819 lines
20 KiB
Plaintext
819 lines
20 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from sklearn.preprocessing import MinMaxScaler"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"data = np.random.randint(0,100,(10,2))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"array([[53, 52],\n",
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" [82, 96],\n",
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" [60, 84],\n",
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" [15, 90],\n",
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" [78, 31],\n",
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" [31, 46],\n",
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" [22, 29]])"
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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"source": [
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"data"
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"scaler_model = MinMaxScaler()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"sklearn.preprocessing._data.MinMaxScaler"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"type(scaler_model)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"MinMaxScaler(copy=True, feature_range=(0, 1))"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"scaler_model.fit(data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"array([[0.62337662, 0.47619048],\n",
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" [0.12987013, 0.92857143],\n",
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"scaler_model.transform(data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[0.62337662, 0.47619048],\n",
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" [0.71428571, 0.85714286],\n",
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" [0.22077922, 0.20238095]])"
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"scaler_model.fit_transform(data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/eddie/.pyenv/versions/3.7.3/envs/tensorflow-1.14/lib/python3.7/site-packages/pandas/compat/__init__.py:117: UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.\n",
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" warnings.warn(msg)\n"
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]
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}
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],
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"source": [
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"mydata = np.random.randint(0,101,(50,4))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"array([[ 97, 70, 23, 81],\n",
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"metadata": {},
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"output_type": "execute_result"
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"source": [
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"mydata"
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.DataFrame(data=mydata, columns = ['f1','f2','f3','label'])"
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]
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},
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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"<div>\n",
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"<style scoped>\n",
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"\n",
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" <th>0</th>\n",
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" <td>97</td>\n",
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" <td>70</td>\n",
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" <td>23</td>\n",
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" <td>81</td>\n",
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" <td>60</td>\n",
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" <td>40</td>\n",
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" <td>73</td>\n",
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" <td>7</td>\n",
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" <td>60</td>\n",
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" <td>88</td>\n",
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" <td>87</td>\n",
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" <td>67</td>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>42</td>\n",
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" <td>25</td>\n",
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" <td>81</td>\n",
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" <td>100</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>45</td>\n",
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" <td>30</td>\n",
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" <td>69</td>\n",
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" <td>72</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>16</td>\n",
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" <td>100</td>\n",
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" <td>70</td>\n",
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" <td>14</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>25</td>\n",
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" <td>76</td>\n",
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" <td>32</td>\n",
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" <td>70</td>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>2</td>\n",
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" <td>29</td>\n",
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" <td>46</td>\n",
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" <td>65</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>71</td>\n",
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" <td>33</td>\n",
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" <td>11</td>\n",
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" <td>79</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
|
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" <td>98</td>\n",
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" <td>16</td>\n",
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" <td>76</td>\n",
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" <td>9</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>10</th>\n",
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" <td>0</td>\n",
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" <td>78</td>\n",
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" <td>61</td>\n",
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" <td>34</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>11</th>\n",
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" <td>78</td>\n",
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" <td>43</td>\n",
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" <td>45</td>\n",
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" <td>87</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>12</th>\n",
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" <td>95</td>\n",
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" <td>78</td>\n",
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" <td>6</td>\n",
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" <td>34</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>13</th>\n",
|
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" <td>14</td>\n",
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" <td>44</td>\n",
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" <td>95</td>\n",
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" <td>27</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>14</th>\n",
|
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" <td>32</td>\n",
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" <td>31</td>\n",
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" <td>90</td>\n",
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" <td>58</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>15</th>\n",
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" <td>72</td>\n",
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" <td>15</td>\n",
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" <td>16</td>\n",
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" <td>59</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>16</th>\n",
|
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" <td>5</td>\n",
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" <td>72</td>\n",
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" <td>31</td>\n",
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" <td>36</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>17</th>\n",
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" <td>72</td>\n",
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" <td>30</td>\n",
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" <td>94</td>\n",
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" <td>55</td>\n",
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" </tr>\n",
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" <tr>\n",
|
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" <th>18</th>\n",
|
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" <td>55</td>\n",
|
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" <td>19</td>\n",
|
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" <td>91</td>\n",
|
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" <td>74</td>\n",
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>19</th>\n",
|
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" <td>9</td>\n",
|
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" <td>20</td>\n",
|
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" <td>10</td>\n",
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" <td>34</td>\n",
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" </tr>\n",
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" <tr>\n",
|
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" <th>20</th>\n",
|
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" <td>3</td>\n",
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" <td>6</td>\n",
|
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" <td>25</td>\n",
|
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" <td>49</td>\n",
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" </tr>\n",
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" <tr>\n",
|
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" <th>21</th>\n",
|
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" <td>63</td>\n",
|
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" <td>61</td>\n",
|
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" <td>86</td>\n",
|
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" <td>55</td>\n",
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" </tr>\n",
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" <tr>\n",
|
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" <th>22</th>\n",
|
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" <td>25</td>\n",
|
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" <td>21</td>\n",
|
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" <td>0</td>\n",
|
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" <td>85</td>\n",
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>23</th>\n",
|
|
" <td>80</td>\n",
|
|
" <td>66</td>\n",
|
|
" <td>73</td>\n",
|
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" <td>51</td>\n",
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" </tr>\n",
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" <tr>\n",
|
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" <th>24</th>\n",
|
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" <td>96</td>\n",
|
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" <td>46</td>\n",
|
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" <td>35</td>\n",
|
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" <td>58</td>\n",
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>25</th>\n",
|
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" <td>7</td>\n",
|
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" <td>4</td>\n",
|
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" <td>89</td>\n",
|
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" <td>25</td>\n",
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" </tr>\n",
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" <tr>\n",
|
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" <th>26</th>\n",
|
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" <td>92</td>\n",
|
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" <td>11</td>\n",
|
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" <td>77</td>\n",
|
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" <td>59</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>27</th>\n",
|
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" <td>38</td>\n",
|
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" <td>94</td>\n",
|
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" <td>19</td>\n",
|
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" <td>46</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>28</th>\n",
|
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" <td>34</td>\n",
|
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" <td>24</td>\n",
|
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" <td>94</td>\n",
|
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" <td>70</td>\n",
|
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" </tr>\n",
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" <tr>\n",
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" <th>29</th>\n",
|
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" <td>100</td>\n",
|
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" <td>91</td>\n",
|
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" <td>46</td>\n",
|
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" <td>76</td>\n",
|
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" </tr>\n",
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" <tr>\n",
|
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" <th>30</th>\n",
|
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" <td>43</td>\n",
|
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" <td>10</td>\n",
|
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" <td>35</td>\n",
|
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" <td>78</td>\n",
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" </tr>\n",
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" <tr>\n",
|
|
" <th>31</th>\n",
|
|
" <td>15</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>57</td>\n",
|
|
" <td>6</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>32</th>\n",
|
|
" <td>51</td>\n",
|
|
" <td>47</td>\n",
|
|
" <td>47</td>\n",
|
|
" <td>55</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>33</th>\n",
|
|
" <td>83</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>84</td>\n",
|
|
" <td>40</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>34</th>\n",
|
|
" <td>100</td>\n",
|
|
" <td>22</td>\n",
|
|
" <td>26</td>\n",
|
|
" <td>72</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>35</th>\n",
|
|
" <td>60</td>\n",
|
|
" <td>83</td>\n",
|
|
" <td>80</td>\n",
|
|
" <td>92</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>36</th>\n",
|
|
" <td>28</td>\n",
|
|
" <td>39</td>\n",
|
|
" <td>82</td>\n",
|
|
" <td>17</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>37</th>\n",
|
|
" <td>56</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>94</td>\n",
|
|
" <td>85</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>38</th>\n",
|
|
" <td>72</td>\n",
|
|
" <td>56</td>\n",
|
|
" <td>63</td>\n",
|
|
" <td>54</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>39</th>\n",
|
|
" <td>15</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>72</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>40</th>\n",
|
|
" <td>41</td>\n",
|
|
" <td>21</td>\n",
|
|
" <td>86</td>\n",
|
|
" <td>54</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>41</th>\n",
|
|
" <td>85</td>\n",
|
|
" <td>7</td>\n",
|
|
" <td>50</td>\n",
|
|
" <td>87</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>42</th>\n",
|
|
" <td>48</td>\n",
|
|
" <td>13</td>\n",
|
|
" <td>69</td>\n",
|
|
" <td>93</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>43</th>\n",
|
|
" <td>75</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>98</td>\n",
|
|
" <td>96</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>44</th>\n",
|
|
" <td>41</td>\n",
|
|
" <td>18</td>\n",
|
|
" <td>14</td>\n",
|
|
" <td>31</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>45</th>\n",
|
|
" <td>84</td>\n",
|
|
" <td>13</td>\n",
|
|
" <td>62</td>\n",
|
|
" <td>6</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>46</th>\n",
|
|
" <td>13</td>\n",
|
|
" <td>40</td>\n",
|
|
" <td>77</td>\n",
|
|
" <td>60</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>47</th>\n",
|
|
" <td>70</td>\n",
|
|
" <td>18</td>\n",
|
|
" <td>84</td>\n",
|
|
" <td>26</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>48</th>\n",
|
|
" <td>97</td>\n",
|
|
" <td>22</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>34</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>49</th>\n",
|
|
" <td>56</td>\n",
|
|
" <td>83</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>95</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
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"text/plain": [
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" f1 f2 f3 label\n",
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"0 97 70 23 81\n",
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"1 60 40 73 7\n",
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"2 60 88 87 67\n",
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"3 42 25 81 100\n",
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"4 45 30 69 72\n",
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"5 16 100 70 14\n",
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"6 25 76 32 70\n",
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"7 2 29 46 65\n",
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"8 71 33 11 79\n",
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"9 98 16 76 9\n",
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"10 0 78 61 34\n",
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"11 78 43 45 87\n",
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"12 95 78 6 34\n",
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"13 14 44 95 27\n",
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"14 32 31 90 58\n",
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"15 72 15 16 59\n",
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"16 5 72 31 36\n",
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"17 72 30 94 55\n",
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"18 55 19 91 74\n",
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"19 9 20 10 34\n",
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"20 3 6 25 49\n",
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"21 63 61 86 55\n",
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"22 25 21 0 85\n",
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"23 80 66 73 51\n",
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"24 96 46 35 58\n",
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"25 7 4 89 25\n",
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"26 92 11 77 59\n",
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"27 38 94 19 46\n",
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"28 34 24 94 70\n",
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"29 100 91 46 76\n",
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"30 43 10 35 78\n",
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"31 15 24 57 6\n",
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"32 51 47 47 55\n",
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"33 83 5 84 40\n",
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"34 100 22 26 72\n",
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"35 60 83 80 92\n",
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"36 28 39 82 17\n",
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"37 56 20 94 85\n",
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"38 72 56 63 54\n",
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"39 15 60 30 72\n",
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"40 41 21 86 54\n",
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"41 85 7 50 87\n",
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"42 48 13 69 93\n",
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"43 75 20 98 96\n",
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"44 41 18 14 31\n",
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"45 84 13 62 6\n",
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"46 13 40 77 60\n",
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"47 70 18 84 26\n",
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"48 97 22 24 34\n",
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"49 56 83 9 95"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"X = df[['f1','f2','f3']]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"y = df['label']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import train_test_split"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
|
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]
|
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(33, 3)"
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]
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},
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"execution_count": 24,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"X_train.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(17, 3)"
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]
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},
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"execution_count": 25,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"X_test.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
|