346 lines
8.1 KiB
Plaintext
346 lines
8.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"text": [
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"/Users/eddie/.pyenv/versions/3.7.6/envs/pytorch/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 numpy as np\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"import pandas as pd\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from torch.utils.data import Dataset"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load the data set using pandas\n",
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"data = pd.read_csv('diabetes.csv')\n"
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Number of times pregnant</th>\n",
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" <th>Plasma glucose concentration</th>\n",
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" <th>Diastolic blood pressure</th>\n",
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" <th>Triceps skin fold thickness</th>\n",
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" <th>2-Hour serum insulin</th>\n",
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" <th>Body mass index</th>\n",
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" <th>Age</th>\n",
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" <th>Class</th>\n",
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" </tr>\n",
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" <tbody>\n",
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" <td>35</td>\n",
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" <td>0</td>\n",
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" <td>33.6</td>\n",
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" <td>50</td>\n",
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" <td>positive</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>26.6</td>\n",
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" <td>negative</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>8</td>\n",
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" <td>183</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>23.3</td>\n",
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" <td>32</td>\n",
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" <td>positive</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1</td>\n",
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" <td>94</td>\n",
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" <td>21</td>\n",
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" <td>negative</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>33</td>\n",
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],
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"text/plain": [
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" Number of times pregnant Plasma glucose concentration \\\n",
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"0 6 148 \n",
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"1 1 85 \n",
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"2 8 183 \n",
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"3 1 89 \n",
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"4 0 137 \n",
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"\n",
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" Diastolic blood pressure Triceps skin fold thickness \\\n",
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"0 72 35 \n",
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"1 66 29 \n",
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"2 64 0 \n",
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"3 66 23 \n",
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"4 40 35 \n",
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"\n",
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" 2-Hour serum insulin Body mass index Age Class \n",
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"0 0 33.6 50 positive \n",
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"1 0 26.6 31 negative \n",
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"2 0 23.3 32 positive \n",
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"3 94 28.1 21 negative \n",
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"4 168 43.1 33 positive "
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]
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},
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"execution_count": 3,
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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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"data.head() "
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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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"x = data.iloc[:,0:-1].values"
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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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"text/plain": [
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"(768, 7)"
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]
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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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],
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"source": [
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"x.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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_string = list(data.iloc[:,-1])"
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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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"768"
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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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"len(y_string)"
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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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"source": [
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"y_int = []\n",
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"for i in y_string:\n",
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" if i == 'positive':\n",
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" y_int.append(1)\n",
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" else:\n",
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" y_int.append(0)\n",
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" "
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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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"source": [
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"y = np.array(y_int, dtype='float64') "
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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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"source": [
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"# data normaalization\n",
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"sc = StandardScaler()\n",
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"x = sc.fit_transform(x)"
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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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"source": [
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"x = torch.tensor(x)\n",
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"y = torch.tensor(y)"
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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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{
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"data": {
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"text/plain": [
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"torch.Size([768, 7])"
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]
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},
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"execution_count": 12,
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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.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": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"y = y.unsqueeze(1)"
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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": 14,
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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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"torch.Size([768, 1])"
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]
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},
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"execution_count": 14,
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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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"y.shape"
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{
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"metadata": {},
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