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[ADD] new files for diabetes classification NN

This commit is contained in:
Eduardo Cueto Mendoza 2020-04-23 13:26:18 -06:00
parent ae23cdd124
commit 5fb53be025
6 changed files with 4860 additions and 5 deletions
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187
Automatic_Differenciation.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Auto grad\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torchvision"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([5., 7., 9.], grad_fn=<AddBackward0>)\n",
"<AddBackward0 object at 0x11f573ad0>\n",
"tensor(21., grad_fn=<SumBackward0>)\n",
"<SumBackward0 object at 0x11f595650>\n"
]
}
],
"source": [
"# The tensor object keeps track of how it was created if requieres_grad is True \n",
"x = torch.tensor([1.,2.,3],requires_grad=True)\n",
"y = torch.tensor([4.,5.,6],requires_grad=True)\n",
"\n",
"z = x + y\n",
"print(z)\n",
"\n",
"print(z.grad_fn)\n",
"s = z.sum()\n",
"\n",
"print(s)\n",
"\n",
"print(s.grad_fn)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([2., 2., 2.])\n"
]
}
],
"source": [
"# To back propagate\n",
"s.backward()\n",
"print(x.grad)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False False\n",
"None\n",
"<AddBackward0 object at 0x11f088650>\n",
"True\n",
"None\n",
"True\n",
"True\n",
"False\n"
]
}
],
"source": [
"x = torch.randn(2,2)\n",
"y = torch.randn(2,2)\n",
"print(x.requires_grad,y.requires_grad)\n",
"\n",
"z = x + y\n",
"\n",
"print(z.grad_fn)\n",
"\n",
"x.requires_grad_()\n",
"y.requires_grad_()\n",
"\n",
"z= x + y\n",
"\n",
"print(z.grad_fn)\n",
"\n",
"print(z.requires_grad)\n",
"\n",
"new_z = z.detach()\n",
"\n",
"print(new_z.grad_fn)\n",
"\n",
"print(x.requires_grad)\n",
"print((x+10).requires_grad)\n",
"\n",
"with torch.no_grad():\n",
" print((x+10).requires_grad)\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[1., 1.],\n",
" [1., 1.]], requires_grad=True)\n",
"tensor([[3., 3.],\n",
" [3., 3.]], grad_fn=<AddBackward0>)\n",
"<AddBackward0 object at 0x11ee83b90>\n",
"tensor([[27., 27.],\n",
" [27., 27.]], grad_fn=<MulBackward0>) tensor(27., grad_fn=<MeanBackward0>)\n",
"tensor([[4.5000, 4.5000],\n",
" [4.5000, 4.5000]])\n"
]
}
],
"source": [
"x = torch.ones(2,2,requires_grad=True)\n",
"print(x)\n",
"y = x + 2\n",
"print(y)\n",
"print(y.grad_fn)\n",
"\n",
"z = y*y*3\n",
"\n",
"out = z.mean()\n",
"\n",
"print(z,out)\n",
"\n",
"out.backward()\n",
"print(x.grad)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

345
Diabetes_NN.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/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",
" warnings.warn(msg)\n"
]
}
],
"source": [
"import numpy as np\n",
"import torch\n",
"import torch.nn as nn\n",
"import pandas as pd\n",
"from sklearn.preprocessing import StandardScaler\n",
"from torch.utils.data import Dataset"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Load the data set using pandas\n",
"data = pd.read_csv('diabetes.csv')\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Number of times pregnant</th>\n",
" <th>Plasma glucose concentration</th>\n",
" <th>Diastolic blood pressure</th>\n",
" <th>Triceps skin fold thickness</th>\n",
" <th>2-Hour serum insulin</th>\n",
" <th>Body mass index</th>\n",
" <th>Age</th>\n",
" <th>Class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6</td>\n",
" <td>148</td>\n",
" <td>72</td>\n",
" <td>35</td>\n",
" <td>0</td>\n",
" <td>33.6</td>\n",
" <td>50</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>85</td>\n",
" <td>66</td>\n",
" <td>29</td>\n",
" <td>0</td>\n",
" <td>26.6</td>\n",
" <td>31</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>8</td>\n",
" <td>183</td>\n",
" <td>64</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>23.3</td>\n",
" <td>32</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>89</td>\n",
" <td>66</td>\n",
" <td>23</td>\n",
" <td>94</td>\n",
" <td>28.1</td>\n",
" <td>21</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>137</td>\n",
" <td>40</td>\n",
" <td>35</td>\n",
" <td>168</td>\n",
" <td>43.1</td>\n",
" <td>33</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Number of times pregnant Plasma glucose concentration \\\n",
"0 6 148 \n",
"1 1 85 \n",
"2 8 183 \n",
"3 1 89 \n",
"4 0 137 \n",
"\n",
" Diastolic blood pressure Triceps skin fold thickness \\\n",
"0 72 35 \n",
"1 66 29 \n",
"2 64 0 \n",
"3 66 23 \n",
"4 40 35 \n",
"\n",
" 2-Hour serum insulin Body mass index Age Class \n",
"0 0 33.6 50 positive \n",
"1 0 26.6 31 negative \n",
"2 0 23.3 32 positive \n",
"3 94 28.1 21 negative \n",
"4 168 43.1 33 positive "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head() "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"x = data.iloc[:,0:-1].values"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(768, 7)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"y_string = list(data.iloc[:,-1])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"768"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(y_string)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"y_int = []\n",
"for i in y_string:\n",
" if i == 'positive':\n",
" y_int.append(1)\n",
" else:\n",
" y_int.append(0)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"y = np.array(y_int, dtype='float64') "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# data normaalization\n",
"sc = StandardScaler()\n",
"x = sc.fit_transform(x)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"x = torch.tensor(x)\n",
"y = torch.tensor(y)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([768, 7])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.shape"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"y = y.unsqueeze(1)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([768, 1])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

114
Numpy_Bridge.ipynb
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{
"data": {
"text/plain": [
"True"
"False"
]
},
"execution_count": 1,
@ -82,7 +82,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@ -91,7 +91,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@ -173,7 +173,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 7,
"metadata": {},
"outputs": [
{
@ -194,7 +194,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 8,
"metadata": {},
"outputs": [
{
@ -214,6 +214,110 @@
"print(T,T.dtype)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tensor Concatenation"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[ 0.7607, 0.2490, -1.5199, 0.4037, 0.0063],\n",
" [ 0.2223, -1.0452, -1.6327, -0.1692, -0.8291]])\n",
"tensor([[-0.2285, 0.7535, -1.4712, 1.1518, 0.4560],\n",
" [-0.4817, -0.6983, -0.9611, -1.5915, -1.7998],\n",
" [-0.3149, 0.4309, 1.4270, 0.1497, -0.4793]])\n",
"\n",
"\n",
"tensor([[ 0.7607, 0.2490, -1.5199, 0.4037, 0.0063],\n",
" [ 0.2223, -1.0452, -1.6327, -0.1692, -0.8291],\n",
" [-0.2285, 0.7535, -1.4712, 1.1518, 0.4560],\n",
" [-0.4817, -0.6983, -0.9611, -1.5915, -1.7998],\n",
" [-0.3149, 0.4309, 1.4270, 0.1497, -0.4793]])\n"
]
}
],
"source": [
"first_1 = torch.randn(2,5)\n",
"print(first_1)\n",
"second_1 = torch.randn(3,5)\n",
"print(second_1)\n",
"# Concaatenate along the 0 dimension row-wise\n",
"con_1 = torch.cat([first_1,second_1])\n",
"print(\"\\n\")\n",
"print(con_1)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[-0.6601, 1.8097, 0.6295],\n",
" [-1.8868, -2.2800, 1.7137]])\n",
"tensor([[ 1.2576, -0.5680, 1.2772, -0.2566, -2.1952],\n",
" [-0.4767, -0.5083, -0.0795, -1.5576, 0.6238]])\n",
"\n",
"\n",
"tensor([[-0.6601, 1.8097, 0.6295, 1.2576, -0.5680, 1.2772, -0.2566, -2.1952],\n",
" [-1.8868, -2.2800, 1.7137, -0.4767, -0.5083, -0.0795, -1.5576, 0.6238]])\n"
]
}
],
"source": [
"first_2 = torch.randn(2,3)\n",
"print(first_2)\n",
"second_2 = torch.randn(2,5)\n",
"print(second_2)\n",
"# Concaatenate along the 1 dimension column-wise\n",
"con_2 = torch.cat([first_2,second_2],1)\n",
"print(\"\\n\")\n",
"print(con_2)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Adding ddimensions to tensors"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[2, 3, 4, 5]])\n",
"torch.Size([1, 4])\n",
"torch.Size([4])\n"
]
}
],
"source": [
"tensor_1 = torch.tensor([2,3,4,5])\n",
"tensor_a = torch.unsqueeze(tensor_1,0)\n",
"print(tensor_a)\n",
"print(tensor_a.shape)\n",
"print(tensor_1.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,

142
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{
"cells": [
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"# The main function that prints \n",
"# all combinations of size r in \n",
"# arr[] of size n. This function \n",
"# mainly uses combinationUtil() \n",
"def printCombination(arr, n, r): \n",
" \n",
" # A temporary array to \n",
" # store all combination \n",
" # one by one \n",
" data = [0]*r; \n",
" \n",
" # Print all combination \n",
" # using temprary array 'data[]' \n",
" combinationUtil(arr, data, 0, \n",
" n - 1, 0, r); \n",
" \n",
"# arr[] ---> Input Array \n",
"# data[] ---> Temporary array to \n",
"# store current combination \n",
"# start & end ---> Staring and Ending \n",
"# indexes in arr[] \n",
"# index ---> Current index in data[] \n",
"# r ---> Size of a combination \n",
"# to be printed \n",
"def combinationUtil(arr, data, start, \n",
" end, index, r): \n",
" \n",
" temp1 =[]\n",
" # Current combination is ready \n",
" # to be printed, print it \n",
" if (index == r): \n",
" temp2 = []\n",
" for j in range(r): \n",
" temp2.append(data[j])\n",
" #print(data[j], end = \" \"); \n",
" #print(); \n",
" #temp1.append(temp2)\n",
" #return temp1\n",
" \n",
" # replace index with all \n",
" # possible elements. The \n",
" # condition \"end-i+1 >= \n",
" # r-index\" makes sure that \n",
" # including one element at \n",
" # index will make a combination \n",
" # with remaining elements at \n",
" # remaining positions \n",
" i = start; \n",
" while(i <= end and end - i + 1 >= r - index): \n",
" data[index] = arr[i]; \n",
" combinationUtil(arr, data, i + 1, \n",
" end, index + 1, r); \n",
" i += 1; \n",
" \n",
" return temp1"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"ename": "IndexError",
"evalue": "list assignment index out of range",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-64-eede45e736f3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0malg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprintCombination\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-63-85ad8d7a3f5f>\u001b[0m in \u001b[0;36mprintCombination\u001b[0;34m(arr, n, r)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;31m# using temprary array 'data[]'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m combinationUtil(arr, data, 0, \n\u001b[0;32m---> 15\u001b[0;31m n - 1, 0, r); \n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;31m# arr[] ---> Input Array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-63-85ad8d7a3f5f>\u001b[0m in \u001b[0;36mcombinationUtil\u001b[0;34m(arr, data, start, end, index, r)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m combinationUtil(arr, data, i + 1, \n\u001b[0;32m---> 52\u001b[0;31m end, index + 1, r); \n\u001b[0m\u001b[1;32m 53\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-63-85ad8d7a3f5f>\u001b[0m in \u001b[0;36mcombinationUtil\u001b[0;34m(arr, data, start, end, index, r)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m combinationUtil(arr, data, i + 1, \n\u001b[0;32m---> 52\u001b[0;31m end, index + 1, r); \n\u001b[0m\u001b[1;32m 53\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-63-85ad8d7a3f5f>\u001b[0m in \u001b[0;36mcombinationUtil\u001b[0;34m(arr, data, start, end, index, r)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m combinationUtil(arr, data, i + 1, \n\u001b[0;32m---> 52\u001b[0;31m end, index + 1, r); \n\u001b[0m\u001b[1;32m 53\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-63-85ad8d7a3f5f>\u001b[0m in \u001b[0;36mcombinationUtil\u001b[0;34m(arr, data, start, end, index, r)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;32mwhile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mend\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 51\u001b[0m combinationUtil(arr, data, i + 1, \n\u001b[1;32m 52\u001b[0m end, index + 1, r); \n",
"\u001b[0;31mIndexError\u001b[0m: list assignment index out of range"
]
}
],
"source": [
"arr =[1,3,5,7,9,11,13,15]\n",
"\n",
"n = len(arr)\n",
"\n",
"alg = printCombination(arr,n,3)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"None\n"
]
}
],
"source": [
"print(alg)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

3308
alice.txt Normal file
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769
diabetes.csv Normal file
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@ -0,0 +1,769 @@
Number of times pregnant,Plasma glucose concentration,Diastolic blood pressure,Triceps skin fold thickness,2-Hour serum insulin,Body mass index,Age,Class
6,148,72,35,0,33.6,50,positive
1,85,66,29,0,26.6,31,negative
8,183,64,0,0,23.3,32,positive
1,89,66,23,94,28.1,21,negative
0,137,40,35,168,43.1,33,positive
5,116,74,0,0,25.6,30,negative
3,78,50,32,88,31,26,positive
10,115,0,0,0,35.3,29,negative
2,197,70,45,543,30.5,53,positive
8,125,96,0,0,0,54,positive
4,110,92,0,0,37.6,30,negative
10,168,74,0,0,38,34,positive
10,139,80,0,0,27.1,57,negative
1,189,60,23,846,30.1,59,positive
5,166,72,19,175,25.8,51,positive
7,100,0,0,0,30,32,positive
0,118,84,47,230,45.8,31,positive
7,107,74,0,0,29.6,31,positive
1,103,30,38,83,43.3,33,negative
1,115,70,30,96,34.6,32,positive
3,126,88,41,235,39.3,27,negative
8,99,84,0,0,35.4,50,negative
7,196,90,0,0,39.8,41,positive
9,119,80,35,0,29,29,positive
11,143,94,33,146,36.6,51,positive
10,125,70,26,115,31.1,41,positive
7,147,76,0,0,39.4,43,positive
1,97,66,15,140,23.2,22,negative
13,145,82,19,110,22.2,57,negative
5,117,92,0,0,34.1,38,negative
5,109,75,26,0,36,60,negative
3,158,76,36,245,31.6,28,positive
3,88,58,11,54,24.8,22,negative
6,92,92,0,0,19.9,28,negative
10,122,78,31,0,27.6,45,negative
4,103,60,33,192,24,33,negative
11,138,76,0,0,33.2,35,negative
9,102,76,37,0,32.9,46,positive
2,90,68,42,0,38.2,27,positive
4,111,72,47,207,37.1,56,positive
3,180,64,25,70,34,26,negative
7,133,84,0,0,40.2,37,negative
7,106,92,18,0,22.7,48,negative
9,171,110,24,240,45.4,54,positive
7,159,64,0,0,27.4,40,negative
0,180,66,39,0,42,25,positive
1,146,56,0,0,29.7,29,negative
2,71,70,27,0,28,22,negative
7,103,66,32,0,39.1,31,positive
7,105,0,0,0,0,24,negative
1,103,80,11,82,19.4,22,negative
1,101,50,15,36,24.2,26,negative
5,88,66,21,23,24.4,30,negative
8,176,90,34,300,33.7,58,positive
7,150,66,42,342,34.7,42,negative
1,73,50,10,0,23,21,negative
7,187,68,39,304,37.7,41,positive
0,100,88,60,110,46.8,31,negative
0,146,82,0,0,40.5,44,negative
0,105,64,41,142,41.5,22,negative
2,84,0,0,0,0,21,negative
8,133,72,0,0,32.9,39,positive
5,44,62,0,0,25,36,negative
2,141,58,34,128,25.4,24,negative
7,114,66,0,0,32.8,42,positive
5,99,74,27,0,29,32,negative
0,109,88,30,0,32.5,38,positive
2,109,92,0,0,42.7,54,negative
1,95,66,13,38,19.6,25,negative
4,146,85,27,100,28.9,27,negative
2,100,66,20,90,32.9,28,positive
5,139,64,35,140,28.6,26,negative
13,126,90,0,0,43.4,42,positive
4,129,86,20,270,35.1,23,negative
1,79,75,30,0,32,22,negative
1,0,48,20,0,24.7,22,negative
7,62,78,0,0,32.6,41,negative
5,95,72,33,0,37.7,27,negative
0,131,0,0,0,43.2,26,positive
2,112,66,22,0,25,24,negative
3,113,44,13,0,22.4,22,negative
2,74,0,0,0,0,22,negative
7,83,78,26,71,29.3,36,negative
0,101,65,28,0,24.6,22,negative
5,137,108,0,0,48.8,37,positive
2,110,74,29,125,32.4,27,negative
13,106,72,54,0,36.6,45,negative
2,100,68,25,71,38.5,26,negative
15,136,70,32,110,37.1,43,positive
1,107,68,19,0,26.5,24,negative
1,80,55,0,0,19.1,21,negative
4,123,80,15,176,32,34,negative
7,81,78,40,48,46.7,42,negative
4,134,72,0,0,23.8,60,positive
2,142,82,18,64,24.7,21,negative
6,144,72,27,228,33.9,40,negative
2,92,62,28,0,31.6,24,negative
1,71,48,18,76,20.4,22,negative
6,93,50,30,64,28.7,23,negative
1,122,90,51,220,49.7,31,positive
1,163,72,0,0,39,33,positive
1,151,60,0,0,26.1,22,negative
0,125,96,0,0,22.5,21,negative
1,81,72,18,40,26.6,24,negative
2,85,65,0,0,39.6,27,negative
1,126,56,29,152,28.7,21,negative
1,96,122,0,0,22.4,27,negative
4,144,58,28,140,29.5,37,negative
3,83,58,31,18,34.3,25,negative
0,95,85,25,36,37.4,24,positive
3,171,72,33,135,33.3,24,positive
8,155,62,26,495,34,46,positive
1,89,76,34,37,31.2,23,negative
4,76,62,0,0,34,25,negative
7,160,54,32,175,30.5,39,positive
4,146,92,0,0,31.2,61,positive
5,124,74,0,0,34,38,positive
5,78,48,0,0,33.7,25,negative
4,97,60,23,0,28.2,22,negative
4,99,76,15,51,23.2,21,negative
0,162,76,56,100,53.2,25,positive
6,111,64,39,0,34.2,24,negative
2,107,74,30,100,33.6,23,negative
5,132,80,0,0,26.8,69,negative
0,113,76,0,0,33.3,23,positive
1,88,30,42,99,55,26,positive
3,120,70,30,135,42.9,30,negative
1,118,58,36,94,33.3,23,negative
1,117,88,24,145,34.5,40,positive
0,105,84,0,0,27.9,62,positive
4,173,70,14,168,29.7,33,positive
9,122,56,0,0,33.3,33,positive
3,170,64,37,225,34.5,30,positive
8,84,74,31,0,38.3,39,negative
2,96,68,13,49,21.1,26,negative
2,125,60,20,140,33.8,31,negative
0,100,70,26,50,30.8,21,negative
0,93,60,25,92,28.7,22,negative
0,129,80,0,0,31.2,29,negative
5,105,72,29,325,36.9,28,negative
3,128,78,0,0,21.1,55,negative
5,106,82,30,0,39.5,38,negative
2,108,52,26,63,32.5,22,negative
10,108,66,0,0,32.4,42,positive
4,154,62,31,284,32.8,23,negative
0,102,75,23,0,0,21,negative
9,57,80,37,0,32.8,41,negative
2,106,64,35,119,30.5,34,negative
5,147,78,0,0,33.7,65,negative
2,90,70,17,0,27.3,22,negative
1,136,74,50,204,37.4,24,negative
4,114,65,0,0,21.9,37,negative
9,156,86,28,155,34.3,42,positive
1,153,82,42,485,40.6,23,negative
8,188,78,0,0,47.9,43,positive
7,152,88,44,0,50,36,positive
2,99,52,15,94,24.6,21,negative
1,109,56,21,135,25.2,23,negative
2,88,74,19,53,29,22,negative
17,163,72,41,114,40.9,47,positive
4,151,90,38,0,29.7,36,negative
7,102,74,40,105,37.2,45,negative
0,114,80,34,285,44.2,27,negative
2,100,64,23,0,29.7,21,negative
0,131,88,0,0,31.6,32,positive
6,104,74,18,156,29.9,41,positive
3,148,66,25,0,32.5,22,negative
4,120,68,0,0,29.6,34,negative
4,110,66,0,0,31.9,29,negative
3,111,90,12,78,28.4,29,negative
6,102,82,0,0,30.8,36,positive
6,134,70,23,130,35.4,29,positive
2,87,0,23,0,28.9,25,negative
1,79,60,42,48,43.5,23,negative
2,75,64,24,55,29.7,33,negative
8,179,72,42,130,32.7,36,positive
6,85,78,0,0,31.2,42,negative
0,129,110,46,130,67.1,26,positive
5,143,78,0,0,45,47,negative
5,130,82,0,0,39.1,37,positive
6,87,80,0,0,23.2,32,negative
0,119,64,18,92,34.9,23,negative
1,0,74,20,23,27.7,21,negative
5,73,60,0,0,26.8,27,negative
4,141,74,0,0,27.6,40,negative
7,194,68,28,0,35.9,41,positive
8,181,68,36,495,30.1,60,positive
1,128,98,41,58,32,33,positive
8,109,76,39,114,27.9,31,positive
5,139,80,35,160,31.6,25,positive
3,111,62,0,0,22.6,21,negative
9,123,70,44,94,33.1,40,negative
7,159,66,0,0,30.4,36,positive
11,135,0,0,0,52.3,40,positive
8,85,55,20,0,24.4,42,negative
5,158,84,41,210,39.4,29,positive
1,105,58,0,0,24.3,21,negative
3,107,62,13,48,22.9,23,positive
4,109,64,44,99,34.8,26,positive
4,148,60,27,318,30.9,29,positive
0,113,80,16,0,31,21,negative
1,138,82,0,0,40.1,28,negative
0,108,68,20,0,27.3,32,negative
2,99,70,16,44,20.4,27,negative
6,103,72,32,190,37.7,55,negative
5,111,72,28,0,23.9,27,negative
8,196,76,29,280,37.5,57,positive
5,162,104,0,0,37.7,52,positive
1,96,64,27,87,33.2,21,negative
7,184,84,33,0,35.5,41,positive
2,81,60,22,0,27.7,25,negative
0,147,85,54,0,42.8,24,negative
7,179,95,31,0,34.2,60,negative
0,140,65,26,130,42.6,24,positive
9,112,82,32,175,34.2,36,positive
12,151,70,40,271,41.8,38,positive
5,109,62,41,129,35.8,25,positive
6,125,68,30,120,30,32,negative
5,85,74,22,0,29,32,positive
5,112,66,0,0,37.8,41,positive
0,177,60,29,478,34.6,21,positive
2,158,90,0,0,31.6,66,positive
7,119,0,0,0,25.2,37,negative
7,142,60,33,190,28.8,61,negative
1,100,66,15,56,23.6,26,negative
1,87,78,27,32,34.6,22,negative
0,101,76,0,0,35.7,26,negative
3,162,52,38,0,37.2,24,positive
4,197,70,39,744,36.7,31,negative
0,117,80,31,53,45.2,24,negative
4,142,86,0,0,44,22,positive
6,134,80,37,370,46.2,46,positive
1,79,80,25,37,25.4,22,negative
4,122,68,0,0,35,29,negative
3,74,68,28,45,29.7,23,negative
4,171,72,0,0,43.6,26,positive
7,181,84,21,192,35.9,51,positive
0,179,90,27,0,44.1,23,positive
9,164,84,21,0,30.8,32,positive
0,104,76,0,0,18.4,27,negative
1,91,64,24,0,29.2,21,negative
4,91,70,32,88,33.1,22,negative
3,139,54,0,0,25.6,22,positive
6,119,50,22,176,27.1,33,positive
2,146,76,35,194,38.2,29,negative
9,184,85,15,0,30,49,positive
10,122,68,0,0,31.2,41,negative
0,165,90,33,680,52.3,23,negative
9,124,70,33,402,35.4,34,negative
1,111,86,19,0,30.1,23,negative
9,106,52,0,0,31.2,42,negative
2,129,84,0,0,28,27,negative
2,90,80,14,55,24.4,24,negative
0,86,68,32,0,35.8,25,negative
12,92,62,7,258,27.6,44,positive
1,113,64,35,0,33.6,21,positive
3,111,56,39,0,30.1,30,negative
2,114,68,22,0,28.7,25,negative
1,193,50,16,375,25.9,24,negative
11,155,76,28,150,33.3,51,positive
3,191,68,15,130,30.9,34,negative
3,141,0,0,0,30,27,positive
4,95,70,32,0,32.1,24,negative
3,142,80,15,0,32.4,63,negative
4,123,62,0,0,32,35,positive
5,96,74,18,67,33.6,43,negative
0,138,0,0,0,36.3,25,positive
2,128,64,42,0,40,24,negative
0,102,52,0,0,25.1,21,negative
2,146,0,0,0,27.5,28,positive
10,101,86,37,0,45.6,38,positive
2,108,62,32,56,25.2,21,negative
3,122,78,0,0,23,40,negative
1,71,78,50,45,33.2,21,negative
13,106,70,0,0,34.2,52,negative
2,100,70,52,57,40.5,25,negative
7,106,60,24,0,26.5,29,positive
0,104,64,23,116,27.8,23,negative
5,114,74,0,0,24.9,57,negative
2,108,62,10,278,25.3,22,negative
0,146,70,0,0,37.9,28,positive
10,129,76,28,122,35.9,39,negative
7,133,88,15,155,32.4,37,negative
7,161,86,0,0,30.4,47,positive
2,108,80,0,0,27,52,positive
7,136,74,26,135,26,51,negative
5,155,84,44,545,38.7,34,negative
1,119,86,39,220,45.6,29,positive
4,96,56,17,49,20.8,26,negative
5,108,72,43,75,36.1,33,negative
0,78,88,29,40,36.9,21,negative
0,107,62,30,74,36.6,25,positive
2,128,78,37,182,43.3,31,positive
1,128,48,45,194,40.5,24,positive
0,161,50,0,0,21.9,65,negative
6,151,62,31,120,35.5,28,negative
2,146,70,38,360,28,29,positive
0,126,84,29,215,30.7,24,negative
14,100,78,25,184,36.6,46,positive
8,112,72,0,0,23.6,58,negative
0,167,0,0,0,32.3,30,positive
2,144,58,33,135,31.6,25,positive
5,77,82,41,42,35.8,35,negative
5,115,98,0,0,52.9,28,positive
3,150,76,0,0,21,37,negative
2,120,76,37,105,39.7,29,negative
10,161,68,23,132,25.5,47,positive
0,137,68,14,148,24.8,21,negative
0,128,68,19,180,30.5,25,positive
2,124,68,28,205,32.9,30,positive
6,80,66,30,0,26.2,41,negative
0,106,70,37,148,39.4,22,negative
2,155,74,17,96,26.6,27,positive
3,113,50,10,85,29.5,25,negative
7,109,80,31,0,35.9,43,positive
2,112,68,22,94,34.1,26,negative
3,99,80,11,64,19.3,30,negative
3,182,74,0,0,30.5,29,positive
3,115,66,39,140,38.1,28,negative
6,194,78,0,0,23.5,59,positive
4,129,60,12,231,27.5,31,negative
3,112,74,30,0,31.6,25,positive
0,124,70,20,0,27.4,36,positive
13,152,90,33,29,26.8,43,positive
2,112,75,32,0,35.7,21,negative
1,157,72,21,168,25.6,24,negative
1,122,64,32,156,35.1,30,positive
10,179,70,0,0,35.1,37,negative
2,102,86,36,120,45.5,23,positive
6,105,70,32,68,30.8,37,negative
8,118,72,19,0,23.1,46,negative
2,87,58,16,52,32.7,25,negative
1,180,0,0,0,43.3,41,positive
12,106,80,0,0,23.6,44,negative
1,95,60,18,58,23.9,22,negative
0,165,76,43,255,47.9,26,negative
0,117,0,0,0,33.8,44,negative
5,115,76,0,0,31.2,44,positive
9,152,78,34,171,34.2,33,positive
7,178,84,0,0,39.9,41,positive
1,130,70,13,105,25.9,22,negative
1,95,74,21,73,25.9,36,negative
1,0,68,35,0,32,22,negative
5,122,86,0,0,34.7,33,negative
8,95,72,0,0,36.8,57,negative
8,126,88,36,108,38.5,49,negative
1,139,46,19,83,28.7,22,negative
3,116,0,0,0,23.5,23,negative
3,99,62,19,74,21.8,26,negative
5,0,80,32,0,41,37,positive
4,92,80,0,0,42.2,29,negative
4,137,84,0,0,31.2,30,negative
3,61,82,28,0,34.4,46,negative
1,90,62,12,43,27.2,24,negative
3,90,78,0,0,42.7,21,negative
9,165,88,0,0,30.4,49,positive
1,125,50,40,167,33.3,28,positive
13,129,0,30,0,39.9,44,positive
12,88,74,40,54,35.3,48,negative
1,196,76,36,249,36.5,29,positive
5,189,64,33,325,31.2,29,positive
5,158,70,0,0,29.8,63,negative
5,103,108,37,0,39.2,65,negative
4,146,78,0,0,38.5,67,positive
4,147,74,25,293,34.9,30,negative
5,99,54,28,83,34,30,negative
6,124,72,0,0,27.6,29,positive
0,101,64,17,0,21,21,negative
3,81,86,16,66,27.5,22,negative
1,133,102,28,140,32.8,45,positive
3,173,82,48,465,38.4,25,positive
0,118,64,23,89,0,21,negative
0,84,64,22,66,35.8,21,negative
2,105,58,40,94,34.9,25,negative
2,122,52,43,158,36.2,28,negative
12,140,82,43,325,39.2,58,positive
0,98,82,15,84,25.2,22,negative
1,87,60,37,75,37.2,22,negative
4,156,75,0,0,48.3,32,positive
0,93,100,39,72,43.4,35,negative
1,107,72,30,82,30.8,24,negative
0,105,68,22,0,20,22,negative
1,109,60,8,182,25.4,21,negative
1,90,62,18,59,25.1,25,negative
1,125,70,24,110,24.3,25,negative
1,119,54,13,50,22.3,24,negative
5,116,74,29,0,32.3,35,positive
8,105,100,36,0,43.3,45,positive
5,144,82,26,285,32,58,positive
3,100,68,23,81,31.6,28,negative
1,100,66,29,196,32,42,negative
5,166,76,0,0,45.7,27,positive
1,131,64,14,415,23.7,21,negative
4,116,72,12,87,22.1,37,negative
4,158,78,0,0,32.9,31,positive
2,127,58,24,275,27.7,25,negative
3,96,56,34,115,24.7,39,negative
0,131,66,40,0,34.3,22,positive
3,82,70,0,0,21.1,25,negative
3,193,70,31,0,34.9,25,positive
4,95,64,0,0,32,31,positive
6,137,61,0,0,24.2,55,negative
5,136,84,41,88,35,35,positive
9,72,78,25,0,31.6,38,negative
5,168,64,0,0,32.9,41,positive
2,123,48,32,165,42.1,26,negative
4,115,72,0,0,28.9,46,positive
0,101,62,0,0,21.9,25,negative
8,197,74,0,0,25.9,39,positive
1,172,68,49,579,42.4,28,positive
6,102,90,39,0,35.7,28,negative
1,112,72,30,176,34.4,25,negative
1,143,84,23,310,42.4,22,negative
1,143,74,22,61,26.2,21,negative
0,138,60,35,167,34.6,21,positive
3,173,84,33,474,35.7,22,positive
1,97,68,21,0,27.2,22,negative
4,144,82,32,0,38.5,37,positive
1,83,68,0,0,18.2,27,negative
3,129,64,29,115,26.4,28,positive
1,119,88,41,170,45.3,26,negative
2,94,68,18,76,26,21,negative
0,102,64,46,78,40.6,21,negative
2,115,64,22,0,30.8,21,negative
8,151,78,32,210,42.9,36,positive
4,184,78,39,277,37,31,positive
0,94,0,0,0,0,25,negative
1,181,64,30,180,34.1,38,positive
0,135,94,46,145,40.6,26,negative
1,95,82,25,180,35,43,positive
2,99,0,0,0,22.2,23,negative
3,89,74,16,85,30.4,38,negative
1,80,74,11,60,30,22,negative
2,139,75,0,0,25.6,29,negative
1,90,68,8,0,24.5,36,negative
0,141,0,0,0,42.4,29,positive
12,140,85,33,0,37.4,41,negative
5,147,75,0,0,29.9,28,negative
1,97,70,15,0,18.2,21,negative
6,107,88,0,0,36.8,31,negative
0,189,104,25,0,34.3,41,positive
2,83,66,23,50,32.2,22,negative
4,117,64,27,120,33.2,24,negative
8,108,70,0,0,30.5,33,positive
4,117,62,12,0,29.7,30,positive
0,180,78,63,14,59.4,25,positive
1,100,72,12,70,25.3,28,negative
0,95,80,45,92,36.5,26,negative
0,104,64,37,64,33.6,22,positive
0,120,74,18,63,30.5,26,negative
1,82,64,13,95,21.2,23,negative
2,134,70,0,0,28.9,23,positive
0,91,68,32,210,39.9,25,negative
2,119,0,0,0,19.6,72,negative
2,100,54,28,105,37.8,24,negative
14,175,62,30,0,33.6,38,positive
1,135,54,0,0,26.7,62,negative
5,86,68,28,71,30.2,24,negative
10,148,84,48,237,37.6,51,positive
9,134,74,33,60,25.9,81,negative
9,120,72,22,56,20.8,48,negative
1,71,62,0,0,21.8,26,negative
8,74,70,40,49,35.3,39,negative
5,88,78,30,0,27.6,37,negative
10,115,98,0,0,24,34,negative
0,124,56,13,105,21.8,21,negative
0,74,52,10,36,27.8,22,negative
0,97,64,36,100,36.8,25,negative
8,120,0,0,0,30,38,positive
6,154,78,41,140,46.1,27,negative
1,144,82,40,0,41.3,28,negative
0,137,70,38,0,33.2,22,negative
0,119,66,27,0,38.8,22,negative
7,136,90,0,0,29.9,50,negative
4,114,64,0,0,28.9,24,negative
0,137,84,27,0,27.3,59,negative
2,105,80,45,191,33.7,29,positive
7,114,76,17,110,23.8,31,negative
8,126,74,38,75,25.9,39,negative
4,132,86,31,0,28,63,negative
3,158,70,30,328,35.5,35,positive
0,123,88,37,0,35.2,29,negative
4,85,58,22,49,27.8,28,negative
0,84,82,31,125,38.2,23,negative
0,145,0,0,0,44.2,31,positive
0,135,68,42,250,42.3,24,positive
1,139,62,41,480,40.7,21,negative
0,173,78,32,265,46.5,58,negative
4,99,72,17,0,25.6,28,negative
8,194,80,0,0,26.1,67,negative
2,83,65,28,66,36.8,24,negative
2,89,90,30,0,33.5,42,negative
4,99,68,38,0,32.8,33,negative
4,125,70,18,122,28.9,45,positive
3,80,0,0,0,0,22,negative
6,166,74,0,0,26.6,66,negative
5,110,68,0,0,26,30,negative
2,81,72,15,76,30.1,25,negative
7,195,70,33,145,25.1,55,positive
6,154,74,32,193,29.3,39,negative
2,117,90,19,71,25.2,21,negative
3,84,72,32,0,37.2,28,negative
6,0,68,41,0,39,41,positive
7,94,64,25,79,33.3,41,negative
3,96,78,39,0,37.3,40,negative
10,75,82,0,0,33.3,38,negative
0,180,90,26,90,36.5,35,positive
1,130,60,23,170,28.6,21,negative
2,84,50,23,76,30.4,21,negative
8,120,78,0,0,25,64,negative
12,84,72,31,0,29.7,46,positive
0,139,62,17,210,22.1,21,negative
9,91,68,0,0,24.2,58,negative
2,91,62,0,0,27.3,22,negative
3,99,54,19,86,25.6,24,negative
3,163,70,18,105,31.6,28,positive
9,145,88,34,165,30.3,53,positive
7,125,86,0,0,37.6,51,negative
13,76,60,0,0,32.8,41,negative
6,129,90,7,326,19.6,60,negative
2,68,70,32,66,25,25,negative
3,124,80,33,130,33.2,26,negative
6,114,0,0,0,0,26,negative
9,130,70,0,0,34.2,45,positive
3,125,58,0,0,31.6,24,negative
3,87,60,18,0,21.8,21,negative
1,97,64,19,82,18.2,21,negative
3,116,74,15,105,26.3,24,negative
0,117,66,31,188,30.8,22,negative
0,111,65,0,0,24.6,31,negative
2,122,60,18,106,29.8,22,negative
0,107,76,0,0,45.3,24,negative
1,86,66,52,65,41.3,29,negative
6,91,0,0,0,29.8,31,negative
1,77,56,30,56,33.3,24,negative
4,132,0,0,0,32.9,23,positive
0,105,90,0,0,29.6,46,negative
0,57,60,0,0,21.7,67,negative
0,127,80,37,210,36.3,23,negative
3,129,92,49,155,36.4,32,positive
8,100,74,40,215,39.4,43,positive
3,128,72,25,190,32.4,27,positive
10,90,85,32,0,34.9,56,positive
4,84,90,23,56,39.5,25,negative
1,88,78,29,76,32,29,negative
8,186,90,35,225,34.5,37,positive
5,187,76,27,207,43.6,53,positive
4,131,68,21,166,33.1,28,negative
1,164,82,43,67,32.8,50,negative
4,189,110,31,0,28.5,37,negative
1,116,70,28,0,27.4,21,negative
3,84,68,30,106,31.9,25,negative
6,114,88,0,0,27.8,66,negative
1,88,62,24,44,29.9,23,negative
1,84,64,23,115,36.9,28,negative
7,124,70,33,215,25.5,37,negative
1,97,70,40,0,38.1,30,negative
8,110,76,0,0,27.8,58,negative
11,103,68,40,0,46.2,42,negative
11,85,74,0,0,30.1,35,negative
6,125,76,0,0,33.8,54,positive
0,198,66,32,274,41.3,28,positive
1,87,68,34,77,37.6,24,negative
6,99,60,19,54,26.9,32,negative
0,91,80,0,0,32.4,27,negative
2,95,54,14,88,26.1,22,negative
1,99,72,30,18,38.6,21,negative
6,92,62,32,126,32,46,negative
4,154,72,29,126,31.3,37,negative
0,121,66,30,165,34.3,33,positive
3,78,70,0,0,32.5,39,negative
2,130,96,0,0,22.6,21,negative
3,111,58,31,44,29.5,22,negative
2,98,60,17,120,34.7,22,negative
1,143,86,30,330,30.1,23,negative
1,119,44,47,63,35.5,25,negative
6,108,44,20,130,24,35,negative
2,118,80,0,0,42.9,21,positive
10,133,68,0,0,27,36,negative
2,197,70,99,0,34.7,62,positive
0,151,90,46,0,42.1,21,positive
6,109,60,27,0,25,27,negative
12,121,78,17,0,26.5,62,negative
8,100,76,0,0,38.7,42,negative
8,124,76,24,600,28.7,52,positive
1,93,56,11,0,22.5,22,negative
8,143,66,0,0,34.9,41,positive
6,103,66,0,0,24.3,29,negative
3,176,86,27,156,33.3,52,positive
0,73,0,0,0,21.1,25,negative
11,111,84,40,0,46.8,45,positive
2,112,78,50,140,39.4,24,negative
3,132,80,0,0,34.4,44,positive
2,82,52,22,115,28.5,25,negative
6,123,72,45,230,33.6,34,negative
0,188,82,14,185,32,22,positive
0,67,76,0,0,45.3,46,negative
1,89,24,19,25,27.8,21,negative
1,173,74,0,0,36.8,38,positive
1,109,38,18,120,23.1,26,negative
1,108,88,19,0,27.1,24,negative
6,96,0,0,0,23.7,28,negative
1,124,74,36,0,27.8,30,negative
7,150,78,29,126,35.2,54,positive
4,183,0,0,0,28.4,36,positive
1,124,60,32,0,35.8,21,negative
1,181,78,42,293,40,22,positive
1,92,62,25,41,19.5,25,negative
0,152,82,39,272,41.5,27,negative
1,111,62,13,182,24,23,negative
3,106,54,21,158,30.9,24,negative
3,174,58,22,194,32.9,36,positive
7,168,88,42,321,38.2,40,positive
6,105,80,28,0,32.5,26,negative
11,138,74,26,144,36.1,50,positive
3,106,72,0,0,25.8,27,negative
6,117,96,0,0,28.7,30,negative
2,68,62,13,15,20.1,23,negative
9,112,82,24,0,28.2,50,positive
0,119,0,0,0,32.4,24,positive
2,112,86,42,160,38.4,28,negative
2,92,76,20,0,24.2,28,negative
6,183,94,0,0,40.8,45,negative
0,94,70,27,115,43.5,21,negative
2,108,64,0,0,30.8,21,negative
4,90,88,47,54,37.7,29,negative
0,125,68,0,0,24.7,21,negative
0,132,78,0,0,32.4,21,negative
5,128,80,0,0,34.6,45,negative
4,94,65,22,0,24.7,21,negative
7,114,64,0,0,27.4,34,positive
0,102,78,40,90,34.5,24,negative
2,111,60,0,0,26.2,23,negative
1,128,82,17,183,27.5,22,negative
10,92,62,0,0,25.9,31,negative
13,104,72,0,0,31.2,38,positive
5,104,74,0,0,28.8,48,negative
2,94,76,18,66,31.6,23,negative
7,97,76,32,91,40.9,32,positive
1,100,74,12,46,19.5,28,negative
0,102,86,17,105,29.3,27,negative
4,128,70,0,0,34.3,24,negative
6,147,80,0,0,29.5,50,positive
4,90,0,0,0,28,31,negative
3,103,72,30,152,27.6,27,negative
2,157,74,35,440,39.4,30,negative
1,167,74,17,144,23.4,33,positive
0,179,50,36,159,37.8,22,positive
11,136,84,35,130,28.3,42,positive
0,107,60,25,0,26.4,23,negative
1,91,54,25,100,25.2,23,negative
1,117,60,23,106,33.8,27,negative
5,123,74,40,77,34.1,28,negative
2,120,54,0,0,26.8,27,negative
1,106,70,28,135,34.2,22,negative
2,155,52,27,540,38.7,25,positive
2,101,58,35,90,21.8,22,negative
1,120,80,48,200,38.9,41,negative
11,127,106,0,0,39,51,negative
3,80,82,31,70,34.2,27,positive
10,162,84,0,0,27.7,54,negative
1,199,76,43,0,42.9,22,positive
8,167,106,46,231,37.6,43,positive
9,145,80,46,130,37.9,40,positive
6,115,60,39,0,33.7,40,positive
1,112,80,45,132,34.8,24,negative
4,145,82,18,0,32.5,70,positive
10,111,70,27,0,27.5,40,positive
6,98,58,33,190,34,43,negative
9,154,78,30,100,30.9,45,negative
6,165,68,26,168,33.6,49,negative
1,99,58,10,0,25.4,21,negative
10,68,106,23,49,35.5,47,negative
3,123,100,35,240,57.3,22,negative
8,91,82,0,0,35.6,68,negative
6,195,70,0,0,30.9,31,positive
9,156,86,0,0,24.8,53,positive
0,93,60,0,0,35.3,25,negative
3,121,52,0,0,36,25,positive
2,101,58,17,265,24.2,23,negative
2,56,56,28,45,24.2,22,negative
0,162,76,36,0,49.6,26,positive
0,95,64,39,105,44.6,22,negative
4,125,80,0,0,32.3,27,positive
5,136,82,0,0,0,69,negative
2,129,74,26,205,33.2,25,negative
3,130,64,0,0,23.1,22,negative
1,107,50,19,0,28.3,29,negative
1,140,74,26,180,24.1,23,negative
1,144,82,46,180,46.1,46,positive
8,107,80,0,0,24.6,34,negative
13,158,114,0,0,42.3,44,positive
2,121,70,32,95,39.1,23,negative
7,129,68,49,125,38.5,43,positive
2,90,60,0,0,23.5,25,negative
7,142,90,24,480,30.4,43,positive
3,169,74,19,125,29.9,31,positive
0,99,0,0,0,25,22,negative
4,127,88,11,155,34.5,28,negative
4,118,70,0,0,44.5,26,negative
2,122,76,27,200,35.9,26,negative
6,125,78,31,0,27.6,49,positive
1,168,88,29,0,35,52,positive
2,129,0,0,0,38.5,41,negative
4,110,76,20,100,28.4,27,negative
6,80,80,36,0,39.8,28,negative
10,115,0,0,0,0,30,positive
2,127,46,21,335,34.4,22,negative
9,164,78,0,0,32.8,45,positive
2,93,64,32,160,38,23,positive
3,158,64,13,387,31.2,24,negative
5,126,78,27,22,29.6,40,negative
10,129,62,36,0,41.2,38,positive
0,134,58,20,291,26.4,21,negative
3,102,74,0,0,29.5,32,negative
7,187,50,33,392,33.9,34,positive
3,173,78,39,185,33.8,31,positive
10,94,72,18,0,23.1,56,negative
1,108,60,46,178,35.5,24,negative
5,97,76,27,0,35.6,52,positive
4,83,86,19,0,29.3,34,negative
1,114,66,36,200,38.1,21,negative
1,149,68,29,127,29.3,42,positive
5,117,86,30,105,39.1,42,negative
1,111,94,0,0,32.8,45,negative
4,112,78,40,0,39.4,38,negative
1,116,78,29,180,36.1,25,negative
0,141,84,26,0,32.4,22,negative
2,175,88,0,0,22.9,22,negative
2,92,52,0,0,30.1,22,negative
3,130,78,23,79,28.4,34,positive
8,120,86,0,0,28.4,22,positive
2,174,88,37,120,44.5,24,positive
2,106,56,27,165,29,22,negative
2,105,75,0,0,23.3,53,negative
4,95,60,32,0,35.4,28,negative
0,126,86,27,120,27.4,21,negative
8,65,72,23,0,32,42,negative
2,99,60,17,160,36.6,21,negative
1,102,74,0,0,39.5,42,positive
11,120,80,37,150,42.3,48,positive
3,102,44,20,94,30.8,26,negative
1,109,58,18,116,28.5,22,negative
9,140,94,0,0,32.7,45,positive
13,153,88,37,140,40.6,39,negative
12,100,84,33,105,30,46,negative
1,147,94,41,0,49.3,27,positive
1,81,74,41,57,46.3,32,negative
3,187,70,22,200,36.4,36,positive
6,162,62,0,0,24.3,50,positive
4,136,70,0,0,31.2,22,positive
1,121,78,39,74,39,28,negative
3,108,62,24,0,26,25,negative
0,181,88,44,510,43.3,26,positive
8,154,78,32,0,32.4,45,positive
1,128,88,39,110,36.5,37,positive
7,137,90,41,0,32,39,negative
0,123,72,0,0,36.3,52,positive
1,106,76,0,0,37.5,26,negative
6,190,92,0,0,35.5,66,positive
2,88,58,26,16,28.4,22,negative
9,170,74,31,0,44,43,positive
9,89,62,0,0,22.5,33,negative
10,101,76,48,180,32.9,63,negative
2,122,70,27,0,36.8,27,negative
5,121,72,23,112,26.2,30,negative
1,126,60,0,0,30.1,47,positive
1,93,70,31,0,30.4,23,negative
1 Number of times pregnant Plasma glucose concentration Diastolic blood pressure Triceps skin fold thickness 2-Hour serum insulin Body mass index Age Class
2 6 148 72 35 0 33.6 50 positive
3 1 85 66 29 0 26.6 31 negative
4 8 183 64 0 0 23.3 32 positive
5 1 89 66 23 94 28.1 21 negative
6 0 137 40 35 168 43.1 33 positive
7 5 116 74 0 0 25.6 30 negative
8 3 78 50 32 88 31 26 positive
9 10 115 0 0 0 35.3 29 negative
10 2 197 70 45 543 30.5 53 positive
11 8 125 96 0 0 0 54 positive
12 4 110 92 0 0 37.6 30 negative
13 10 168 74 0 0 38 34 positive
14 10 139 80 0 0 27.1 57 negative
15 1 189 60 23 846 30.1 59 positive
16 5 166 72 19 175 25.8 51 positive
17 7 100 0 0 0 30 32 positive
18 0 118 84 47 230 45.8 31 positive
19 7 107 74 0 0 29.6 31 positive
20 1 103 30 38 83 43.3 33 negative
21 1 115 70 30 96 34.6 32 positive
22 3 126 88 41 235 39.3 27 negative
23 8 99 84 0 0 35.4 50 negative
24 7 196 90 0 0 39.8 41 positive
25 9 119 80 35 0 29 29 positive
26 11 143 94 33 146 36.6 51 positive
27 10 125 70 26 115 31.1 41 positive
28 7 147 76 0 0 39.4 43 positive
29 1 97 66 15 140 23.2 22 negative
30 13 145 82 19 110 22.2 57 negative
31 5 117 92 0 0 34.1 38 negative
32 5 109 75 26 0 36 60 negative
33 3 158 76 36 245 31.6 28 positive
34 3 88 58 11 54 24.8 22 negative
35 6 92 92 0 0 19.9 28 negative
36 10 122 78 31 0 27.6 45 negative
37 4 103 60 33 192 24 33 negative
38 11 138 76 0 0 33.2 35 negative
39 9 102 76 37 0 32.9 46 positive
40 2 90 68 42 0 38.2 27 positive
41 4 111 72 47 207 37.1 56 positive
42 3 180 64 25 70 34 26 negative
43 7 133 84 0 0 40.2 37 negative
44 7 106 92 18 0 22.7 48 negative
45 9 171 110 24 240 45.4 54 positive
46 7 159 64 0 0 27.4 40 negative
47 0 180 66 39 0 42 25 positive
48 1 146 56 0 0 29.7 29 negative
49 2 71 70 27 0 28 22 negative
50 7 103 66 32 0 39.1 31 positive
51 7 105 0 0 0 0 24 negative
52 1 103 80 11 82 19.4 22 negative
53 1 101 50 15 36 24.2 26 negative
54 5 88 66 21 23 24.4 30 negative
55 8 176 90 34 300 33.7 58 positive
56 7 150 66 42 342 34.7 42 negative
57 1 73 50 10 0 23 21 negative
58 7 187 68 39 304 37.7 41 positive
59 0 100 88 60 110 46.8 31 negative
60 0 146 82 0 0 40.5 44 negative
61 0 105 64 41 142 41.5 22 negative
62 2 84 0 0 0 0 21 negative
63 8 133 72 0 0 32.9 39 positive
64 5 44 62 0 0 25 36 negative
65 2 141 58 34 128 25.4 24 negative
66 7 114 66 0 0 32.8 42 positive
67 5 99 74 27 0 29 32 negative
68 0 109 88 30 0 32.5 38 positive
69 2 109 92 0 0 42.7 54 negative
70 1 95 66 13 38 19.6 25 negative
71 4 146 85 27 100 28.9 27 negative
72 2 100 66 20 90 32.9 28 positive
73 5 139 64 35 140 28.6 26 negative
74 13 126 90 0 0 43.4 42 positive
75 4 129 86 20 270 35.1 23 negative
76 1 79 75 30 0 32 22 negative
77 1 0 48 20 0 24.7 22 negative
78 7 62 78 0 0 32.6 41 negative
79 5 95 72 33 0 37.7 27 negative
80 0 131 0 0 0 43.2 26 positive
81 2 112 66 22 0 25 24 negative
82 3 113 44 13 0 22.4 22 negative
83 2 74 0 0 0 0 22 negative
84 7 83 78 26 71 29.3 36 negative
85 0 101 65 28 0 24.6 22 negative
86 5 137 108 0 0 48.8 37 positive
87 2 110 74 29 125 32.4 27 negative
88 13 106 72 54 0 36.6 45 negative
89 2 100 68 25 71 38.5 26 negative
90 15 136 70 32 110 37.1 43 positive
91 1 107 68 19 0 26.5 24 negative
92 1 80 55 0 0 19.1 21 negative
93 4 123 80 15 176 32 34 negative
94 7 81 78 40 48 46.7 42 negative
95 4 134 72 0 0 23.8 60 positive
96 2 142 82 18 64 24.7 21 negative
97 6 144 72 27 228 33.9 40 negative
98 2 92 62 28 0 31.6 24 negative
99 1 71 48 18 76 20.4 22 negative
100 6 93 50 30 64 28.7 23 negative
101 1 122 90 51 220 49.7 31 positive
102 1 163 72 0 0 39 33 positive
103 1 151 60 0 0 26.1 22 negative
104 0 125 96 0 0 22.5 21 negative
105 1 81 72 18 40 26.6 24 negative
106 2 85 65 0 0 39.6 27 negative
107 1 126 56 29 152 28.7 21 negative
108 1 96 122 0 0 22.4 27 negative
109 4 144 58 28 140 29.5 37 negative
110 3 83 58 31 18 34.3 25 negative
111 0 95 85 25 36 37.4 24 positive
112 3 171 72 33 135 33.3 24 positive
113 8 155 62 26 495 34 46 positive
114 1 89 76 34 37 31.2 23 negative
115 4 76 62 0 0 34 25 negative
116 7 160 54 32 175 30.5 39 positive
117 4 146 92 0 0 31.2 61 positive
118 5 124 74 0 0 34 38 positive
119 5 78 48 0 0 33.7 25 negative
120 4 97 60 23 0 28.2 22 negative
121 4 99 76 15 51 23.2 21 negative
122 0 162 76 56 100 53.2 25 positive
123 6 111 64 39 0 34.2 24 negative
124 2 107 74 30 100 33.6 23 negative
125 5 132 80 0 0 26.8 69 negative
126 0 113 76 0 0 33.3 23 positive
127 1 88 30 42 99 55 26 positive
128 3 120 70 30 135 42.9 30 negative
129 1 118 58 36 94 33.3 23 negative
130 1 117 88 24 145 34.5 40 positive
131 0 105 84 0 0 27.9 62 positive
132 4 173 70 14 168 29.7 33 positive
133 9 122 56 0 0 33.3 33 positive
134 3 170 64 37 225 34.5 30 positive
135 8 84 74 31 0 38.3 39 negative
136 2 96 68 13 49 21.1 26 negative
137 2 125 60 20 140 33.8 31 negative
138 0 100 70 26 50 30.8 21 negative
139 0 93 60 25 92 28.7 22 negative
140 0 129 80 0 0 31.2 29 negative
141 5 105 72 29 325 36.9 28 negative
142 3 128 78 0 0 21.1 55 negative
143 5 106 82 30 0 39.5 38 negative
144 2 108 52 26 63 32.5 22 negative
145 10 108 66 0 0 32.4 42 positive
146 4 154 62 31 284 32.8 23 negative
147 0 102 75 23 0 0 21 negative
148 9 57 80 37 0 32.8 41 negative
149 2 106 64 35 119 30.5 34 negative
150 5 147 78 0 0 33.7 65 negative
151 2 90 70 17 0 27.3 22 negative
152 1 136 74 50 204 37.4 24 negative
153 4 114 65 0 0 21.9 37 negative
154 9 156 86 28 155 34.3 42 positive
155 1 153 82 42 485 40.6 23 negative
156 8 188 78 0 0 47.9 43 positive
157 7 152 88 44 0 50 36 positive
158 2 99 52 15 94 24.6 21 negative
159 1 109 56 21 135 25.2 23 negative
160 2 88 74 19 53 29 22 negative
161 17 163 72 41 114 40.9 47 positive
162 4 151 90 38 0 29.7 36 negative
163 7 102 74 40 105 37.2 45 negative
164 0 114 80 34 285 44.2 27 negative
165 2 100 64 23 0 29.7 21 negative
166 0 131 88 0 0 31.6 32 positive
167 6 104 74 18 156 29.9 41 positive
168 3 148 66 25 0 32.5 22 negative
169 4 120 68 0 0 29.6 34 negative
170 4 110 66 0 0 31.9 29 negative
171 3 111 90 12 78 28.4 29 negative
172 6 102 82 0 0 30.8 36 positive
173 6 134 70 23 130 35.4 29 positive
174 2 87 0 23 0 28.9 25 negative
175 1 79 60 42 48 43.5 23 negative
176 2 75 64 24 55 29.7 33 negative
177 8 179 72 42 130 32.7 36 positive
178 6 85 78 0 0 31.2 42 negative
179 0 129 110 46 130 67.1 26 positive
180 5 143 78 0 0 45 47 negative
181 5 130 82 0 0 39.1 37 positive
182 6 87 80 0 0 23.2 32 negative
183 0 119 64 18 92 34.9 23 negative
184 1 0 74 20 23 27.7 21 negative
185 5 73 60 0 0 26.8 27 negative
186 4 141 74 0 0 27.6 40 negative
187 7 194 68 28 0 35.9 41 positive
188 8 181 68 36 495 30.1 60 positive
189 1 128 98 41 58 32 33 positive
190 8 109 76 39 114 27.9 31 positive
191 5 139 80 35 160 31.6 25 positive
192 3 111 62 0 0 22.6 21 negative
193 9 123 70 44 94 33.1 40 negative
194 7 159 66 0 0 30.4 36 positive
195 11 135 0 0 0 52.3 40 positive
196 8 85 55 20 0 24.4 42 negative
197 5 158 84 41 210 39.4 29 positive
198 1 105 58 0 0 24.3 21 negative
199 3 107 62 13 48 22.9 23 positive
200 4 109 64 44 99 34.8 26 positive
201 4 148 60 27 318 30.9 29 positive
202 0 113 80 16 0 31 21 negative
203 1 138 82 0 0 40.1 28 negative
204 0 108 68 20 0 27.3 32 negative
205 2 99 70 16 44 20.4 27 negative
206 6 103 72 32 190 37.7 55 negative
207 5 111 72 28 0 23.9 27 negative
208 8 196 76 29 280 37.5 57 positive
209 5 162 104 0 0 37.7 52 positive
210 1 96 64 27 87 33.2 21 negative
211 7 184 84 33 0 35.5 41 positive
212 2 81 60 22 0 27.7 25 negative
213 0 147 85 54 0 42.8 24 negative
214 7 179 95 31 0 34.2 60 negative
215 0 140 65 26 130 42.6 24 positive
216 9 112 82 32 175 34.2 36 positive
217 12 151 70 40 271 41.8 38 positive
218 5 109 62 41 129 35.8 25 positive
219 6 125 68 30 120 30 32 negative
220 5 85 74 22 0 29 32 positive
221 5 112 66 0 0 37.8 41 positive
222 0 177 60 29 478 34.6 21 positive
223 2 158 90 0 0 31.6 66 positive
224 7 119 0 0 0 25.2 37 negative
225 7 142 60 33 190 28.8 61 negative
226 1 100 66 15 56 23.6 26 negative
227 1 87 78 27 32 34.6 22 negative
228 0 101 76 0 0 35.7 26 negative
229 3 162 52 38 0 37.2 24 positive
230 4 197 70 39 744 36.7 31 negative
231 0 117 80 31 53 45.2 24 negative
232 4 142 86 0 0 44 22 positive
233 6 134 80 37 370 46.2 46 positive
234 1 79 80 25 37 25.4 22 negative
235 4 122 68 0 0 35 29 negative
236 3 74 68 28 45 29.7 23 negative
237 4 171 72 0 0 43.6 26 positive
238 7 181 84 21 192 35.9 51 positive
239 0 179 90 27 0 44.1 23 positive
240 9 164 84 21 0 30.8 32 positive
241 0 104 76 0 0 18.4 27 negative
242 1 91 64 24 0 29.2 21 negative
243 4 91 70 32 88 33.1 22 negative
244 3 139 54 0 0 25.6 22 positive
245 6 119 50 22 176 27.1 33 positive
246 2 146 76 35 194 38.2 29 negative
247 9 184 85 15 0 30 49 positive
248 10 122 68 0 0 31.2 41 negative
249 0 165 90 33 680 52.3 23 negative
250 9 124 70 33 402 35.4 34 negative
251 1 111 86 19 0 30.1 23 negative
252 9 106 52 0 0 31.2 42 negative
253 2 129 84 0 0 28 27 negative
254 2 90 80 14 55 24.4 24 negative
255 0 86 68 32 0 35.8 25 negative
256 12 92 62 7 258 27.6 44 positive
257 1 113 64 35 0 33.6 21 positive
258 3 111 56 39 0 30.1 30 negative
259 2 114 68 22 0 28.7 25 negative
260 1 193 50 16 375 25.9 24 negative
261 11 155 76 28 150 33.3 51 positive
262 3 191 68 15 130 30.9 34 negative
263 3 141 0 0 0 30 27 positive
264 4 95 70 32 0 32.1 24 negative
265 3 142 80 15 0 32.4 63 negative
266 4 123 62 0 0 32 35 positive
267 5 96 74 18 67 33.6 43 negative
268 0 138 0 0 0 36.3 25 positive
269 2 128 64 42 0 40 24 negative
270 0 102 52 0 0 25.1 21 negative
271 2 146 0 0 0 27.5 28 positive
272 10 101 86 37 0 45.6 38 positive
273 2 108 62 32 56 25.2 21 negative
274 3 122 78 0 0 23 40 negative
275 1 71 78 50 45 33.2 21 negative
276 13 106 70 0 0 34.2 52 negative
277 2 100 70 52 57 40.5 25 negative
278 7 106 60 24 0 26.5 29 positive
279 0 104 64 23 116 27.8 23 negative
280 5 114 74 0 0 24.9 57 negative
281 2 108 62 10 278 25.3 22 negative
282 0 146 70 0 0 37.9 28 positive
283 10 129 76 28 122 35.9 39 negative
284 7 133 88 15 155 32.4 37 negative
285 7 161 86 0 0 30.4 47 positive
286 2 108 80 0 0 27 52 positive
287 7 136 74 26 135 26 51 negative
288 5 155 84 44 545 38.7 34 negative
289 1 119 86 39 220 45.6 29 positive
290 4 96 56 17 49 20.8 26 negative
291 5 108 72 43 75 36.1 33 negative
292 0 78 88 29 40 36.9 21 negative
293 0 107 62 30 74 36.6 25 positive
294 2 128 78 37 182 43.3 31 positive
295 1 128 48 45 194 40.5 24 positive
296 0 161 50 0 0 21.9 65 negative
297 6 151 62 31 120 35.5 28 negative
298 2 146 70 38 360 28 29 positive
299 0 126 84 29 215 30.7 24 negative
300 14 100 78 25 184 36.6 46 positive
301 8 112 72 0 0 23.6 58 negative
302 0 167 0 0 0 32.3 30 positive
303 2 144 58 33 135 31.6 25 positive
304 5 77 82 41 42 35.8 35 negative
305 5 115 98 0 0 52.9 28 positive
306 3 150 76 0 0 21 37 negative
307 2 120 76 37 105 39.7 29 negative
308 10 161 68 23 132 25.5 47 positive
309 0 137 68 14 148 24.8 21 negative
310 0 128 68 19 180 30.5 25 positive
311 2 124 68 28 205 32.9 30 positive
312 6 80 66 30 0 26.2 41 negative
313 0 106 70 37 148 39.4 22 negative
314 2 155 74 17 96 26.6 27 positive
315 3 113 50 10 85 29.5 25 negative
316 7 109 80 31 0 35.9 43 positive
317 2 112 68 22 94 34.1 26 negative
318 3 99 80 11 64 19.3 30 negative
319 3 182 74 0 0 30.5 29 positive
320 3 115 66 39 140 38.1 28 negative
321 6 194 78 0 0 23.5 59 positive
322 4 129 60 12 231 27.5 31 negative
323 3 112 74 30 0 31.6 25 positive
324 0 124 70 20 0 27.4 36 positive
325 13 152 90 33 29 26.8 43 positive
326 2 112 75 32 0 35.7 21 negative
327 1 157 72 21 168 25.6 24 negative
328 1 122 64 32 156 35.1 30 positive
329 10 179 70 0 0 35.1 37 negative
330 2 102 86 36 120 45.5 23 positive
331 6 105 70 32 68 30.8 37 negative
332 8 118 72 19 0 23.1 46 negative
333 2 87 58 16 52 32.7 25 negative
334 1 180 0 0 0 43.3 41 positive
335 12 106 80 0 0 23.6 44 negative
336 1 95 60 18 58 23.9 22 negative
337 0 165 76 43 255 47.9 26 negative
338 0 117 0 0 0 33.8 44 negative
339 5 115 76 0 0 31.2 44 positive
340 9 152 78 34 171 34.2 33 positive
341 7 178 84 0 0 39.9 41 positive
342 1 130 70 13 105 25.9 22 negative
343 1 95 74 21 73 25.9 36 negative
344 1 0 68 35 0 32 22 negative
345 5 122 86 0 0 34.7 33 negative
346 8 95 72 0 0 36.8 57 negative
347 8 126 88 36 108 38.5 49 negative
348 1 139 46 19 83 28.7 22 negative
349 3 116 0 0 0 23.5 23 negative
350 3 99 62 19 74 21.8 26 negative
351 5 0 80 32 0 41 37 positive
352 4 92 80 0 0 42.2 29 negative
353 4 137 84 0 0 31.2 30 negative
354 3 61 82 28 0 34.4 46 negative
355 1 90 62 12 43 27.2 24 negative
356 3 90 78 0 0 42.7 21 negative
357 9 165 88 0 0 30.4 49 positive
358 1 125 50 40 167 33.3 28 positive
359 13 129 0 30 0 39.9 44 positive
360 12 88 74 40 54 35.3 48 negative
361 1 196 76 36 249 36.5 29 positive
362 5 189 64 33 325 31.2 29 positive
363 5 158 70 0 0 29.8 63 negative
364 5 103 108 37 0 39.2 65 negative
365 4 146 78 0 0 38.5 67 positive
366 4 147 74 25 293 34.9 30 negative
367 5 99 54 28 83 34 30 negative
368 6 124 72 0 0 27.6 29 positive
369 0 101 64 17 0 21 21 negative
370 3 81 86 16 66 27.5 22 negative
371 1 133 102 28 140 32.8 45 positive
372 3 173 82 48 465 38.4 25 positive
373 0 118 64 23 89 0 21 negative
374 0 84 64 22 66 35.8 21 negative
375 2 105 58 40 94 34.9 25 negative
376 2 122 52 43 158 36.2 28 negative
377 12 140 82 43 325 39.2 58 positive
378 0 98 82 15 84 25.2 22 negative
379 1 87 60 37 75 37.2 22 negative
380 4 156 75 0 0 48.3 32 positive
381 0 93 100 39 72 43.4 35 negative
382 1 107 72 30 82 30.8 24 negative
383 0 105 68 22 0 20 22 negative
384 1 109 60 8 182 25.4 21 negative
385 1 90 62 18 59 25.1 25 negative
386 1 125 70 24 110 24.3 25 negative
387 1 119 54 13 50 22.3 24 negative
388 5 116 74 29 0 32.3 35 positive
389 8 105 100 36 0 43.3 45 positive
390 5 144 82 26 285 32 58 positive
391 3 100 68 23 81 31.6 28 negative
392 1 100 66 29 196 32 42 negative
393 5 166 76 0 0 45.7 27 positive
394 1 131 64 14 415 23.7 21 negative
395 4 116 72 12 87 22.1 37 negative
396 4 158 78 0 0 32.9 31 positive
397 2 127 58 24 275 27.7 25 negative
398 3 96 56 34 115 24.7 39 negative
399 0 131 66 40 0 34.3 22 positive
400 3 82 70 0 0 21.1 25 negative
401 3 193 70 31 0 34.9 25 positive
402 4 95 64 0 0 32 31 positive
403 6 137 61 0 0 24.2 55 negative
404 5 136 84 41 88 35 35 positive
405 9 72 78 25 0 31.6 38 negative
406 5 168 64 0 0 32.9 41 positive
407 2 123 48 32 165 42.1 26 negative
408 4 115 72 0 0 28.9 46 positive
409 0 101 62 0 0 21.9 25 negative
410 8 197 74 0 0 25.9 39 positive
411 1 172 68 49 579 42.4 28 positive
412 6 102 90 39 0 35.7 28 negative
413 1 112 72 30 176 34.4 25 negative
414 1 143 84 23 310 42.4 22 negative
415 1 143 74 22 61 26.2 21 negative
416 0 138 60 35 167 34.6 21 positive
417 3 173 84 33 474 35.7 22 positive
418 1 97 68 21 0 27.2 22 negative
419 4 144 82 32 0 38.5 37 positive
420 1 83 68 0 0 18.2 27 negative
421 3 129 64 29 115 26.4 28 positive
422 1 119 88 41 170 45.3 26 negative
423 2 94 68 18 76 26 21 negative
424 0 102 64 46 78 40.6 21 negative
425 2 115 64 22 0 30.8 21 negative
426 8 151 78 32 210 42.9 36 positive
427 4 184 78 39 277 37 31 positive
428 0 94 0 0 0 0 25 negative
429 1 181 64 30 180 34.1 38 positive
430 0 135 94 46 145 40.6 26 negative
431 1 95 82 25 180 35 43 positive
432 2 99 0 0 0 22.2 23 negative
433 3 89 74 16 85 30.4 38 negative
434 1 80 74 11 60 30 22 negative
435 2 139 75 0 0 25.6 29 negative
436 1 90 68 8 0 24.5 36 negative
437 0 141 0 0 0 42.4 29 positive
438 12 140 85 33 0 37.4 41 negative
439 5 147 75 0 0 29.9 28 negative
440 1 97 70 15 0 18.2 21 negative
441 6 107 88 0 0 36.8 31 negative
442 0 189 104 25 0 34.3 41 positive
443 2 83 66 23 50 32.2 22 negative
444 4 117 64 27 120 33.2 24 negative
445 8 108 70 0 0 30.5 33 positive
446 4 117 62 12 0 29.7 30 positive
447 0 180 78 63 14 59.4 25 positive
448 1 100 72 12 70 25.3 28 negative
449 0 95 80 45 92 36.5 26 negative
450 0 104 64 37 64 33.6 22 positive
451 0 120 74 18 63 30.5 26 negative
452 1 82 64 13 95 21.2 23 negative
453 2 134 70 0 0 28.9 23 positive
454 0 91 68 32 210 39.9 25 negative
455 2 119 0 0 0 19.6 72 negative
456 2 100 54 28 105 37.8 24 negative
457 14 175 62 30 0 33.6 38 positive
458 1 135 54 0 0 26.7 62 negative
459 5 86 68 28 71 30.2 24 negative
460 10 148 84 48 237 37.6 51 positive
461 9 134 74 33 60 25.9 81 negative
462 9 120 72 22 56 20.8 48 negative
463 1 71 62 0 0 21.8 26 negative
464 8 74 70 40 49 35.3 39 negative
465 5 88 78 30 0 27.6 37 negative
466 10 115 98 0 0 24 34 negative
467 0 124 56 13 105 21.8 21 negative
468 0 74 52 10 36 27.8 22 negative
469 0 97 64 36 100 36.8 25 negative
470 8 120 0 0 0 30 38 positive
471 6 154 78 41 140 46.1 27 negative
472 1 144 82 40 0 41.3 28 negative
473 0 137 70 38 0 33.2 22 negative
474 0 119 66 27 0 38.8 22 negative
475 7 136 90 0 0 29.9 50 negative
476 4 114 64 0 0 28.9 24 negative
477 0 137 84 27 0 27.3 59 negative
478 2 105 80 45 191 33.7 29 positive
479 7 114 76 17 110 23.8 31 negative
480 8 126 74 38 75 25.9 39 negative
481 4 132 86 31 0 28 63 negative
482 3 158 70 30 328 35.5 35 positive
483 0 123 88 37 0 35.2 29 negative
484 4 85 58 22 49 27.8 28 negative
485 0 84 82 31 125 38.2 23 negative
486 0 145 0 0 0 44.2 31 positive
487 0 135 68 42 250 42.3 24 positive
488 1 139 62 41 480 40.7 21 negative
489 0 173 78 32 265 46.5 58 negative
490 4 99 72 17 0 25.6 28 negative
491 8 194 80 0 0 26.1 67 negative
492 2 83 65 28 66 36.8 24 negative
493 2 89 90 30 0 33.5 42 negative
494 4 99 68 38 0 32.8 33 negative
495 4 125 70 18 122 28.9 45 positive
496 3 80 0 0 0 0 22 negative
497 6 166 74 0 0 26.6 66 negative
498 5 110 68 0 0 26 30 negative
499 2 81 72 15 76 30.1 25 negative
500 7 195 70 33 145 25.1 55 positive
501 6 154 74 32 193 29.3 39 negative
502 2 117 90 19 71 25.2 21 negative
503 3 84 72 32 0 37.2 28 negative
504 6 0 68 41 0 39 41 positive
505 7 94 64 25 79 33.3 41 negative
506 3 96 78 39 0 37.3 40 negative
507 10 75 82 0 0 33.3 38 negative
508 0 180 90 26 90 36.5 35 positive
509 1 130 60 23 170 28.6 21 negative
510 2 84 50 23 76 30.4 21 negative
511 8 120 78 0 0 25 64 negative
512 12 84 72 31 0 29.7 46 positive
513 0 139 62 17 210 22.1 21 negative
514 9 91 68 0 0 24.2 58 negative
515 2 91 62 0 0 27.3 22 negative
516 3 99 54 19 86 25.6 24 negative
517 3 163 70 18 105 31.6 28 positive
518 9 145 88 34 165 30.3 53 positive
519 7 125 86 0 0 37.6 51 negative
520 13 76 60 0 0 32.8 41 negative
521 6 129 90 7 326 19.6 60 negative
522 2 68 70 32 66 25 25 negative
523 3 124 80 33 130 33.2 26 negative
524 6 114 0 0 0 0 26 negative
525 9 130 70 0 0 34.2 45 positive
526 3 125 58 0 0 31.6 24 negative
527 3 87 60 18 0 21.8 21 negative
528 1 97 64 19 82 18.2 21 negative
529 3 116 74 15 105 26.3 24 negative
530 0 117 66 31 188 30.8 22 negative
531 0 111 65 0 0 24.6 31 negative
532 2 122 60 18 106 29.8 22 negative
533 0 107 76 0 0 45.3 24 negative
534 1 86 66 52 65 41.3 29 negative
535 6 91 0 0 0 29.8 31 negative
536 1 77 56 30 56 33.3 24 negative
537 4 132 0 0 0 32.9 23 positive
538 0 105 90 0 0 29.6 46 negative
539 0 57 60 0 0 21.7 67 negative
540 0 127 80 37 210 36.3 23 negative
541 3 129 92 49 155 36.4 32 positive
542 8 100 74 40 215 39.4 43 positive
543 3 128 72 25 190 32.4 27 positive
544 10 90 85 32 0 34.9 56 positive
545 4 84 90 23 56 39.5 25 negative
546 1 88 78 29 76 32 29 negative
547 8 186 90 35 225 34.5 37 positive
548 5 187 76 27 207 43.6 53 positive
549 4 131 68 21 166 33.1 28 negative
550 1 164 82 43 67 32.8 50 negative
551 4 189 110 31 0 28.5 37 negative
552 1 116 70 28 0 27.4 21 negative
553 3 84 68 30 106 31.9 25 negative
554 6 114 88 0 0 27.8 66 negative
555 1 88 62 24 44 29.9 23 negative
556 1 84 64 23 115 36.9 28 negative
557 7 124 70 33 215 25.5 37 negative
558 1 97 70 40 0 38.1 30 negative
559 8 110 76 0 0 27.8 58 negative
560 11 103 68 40 0 46.2 42 negative
561 11 85 74 0 0 30.1 35 negative
562 6 125 76 0 0 33.8 54 positive
563 0 198 66 32 274 41.3 28 positive
564 1 87 68 34 77 37.6 24 negative
565 6 99 60 19 54 26.9 32 negative
566 0 91 80 0 0 32.4 27 negative
567 2 95 54 14 88 26.1 22 negative
568 1 99 72 30 18 38.6 21 negative
569 6 92 62 32 126 32 46 negative
570 4 154 72 29 126 31.3 37 negative
571 0 121 66 30 165 34.3 33 positive
572 3 78 70 0 0 32.5 39 negative
573 2 130 96 0 0 22.6 21 negative
574 3 111 58 31 44 29.5 22 negative
575 2 98 60 17 120 34.7 22 negative
576 1 143 86 30 330 30.1 23 negative
577 1 119 44 47 63 35.5 25 negative
578 6 108 44 20 130 24 35 negative
579 2 118 80 0 0 42.9 21 positive
580 10 133 68 0 0 27 36 negative
581 2 197 70 99 0 34.7 62 positive
582 0 151 90 46 0 42.1 21 positive
583 6 109 60 27 0 25 27 negative
584 12 121 78 17 0 26.5 62 negative
585 8 100 76 0 0 38.7 42 negative
586 8 124 76 24 600 28.7 52 positive
587 1 93 56 11 0 22.5 22 negative
588 8 143 66 0 0 34.9 41 positive
589 6 103 66 0 0 24.3 29 negative
590 3 176 86 27 156 33.3 52 positive
591 0 73 0 0 0 21.1 25 negative
592 11 111 84 40 0 46.8 45 positive
593 2 112 78 50 140 39.4 24 negative
594 3 132 80 0 0 34.4 44 positive
595 2 82 52 22 115 28.5 25 negative
596 6 123 72 45 230 33.6 34 negative
597 0 188 82 14 185 32 22 positive
598 0 67 76 0 0 45.3 46 negative
599 1 89 24 19 25 27.8 21 negative
600 1 173 74 0 0 36.8 38 positive
601 1 109 38 18 120 23.1 26 negative
602 1 108 88 19 0 27.1 24 negative
603 6 96 0 0 0 23.7 28 negative
604 1 124 74 36 0 27.8 30 negative
605 7 150 78 29 126 35.2 54 positive
606 4 183 0 0 0 28.4 36 positive
607 1 124 60 32 0 35.8 21 negative
608 1 181 78 42 293 40 22 positive
609 1 92 62 25 41 19.5 25 negative
610 0 152 82 39 272 41.5 27 negative
611 1 111 62 13 182 24 23 negative
612 3 106 54 21 158 30.9 24 negative
613 3 174 58 22 194 32.9 36 positive
614 7 168 88 42 321 38.2 40 positive
615 6 105 80 28 0 32.5 26 negative
616 11 138 74 26 144 36.1 50 positive
617 3 106 72 0 0 25.8 27 negative
618 6 117 96 0 0 28.7 30 negative
619 2 68 62 13 15 20.1 23 negative
620 9 112 82 24 0 28.2 50 positive
621 0 119 0 0 0 32.4 24 positive
622 2 112 86 42 160 38.4 28 negative
623 2 92 76 20 0 24.2 28 negative
624 6 183 94 0 0 40.8 45 negative
625 0 94 70 27 115 43.5 21 negative
626 2 108 64 0 0 30.8 21 negative
627 4 90 88 47 54 37.7 29 negative
628 0 125 68 0 0 24.7 21 negative
629 0 132 78 0 0 32.4 21 negative
630 5 128 80 0 0 34.6 45 negative
631 4 94 65 22 0 24.7 21 negative
632 7 114 64 0 0 27.4 34 positive
633 0 102 78 40 90 34.5 24 negative
634 2 111 60 0 0 26.2 23 negative
635 1 128 82 17 183 27.5 22 negative
636 10 92 62 0 0 25.9 31 negative
637 13 104 72 0 0 31.2 38 positive
638 5 104 74 0 0 28.8 48 negative
639 2 94 76 18 66 31.6 23 negative
640 7 97 76 32 91 40.9 32 positive
641 1 100 74 12 46 19.5 28 negative
642 0 102 86 17 105 29.3 27 negative
643 4 128 70 0 0 34.3 24 negative
644 6 147 80 0 0 29.5 50 positive
645 4 90 0 0 0 28 31 negative
646 3 103 72 30 152 27.6 27 negative
647 2 157 74 35 440 39.4 30 negative
648 1 167 74 17 144 23.4 33 positive
649 0 179 50 36 159 37.8 22 positive
650 11 136 84 35 130 28.3 42 positive
651 0 107 60 25 0 26.4 23 negative
652 1 91 54 25 100 25.2 23 negative
653 1 117 60 23 106 33.8 27 negative
654 5 123 74 40 77 34.1 28 negative
655 2 120 54 0 0 26.8 27 negative
656 1 106 70 28 135 34.2 22 negative
657 2 155 52 27 540 38.7 25 positive
658 2 101 58 35 90 21.8 22 negative
659 1 120 80 48 200 38.9 41 negative
660 11 127 106 0 0 39 51 negative
661 3 80 82 31 70 34.2 27 positive
662 10 162 84 0 0 27.7 54 negative
663 1 199 76 43 0 42.9 22 positive
664 8 167 106 46 231 37.6 43 positive
665 9 145 80 46 130 37.9 40 positive
666 6 115 60 39 0 33.7 40 positive
667 1 112 80 45 132 34.8 24 negative
668 4 145 82 18 0 32.5 70 positive
669 10 111 70 27 0 27.5 40 positive
670 6 98 58 33 190 34 43 negative
671 9 154 78 30 100 30.9 45 negative
672 6 165 68 26 168 33.6 49 negative
673 1 99 58 10 0 25.4 21 negative
674 10 68 106 23 49 35.5 47 negative
675 3 123 100 35 240 57.3 22 negative
676 8 91 82 0 0 35.6 68 negative
677 6 195 70 0 0 30.9 31 positive
678 9 156 86 0 0 24.8 53 positive
679 0 93 60 0 0 35.3 25 negative
680 3 121 52 0 0 36 25 positive
681 2 101 58 17 265 24.2 23 negative
682 2 56 56 28 45 24.2 22 negative
683 0 162 76 36 0 49.6 26 positive
684 0 95 64 39 105 44.6 22 negative
685 4 125 80 0 0 32.3 27 positive
686 5 136 82 0 0 0 69 negative
687 2 129 74 26 205 33.2 25 negative
688 3 130 64 0 0 23.1 22 negative
689 1 107 50 19 0 28.3 29 negative
690 1 140 74 26 180 24.1 23 negative
691 1 144 82 46 180 46.1 46 positive
692 8 107 80 0 0 24.6 34 negative
693 13 158 114 0 0 42.3 44 positive
694 2 121 70 32 95 39.1 23 negative
695 7 129 68 49 125 38.5 43 positive
696 2 90 60 0 0 23.5 25 negative
697 7 142 90 24 480 30.4 43 positive
698 3 169 74 19 125 29.9 31 positive
699 0 99 0 0 0 25 22 negative
700 4 127 88 11 155 34.5 28 negative
701 4 118 70 0 0 44.5 26 negative
702 2 122 76 27 200 35.9 26 negative
703 6 125 78 31 0 27.6 49 positive
704 1 168 88 29 0 35 52 positive
705 2 129 0 0 0 38.5 41 negative
706 4 110 76 20 100 28.4 27 negative
707 6 80 80 36 0 39.8 28 negative
708 10 115 0 0 0 0 30 positive
709 2 127 46 21 335 34.4 22 negative
710 9 164 78 0 0 32.8 45 positive
711 2 93 64 32 160 38 23 positive
712 3 158 64 13 387 31.2 24 negative
713 5 126 78 27 22 29.6 40 negative
714 10 129 62 36 0 41.2 38 positive
715 0 134 58 20 291 26.4 21 negative
716 3 102 74 0 0 29.5 32 negative
717 7 187 50 33 392 33.9 34 positive
718 3 173 78 39 185 33.8 31 positive
719 10 94 72 18 0 23.1 56 negative
720 1 108 60 46 178 35.5 24 negative
721 5 97 76 27 0 35.6 52 positive
722 4 83 86 19 0 29.3 34 negative
723 1 114 66 36 200 38.1 21 negative
724 1 149 68 29 127 29.3 42 positive
725 5 117 86 30 105 39.1 42 negative
726 1 111 94 0 0 32.8 45 negative
727 4 112 78 40 0 39.4 38 negative
728 1 116 78 29 180 36.1 25 negative
729 0 141 84 26 0 32.4 22 negative
730 2 175 88 0 0 22.9 22 negative
731 2 92 52 0 0 30.1 22 negative
732 3 130 78 23 79 28.4 34 positive
733 8 120 86 0 0 28.4 22 positive
734 2 174 88 37 120 44.5 24 positive
735 2 106 56 27 165 29 22 negative
736 2 105 75 0 0 23.3 53 negative
737 4 95 60 32 0 35.4 28 negative
738 0 126 86 27 120 27.4 21 negative
739 8 65 72 23 0 32 42 negative
740 2 99 60 17 160 36.6 21 negative
741 1 102 74 0 0 39.5 42 positive
742 11 120 80 37 150 42.3 48 positive
743 3 102 44 20 94 30.8 26 negative
744 1 109 58 18 116 28.5 22 negative
745 9 140 94 0 0 32.7 45 positive
746 13 153 88 37 140 40.6 39 negative
747 12 100 84 33 105 30 46 negative
748 1 147 94 41 0 49.3 27 positive
749 1 81 74 41 57 46.3 32 negative
750 3 187 70 22 200 36.4 36 positive
751 6 162 62 0 0 24.3 50 positive
752 4 136 70 0 0 31.2 22 positive
753 1 121 78 39 74 39 28 negative
754 3 108 62 24 0 26 25 negative
755 0 181 88 44 510 43.3 26 positive
756 8 154 78 32 0 32.4 45 positive
757 1 128 88 39 110 36.5 37 positive
758 7 137 90 41 0 32 39 negative
759 0 123 72 0 0 36.3 52 positive
760 1 106 76 0 0 37.5 26 negative
761 6 190 92 0 0 35.5 66 positive
762 2 88 58 26 16 28.4 22 negative
763 9 170 74 31 0 44 43 positive
764 9 89 62 0 0 22.5 33 negative
765 10 101 76 48 180 32.9 63 negative
766 2 122 70 27 0 36.8 27 negative
767 5 121 72 23 112 26.2 30 negative
768 1 126 60 0 0 30.1 47 positive
769 1 93 70 31 0 30.4 23 negative