{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np \n", "import sklearn.metrics.pairwise as pw\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "A = [[3, 4000, 5]]\n", "B = [[3, 4000, 4]]\n", "C = [[3, 4100, 5]]\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Euclidean: \t [[1.]]\n", "Cosine: \t [[0.99999997]]\n", "Manhattan: \t [[1.]]\n" ] } ], "source": [ "print('Euclidean: \\t',pw.euclidean_distances(A, B))\n", "print('Cosine: \\t',pw.cosine_similarity(A, B))\n", "print('Manhattan: \\t',pw.manhattan_distances(A, B))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Euclidean: \t [[100.]]\n", "Cosine: \t [[1.]]\n", "Manhattan: \t [[100.]]\n" ] } ], "source": [ "print('Euclidean: \\t',pw.euclidean_distances(A, C))\n", "print('Cosine: \\t',pw.cosine_similarity(A, C))\n", "print('Manhattan: \\t',pw.manhattan_distances(A, C))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Euclidean E-D: \t [[3.31662479]]\n", "Euclidean E-F: \t [[2.82842712]]\n", "\n", "Cosine: E-D \t [[0.99014754]]\n", "Cosine E-F: \t [[0.7592566]]\n", "\n", "Manhattan: E-D \t [[5.]]\n", "Manhattan E-F: \t [[6.]]\n" ] } ], "source": [ "import sklearn.metrics.pairwise as pw\n", "\n", "D = [[6,0,0,3,3]]\n", "E = [[3,0,0,2,2]]\n", "F = [[1,1,1,1,1]]\n", "\n", "print('Euclidean E-D: \\t',pw.euclidean_distances(D, E))\n", "print('Euclidean E-F: \\t',pw.euclidean_distances(E, F))\n", "\n", "print('\\nCosine: E-D \\t',pw.cosine_similarity(D, E))\n", "print('Cosine E-F: \\t',pw.cosine_similarity(E, F))\n", "\n", "print('\\nManhattan: E-D \\t',pw.manhattan_distances(D, E))\n", "print('Manhattan E-F: \\t',pw.manhattan_distances(E, F))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ACDB_greenB_redB_yellow
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" ], "text/plain": [ " A C D B_green B_red B_yellow\n", "0 1.0 3300 MSK 0 1 0\n", "1 2.0 1250 SPB 0 1 0\n", "2 NaN 4600 EKB 0 0 1\n", "3 2.0 4500 MSK 1 0 0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# OHE encoding\n", "dataset = pd.get_dummies(dataset, columns = ['B'])\n", "dataset" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
LabelEncoder()
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" ], "text/plain": [ "LabelEncoder()" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Label encoding\n", "from sklearn import preprocessing\n", "le = preprocessing.LabelEncoder()\n", "le.fit(dataset['D'])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ACDB_greenB_redB_yellow
01.033001010
12.012502010
2NaN46000001
32.045001100
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" ], "text/plain": [ " A C D B_green B_red B_yellow\n", "0 1.0 3300 1 0 1 0\n", "1 2.0 1250 2 0 1 0\n", "2 NaN 4600 0 0 0 1\n", "3 2.0 4500 1 1 0 0" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset['D'] = le.transform(dataset['D'])\n", "dataset" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ACDB_greenB_redB_yellow
01.00000033001010
12.00000012502010
21.66666746000001
32.00000045001100
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" ], "text/plain": [ " A C D B_green B_red B_yellow\n", "0 1.000000 3300 1 0 1 0\n", "1 2.000000 1250 2 0 1 0\n", "2 1.666667 4600 0 0 0 1\n", "3 2.000000 4500 1 1 0 0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Заполняем пропущенные данные\n", "dataset['A'] = dataset['A'].fillna(np.mean(dataset['A']))\n", "dataset" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ACDB_greenB_redB_yellowC_normalizedC_standardized
01.000000330010100.611940-0.072209
12.000000125020100.000000-1.388018
21.666667460000011.0000000.762206
32.000000450011000.9701490.698021
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" ], "text/plain": [ " A C D B_green B_red B_yellow C_normalized C_standardized\n", "0 1.000000 3300 1 0 1 0 0.611940 -0.072209\n", "1 2.000000 1250 2 0 1 0 0.000000 -1.388018\n", "2 1.666667 4600 0 0 0 1 1.000000 0.762206\n", "3 2.000000 4500 1 1 0 0 0.970149 0.698021" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset['C_normalized'] = (dataset['C'] - dataset['C'].min()) / (dataset['C'].max() - dataset['C'].min())\n", "dataset['C_standardized'] = (dataset['C'] - dataset['C'].mean()) / dataset['C'].std()\n", "dataset" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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cPn1apaWlioiIKLc+IiJCWVlZlW6Tl5dX6fi8vLxKx6empmrevHkV1q9fv55fUpbLycnRjBkzqjX2F69Xr+bixYvVoUOHWnQFoCpHCiQpSFu2bNGhsNrXS0tLq3UNX/cE3ysqKqr2WL+Gl7qQkpJS7khNfn6+YmJilJiYKLfbXY+dobaKioo0ePDgq44pOF+sdZ98qqT/9yOFNXZWWZMjL4D/fXUsX4v2btfgwYN1S/S1vw97PB6lpaVpxIgRCg4ObhA9wX8unzmpDr+GlxYtWigwMFAnTpwot/7EiROKjIysdJvIyMgajXc6nXI6K/7SCg4OrvU/dtSvpk2bql+/flcd4/F49N3Zb/T/Bg5gfwMNRFBQkPerL+alL97Pfd0TfK8m+8WvF+w2atRIffv21YYNG7zrysrKtGHDBiUkJFS6TUJCQrnx0qVDhlcaDwAAbix+P22UnJysCRMm6NZbb1W/fv20dOlSFRYWatKkSZKk8ePHq3Xr1kpNTZUkPfrooxoyZIgWL16su+66S2+99ZY+++wzvfzyy/5uFQAAWMDv4WXs2LE6deqUZs+erby8PPXq1Utr1671XpR7+PBhBQT83wGggQMH6s0339SsWbP01FNPqVOnTlqzZo26devm71YBAIAF6uSC3WnTpmnatGmVPrdp06YK68aMGaMxY8b4uSsAAGAj7m0EAACsQngBAABWIbwAAACrEF4AAIBVCC8AAMAqhBcAAGAVwgsAALAK4QUAAFiF8AIAAKxCeAEAAFYhvAAAAKsQXgAAgFUILwAAwCqEFwAAYBXCCwAAsArhBQAAWIXwAgAArEJ4AQAAViG8AAAAqxBeAACAVQgvAADAKoQXAABgFcILAACwCuEFAABYhfACAACsQngBAABWIbwAAACrEF4AAIBVCC8AAMAqhBcAAGAVwgsAALAK4QUAAFiF8AIAAKxCeAEAAFYhvAAAAKsQXgAAgFUILwAAwCqEFwAAYBXCCwAAsArhBQAAWIXwAgAArEJ4AQAAViG8AAAAqxBeAACAVQgvAADAKoQXAABgFcILAACwCuEFAABYJai+GwAAXF/Oe0olSV8ePVerOoXni/XZKSny0LcKbeysVa0DJwtqtT0aFsILAMCncv43KDz5zl4fVAvSvx/41Ad1Lgl18mvvesBeBAD4VOItkZKkDq3C1Dg48JrrZB8/pxl/26vF/9RdXaKa1rqvUGeQ2rUIrXUd1D/CCwDAp5qFNtJ9/W6udZ2SkhJJUoeWoerWuvbhBdcPLtgFAABWIbwAAACrEF4AAIBVCC8AAMAqhBcAAGAVwgsAALAK4QUAAFjFr+Hlm2++0bhx4+R2uxUeHq4HHnhABQVX/4jmoUOHyuFwlHv88pe/9GebAADAIn79kLpx48bp+PHjSktLk8fj0aRJk/Tggw/qzTffvOp2U6ZM0fz5873LLpfLn20CAACL+C28ZGZmau3atfr000916623SpL+8Ic/6M4779SiRYsUHR19xW1dLpciIyP91RoAALCY38JLenq6wsPDvcFFkoYPH66AgADt2LFD99xzzxW3feONN/SXv/xFkZGRGjVqlH7zm99c8ehLcXGxiouLvcv5+fmSJI/HI4/H46PvBg3V5X3MvgauP5dvD1BSUsIcvwHUZB/7Lbzk5eWpVatW5V8sKEjNmjVTXl7eFbf7+c9/rtjYWEVHR+uLL77QzJkzlZ2drXfeeafS8ampqZo3b16F9evXr+d00w0kLS2tvlsA4GNHCiQpSNu3b9fRL+u7G/hbUVFRtcfWOLw8+eSTeu655646JjMzs6ZlvR588EHvf3fv3l1RUVEaNmyYcnJy1KFDhwrjU1JSlJyc7F3Oz89XTEyMEhMT5Xa7r7kP2MHj8SgtLU0jRoxQcHBwfbcDwIc+P/yNtPczDRgwQD1vblbf7cDPLp85qY4ah5cZM2Zo4sSJVx3Tvn17RUZG6uTJk+XWl5SU6JtvvqnR9Sz9+/eXJB04cKDS8OJ0OuV0OiusDw4O5pfZDYT9DVx/goKCvF+Z39e/muzjGoeXli1bqmXLllWOS0hI0NmzZ7Vr1y717dtXkvTRRx+prKzMG0iqIyMjQ5IUFRVV01YBAMB1yG+f8xIfH6+RI0dqypQp2rlzp7Zu3app06bpvvvu8/6l0dGjRxUXF6edO3dKknJycrRgwQLt2rVLBw8e1Lvvvqvx48frxz/+sXr06OGvVgEAgEX8+iF1b7zxhuLi4jRs2DDdeeedGjx4sF5++WXv8x6PR9nZ2d6LdBo1aqQPP/xQiYmJiouL04wZM3Tvvffqv/7rv/zZJgAAsIhfP6SuWbNmV/1AurZt28oY412OiYnR5s2b/dkSAACwHPc2AgAAViG8AAAAqxBeAACAVQgvAADAKoQXAABgFcILAACwCuEFAABYhfACAACsQngBAABWIbwAAACrEF4AAIBVCC8AAMAqhBcAAGAVwgsAALAK4QUAAFiF8AIAAKxCeAEAAFYhvAAAAKsQXgAAgFUILwAAwCqEFwAAYBXCCwAAsArhBQAAWIXwAgAArEJ4AQAAViG8AAAAqxBeAACAVQgvAADAKoQXAABgFcILAACwCuEFAABYhfACAACsQngBAABWIbwAAACrEF4AAIBVCC8AAMAqhBcAAGAVwgsAALAK4QUAAFiF8AIAAKxCeAEAAFYhvAAAAKsQXgAAgFUILwAAwCqEFwAAYBXCCwAAsArhBQAAWIXwAgAArEJ4AQAAViG8AAAAqxBeAACAVQgvAADAKoQXAABgFcILAACwCuEFAABYhfACAACsQngBAABWIbwAAACrEF4AAIBVCC8AAMAqfgsvzzzzjAYOHCiXy6Xw8PBqbWOM0ezZsxUVFaXGjRtr+PDh2r9/v79aBAAAFvJbeLl48aLGjBmjf/3Xf632Nr///e+1bNkyvfTSS9qxY4dCQ0OVlJSkCxcu+KtNAABgmSB/FZ43b54kaeXKldUab4zR0qVLNWvWLI0ePVqS9Oc//1kRERFas2aN7rvvPn+1CgAALOK38FJTubm5ysvL0/Dhw73rmjZtqv79+ys9Pf2K4aW4uFjFxcXe5fz8fEmSx+ORx+Pxb9Ood5f3MfsauP6UlJR4vzLHr3812ccNJrzk5eVJkiIiIsqtj4iI8D5XmdTUVO9Rnu9bv369XC6Xb5tEg5WWllbfLQDwsSMFkhSk7du36+iX9d0N/K2oqKjaY2sUXp588kk999xzVx2TmZmpuLi4mpStlZSUFCUnJ3uX8/PzFRMTo8TERLnd7jrrA/XD4/EoLS1NI0aMUHBwcH23A8CHPj/8jbT3Mw0YMEA9b25W3+3Azy6fOamOGoWXGTNmaOLEiVcd0759+5qU9IqMjJQknThxQlFRUd71J06cUK9eva64ndPplNPprLA+ODiYX2Y3EPY3cP0JCgryfmV+X/9qso9rFF5atmypli1b1rih6mjXrp0iIyO1YcMGb1jJz8/Xjh07avQXSwAA4Prmtz+VPnz4sDIyMnT48GGVlpYqIyNDGRkZKigo8I6Ji4vT6tWrJUkOh0PTp0/Xb3/7W7377rvau3evxo8fr+joaN19993+ahMAAFjGbxfszp49W6+//rp3uXfv3pKkjRs3aujQoZKk7OxsnTt3zjvmiSeeUGFhoR588EGdPXtWgwcP1tq1axUSEuKvNgEAgGX8Fl5WrlxZ5We8GGPKLTscDs2fP1/z58/3V1sAAMBy3NsIAABYhfACAACsQngBAABWIbwAAACrEF4AAIBVCC8AAMAqhBcAAGAVwgsAALAK4QUAAFiF8AIAAKxCeAEAAFYhvAAAAKsQXgAAgFUILwAAwCqEFwAAYBXCCwAAsArhBQAAWIXwAgAArEJ4AQAAViG8AAAAqxBeAACAVQgvAADAKoQXAABgFcILAACwCuEFAABYhfACAACsQngBAABWIbwAAACrEF4AAIBVCC8AAMAqhBcAAGAVwgsAALAK4QUAAFiF8AIAAKxCeAEAAFYhvAAAAKsQXgAAgFUILwAAwCqEFwAAYBXCCwAAsArhBQAAWIXwAgAArEJ4AQAAViG8AAAAqxBeAACAVQgvAADAKkH13QAA4MZTVFSkrKysq47JPn5WxXkHlPllY5WdCa+yZlxcnFwul486RENGeAEA1LmsrCz17du3WmN//nr1au7atUt9+vSpRVewBeEFAFDn4uLitGvXrquOKThfrPc2puuu2xIU1thZrZq4MRBeAAB1zuVyVXmUxOPx6NvTJ5XQ71YFBwfXUWewARfsAgAAqxBeAACAVQgvAADAKoQXAABgFcILAACwCuEFAABYhfACAACsQngBAABWIbwAAACr+C28PPPMMxo4cKBcLpfCw8Ortc3EiRPlcDjKPUaOHOmvFgEAgIX8dnuAixcvasyYMUpISNCrr75a7e1GjhypFStWeJedzqrvZwEAAG4cfgsv8+bNkyStXLmyRts5nU5FRkb6oSMAAHA9aHA3Zty0aZNatWqlm266Sbfffrt++9vfqnnz5lccX1xcrOLiYu9yfn6+pEs39PJ4PH7vF/Xr8j5mXwPXH+b3jaUm+7lBhZeRI0fqZz/7mdq1a6ecnBw99dRTuuOOO5Senq7AwMBKt0lNTfUe5fm+NWvWyOVy+btlNBB///vf67sFAH7C/L4xFBUVSZKMMVUPNjUwc+ZMI+mqj8zMzHLbrFixwjRt2rQmL+OVk5NjJJkPP/zwimMuXLhgzp0753384x//qLJHHjx48ODBg0fDfBw5cqTKfFCjIy8zZszQxIkTrzqmffv2NSlZZa0WLVrowIEDGjZsWKVjnE5nuYt6w8LCdOTIETVp0kQOh8NnvaBhys/PV0xMjI4cOSK3213f7QDwIeb3jcUYo++++07R0dFVjq1ReGnZsqVatmx5zY3V1Ndff60zZ84oKiqq2tsEBASoTZs2fuwKDZHb7ebNDbhOMb9vHE2bNq3WOL99zsvhw4eVkZGhw4cPq7S0VBkZGcrIyFBBQYF3TFxcnFavXi1JKigo0OOPP67t27fr4MGD2rBhg0aPHq2OHTsqKSnJX20CAADL+O2C3dmzZ+v111/3Lvfu3VuStHHjRg0dOlSSlJ2drXPnzkmSAgMD9cUXX+j111/X2bNnFR0drcTERC1YsIDPegEAAF4OY6pzWS/QMBUXFys1NVUpKSmEXOA6w/zGlRBeAACAVbgxIwAAsArhBQAAWIXwAgAArEJ4wQ3t4MGDcjgcysjIkHTp3loOh0Nnz57122vOnTtXvXr18lt94HrhcDi0Zs0av9f+4fuAP6xcuVLh4eF+q3+jIbyggry8PD388MNq3769nE6nYmJiNGrUKG3YsKG+W/O7gQMH6vjx49X+oCSgIfLXHG7btq2WLl3qmyYbkJiYGB0/flzdunWr71ZQTQ3qxoyofwcPHtSgQYMUHh6uhQsXqnv37vJ4PFq3bp1+9atfKSsrq857unjxoho1alQnr9WoUSNFRkbWyWsB/tAQ53B9qe57R2BgIPPeMhx5QTlTp06Vw+HQzp07de+996pz58665ZZblJycrO3bt1e5vcPh0CuvvKJ77rlHLpdLnTp10rvvvltuzObNm9WvXz85nU5FRUXpySefVElJiff5oUOHatq0aZo+fbpatGihpKQk7+mcdevWqXfv3mrcuLFuv/12nTx5Uh988IHi4+Pldrv185//3HtnUklau3atBg8erPDwcDVv3lw/+clPlJOTc8X+f3jaaOjQoXI4HBUeBw8elCSdPXtWkydPVsuWLeV2u3X77bfr888/L1fz2WefVUREhJo0aaIHHnhAFy5cqPLnCFyr2sxhY4zmzp2rm2++WU6nU9HR0XrkkUckXZoLhw4d0mOPPeadB5J05swZ3X///WrdurVcLpe6d++u//iP/yhXd+jQoXrkkUf0xBNPqFmzZoqMjNTcuXPLjdm/f79+/OMfKyQkRF27dlVaWlqF/mbOnKnOnTvL5XKpffv2+s1vfiOPx+N9/vIp2VdeeUXt2rVTSEhItWr/8LTRxIkTK533mzZtknTp82d+/etfq3Xr1goNDVX//v29z122cuVK3XzzzXK5XLrnnnt05syZq/7sUUPXdLtnXJfOnDljHA6H+d3vfnfNNSSZNm3amDfffNPs37/fPPLIIyYsLMycOXPGGGPM119/bVwul5k6darJzMw0q1evNi1atDBz5szx1hgyZIgJCwszjz/+uMnKyjJZWVlm48aNRpIZMGCA2bJli9m9e7fp2LGjGTJkiElMTDS7d+82H3/8sWnevLl59tlnvbX+9re/mbffftvs37/f7Nmzx4waNcp0797dlJaWGmOMyc3NNZLMnj17jDHG+zrffvut92dy/Phx7+NnP/uZ6dKliykqKjLGGDN8+HAzatQo8+mnn5p9+/aZGTNmmObNm3u/31WrVhmn02leeeUVk5WVZZ5++mnTpEkT07Nnz2v+GQNXUts5/Ne//tW43W7z/vvvm0OHDpkdO3aYl19+2Vu7TZs2Zv78+d75YMylOb1w4UKzZ88ek5OTY5YtW2YCAwPNjh07vHWHDBli3G63mTt3rtm3b595/fXXjcPhMOvXrzfGGFNaWmq6detmhg0bZjIyMszmzZtN7969jSSzevVqb50FCxaYrVu3mtzcXPPuu++aiIgI89xzz3mfnzNnjgkNDTUjR440u3fvNp9//nm1av/wfeDs2bPl5v2jjz5qWrVq5f2eJ0+ebAYOHGg+/vhjc+DAAbNw4ULjdDrNvn37jDHGbN++3QQEBJjnnnvOZGdnmxdeeMGEh4ebpk2bXtN+QUWEF3jt2LHDSDLvvPPONdeQZGbNmuVdLigoMJLMBx98YIwx5qmnnjJdunQxZWVl3jHLly83YWFh3kAxZMgQ07t373J1L4eKDz/80LsuNTXVSDI5OTnedQ899JBJSkq6Yn+nTp0ykszevXuNMVWHl+97/vnnTXh4uMnOzjbGGPPJJ58Yt9ttLly4UG5chw4dzJ/+9CdjjDEJCQlm6tSp5Z7v378/4QV+Uds5vHjxYtO5c2dz8eLFSp+PjY01S5YsqbLOXXfdZWbMmOFdHjJkiBk8eHC5MT/60Y/MzJkzjTHGrFu3zgQFBZmjR496n//ggw8qhJcfWrhwoenbt693ec6cOSY4ONicPHnSu646tX/4PvB9b7/9tgkJCTFbtmwxxhhz6NAhExgYWK6eMcYMGzbMpKSkGGOMuf/++82dd95Z7vmxY8cSXnyI00bwMj76sOUePXp4/zs0NFRut1snT56UJGVmZiohIcF7yFmSBg0apIKCAn399dfedX379q2ydkREhPfw8ffXXX4t6dLh4vvvv1/t27eX2+1W27ZtJV26cWhNfPDBB3ryySe1atUqde7cWZL0+eefq6CgQM2bN1dYWJj3kZub6z01lZmZqf79+5erlZCQUKPXBqqrtnN4zJgxOn/+vNq3b68pU6Zo9erV5U7pVqa0tFQLFixQ9+7d1axZM4WFhWndunUV5tj3564kRUVFlXtfiImJUXR0tPf5yubJqlWrNGjQIEVGRiosLEyzZs2q8DqxsbFq2bKld7m6tSuzZ88e/eIXv9CLL76oQYMGSZL27t2r0tJSde7cudy837x5M/O+DnHBLrw6deokh8NR6wv6goODyy07HA6VlZXVqEZoaGiVtR0OR5WvNWrUKMXGxurf/u3fFB0drbKyMnXr1k0XL16sdi//+Mc/dN999+nZZ59VYmKid31BQYGioqIqnOuWxJ9Eol7Udg7HxMQoOztbH374odLS0jR16lQtXLhQmzdvrjDXLlu4cKFeeOEFLV26VN27d1doaKimT59eYY7V9n0hPT1d48aN07x585SUlKSmTZvqrbfe0uLFi8uNu9J7R03l5eXppz/9qSZPnqwHHnjAu76goECBgYHatWuXAgMDy20TFhbmk9dG1TjyAq9mzZopKSlJy5cvV2FhYYXnffHZJ/Hx8UpPTy/3f4hbt25VkyZN1KZNm1rX/74zZ84oOztbs2bN0rBhwxQfH69vv/22RjVOnz6tUaNG6d5779Vjjz1W7rk+ffooLy9PQUFB6tixY7lHixYtJF36fnfs2FFuu+pc+AxcC1/M4caNG2vUqFFatmyZNm3apPT0dO3du1fSpb/GKy0tLTd+69atGj16tP75n/9ZPXv2VPv27bVv374a9R0fH68jR47o+PHj3nU/nCfbtm1TbGysnn76ad16663q1KmTDh065JPaP3ThwgWNHj1acXFxev7558s917t3b5WWlurkyZMV5v3lv1hi3vsf4QXlLF++XKWlperXr5/efvtt7d+/X5mZmVq2bJlPDntOnTpVR44c0cMPP6ysrCz9/e9/15w5c5ScnKyAAN/+c7zpppvUvHlzvfzyyzpw4IA++ugjJScn16jGvffeK5fLpblz5yovL8/7KC0t1fDhw5WQkKC7775b69ev18GDB7Vt2zY9/fTT+uyzzyRJjz76qF577TWtWLFC+/bt05w5c/TVV1/59PsEvq82c3jlypV69dVX9eWXX+p//ud/9Je//EWNGzdWbGyspEuf8/Lxxx/r6NGjOn36tKRLR3vS0tK0bds2ZWZm6qGHHtKJEydq1PPw4cPVuXNnTZgwQZ9//rk++eQTPf300+XGdOrUSYcPH9Zbb72lnJwcLVu2TKtXr/ZJ7R966KGHdOTIES1btkynTp3yzvuLFy+qc+fOGjdunMaPH6933nlHubm52rlzp1JTU/Xee+9Jkh555BGtXbtWixYt0v79+/Xiiy9q7dq1NfqZoAr1fM0NGqBjx46ZX/3qVyY2NtY0atTItG7d2vz0pz81GzdurHJbVXKBXdOmTc2KFSu8y5s2bTI/+tGPTKNGjUxkZKSZOXOm8Xg83ueHDBliHn300XI1KruQdsWKFRUugJszZ065i2HT0tJMfHy8cTqdpkePHmbTpk1XvVDvh68jqdJHbm6uMcaY/Px88/DDD5vo6GgTHBxsYmJizLhx48zhw4e9PTzzzDOmRYsWJiwszEyYMME88cQTXLALv7rWObx69WrTv39/43a7TWhoqBkwYEC5i+TT09NNjx49jNPpNJd/fZw5c8aMHj3ahIWFmVatWplZs2aZ8ePHm9GjR3u3q2xOjx492kyYMMG7nJ2dbQYPHmwaNWpkOnfubNauXVvh/eTxxx83zZs3N2FhYWbs2LFmyZIl5d4Dfjj/q1v7h+8DsbGxlc77yz+/ixcvmtmzZ5u2bdua4OBgExUVZe655x7zxRdfeF/z1VdfNW3atDGNGzc2o0aNMosWLeKCXR9yGOOjqzQBAADqAKeNAACAVQgvqLY33nij3J8Gfv9xyy231Hd7AKrAHMb1gtNGqLbvvvvuihfiBQcHey/qA9AwMYdxvSC8AAAAq3DaCAAAWIXwAgAArEJ4AQAAViG8AAAAqxBeAACAVQgvAADAKoQXAABgFcILAACwyv8HwFOqZdpnfAUAAAAASUVORK5CYII=\n", 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