6devG4cOHrRKP1atXM3bs2Ds69oIFC1i3bh2ffvopHh4eNst0796dQ4cO8csvvwCwePFihgwZcqeXI1J0CjIGJCJCSYiUOHc9CH3z5s2sXbuWbdu2kZKSQqtWrRg5ciRDhw6levXqOcovX76cJ598koSEhLs57R3TYEMRkZwKc8DhlStXGDFiBD/99BNpaWmUKVOG0NBQpk+fTq1atQp8vLi4OJo1a0bdunWpUqUKAOXLl+fzzz/n5ZdfxsfHh4kTJwKwZcsWnn/+edLS0mjSpAmLFi2iatWqdo+vekEMVdAB6Jk0FkQMVNSD0O86AalWrRq1a9cmJCSEkSNHcu+999otv2/fPhYvXszbb799N6e9Y6poRERyKqzK5uzZs8yfP58PPviAmzdvYjKZmDNnDpMmTSqEKIuG6gUxjN4DIsVEUScgd90xNioqik6dOuW7fOvWrWnduvXdnlZERJxMeHg4a9aswWQyMW7cOMLDw5k7dy5PP/00SUlJPP74444OUcSxeveGJk1yvgkd/mzhyK2FpEkTy/4iJcBdt4AUN/qlS0Qkp8L4tatWrVqMHTuWJ554Ah8fn6z1b775Jv/+978JCQlh/vz5lC1b9m7DLVSqF8RQ8fEQHGydhGTvXpU9CWnSBHbuBG9vo6KUUs7pu2AVN6poRERyKozK5vz581aJx+02bdrElClTuP/++9myZctdnaewqV4Qw92ehOQ2tiMzCVHyIQ6gBKSQqaIREcnJiDehHzx4kIcffphjtrqfOJDqBXGI+HiIjrY/pmP5cku3KyUfYjAlIIVMFY2ISE5GJCAA586dw9fXt8jPUxCqF0RErBV1naBvXBERMYyzJR8iImI8JSAiIiIiImIYJSAiIiIiImIYJSAiIiIiImIYJSAiIiIiImIYJSAiIiIiImIYp0xATp48Sc+ePWndujXBwcF254w3m80MGDCAgIAAAyMUEREREZE74ZQJSEREBKGhoezbt4+IiAjCwsJyLbtw4ULq1atnYHQiIiIiInKnnC4BSUhI4ODBg4SEhAAwcOBA4uLiOHXqVI6yx44dY/PmzTz++ONGhykiIlJkrqXCiUQ4dgUu3XB0NCIihcvV0QFkFxcXh7e3N66ultBMJhN+fn7ExsZSv379rHKpqalMnz6dt956S2+wFRGRYi/DDF/EwcIfYeMZSDf/ua2FJ4Q1g1H3QuWyDgtRRKRQFNsn99dee40BAwbQqFEjR4ciIiJyVy5eh+AN0GMjfHraOvkAOHgJJn8FDT6Cr885JkYRkcLidAlI7dq1iY+PJy0tDbAMMj979iz+/v5W5fbs2cO7775L8+bN6dOnD8nJyTRv3pxLly45ImwREZE7knADOn0KX5/Pu+yF65Yk5bOzRR6WiEiRcboExMvLi8DAQFavXg1AVFQUvr6+Vt2vALZu3cqPP/7IkSNH2Lp1K25ubhw5cgRPT09HhC0iIlJgZjOEbIdf/sj/PrcyYOg2iL1adHGJiBQlp0tAACIjI1m2bBmtW7fmjTfeYOHChQCEh4ezZcsWB0cnIiJGevrpp2nevDkeHh4cPnzYZpldu3ZRq1YtOnbsmPXvxg3nH7399XnYma1LVa1KsKATXJoAKZNhU19oX8u6TNItmG/7oxARcXqmxMREc97FSg43NzcNWhcRySYjI4Pk5GRHh2HTnj17qFu3Lr1792blypUEBgbmKLNr1y5mzJjB7t27C3x8R9YLIdthzck/l2tWhP3DoHYV63LpGTA4Gjb+9ue6auUhbhxUdLrpZESkuCvqOkFP4iIi4tQ6dOhA7dq1HR1GobuVbhlwfrtnWuZMPgDKuMC89tbrrtzUWBARKZ6UgIiISIlw5swZOnfuTHBwMO+//76jw8nTxRuW8Ry3G35P7uXv9bBMx3s7jQMRkeJIDbciIlLs3X///Rw9epSqVasSFxfH8OHDqVGjBoMHD3Z0aLnKPtUu5N2dqmIZ6+W0DNvlREScmVpARESk2HN3d6dq1aqAZTr3YcOGsXfvXgdHZZ9nBTBlW7c9Nvfyv6fAvgTrdd6VCj0sEZEipwRERESKvQsXLpCRYWkOSE5OZtu2bTYHqzuTymWhk4/1ulcPwM102+VfO2DdZausCwT7Fl18IiJFRQmIiIg4tYiICJo2bcq5c+cYOnQoLVu2BKynZo+KiqJ9+/Z06NCBHj160LVrV8aMGePIsPMl7D7r5cO/Q8+NcOC2lo746/D3vTDnoHXZ4fdATbWAiEgxpGl4RUTEqafhLWqOrBdupUOTVXAqKee2BlXBrSwcuZxzrIcJ+G4otKlpSJgiUspoGl4REZESqlwZWNcTqpTNue3XP+DAJdsDzee2V/IhIsWXEhAREREHaukFnw+wvIQwLy4meL09PHF/0cclIlJUlICIiIg42F+84WgI/DsIfCvn3F6hDIxvBPuGweNKPkSkmNMYEBER0RgQJ6oXUtNh9wWISYbUDEvLSEcfqF7B0ZGJSGlR1HWCXkQoIiLiRMqWgeDajo5CRKToOM9PPiIiIiIiUuIpAREREREREcMoAREREREREcMoAREREREREcMoAREREREREcMoAREREREREcMoAREREREREcMoAREREREREcMoAREREZE7lp4BZrOjoxCR4kRvQhcREZF8u5kO60/Cf47BdxfhehqULwP3VYeJjWFMQ3Av5+goRcSZmRITE0vV7xZubm64uKjhR0TkdhkZGSQnJzs6DIdQvZB/m87AI1/Bheu5l3ErC7PbwaPNDAtLRApZUdcJ+sYVERGRPK34GQZF208+AJJTYerX8K/vjIlLRIofJSAiIiJi157zMHEnZBSgz8TMfZakRUQkOyUgIiLi1J5++mmaN2+Oh4cHhw8fzrXcihUraNWqFS1atGDatGmkpqYaGGXJ9s9vIT1b8vGAN3zYHX4MgU97w4A6Ofeb8S2kphsTo4gUH0pARETEqQ0aNIjo6Gj8/f1zLXPmzBleeeUVtm7dyoEDB7h48SLLli0zLsgS7Mff4evz1uvCm8PewTC6ITSrDoPqQVRfeLuTdblz12Djb8bFKiLFgxIQERFxah06dKB27dp2y0RFRdGnTx+8vb0xmUxMnDiR9evXGxRhyfbhCetl74owpx2YTDnLTr0P2ta0XqduWCKSnVMmICdPnqRnz560bt2a4OBgjh07lqPMV199Rbdu3Wjbti0PPPAAzz33HBkZGQ6IVkREHC02NtaqhSQgIICzZ886MKKS49c/rJdHNLBMu5ubsY2sl08mFX5MIlK8OWUCEhERQWhoKPv27SMiIoKwsLAcZTw8PFiyZAnffvstX375Jd999x0ff/yxA6IVEREpuW5mG8NRs6L98t7ZtqdoDIiIZON0CUhCQgIHDx4kJCQEgIEDBxIXF8epU6esyt1///3UrVsXgAoVKtC8eXNiYmKMDldERJyAv78/sbGxWcsxMTH4+fk5MKKSo3p56+U9522Xy7Q72/bs+4uIOF0CEhcXh7e3N66ulpe0m0wm/Pz8rCqW7OLj49mwYQO9e/c2KkwREXEiAwcOZOvWrcTHx2M2m1myZAlDhgxxdFglQmdf6+VtsXD4d9tlL92ApdnGfHT2KZq4RKT4croEpKCSkpIYOXIk06ZNo2XLlo4OR0RECllERARNmzbl3LlzDB06NOu7Pjw8nC1btgBQt25dZsyYQa9evWjZsiWenp5MmDDBkWGXGCMbQNVyfy6bgYFb4etz1uV+ugx9NkPSLev1U/RGdBHJxpSYmFiA1woVvYSEBFq1asXp06dxdXXFbDbTqFEjoqOjqV+/vlXZ5ORkhg4dSo8ePXjqqafydXw3NzdcXIp93iUiUqgyMjJITk52dBgOoXohb0/thbmHcq4PrAEtasCvSbD3Qs7tA+pYpucVkeKlqOsEp/vG9fLyIjAwkNWrVwOWqRV9fX1zJB9Xr15l2LBhdO/ePd/Jh4iIiBTc80HQ0jPn+sO/w4pfbCcfvpVhYeeij01Eih+nS0AAIiMjWbZsGa1bt+aNN95g4cKFgHVz+zvvvMO+ffvYuHEjHTt2pGPHjsydO9eRYYuIiJRIVcpCdD9o45W/8nXc4LMB4F+laOMSkeLJ6bpgFTU1tYuI5KQuWKoX8uNGGrx5BN7+EWKu5txevTxMagL/aAk1Khgfn4gUjqKuE5SAiIiIEhDVCwWSngHbY+G7i/DHLahcFppXh4F1oYKro6MTkbtV1HWCviZERESkQMq4QJ86ln8iIgWln3xERERERMQwSkBERERERMQwSkBERERERMQwSkBERERERMQwSkBERERERMQwSkBERERERMQwSkBERERERMQwSkBERERERMQwSkBERERERMQwehO6iIiIlDiJN2H5z7DhNJy/blnnUwkG1YPQRuBR3rHxSSGKj4foaAgNzb3M8uXQuzd4exsXl+RKCYiIiIiUGLfS4dlv4e2jcD3NetvxRNh5Dv75LUxtBq+0hXJlHBKmFJb4eAgOhmPHIDERpk/PWWb+fIiIgCZNYOdOJSFOQF2wREREpES4ngp9NsPcQzmTD6tyaTDvkKXs9VTj4pNCdnvyAZYkY/586zKZyQdYygUHW/YTh1ICIiIiIsWe2QyhX8AXcfnf54s4GPu5ZV8pZrInH5luT0JuTz4yKQlxCkpARETE6Z08eZKePXvSunVrgoODOZb9oQPYtWsXtWrVomPHjln/bty44YBoxRE+j4N1p6zXmYBh9WF5N8u/4fdY1t3uv6ct+0oxEx2dM/nIFBEB9evnTD4yHTtm2V8cRmNARETE6UVERBAaGsro0aPZsGEDYWFh7Ny5M0e5Bg0asHv3bgdEKI729o/Wy+7l4LMBEFTzz3XjGsH3F+HBjZB068/1C3+EB/2MiVMKSWioZcxHbknG6dO57xsZaX/AuhQ5tYCIiIhTS0hI4ODBg4SEhAAwcOBA4uLiOHXqVB57Smlx6QZEnbFe91KQdfKRKaimZdvtos5AghrLip/p0y3JREFERtoeqC6GUgIiIiJOLS4uDm9vb1xdLY32JpMJPz8/YmNjc5Q9c+YMnTt3Jjg4mPfff9/oUMVBjl2B9NvGcbiYYHzj3MuPb2wpkynDbDmGFEMFSUKUfDgNdcESEZES4f777+fo0aNUrVqVuLg4hg8fTo0aNRg8eLCjQ5Midi3bjFfeFS1dsHLjXs5SJvP9IADXNBtW8TV9umXAub1uV/XqKflwImoBERERp1a7dm3i4+NJS7M8ZZrNZs6ePYu/v79VOXd3d6pWrZq1z7Bhw9i7d6/h8Yrxsicb569D/HXbZcGy7Xy27VX1YsLiK6/kAyzbs0/RKw6jBERERJyal5cXgYGBrF69GoCoqCh8fX2pX7++VbkLFy6QkZEBQHJyMtu2bSMwMNDweMV491WHCtleKLjwR9tlIeeA9QplLMeQYsjWVLu5sfWeEHEIJSB2XE+FU0lw/Irl15KSPk94WgbEJMNPlyH2qmW5JDOb4cJ1y9/3dBLcsPPSKhFxrMjISJYtW0br1q154403WLhwIQDh4eFs2bIFsCQm7du3p0OHDvTo0YOuXbsyZswYR4YtBnEvByMbWK979QCsOmFdd5vNsPpXmHXAuuzIBva7bImTKkjykUlJiFMwJSYmlvDHamtubm64uOSed2WYYUcsvH0UNv9mPaitnhtMbgqTmoBXRQOCNciR32HRUfjwF0i+rQ9s1XIwtiFMbQZNS9AvQxeuw+Jj8N5PEHP1z/WuLjCgDoTdB91rgyn7ZPEiJVhGRgbJycmODsMh8qoXpHjYlwBt1uVcH1jD8t0OsOk3OPR7zjLfD4U2NmbMEie2fDmMH5/79nr17HfLWrZMU/HaUdR1ghKQ2/z4Ozz8Gfx42f4xyrnAc23gn62K90Nq4k2YsBM+zaPbJFhe3rS4K7gV41+IMszwwveWX8VS82jdaeEJqx6ERtWMiU3E0ZSAKAEpCZ7YA28cLtg+EYHwRoeiiUeKUG5vQoc/Z7vKrYWkSRPYuRO8vYs6ymJLCUghy62i+d8F6LXZ+sVEeZnYGP7T1Xoqv+Ii4QZ0i8o72bpdS0/4fCBUK4YD9dIzYNwX8NGJ/O9TrTzsGACtvYouLhFnoQRECUhJkJ4Bk7+CJcfzV35iY3ivC5TRn794spWEZJ9qN3sSouQjX4q6TtD/5YAzSdB/a8GSD7B8wf37+6KJqSilpsOgrQVLPgAOXIIh0ZYv+OLmn98WLPkAuHIT+m2GuKt5lxUREccr4wLvd7UkFfXdcy9Xzw3e7WIpq+SjGPP2tiQTTZpYlm295+P294Qo+XAaagEBRn+W8+G0QVV48n4YUh88ysG3F+Gdo7AyW7kyJjg1GgLcijjwQvT+T/DIV9br3MvB9OYwrhHUdbMMvl96HN46knN+9Q+7w+iGxsV7t04kQqOP4fYb3QSENoIpTS0tHFduwtpTMPcgnMmW8E9qDO8HGxeviCOoBURPoSVNhhm2xcCGM3DummWdb2UYVBd6BRTP3guSi/h4iI62P6Zj+XLo3VvJRz6Vyi5YJ0+eZOrUqfz++++4u7vz9ttv0yQzu73NihUriIyMJCMjg86dOzNv3jzKli1r99jZK5qL18HvA+sxAT394dPeUNHGaxo3nIbB0dYPs8+2gpltC3qVjmE2Q6t1cPDSn+v8KsOuh6CujV+Lfv0DOn4C8Tf+XNe+FuwpRu/1+vteeP3Qn8tlTLCxD/Spk7PstVTotwW+OvfnuoquEDeueHY9E8kvJSBKQEREMpXKLlgRERGEhoayb98+IiIiCAsLy1HmzJkzvPLKK2zdupUDBw5w8eJFli1bVuBzLf3ZOvkoXwZWdLOdfAAMqgd/zZYL/edY8Zmy9vuL1skHwFudbCcfYGkJyj44b+8FOGxjFhFndDPd0pJzu8fus518AFQuCx90t8yIlelGGnzwc9HFKCIiIlKaOF0CkpCQwMGDBwkJCQFg4MCBxMXFcerUKatyUVFR9OnTB29vb0wmExMnTmT9+vUFPt/X56yXh98D3pXs7zM923utLt6AE38U+NQO8fV562X/Kn9OT5ibYfWhZrZph3eft13W2fx42dK96nbhze3v418FBmb7THYVk+sVERERcXZOl4DExcXh7e2Nq6ulCcJkMuHn50dsbKxVudjYWPz9/bOWAwICOHv2bIHP93uK9XKHWnnv06w6VMnW0+vSDdtlnU32623nnfcAvLJloG22+dEvpdgu62yyX69XBUurTl46+lgvF5frFREREXF2TpeAGK1stk/gej7ehp1hzvnW7OzHcVZ3cr22yhXn683PG+2zD7wvLtcrIiIi4uyc7rGqdu3axMfHk5ZmeQI0m82cPXvWqrUDwN/f36pVJCYmBj8/vwKfz6ey9fLGM3nvsy3G+g3pYJlZozjwyda97MtzkJzH9MOXU2DPBet1vnl0U3MW2a/3Wpr1APPcZL8PisvfV0RERMTZOV0C4uXlRWBgIKtXrwYsYz18fX2pX7++VbmBAweydetW4uPjMZvNLFmyhCFDhhT4fA/VtV7+8hx8G597+QwzvHbQel0LT8u4geKgf13rqQevpsLbR+3v8+YRSEn/c7msC/QOKJLwCl0jD2jsYb3u1QP2W0G+jIPvLlqvG1i3kAMTERERKaWcLgEBiIyMZNmyZbRu3Zo33niDhQsXAhAeHs6WLVsAqFu3LjNmzKBXr160bNkST09PJkyYUOBzDb3HMi7Aat02OJCQs+zNdHjs65y/oIc1A1MxmU/cv0rOh+n/+w5W/pLzodxstswgNXOf9fph9fMeqO8sTCaY2sx63bZYiNgDt9Jzlv8uHh7+zHpd7cpKQEREREQKi1O+B6Qo2Zrv/fnv4MVsD9kmoF+dP19E+N1Fy8N4fLbB5p4V4MwYy/StxcWXcRAclXN9S08Y29Dy9tiTSbD8Z9vT7f5vCLQtRu/xSbwJ9T6ExGxdzXwrw/hG0MYLLt+E9adga0zO/We1hWdaGROriKPoPSBO+XuciIhDlMoXERYlWxXNjTToHgXf2Ol6ZUsZE2zqW3y6I93uiT3wxuGC7/fPVvByMXnp4u1svUAyP7r6QnR/y/thREoyJSBKQEREMpXKFxEaraIrbOybc6pZe8q5wMoHi2fyATC3PUxpWrB9pjeHmX8pmniK2qB6sKyb9QsG89LJBz7preRDREREpDApAfn/alSALwbCUy3AvZz9sh1rwVeDIKSBIaEVCRcTLOoM/+kCdd3sl73HHZYFQ2TH4jPWxZZxjeCLAZZ3n9jjUQ5mtITt/cGjvDGxiYiIiJQW6oJlw9VU+OgErD0JcdcgJQ2qV4AHvGFyUwisYVCwBknPgOhYWHYcfk6E5FRwKwtNq1vGSPT0t545qyQ4kAD/OWaZ8ezKTajgahmgP+IeeLgBVCpGY3pECoO6YOn3OBGRTBoDUshU0YiI5KQERPWCiEgmjQEREREREZESQwmIiIg4vZMnT9KzZ09at25NcHAwx44ds1luxYoVtGrVihYtWjBt2jRSU1MNjlRERPJS6rpgVa5cWU3tIiLZZGRkcO3aNUeHkasBAwYwcuRIRo8ezYYNG4iMjGTnzp1WZc6cOUPv3r356quvqFmzJg8//DDdu3fnkUcesXts1QsiItaKuk4odQmIiIgULwkJCbRq1YrTp0/j6uqK2WymUaNGREdHU79+/axyb775JqdPn+aNN94AYPv27bz++utER0c7KnQREbFBP/mIiIhTi4uLw9vbG1dXVwBMJhN+fn7ExsZalYuNjcXf3z9rOSAggLNnzxoaq4iI5E0JiIiIiIiIGEYJiIiIOLXatWsTHx9PWloaAGazmbNnz1q1dgD4+/tbtYrExMTg5+dnaKwiIpI3JSAiIuLUvLy8CAwMZPXq1QBERUXh6+trNf4DYODAgWzdupX4+HjMZjNLlixhyJAhjghZRETs0CB0ERFxeidOnCAsLIzLly/j5ubGwoULadasGeHh4fTp04e+ffsCsHz58qxB6B07duSNN96gbNmyjgxdRESyUQuIHfmdd76kePrpp2nevDkeHh4cPnzY0eEUuZSUFEaNGkXr1q3p0KEDDz30EKdOnXJ0WEVq8ODBtG/fno4dO9KnTx8OHTrk6JAM8eGHH+Lh4cGmTZscHUqRa968OW3atKFjx4507NiR//73v44OqVDce++97Nixg3379vHll1/SrFkzAN56662s5AMgNDSUgwcPcvDgQRYsWEBMTIzTvz8kP3XNrl27qFWrVtbftWPHjty4ccOwGPNbPzjyc8xPjI7+HAtS70RHRxMUFESrVq0YM2YMSUlJThfnb7/9RvXq1a0+z9OnTxsWZ37rNEe/Hyg/cTr63syUV31ZWPelEhA7IiIiCA0NZd++fURERBAWFubokIrUoEGDiI6OztGvuiQbP348P/zwA3v27KFv376Eh4c7OqQitXTpUvbu3cvu3bsJCwsr8fc0WCrIFStWEBQU5OhQDLNkyRJ2797N7t27S30XpPx8j585c4ZXXnmFrVu3cuDAAS5evMiyZcucKkaABg0aZP1dd+/eTcWKFQ2LMT/1g6M/x/zWYY78HCF/9c7Vq1cJDw9n5cqV7N+/Hx8fH+bMmeN0cQJUqVLF6vOsV6+eYTHmp05z9H2Z3zjB8fdmXvVlYd6XSkBykZCQwMGDBwkJCQEsfYvj4uJK9C/kHTp0oHbt2o4OwzAVKlSgZ8+emEwmAIKCgoiJiXFwVEXLw8Mj67+TkpKyrr2kysjIYNq0acyePZty5co5OhwxWH6/x6OioujTpw/e3t6YTCYmTpzI+vXrnSpGR8tP/eDIzxGKRx2W33rns88+IzAwkIYNGwIwadIkQz/L4lI/5qdOc/R9CcWj7s1PfVmY96XrHUdawtmbdz77wEcpGRYtWmTVlaOkmjJlCrt37wZgzZo1Do6maC1cuJC2bdvSokULR4diqEcffRSAVq1a8cILL+Dp6engiBwjv9/jjnx/SEHqmjNnztC5c2fKlCnD6NGj+etf/2pIjPlVXN7D4kyfY271jq3P8sKFC6SlpWXdK0ayVz9ev36d4OBg0tPT6devH08++SRlypQxLLa86jRnuS/zU/c68t7MT31ZmPelEhARYN68eZw+fZrIyEhHh1Lk3n33XQA++ugjXnjhBdauXevgiIrGTz/9RFRUFFu2bHF0KIbasmUL/v7+pKamMnPmTKZOnVpi/8alyf3338/Ro0epWrUqcXFxDB8+nBo1ajB48GBHh1asONPnWFzqHXtx1qpVi2PHjuHl5cWVK1eYMGECCxYsYPr06YbFV1zqtLzidOS96Yj6Ul2wcpHfeeel+HvrrbfYuHEja9eupVKlSo4OxzCjRo1i165dXL582dGhFIlvvvmGmJgYWrVqRfPmzfnhhx+IiIhg8eLFjg6tSGV+R5UtW5apU6fyzTffODgixykO7w/Jb4zu7u5UrVo1a59hw4axd+9eQ2LMr+LwHhZn+RzzqndsfZa1atUyvPUjrzjLly+Pl5cXANWqVWPMmDEOuy9zq9Oc7b7MLU5H3pv5rS8L875UApKL/M47L8XbggULWLduHZ9++qlVH82SKDExkfPnz2ctb9q0ierVq1OtWjUHRlV0Jk2axM8//8yRI0c4cuQIbdq0ITIykkmTJjk6tCJz7do1EhMTs5bXrVtH8+bNHReQgxWH94fkN8YLFy6QkZEBQHJyMtu2bSMwMNCQGPOrOLyHxRk+x/zUO927d+fQoUP88ssvACxevNjwzzI/cSYkJGTNKHXz5k02btxo2OeZ3zrN0fdlfuN05L2Z3/qyMO9LdcGyIzIykrCwMF5//fWseedLsoiICLZv3058fDxDhw6lSpUqHDhwwNFhFZm4uDj+7//+j7p169K/f3/A8mvO559/7uDIikZSUhLjx48nJSUFk8mEp6cnq1atcsrBcHJnEhISGDt2LOnp6QDUqVOHd955x8FROVZu3+O3vz+kbt26zJgxg169egGW94dMmDDBqWKMiopiyZIllClThvT0dAYNGsSYMWMMizG3+sGZPsf8xOjoz9FevfPyyy/j4+PDxIkTcXNz480332T06NGkpaXRpEkTFi1a5HRxfvPNN8yaNQsXFxfS09Pp1KkTTz75pCEx2qvTnOm+zG+cjr43c1NU96VeRCgiIiIiIoZRFywRERERETGMEhARERERETGMEhARERERETGMEhARERERETGMEhARERERETGMEhARERERETGMEhARERERETGMEhARERERETGMEhARERERETGMEhARERERETGMEhARERERg5w7dw5vb28ee+wxq/Vffvklnp6ezJgxw0GRiRhHCYiIQW7cuEFQUBBBQUHcuHEja/2VK1do1KgRPXv2JD093YERiohIUfP19WXcuHGsWbOGmJgYAH755RdCQ0N58MEHefnllx0coUjRUwIiYpCKFSuyaNEiTp06xUsvvZS1/sknnyQpKYm3336bMmXKODBCERExwuOPP46Liwvz58/n8uXLhISEEBAQwOLFi3Fx0aOZlHyujg5ApDRp06YN06dPJzIykv79+5OQkMD69euZNWsWDRo0cHR4IiJigMxWkBUrVnDo0CFSUlLYvHkzlStXdnRoIoYwJSYmmh0dhEhpcuvWLbp27cq1a9e4du0ajRo1YtOmTZhMJkeHJiIiBomJiSEwMJCKFSuydetWWrRoYbX90qVLhIWFsXv3bnx9fZk3bx5dunRxTLAihUztfCIGK1euHAsXLuS3337j6tWrLFy4UMmHiEgpM2/ePADS0tLw8PDIsf3JJ5+kZs2a/Prrr7z44ouMHz+eK1euGBylSNFQAiLiAJ9//jkAKSkpnDp1ysHRiIiIkd58801WrFjBnDlzcHV1zUpGMl29epXNmzczY8YMKlWqRN++fWnatCmbN292UMQihUsJiIjBfvzxR2bPns3o0aMJDAxk2rRp/PHHH44OS0REDLBp0yZeeOEFnn32WR555BFCQ0NZtWoVZ86cySpz8uRJKleuTO3atbPWNW3alOPHjzsgYpHCpwRExECpqamEhYVRq1YtXn31Vd5++20uXrzIP//5T0eHJiIiRezgwYNMnjyZ4cOH8+STTwIwffp0XFxcrFpBrl27hpubm9W+7u7uXLt2zdB4RYqKEhARA82ZM4cjR46wYMEC3NzcuO+++3j66adZuXIl27dvd3R4IiJSROLi4hg5ciSBgYG8+eabWet9fHwYM2aMVStI5cqVSU5Otto/KSlJs2RJiaFZsEQMcvDgQR588EEmTpzI7Nmzs9anp6fTo0cPzp8/zzfffGNzMKKIiJQeV69epV69ehw6dAhfX18A+vfvz8iRIxkzZoyDoxO5e0pARERERJxMaGgo7u7uzJ49m6+++oqpU6eyf/9+qlWr5ujQRO6aXkQoIiIi4mTmzZvH1KlTqV+/Pr6+vixdulTJh5QYagERERERERHDaBC6iIiIiIgYRgmIiIiIiIgYRgmIiIiIiIgYRgmIiIiIiIgYRgmIiIiIiIgYRgmIiIiIiIgYRgmIiIiIiIgYRgmIiIiIiIgYRgmIiIiIiIgYRgmIiIiIiIgYRgmIiIiIiIgYRgmIiIiIiIgYRgmIiIiIiIgY5v8BnjvVSm/Tz0sAAAAASUVORK5CYII=", + "text/html": [ + "\n", + "
\n", + "
\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "059090a31e5840dbb92f25f852179e26" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js" + } + } + } + } + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "mlF_HuIh4hMB" + }, + "execution_count": 64, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/searching/one.html b/searching/one.html new file mode 100644 index 0000000..e69de29