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Repository: FlorianMuellerklein/Machine-Learning
Branch: master
Commit: c6fdda905ea5
Files: 27
Total size: 15.7 MB
Directory structure:
gitextract_r00bh6ch/
├── .idea/
│ ├── .name
│ ├── encodings.xml
│ ├── inspectionProfiles/
│ │ ├── Project_Default.xml
│ │ └── profiles_settings.xml
│ ├── misc.xml
│ ├── ml.iml
│ ├── modules.xml
│ ├── scopes/
│ │ └── scope_settings.xml
│ ├── vcs.xml
│ └── workspace.xml
├── 1_Perceptron.ipynb
├── Data/
│ ├── denver.csv
│ ├── denver_data_description.txt
│ ├── ex1data1.txt
│ ├── ex1data2.txt
│ ├── heart.txt
│ ├── heart_test.txt
│ ├── ionosphere.csv
│ ├── sklearn_digits.csv
│ ├── stackloss.csv
│ └── train_binary.csv
├── LinearRegression.py
├── LogisticClassifier.py
├── MultiLayerPerceptron.py
├── Old/
│ ├── BackPropagationNN.py
│ └── README.md
└── README.md
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FILE CONTENTS
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================================================
FILE: 1_Perceptron.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Perceptron Tutorial\n",
"\n",
"The perceptron algorithm is a supervised algorithm for binary classification. This linear classifier was introduced by Frank Rosenblatt in 1957 in his paper, 'The Perceptron--a perceiving and recognizing automaton.' The perceptron makes predictions based on a linear combination of it's weights with the feature vector. It also allows for online learning, which means that it can process each element in a training set one at a time. \n",
"\n",
"The perceptron is trained with the 'perceptron learning rule' which is defined as follows:\n",
"\n",
"1. Initialize the weight vector (Wo), either to 0 or small random numbers. \n",
"2. Calculate the initial 'guess' as a linear combination of that weight vector and the first input.\n",
"3. Compare the perceptron output to the target output (error). Since this is a binary classification algorithm the outputs and targets will either be values of 0 or 1.\n",
"4. Update the new weight vector (Wn) depending on how the target compares to the perceptron output. If the target is less than the output than the weights will be decreased and if the target is greater than the output the weights will be increased. \n",
"5. Steps 1-4 are repeated until every output matches the target value for each training example.\n",
"\n",
"Here we are going to code up a perceptron and train it with the perceptron learning rule using a hard limit as the activation function. I am using numpy here because it is readible and optimized for linear algebra operations. I am defining the perceptron rule as:\n",
"\n",
"```\n",
"Wn = Wo + alpha * error * input.T\n",
"error = true - predicted\n",
"```\n",
"\n",
"The complete code for the perceptron is below."
]
},
{
"cell_type": "code",
"execution_count": 316,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import random\n",
"import numpy as np\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.style.use('ggplot')\n",
"\n",
"def hardlim(a):\n",
" if a > 0.0:\n",
" return 1.\n",
" else:\n",
" return 0.\n",
"\n",
"class Perceptron(object):\n",
" '''\n",
" simple feed forward perceptron with a hard limit activation\n",
" trained with the perceptron learning rule\n",
" '''\n",
" def __init__(self):\n",
" self.alpha = None\n",
" self.w = None\n",
"\n",
" def response(self, X):\n",
" '''\n",
" perceptron response\n",
" :param X: input vector\n",
" :return: perceptron out\n",
" '''\n",
" a = hardlim(np.dot(self.w.T, X))\n",
" return a\n",
"\n",
" def updateWeight(self, X, error):\n",
" '''\n",
" update the vector of input weights\n",
" :param X: input data\n",
" :param error: prediction != true\n",
" :return: updated weight vector\n",
" '''\n",
" self.w += self.alpha * error * X\n",
"\n",
" def train(self, X, y, alpha, iterations):\n",
" '''\n",
" trains perceptron on vector data by looping each row and updating the weight vector\n",
" :param X: input data\n",
" :param y: correct y value\n",
" :return: updated parameters\n",
" '''\n",
" # initialize the learning rate and count data size\n",
" self.alpha = alpha\n",
" num_examples, num_features = np.shape(X)\n",
"\n",
" # set up bias\n",
" bias = np.ones(shape=(num_examples,1))\n",
" X = np.hstack((X, bias))\n",
"\n",
" # initialize weight vector\n",
" self.w = np.random.rand(num_features + 1)\n",
" \n",
" error_count = []\n",
" for i in range(iterations):\n",
" for j in range(num_examples):\n",
" prediction = self.response(X[j])\n",
" error = int(y[j]) - prediction \n",
" self.updateWeight(X[j], error)\n",
" error_count.append(error)\n",
" \n",
" error_count = np.array(error_count)\n",
" plt.plot(error_count)\n",
" plt.ylim([-1,1])\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The perceptron is contained in a python class. Within the class there are functions to create the output, update the weights based on the error and a function to contain the training loop. \n",
"\n",
"* **Response** - This simply calcualtes the linear combination of the weights and input vectors. Then pushes that output through the hard limit function which will generate either a 0 or 1 for the class prediction. \n",
"* **UpdateWeight** - This function will take the error term and update the perceptron weights as defined by the perceptron learning rule.\n",
"* **Train** - The train function first initializes some of the parameters of the perceptron. It will take an 'alpha' which should be any number between 0 and 1. Then it will determine the proper size of the weight vector by setting it to the corresponding size of the input vectors. Finally before training it will initialize that weight vector to small random numbers and tack on a bias. During training the loop will run for a predetermined number of iterations. On each iteration the perceptron will; generate it's prediciton, compare that prediction to the target (error), then update the weights as necessary based on the error term.\n",
"\n",
"At the end of training the perceptron will generate a plot which shows the error generated by each training example. When the plot flatlines at 0 it may be safe to assume that the perceptron converged to a good solution.\n",
"\n",
"Now we will come up with some data for the perceptron to solve. Let's take the data from [Neural Network Design](http://hagan.okstate.edu/NNDesign.pdf) page 4-7 and plot the points below."
]
},
{
"cell_type": "code",
"execution_count": 309,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x112c53610>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# generate training data\n",
"X = np.array([[-1,-1],\n",
" [-1,1],\n",
" [1,-1],\n",
" [1,1]])\n",
"\n",
"y = np.array([[0],\n",
" [0],\n",
" [0],\n",
" [1]])\n",
"\n",
"# plot the points\n",
"plt.plot(X[0][0],X[0][1], 'ro', markersize=15)\n",
"plt.plot(X[1][0],X[1][1], 'ro', markersize=15)\n",
"plt.plot(X[2][0],X[2][1], 'ro', markersize=15)\n",
"plt.plot(X[3][0],X[3][1], 'bo', markersize=15)\n",
"plt.axis([-1.1,1.1,-1.1,1.1])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we are trying to draw a decision boundary to seperate the blue from the red circles. \n",
"\n",
"Lets train the network and see how it does. "
]
},
{
"cell_type": "code",
"execution_count": 321,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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goQFoinkPDrKz1VqqIuwwvDfJksc8EfCamNNUISTsMddVIlbSZ2WO6ohOV/tiLzt5fZeI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\nehfxmxiuU6M9Ek6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=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11304b4d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model = Perceptron()\n",
"model.train(X, y, alpha=0.1, iterations=10) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This plot allows us to see how many examples the perceptron needed to see in order to converge to a solution. Anytime the error shows -1 the true prediction was 0 and our perceptron predicted 1. Anytime the error shows 1 the true was 1 and the perceptron predicted 0. When the error is 0 that's when the perceptron got that training case correct.\n",
"\n",
"\n",
"### Decision Boundaries\n",
"\n",
"Now lets plot the final decision boundary from the perceptron."
]
},
{
"cell_type": "code",
"execution_count": 320,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x110907e50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n = np.linalg.norm(model.w[0:2])\n",
"ww = (model.w[0:2]) / n\n",
"ww1 = [float(ww[1])* 2., -1.0*float(ww[0])* 2.] \n",
"ww2 = [-1.0*float(ww[1]) * 2., float(ww[0]) * 2.]\n",
"plt.plot([ww1[0]-model.w[2],ww2[0]-model.w[2]], [ww1[1]-model.w[2],ww2[1]-model.w[2]])\n",
"plt.plot(X[0][0],X[0][1], 'ro', markersize=15)\n",
"plt.plot(X[1][0],X[1][1], 'ro', markersize=15)\n",
"plt.plot(X[2][0],X[2][1], 'ro', markersize=15)\n",
"plt.plot(X[3][0],X[3][1], 'bo', markersize=15)\n",
"plt.axis([-1.1,1.1,-1.1,1.1])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"\n",
"\n",
"\n",
"\n",
"Here is a cool animation of the how the decision boundary updates after every full iteration through the training data. This animation was created on a different run of the algorithm so the final boundary is not the same as the one above. It is worth noting that the decision boundary may not always be the same.\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: Data/denver.csv
================================================
6.9,1.8,30.2,58.3,27.3,84.9,-14.2
8.4,28.5,38.8,87.5,39.8,172.6,-34.1
5.7,7.8,31.7,83.5,26,154.2,-15.8
7.4,2.3,24.2,14.2,29.4,35.2,-13.9
8.5,-0.7,28.1,46.7,26.6,69.2,-13.9
13.8,7.2,10.4,57.9,26.2,111,-22.6
1.7,32.2,7.5,73.8,50.5,704.1,-40.9
3.6,7.4,30,61.3,26.4,69.9,4
8.2,10.2,12.1,41,11.7,65.4,-32.5
5,10.5,13.6,17.4,14.7,132.1,-8.1
2.1,0.3,18.3,34.4,24.2,179.9,12.3
4.2,8.1,21.3,64.9,21.7,139.9,-35
3.9,2,33.1,82,26.3,108.7,-2
4.1,10.8,38.3,83.3,32.6,123.2,-2.2
4.2,1.9,36.9,61.8,21.6,104.7,-14.2
9.4,-1.5,22.4,22.2,33.5,61.5,-32.7
3.6,-0.3,19.6,8.6,27,68.2,-13.4
7.6,5.5,29.1,62.8,32.2,96.9,-8.7
8.5,4.8,32.8,86.2,16,258,0.5
7.5,2.3,26.5,18.7,23.7,32,-0.6
4.1,17.3,41.5,78.6,23.5,127,-12.5
4.6,68.6,39,14.6,38.2,27.1,45.4
7.2,3,20.2,41.4,27.6,70.7,-38.2
13.4,7.1,20.4,13.9,22.5,38.3,-33.6
10.3,1.4,29.8,43.7,29.4,54,-10
9.4,4.6,36,78.2,29.9,101.5,-14.6
2.5,-3.3,37.6,88.5,27.5,185.9,-7.6
10.3,-0.5,31.8,57.2,27.2,61.2,-17.6
7.5,22.3,28.6,5.7,31.3,38.6,27.2
18.7,6.2,39.7,55.8,28.7,52.6,-2.9
5.1,-2,23.8,29,29.3,62.6,-10.3
3.7,19.6,12.3,77.3,32,207.7,-45.6
10.3,3,31.1,51.7,26.2,42.4,-31.9
7.3,19.2,32.9,68.1,25.2,105.2,-35.7
4.2,7,22.1,41.2,21.4,68.6,-8.8
2.1,5.4,27.1,60,23.5,157.3,6.2
2.5,2.8,20.3,29.8,24.1,58.5,-27.5
8.1,8.5,30,66.4,26,63.1,-37.4
10.3,-1.9,15.9,39.9,38.5,86.4,-13.5
10.5,2.8,36.4,72.3,26,77.5,-21.6
5.8,2,24.2,19.5,28.3,63.5,2.2
6.9,2.9,20.7,6.6,25.8,68.9,-2.4
9.3,4.9,34.9,82.4,18.4,102.8,-12
11.4,2.6,38.7,78.2,18.4,86.6,-12.8
================================================
FILE: Data/denver_data_description.txt
================================================
X1 = total population (in thousands)
X2 = % change in population over past several years
X3 = % of children (under 18) in population
X4 = % free school lunch participation
X5 = % change in household income over past several years
X6 = crime rate (per 1000 population)
X7 = % change in crime rate over past
================================================
FILE: Data/ex1data1.txt
================================================
6.1101,17.592
5.5277,9.1302
8.5186,13.662
7.0032,11.854
5.8598,6.8233
8.3829,11.886
7.4764,4.3483
8.5781,12
6.4862,6.5987
5.0546,3.8166
5.7107,3.2522
14.164,15.505
5.734,3.1551
8.4084,7.2258
5.6407,0.71618
5.3794,3.5129
6.3654,5.3048
5.1301,0.56077
6.4296,3.6518
7.0708,5.3893
6.1891,3.1386
20.27,21.767
5.4901,4.263
6.3261,5.1875
5.5649,3.0825
18.945,22.638
12.828,13.501
10.957,7.0467
13.176,14.692
22.203,24.147
5.2524,-1.22
6.5894,5.9966
9.2482,12.134
5.8918,1.8495
8.2111,6.5426
7.9334,4.5623
8.0959,4.1164
5.6063,3.3928
12.836,10.117
6.3534,5.4974
5.4069,0.55657
6.8825,3.9115
11.708,5.3854
5.7737,2.4406
7.8247,6.7318
7.0931,1.0463
5.0702,5.1337
5.8014,1.844
11.7,8.0043
5.5416,1.0179
7.5402,6.7504
5.3077,1.8396
7.4239,4.2885
7.6031,4.9981
6.3328,1.4233
6.3589,-1.4211
6.2742,2.4756
5.6397,4.6042
9.3102,3.9624
9.4536,5.4141
8.8254,5.1694
5.1793,-0.74279
21.279,17.929
14.908,12.054
18.959,17.054
7.2182,4.8852
8.2951,5.7442
10.236,7.7754
5.4994,1.0173
20.341,20.992
10.136,6.6799
7.3345,4.0259
6.0062,1.2784
7.2259,3.3411
5.0269,-2.6807
6.5479,0.29678
7.5386,3.8845
5.0365,5.7014
10.274,6.7526
5.1077,2.0576
5.7292,0.47953
5.1884,0.20421
6.3557,0.67861
9.7687,7.5435
6.5159,5.3436
8.5172,4.2415
9.1802,6.7981
6.002,0.92695
5.5204,0.152
5.0594,2.8214
5.7077,1.8451
7.6366,4.2959
5.8707,7.2029
5.3054,1.9869
8.2934,0.14454
13.394,9.0551
5.4369,0.61705
================================================
FILE: Data/ex1data2.txt
================================================
2104,3,399900
1600,3,329900
2400,3,369000
1416,2,232000
3000,4,539900
1985,4,299900
1534,3,314900
1427,3,198999
1380,3,212000
1494,3,242500
1940,4,239999
2000,3,347000
1890,3,329999
4478,5,699900
1268,3,259900
2300,4,449900
1320,2,299900
1236,3,199900
2609,4,499998
3031,4,599000
1767,3,252900
1888,2,255000
1604,3,242900
1962,4,259900
3890,3,573900
1100,3,249900
1458,3,464500
2526,3,469000
2200,3,475000
2637,3,299900
1839,2,349900
1000,1,169900
2040,4,314900
3137,3,579900
1811,4,285900
1437,3,249900
1239,3,229900
2132,4,345000
4215,4,549000
2162,4,287000
1664,2,368500
2238,3,329900
2567,4,314000
1200,3,299000
852,2,179900
1852,4,299900
1203,3,239500
================================================
FILE: Data/heart.txt
================================================
1,59,52,70,67,73,66,72,61,58,52,72,71,70,77,66,65,67,55,61,57,68,66,72,74,63,64,56,54,67,54,76,74,65,67,66,56,62,56,72,62,74,74,64,67
1,72,62,69,67,78,82,74,65,69,63,70,70,72,74,70,71,72,75,66,65,73,78,74,79,74,69,69,70,71,69,72,70,62,65,65,71,63,60,69,73,67,71,56,58
1,71,62,70,64,67,64,79,65,70,69,72,71,68,65,61,61,73,71,75,74,80,74,54,47,53,37,77,68,72,59,72,68,60,60,73,70,66,65,64,55,61,41,51,46
1,69,71,70,78,61,63,67,65,59,59,66,69,71,75,65,58,60,55,62,59,67,66,74,74,64,60,57,54,70,73,69,76,62,64,61,61,66,65,72,73,68,68,59,63
1,70,66,61,66,61,58,69,69,72,68,62,71,71,71,63,59,74,75,70,69,83,77,73,70,41,37,39,40,58,46,75,73,65,66,67,69,70,66,70,64,60,55,49,41
1,57,69,68,75,69,74,73,71,57,61,72,74,73,69,61,58,60,55,71,62,79,70,77,71,65,63,69,55,61,68,75,74,63,64,63,58,69,67,79,77,72,70,61,65
1,69,66,62,75,67,71,72,76,69,70,66,69,71,80,66,64,71,77,65,61,72,67,71,69,65,57,69,65,68,65,76,73,63,64,69,70,72,72,69,68,70,73,63,59
1,61,60,60,62,64,72,68,67,74,68,76,70,74,71,76,74,74,70,75,66,69,62,65,60,66,65,68,59,64,59,72,65,55,56,66,66,66,60,60,58,60,67,49,52
1,65,62,67,68,65,67,71,71,64,56,73,72,68,69,56,57,67,62,74,66,80,76,80,78,53,47,48,36,68,65,74,73,60,60,67,63,74,63,77,79,68,70,59,56
1,74,73,72,79,66,61,76,66,65,64,78,74,62,57,48,36,62,50,67,63,79,70,61,57,52,36,69,49,55,65,74,73,58,60,64,62,73,69,62,67,60,56,53,46
1,70,69,60,62,58,60,71,77,69,69,73,68,68,70,69,65,76,75,63,64,67,74,56,60,54,44,68,69,68,68,74,73,61,59,68,67,64,68,64,76,64,61,54,49
1,67,66,65,77,66,70,72,72,72,67,76,72,73,76,74,71,74,73,69,61,78,70,76,73,70,70,62,51,70,68,79,77,75,68,72,71,69,63,65,61,73,73,64,67
1,76,69,78,73,68,67,75,70,77,70,79,73,79,75,74,71,76,68,81,79,77,78,75,76,66,76,65,67,67,57,68,75,50,62,62,59,47,49,75,65,74,70,51,48
1,70,69,67,66,68,60,76,77,70,67,71,71,79,79,70,64,77,76,63,54,68,65,72,67,59,52,56,50,67,61,74,72,67,59,68,66,73,68,74,68,77,69,65,62
1,78,73,68,74,68,69,63,74,68,67,73,73,66,71,64,67,66,68,61,66,75,71,60,62,64,66,65,67,66,62,74,75,61,61,63,66,68,65,71,62,69,67,61,59
1,67,51,73,65,69,56,72,63,64,56,68,67,70,62,70,58,68,62,65,59,77,69,68,59,64,55,65,60,70,60,72,65,58,51,66,59,71,62,74,60,76,65,62,56
1,70,54,66,66,76,46,74,58,68,52,81,58,67,58,68,32,73,59,76,51,82,57,76,54,58,30,69,41,59,59,67,73,62,55,60,55,65,56,65,44,73,36,51,28
1,63,63,69,72,67,62,65,57,68,53,68,71,73,78,64,58,61,54,70,61,72,67,72,68,57,55,61,53,66,60,76,77,73,66,66,58,75,70,77,67,78,68,64,58
1,62,56,66,57,74,75,68,59,65,59,74,66,71,65,67,69,66,66,71,72,80,74,71,63,62,72,62,66,66,61,73,68,59,59,63,62,73,73,76,67,77,71,62,58
1,80,74,82,77,74,74,73,77,64,61,73,73,73,72,62,66,66,68,63,61,69,75,70,75,59,64,63,69,66,66,70,77,62,62,60,65,67,66,74,71,66,71,59,62
1,63,58,66,55,56,58,69,74,44,48,63,60,76,67,73,73,58,66,68,63,72,74,70,72,77,75,70,71,71,67,75,73,60,59,71,70,65,62,70,69,71,70,58,61
1,70,65,65,62,68,67,77,74,62,61,66,61,69,74,64,62,71,71,74,70,77,78,69,70,67,65,67,70,49,48,73,71,65,73,73,73,75,71,73,72,73,70,65,64
1,61,63,58,62,56,60,67,75,61,57,64,71,56,59,66,62,73,76,71,75,83,84,69,71,69,69,69,71,49,43,59,64,56,61,64,72,70,73,69,70,65,68,65,62
1,70,64,52,58,75,89,70,72,26,30,46,55,54,59,40,40,39,37,35,17,59,52,66,72,23,46,8,31,17,20,49,72,61,70,31,13,40,23,31,30,57,67,41,57
1,75,71,54,51,53,50,68,69,46,55,11,12,43,48,61,60,73,77,46,45,68,59,65,73,54,60,55,66,54,41,53,52,58,63,72,65,33,23,64,54,36,46,45,52
1,77,61,62,68,62,58,72,68,77,71,76,77,72,75,62,57,77,74,61,58,72,76,69,68,56,53,57,54,69,70,73,79,65,70,66,68,67,66,71,67,60,60,53,57
1,75,72,75,79,72,68,79,77,69,66,73,77,67,73,57,58,69,69,67,65,77,68,69,65,58,54,68,60,67,66,75,78,63,66,68,64,72,69,73,61,52,44,34,37
1,78,76,71,72,65,71,75,74,70,64,65,76,65,73,59,57,65,65,73,73,81,80,68,66,59,44,62,63,62,59,71,74,59,60,64,61,77,76,62,67,44,42,44,30
1,69,68,75,74,78,72,75,72,61,57,72,71,75,72,70,68,68,62,62,66,67,67,74,78,64,68,62,62,64,63,75,77,66,67,69,65,62,63,64,59,74,75,63,67
1,72,66,75,67,61,59,64,63,61,67,75,76,66,48,61,56,69,68,68,68,68,75,69,67,68,71,70,68,48,47,74,79,63,75,62,62,64,67,56,52,69,83,59,73
1,64,64,70,75,70,71,74,71,59,60,62,68,70,66,69,72,69,69,61,63,56,60,62,66,69,71,62,63,67,65,62,58,52,51,67,66,61,56,64,65,71,73,57,63
1,72,63,68,62,72,63,79,61,57,49,76,75,55,57,43,37,54,52,57,56,78,78,57,55,35,37,57,57,41,35,67,70,75,72,53,46,63,62,53,43,38,35,32,26
1,79,78,66,63,69,62,78,70,72,71,73,78,75,65,68,62,76,71,68,68,72,71,52,48,23,26,66,59,66,66,72,74,56,58,67,63,66,69,70,74,34,33,11,12
1,66,81,75,72,69,67,74,81,67,73,76,75,69,64,58,57,74,74,56,62,74,78,55,50,37,38,65,73,61,61,73,73,61,63,67,66,60,71,46,58,35,37,24,20
1,65,66,71,72,67,75,76,83,70,74,70,76,70,68,63,71,69,76,72,73,76,80,69,68,57,59,59,56,62,68,75,73,65,61,69,73,66,70,63,65,65,67,53,42
1,71,75,76,74,71,68,67,68,69,75,75,74,59,58,71,69,70,74,74,72,71,78,69,72,69,72,63,61,59,70,72,75,61,66,70,71,59,64,64,60,72,61,55,63
1,70,66,66,68,71,69,64,61,68,67,50,53,73,71,73,63,71,73,80,81,82,82,67,71,52,47,67,64,66,67,66,75,58,62,65,65,71,67,70,71,67,64,52,53
1,73,76,68,74,56,59,73,76,54,48,75,78,47,53,25,19,60,56,56,54,80,79,47,53,19,14,58,50,67,71,63,54,49,48,66,65,62,58,57,72,31,30,15,11
1,68,76,79,78,63,73,68,78,64,71,73,77,67,71,58,57,61,63,52,64,64,74,53,72,36,44,52,54,49,56,73,81,65,80,53,60,63,70,58,64,52,57,49,50
1,68,64,65,68,63,64,77,73,75,72,80,77,70,71,61,61,73,68,63,62,76,73,69,69,48,59,62,44,66,59,75,74,64,64,63,61,70,69,74,67,51,48,45,45
0,62,67,64,70,59,58,67,74,60,66,68,68,73,71,60,63,64,74,64,65,74,77,69,73,59,58,58,67,65,69,78,76,61,62,64,67,72,74,71,71,71,69,66,61
0,62,67,68,70,65,70,73,77,69,70,69,73,71,74,71,71,76,75,66,67,73,73,70,74,63,67,58,68,66,69,78,79,69,70,71,73,72,71,73,77,72,76,64,66
0,59,68,69,67,69,59,78,73,66,65,77,73,74,66,66,55,71,66,69,68,75,73,80,79,69,65,69,66,68,65,75,71,59,61,65,64,73,71,81,75,74,65,69,66
0,75,75,70,77,67,75,75,75,67,66,74,73,68,72,64,70,76,70,67,63,74,75,72,68,69,68,75,69,71,74,75,76,63,70,71,69,66,63,70,73,66,68,58,59
0,77,79,79,77,74,76,76,81,65,68,66,66,74,73,72,68,67,73,63,62,72,67,76,69,68,64,64,61,69,68,73,75,70,66,64,70,70,70,73,76,79,73,65,63
0,68,64,74,80,76,72,78,75,67,64,75,80,78,77,66,64,67,67,70,60,78,82,70,68,63,60,64,60,54,56,70,73,59,65,55,58,50,51,73,70,69,65,42,41
0,76,73,74,76,60,69,76,76,68,69,78,79,57,62,69,69,67,66,73,69,80,81,58,68,75,69,73,70,58,65,79,76,74,71,66,64,65,62,78,68,75,68,62,60
0,61,76,71,68,77,69,77,69,64,75,71,81,75,72,71,69,70,73,61,71,69,79,64,65,62,66,61,65,71,68,67,71,59,64,66,65,60,68,74,71,69,68,63,59
0,67,65,77,74,67,66,67,70,65,64,75,78,66,74,62,60,65,65,73,72,75,76,74,81,66,65,65,63,63,67,76,80,63,64,63,64,73,72,76,75,72,74,65,64
0,71,61,74,74,76,74,69,56,68,78,71,78,58,64,70,72,71,68,72,71,79,78,67,68,63,60,67,67,76,74,67,79,67,71,71,64,70,74,83,76,74,73,54,54
0,64,70,71,69,72,70,75,78,61,66,69,68,68,70,71,70,75,76,73,72,80,78,79,81,74,70,72,79,73,75,77,73,65,64,72,72,59,62,71,74,68,67,58,57
0,76,75,68,78,71,72,72,75,61,65,67,70,67,75,60,58,63,67,59,63,67,72,74,73,56,56,52,52,67,68,73,78,65,68,61,67,69,74,77,75,74,70,63,61
0,74,73,72,75,63,62,67,67,73,74,75,79,70,71,64,67,65,69,79,78,81,80,71,73,60,62,69,67,69,69,75,75,66,67,67,66,71,73,66,69,62,65,55,56
0,65,67,69,76,62,68,65,66,65,64,74,73,60,75,66,63,64,62,73,65,77,74,69,69,66,59,68,59,69,69,76,79,65,63,60,60,69,64,69,74,69,70,62,57
0,59,75,70,76,62,70,65,74,65,67,75,76,70,73,63,61,74,67,78,69,75,73,70,68,67,64,79,68,70,75,76,77,59,63,72,69,64,64,65,72,61,61,51,55
0,76,72,73,69,67,73,74,72,60,65,73,66,66,73,68,67,69,70,60,58,66,76,69,75,65,64,63,60,74,71,77,79,61,68,71,70,62,63,73,76,62,69,52,59
0,71,75,78,78,68,67,75,72,67,68,72,75,74,74,67,66,66,67,66,66,78,80,73,75,67,72,67,67,67,65,77,78,61,64,63,66,51,57,77,67,78,76,60,59
0,80,76,75,75,69,68,74,75,77,77,76,78,74,70,66,65,67,75,74,73,74,77,68,67,61,58,60,67,61,63,75,75,66,62,59,61,77,74,69,67,65,66,61,58
0,68,70,66,72,63,71,77,82,61,63,61,62,61,65,65,62,72,77,69,73,72,78,74,77,69,69,77,74,64,63,66,70,58,60,65,69,75,77,77,77,69,77,65,64
0,67,57,73,78,63,68,72,73,61,59,59,76,71,72,69,66,70,68,65,77,79,68,71,75,62,66,70,78,68,69,70,76,65,65,66,67,62,72,69,72,70,68,60,59
0,72,74,67,69,69,70,74,81,66,70,73,78,69,80,65,65,70,77,69,70,73,79,64,68,56,58,67,64,68,68,69,74,62,67,66,70,73,77,74,77,71,72,63,65
0,62,71,78,84,64,68,72,74,53,57,71,70,71,72,54,53,63,67,54,57,71,71,70,74,54,57,54,57,62,64,75,72,62,65,60,64,64,67,72,77,62,62,61,65
0,69,78,74,76,70,67,69,73,68,75,75,76,71,77,58,61,66,70,67,72,76,72,56,62,56,61,57,62,67,73,76,74,58,63,64,68,66,76,70,72,64,68,60,56
0,59,65,53,60,72,74,67,69,64,67,64,65,71,70,68,71,72,70,70,70,75,78,72,71,71,71,67,64,67,71,71,74,62,66,72,73,57,57,64,71,70,69,53,49
0,66,67,63,70,69,70,73,72,61,62,68,68,70,71,71,67,71,69,65,65,76,78,71,70,65,64,68,71,70,71,73,74,58,63,68,71,70,72,77,79,79,79,66,66
0,62,66,66,68,73,76,68,71,62,62,63,68,74,75,63,68,71,68,58,58,65,66,65,76,64,70,63,60,70,72,71,77,67,69,72,71,63,63,70,72,75,79,62,59
0,71,71,69,71,65,65,76,73,67,66,69,79,76,76,63,62,74,71,58,66,76,78,78,74,68,66,69,68,68,68,71,74,59,57,65,66,73,71,78,74,68,70,57,55
0,63,61,75,72,68,71,69,70,64,56,70,75,76,73,70,72,76,73,68,68,79,76,68,76,66,73,58,68,72,75,72,75,65,61,66,67,58,57,72,72,68,71,57,58
0,74,81,80,78,70,69,74,77,69,71,73,76,68,68,62,61,68,68,67,70,74,80,68,74,57,62,57,65,61,65,71,76,63,65,67,67,70,74,63,72,68,70,61,64
0,69,64,73,72,49,70,66,71,57,56,64,62,76,74,65,62,63,58,63,63,75,76,78,80,75,77,51,62,74,68,77,77,70,68,68,64,59,58,69,66,74,75,62,59
0,70,64,68,67,76,68,76,69,67,64,69,65,62,65,70,67,74,68,65,65,74,75,64,69,63,63,64,64,56,61,62,68,66,66,62,58,57,48,75,64,79,74,59,58
0,65,68,70,78,65,72,72,74,64,69,71,73,72,68,62,62,65,62,72,75,79,78,72,76,66,67,62,61,68,76,72,78,65,64,67,63,64,67,67,77,66,66,59,57
0,64,53,74,70,65,63,70,70,57,57,64,64,73,74,65,59,65,63,63,62,72,73,79,76,75,68,60,58,69,66,72,76,64,65,63,65,63,65,75,80,74,67,71,67
0,70,71,71,74,68,66,72,70,66,69,74,72,70,69,64,66,73,72,75,73,81,81,76,75,71,70,74,82,69,72,76,74,60,56,66,66,64,64,73,72,66,63,54,58
0,72,70,75,80,73,70,76,73,66,56,72,70,73,75,67,65,69,69,73,72,81,77,80,79,67,64,64,66,69,68,70,75,63,59,66,63,74,77,81,78,79,75,65,66
0,70,75,72,72,67,71,71,78,63,67,73,76,71,74,59,61,67,64,74,71,77,77,70,72,61,61,62,58,63,69,76,75,64,65,66,67,68,70,70,71,64,67,56,54
0,59,57,67,71,66,68,68,70,56,62,77,61,67,71,75,71,67,64,62,54,64,75,71,72,76,79,75,70,71,77,71,69,56,54,62,64,56,53,71,68,64,63,56,56
0,67,64,73,75,77,77,74,70,65,62,74,75,65,67,68,70,66,69,67,60,74,75,62,64,66,71,62,61,64,69,73,76,64,66,61,64,65,60,68,75,74,80,67,68
0,68,65,72,72,47,74,76,74,67,66,71,69,69,67,63,64,68,68,70,74,77,77,73,60,49,48,42,69,70,69,76,79,63,66,64,69,71,73,73,75,68,56,58,44
0,66,54,69,66,69,69,75,72,63,62,68,66,68,70,71,68,70,69,66,68,73,72,65,73,67,63,60,57,70,68,75,75,65,67,69,65,65,64,67,69,71,68,59,59
================================================
FILE: Data/heart_test.txt
================================================
1, 67, 68, 73, 78, 65, 63, 67, 60, 63, 62, 71, 68, 76, 73, 59, 61, 62, 56, 74, 73, 78, 76, 79, 79, 70, 70, 68, 67, 65, 67, 76, 75, 63, 61, 61, 56, 76, 75, 74, 77, 76, 74, 59, 68
1, 75, 74, 71, 71, 62, 58, 70, 64, 71, 68, 76, 68, 71, 71, 58, 58, 70, 69, 70, 72, 75, 73, 74, 72, 66, 60, 63, 66, 70, 64, 75, 70, 64, 62, 66, 62, 68, 69, 69, 66, 64, 58, 57, 52
1, 83, 64, 66, 67, 67, 74, 74, 72, 64, 68, 75, 73, 78, 73, 72, 57, 71, 67, 73, 65, 78, 73, 76, 69, 63, 57, 63, 53, 67, 60, 77, 74, 69, 64, 67, 64, 69, 63, 68, 54, 65, 64, 43, 42
1, 72, 66, 65, 65, 64, 61, 71, 78, 73, 69, 68, 65, 62, 65, 66, 66, 72, 74, 67, 61, 77, 71, 68, 65, 64, 60, 73, 69, 70, 69, 74, 72, 61, 63, 69, 68, 68, 63, 71, 72, 65, 63, 58, 60
1, 62, 60, 69, 61, 63, 63, 70, 68, 70, 65, 77, 56, 71, 65, 69, 68, 74, 78, 77, 70, 80, 73, 79, 75, 76, 67, 74, 69, 66, 71, 70, 61, 54, 54, 66, 66, 58, 56, 72, 73, 71, 64, 49, 42
1, 68, 63, 67, 67, 65, 72, 74, 72, 70, 71, 79, 71, 72, 67, 68, 69, 75, 79, 67, 65, 78, 69, 72, 67, 64, 59, 67, 65, 73, 70, 80, 69, 63, 61, 70, 70, 70, 67, 77, 71, 77, 72, 68, 59
1, 80, 76, 77, 76, 67, 68, 71, 76, 69, 66, 76, 78, 61, 71, 58, 61, 61, 68, 78, 72, 72, 75, 61, 66, 58, 62, 69, 66, 55, 40, 70, 71, 67, 58, 57, 58, 62, 65, 59, 45, 53, 58, 54, 55
1, 68, 63, 62, 58, 60, 57, 69, 78, 59, 53, 61, 58, 48, 50, 52, 50, 72, 70, 59, 59, 71, 77, 50, 49, 53, 44, 76, 74, 64, 66, 68, 63, 52, 52, 66, 67, 74, 70, 77, 74, 66, 60, 59, 56
1, 77, 61, 71, 69, 70, 66, 57, 55, 67, 67, 79, 72, 79, 71, 64, 54, 62, 63, 39, 47, 75, 76, 67, 71, 63, 44, 22, 25, 21, 18, 72, 79, 57, 58, 48, 40, 41, 39, 19, 16, 68, 72, 56, 65
1, 69, 68, 73, 74, 62, 67, 74, 73, 67, 65, 70, 75, 64, 68, 58, 57, 66, 70, 57, 63, 68, 71, 68, 69, 40, 38, 55, 59, 67, 67, 76, 78, 65, 67, 61, 65, 76, 79, 74, 73, 64, 56, 53, 45
1, 65, 69, 70, 71, 56, 63, 63, 70, 44, 51, 78, 74, 48, 49, 49, 52, 58, 60, 49, 51, 80, 65, 51, 48, 58, 43, 64, 62, 71, 70, 78, 73, 71, 57, 71, 73, 68, 64, 62, 59, 59, 64, 53, 55
1, 65, 63, 73, 75, 67, 61, 76, 65, 62, 62, 79, 75, 74, 70, 57, 52, 62, 62, 62, 63, 78, 72, 57, 50, 39, 36, 51, 51, 41, 43, 64, 70, 56, 54, 50, 46, 66, 67, 49, 43, 43, 43, 41, 43
1, 71, 73, 65, 80, 62, 67, 65, 77, 66, 68, 72, 80, 71, 74, 58, 62, 67, 73, 73, 73, 83, 78, 66, 77, 60, 59, 61, 61, 53, 46, 73, 75, 67, 65, 62, 65, 62, 66, 62, 60, 61, 68, 43, 56
1, 64, 75, 52, 46, 59, 54, 79, 78, 61, 68, 61, 71, 36, 28, 59, 58, 67, 74, 51, 55, 57, 78, 31, 32, 40, 43, 66, 78, 49, 49, 47, 39, 31, 27, 59, 75, 51, 73, 60, 56, 41, 21, 33, 22
1, 62, 54, 65, 65, 24, 31, 79, 66, 53, 54, 76, 72, 47, 50, 30, 25, 73, 74, 62, 55, 76, 78, 56, 47, 27, 34, 56, 78, 68, 68, 66, 68, 60, 60, 66, 68, 55, 51, 68, 71, 31, 29, 27, 18
1, 57, 43, 62, 50, 43, 57, 61, 44, 44, 24, 53, 36, 60, 69, 58, 61, 30, 32, 61, 63, 75, 72, 62, 71, 42, 44, 67, 74, 29, 19, 76, 77, 65, 66, 41, 41, 57, 46, 27, 12, 41, 52, 42, 49
1, 67, 65, 61, 61, 60, 62, 75, 74, 68, 68, 72, 75, 70, 75, 63, 68, 72, 75, 64, 70, 76, 75, 70, 70, 67, 63, 66, 67, 69, 66, 77, 77, 64, 66, 68, 69, 70, 69, 75, 66, 71, 70, 57, 63
1, 62, 54, 73, 68, 72, 71, 76, 76, 62, 52, 66, 58, 73, 65, 70, 70, 73, 73, 59, 52, 65, 59, 70, 55, 72, 65, 68, 70, 69, 66, 69, 66, 61, 51, 69, 72, 60, 54, 74, 68, 73, 67, 56, 52
1, 67, 59, 54, 48, 63, 67, 77, 78, 71, 61, 57, 57, 59, 50, 72, 72, 77, 77, 76, 73, 85, 67, 58, 50, 39, 33, 54, 50, 57, 50, 45, 41, 38, 43, 76, 77, 80, 69, 61, 48, 63, 61, 37, 36
1, 65, 56, 67, 58, 76, 79, 70, 71, 59, 50, 76, 61, 72, 64, 69, 73, 71, 68, 67, 50, 75, 65, 71, 62, 63, 70, 66, 55, 59, 52, 74, 60, 59, 58, 68, 63, 54, 38, 57, 52, 71, 74, 59, 65
1, 71, 56, 75, 74, 61, 69, 73, 73, 65, 63, 71, 69, 70, 74, 61, 64, 70, 66, 70, 69, 76, 81, 62, 60, 39, 39, 72, 72, 58, 64, 71, 71, 56, 55, 62, 61, 70, 75, 57, 63, 41, 61, 34, 40
1, 73, 59, 76, 61, 67, 52, 66, 46, 70, 59, 76, 72, 71, 75, 63, 33, 68, 49, 68, 49, 73, 66, 70, 58, 30, 14, 54, 41, 54, 41, 76, 66, 77, 57, 62, 26, 74, 57, 58, 47, 40, 9, 28, 19
1, 77, 69, 77, 79, 60, 59, 65, 68, 57, 54, 76, 76, 60, 66, 39, 36, 63, 65, 54, 60, 75, 71, 59, 63, 43, 38, 64, 49, 53, 42, 67, 73, 62, 60, 55, 52, 59, 58, 55, 43, 41, 37, 39, 26
1, 66, 65, 72, 74, 59, 61, 67, 65, 59, 58, 70, 72, 71, 67, 58, 52, 64, 63, 73, 71, 78, 59, 75, 72, 54, 45, 66, 64, 61, 60, 73, 74, 58, 57, 60, 59, 74, 71, 75, 74, 70, 66, 63, 61
1, 69, 80, 67, 67, 62, 61, 65, 67, 62, 71, 77, 75, 72, 77, 56, 54, 65, 60, 57, 63, 80, 69, 71, 72, 51, 39, 57, 50, 64, 63, 75, 70, 59, 56, 60, 55, 65, 68, 66, 67, 67, 68, 54, 54
1, 74, 67, 69, 75, 59, 57, 70, 68, 70, 62, 79, 76, 74, 70, 64, 56, 71, 73, 72, 64, 81, 72, 75, 66, 60, 57, 68, 62, 59, 38, 74, 66, 71, 60, 67, 59, 66, 58, 70, 50, 45, 31, 36, 35
1, 66, 70, 78, 75, 69, 64, 70, 66, 66, 69, 75, 76, 66, 65, 54, 52, 61, 57, 68, 65, 79, 69, 67, 63, 51, 50, 54, 43, 41, 49, 71, 74, 67, 67, 54, 52, 76, 72, 65, 69, 69, 64, 58, 56
1, 79, 58, 73, 78, 67, 65, 74, 71, 64, 54, 76, 68, 72, 69, 70, 64, 69, 64, 63, 53, 74, 64, 73, 67, 60, 57, 58, 51, 65, 68, 76, 75, 66, 63, 66, 62, 66, 58, 71, 67, 71, 68, 62, 60
1, 61, 66, 64, 65, 62, 66, 74, 67, 60, 62, 65, 64, 69, 70, 70, 67, 75, 73, 69, 69, 76, 78, 77, 80, 76, 76, 69, 73, 65, 73, 69, 63, 53, 58, 68, 65, 62, 60, 73, 73, 67, 63, 58, 61
1, 63, 69, 70, 72, 66, 70, 66, 72, 71, 67, 74, 77, 70, 71, 67, 69, 69, 68, 75, 73, 73, 77, 67, 66, 62, 59, 62, 58, 61, 65, 74, 75, 63, 67, 65, 67, 71, 71, 68, 70, 74, 72, 56, 60
1, 73, 67, 74, 73, 79, 75, 76, 77, 61, 60, 70, 73, 76, 77, 71, 71, 65, 65, 69, 64, 75, 76, 78, 80, 72, 69, 61, 59, 71, 68, 76, 79, 68, 67, 71, 67, 63, 59, 68, 66, 74, 71, 59, 57
1, 66, 68, 67, 72, 65, 71, 71, 71, 60, 67, 69, 78, 75, 72, 68, 66, 69, 68, 77, 77, 81, 80, 68, 69, 70, 65, 66, 65, 62, 66, 74, 76, 59, 62, 64, 63, 67, 75, 70, 75, 74, 76, 62, 65
1, 55, 54, 71, 74, 21, 25, 60, 27, 69, 64, 74, 79, 64, 70, 58, 38, 72, 64, 63, 62, 77, 75, 61, 58, 42, 37, 64, 54, 20, 15, 66, 73, 66, 67, 43, 36, 77, 59, 24, 35, 18, 27, 29, 22
1, 70, 68, 68, 70, 56, 54, 66, 67, 68, 70, 75, 73, 59, 59, 60, 62, 75, 71, 50, 45, 55, 55, 61, 61, 46, 49, 55, 52, 66, 72, 66, 65, 62, 65, 68, 70, 69, 69, 67, 70, 56, 57, 56, 52
1, 71, 71, 77, 82, 64, 63, 73, 71, 66, 64, 72, 79, 66, 59, 57, 57, 70, 63, 71, 68, 78, 79, 67, 65, 63, 63, 70, 67, 68, 70, 75, 77, 60, 62, 70, 67, 77, 74, 71, 75, 72, 67, 62, 60
1, 57, 44, 74, 68, 82, 76, 78, 62, 66, 67, 74, 72, 71, 70, 67, 69, 68, 68, 69, 74, 75, 71, 73, 71, 69, 65, 58, 63, 54, 63, 68, 74, 73, 76, 66, 65, 63, 66, 71, 71, 71, 68, 66, 60
1, 72, 71, 70, 72, 63, 69, 71, 73, 71, 71, 79, 75, 71, 71, 65, 74, 68, 74, 71, 71, 70, 78, 62, 67, 62, 60, 59, 59, 69, 66, 75, 75, 57, 57, 66, 69, 68, 67, 69, 64, 58, 62, 52, 54
1, 60, 58, 74, 68, 44, 48, 85, 60, 56, 48, 71, 70, 46, 43, 57, 43, 75, 60, 62, 44, 69, 52, 57, 42, 20, 23, 58, 60, 38, 41, 67, 70, 64, 68, 59, 47, 69, 55, 65, 55, 19, 47, 18, 17
1, 33, 21, 38, 54, 60, 52, 41, 50, 26, 29, 58, 58, 76, 71, 59, 68, 17, 13, 75, 73, 73, 78, 65, 67, 62, 47, 59, 57, 23, 26, 73, 74, 52, 58, 40, 38, 49, 45, 11, 13, 62, 64, 46, 40
1, 58, 70, 62, 73, 34, 41, 55, 54, 65, 77, 73, 78, 65, 65, 33, 48, 57, 64, 69, 67, 68, 64, 61, 58, 60, 59, 73, 70, 45, 40, 71, 74, 52, 56, 59, 62, 67, 75, 36, 39, 22, 19, 29, 24
1, 58, 40, 57, 43, 74, 57, 78, 53, 68, 58, 65, 54, 75, 50, 59, 73, 80, 77, 60, 59, 61, 61, 55, 56, 48, 63, 75, 77, 13, 7, 47, 52, 54, 58, 51, 31, 53, 18, 17, 6, 15, 69, 30, 40
1, 77, 77, 70, 71, 68, 66, 75, 75, 73, 72, 71, 77, 66, 65, 60, 58, 70, 70, 71, 74, 80, 79, 64, 64, 60, 55, 70, 68, 76, 74, 75, 75, 65, 65, 73, 73, 71, 70, 73, 73, 63, 62, 52, 52
1, 65, 67, 67, 61, 55, 57, 66, 74, 64, 69, 61, 62, 71, 71, 53, 53, 64, 71, 65, 66, 71, 73, 65, 70, 47, 39, 80, 75, 67, 64, 72, 66, 53, 55, 63, 64, 60, 61, 74, 63, 59, 58, 50, 49
1, 77, 59, 76, 78, 40, 34, 75, 63, 65, 26, 70, 28, 50, 46, 25, 66, 46, 52, 63, 63, 68, 56, 46, 47, 12, 6, 28, 23, 56, 48, 68, 55, 63, 54, 40, 20, 65, 68, 56, 57, 5, 10, 13, 10
1, 64, 59, 76, 72, 55, 52, 63, 66, 68, 63, 75, 72, 70, 64, 60, 57, 75, 68, 62, 59, 69, 73, 74, 67, 49, 42, 66, 51, 58, 48, 75, 73, 76, 75, 64, 62, 73, 70, 72, 51, 71, 42, 61, 39
1, 57, 63, 67, 82, 69, 67, 63, 70, 74, 64, 71, 71, 72, 73, 59, 54, 65, 63, 79, 62, 77, 69, 70, 63, 54, 50, 61, 53, 59, 60, 72, 74, 72, 59, 64, 54, 70, 65, 73, 70, 64, 58, 60, 57
1, 58, 57, 74, 71, 59, 60, 54, 59, 33, 39, 65, 74, 71, 75, 53, 57, 47, 60, 43, 44, 75, 78, 54, 57, 56, 56, 57, 61, 60, 62, 77, 76, 59, 57, 53, 60, 53, 57, 61, 74, 77, 82, 66, 68
1, 61, 66, 64, 74, 51, 55, 64, 63, 67, 67, 73, 76, 69, 67, 55, 53, 70, 67, 65, 61, 70, 66, 70, 62, 43, 35, 68, 65, 61, 56, 70, 73, 59, 62, 64, 62, 67, 68, 55, 61, 53, 42, 37, 28
1, 67, 80, 73, 76, 74, 69, 78, 81, 67, 68, 72, 79, 74, 77, 65, 65, 64, 66, 71, 74, 79, 81, 68, 69, 59, 63, 62, 63, 67, 66, 73, 73, 61, 65, 65, 66, 65, 72, 69, 63, 70, 67, 58, 56
1, 69, 55, 75, 73, 63, 64, 70, 66, 61, 75, 77, 75, 74, 76, 63, 61, 73, 75, 49, 67, 72, 69, 66, 74, 48, 48, 53, 66, 64, 68, 71, 71, 60, 64, 62, 68, 67, 74, 65, 70, 63, 64, 50, 52
1, 77, 63, 68, 70, 66, 70, 80, 79, 60, 57, 75, 68, 63, 66, 63, 69, 69, 70, 36, 34, 48, 47, 72, 77, 62, 66, 62, 62, 57, 69, 73, 75, 62, 63, 69, 71, 56, 53, 74, 76, 72, 73, 64, 63
1, 46, 47, 65, 68, 41, 48, 52, 45, 54, 47, 74, 72, 63, 63, 47, 45, 31, 36, 60, 65, 66, 79, 57, 60, 49, 52, 45, 45, 31, 36, 70, 71, 63, 72, 34, 34, 66, 78, 26, 35, 60, 43, 52, 51
1, 67, 74, 73, 74, 54, 63, 58, 72, 59, 71, 75, 82, 59, 77, 38, 38, 56, 70, 60, 70, 70, 84, 63, 67, 33, 38, 57, 69, 62, 65, 71, 72, 54, 60, 57, 61, 53, 73, 58, 67, 49, 56, 27, 36
1, 73, 70, 69, 75, 62, 62, 67, 70, 70, 76, 75, 74, 73, 77, 63, 58, 68, 71, 64, 66, 76, 74, 67, 69, 56, 44, 65, 65, 68, 69, 72, 71, 57, 61, 62, 65, 62, 66, 66, 62, 54, 56, 44, 35
1, 68, 77, 67, 70, 64, 62, 79, 80, 64, 64, 76, 74, 65, 64, 64, 60, 62, 67, 74, 75, 82, 77, 75, 74, 72, 73, 72, 71, 67, 69, 75, 73, 64, 62, 63, 65, 68, 70, 77, 69, 72, 71, 65, 57
1, 57, 70, 65, 71, 58, 69, 74, 81, 67, 72, 75, 78, 69, 71, 65, 63, 71, 75, 61, 68, 72, 78, 66, 72, 41, 38, 48, 48, 63, 68, 69, 77, 62, 67, 65, 69, 70, 74, 77, 81, 67, 69, 55, 60
1, 47, 47, 48, 69, 80, 52, 52, 52, 58, 46, 45, 48, 42, 59, 40, 38, 62, 54, 63, 69, 64, 63, 79, 70, 33, 19, 49, 46, 44, 43, 49, 57, 43, 75, 49, 27, 36, 39, 47, 57, 14, 20, 21, 4
1, 64, 75, 74, 73, 57, 61, 63, 70, 63, 72, 75, 81, 73, 77, 36, 38, 63, 73, 63, 65, 66, 63, 74, 77, 29, 28, 53, 52, 64, 68, 68, 73, 59, 61, 64, 66, 58, 69, 56, 73, 37, 38, 24, 18
1, 58, 66, 63, 61, 66, 68, 75, 79, 61, 63, 57, 62, 66, 66, 59, 59, 75, 74, 62, 65, 78, 76, 72, 72, 60, 60, 64, 65, 64, 70, 68, 68, 59, 67, 67, 66, 58, 57, 58, 73, 48, 59, 39, 51
1, 73, 71, 71, 68, 64, 60, 72, 76, 75, 70, 77, 76, 67, 67, 65, 59, 69, 69, 70, 69, 77, 73, 64, 58, 56, 51, 58, 57, 71, 68, 75, 77, 72, 68, 72, 70, 72, 69, 72, 64, 62, 61, 53, 53
1, 67, 66, 67, 65, 74, 65, 76, 70, 71, 68, 70, 68, 69, 65, 70, 70, 72, 72, 76, 71, 77, 72, 64, 49, 45, 35, 71, 67, 67, 64, 72, 66, 62, 57, 74, 72, 72, 69, 72, 66, 76, 71, 58, 45
1, 78, 75, 69, 66, 69, 71, 75, 75, 67, 67, 58, 50, 44, 47, 63, 73, 75, 77, 73, 76, 36, 42, 48, 43, 61, 61, 71, 73, 62, 64, 39, 37, 28, 30, 68, 71, 73, 69, 72, 74, 65, 68, 56, 55
1, 70, 69, 69, 67, 69, 68, 72, 73, 63, 71, 73, 73, 69, 69, 70, 65, 79, 76, 54, 61, 73, 70, 65, 72, 56, 55, 67, 71, 69, 70, 70, 69, 56, 62, 69, 67, 68, 69, 80, 77, 71, 69, 52, 50
1, 65, 65, 71, 77, 70, 68, 73, 72, 68, 65, 78, 75, 77, 65, 61, 58, 71, 61, 76, 77, 76, 75, 65, 72, 57, 43, 69, 68, 67, 67, 70, 66, 57, 59, 66, 59, 64, 61, 63, 58, 63, 54, 34, 27
1, 66, 73, 75, 74, 67, 67, 73, 71, 72, 63, 79, 78, 64, 74, 66, 74, 77, 73, 75, 62, 79, 67, 67, 72, 66, 59, 73, 69, 70, 73, 77, 74, 62, 66, 69, 66, 64, 62, 78, 75, 73, 63, 50, 46
1, 59, 55, 51, 65, 68, 45, 21, 25, 45, 41, 57, 74, 68, 77, 28, 25, 3, 11, 54, 67, 58, 75, 65, 77, 33, 31, 14, 17, 39, 43, 65, 73, 61, 71, 18, 24, 44, 59, 36, 47, 29, 28, 52, 13
1, 72, 69, 65, 65, 66, 67, 76, 78, 72, 70, 77, 79, 66, 67, 65, 60, 71, 72, 74, 67, 79, 79, 72, 67, 57, 51, 68, 56, 68, 63, 76, 77, 61, 64, 68, 65, 77, 76, 76, 64, 71, 57, 41, 39
1, 65, 50, 74, 72, 62, 59, 71, 66, 67, 55, 72, 73, 70, 72, 61, 62, 74, 73, 63, 59, 79, 80, 68, 67, 60, 68, 64, 68, 48, 40, 63, 74, 56, 58, 59, 59, 70, 59, 62, 47, 73, 68, 54, 45
1, 63, 54, 76, 69, 72, 66, 69, 66, 61, 53, 72, 65, 72, 74, 69, 65, 71, 67, 59, 59, 70, 70, 76, 71, 62, 65, 64, 59, 69, 62, 77, 77, 66, 65, 71, 69, 59, 54, 71, 62, 65, 69, 54, 56
1, 54, 52, 58, 55, 62, 54, 75, 74, 61, 58, 66, 66, 56, 50, 49, 40, 74, 82, 61, 47, 58, 71, 27, 40, 40, 19, 77, 77, 55, 56, 68, 68, 44, 41, 69, 68, 62, 56, 61, 62, 45, 20, 37, 11
1, 67, 58, 59, 54, 60, 65, 75, 75, 63, 61, 70, 67, 72, 61, 64, 57, 73, 66, 72, 79, 78, 81, 79, 76, 74, 50, 69, 66, 57, 47, 61, 51, 54, 56, 65, 66, 63, 71, 68, 59, 62, 47, 61, 48
1, 60, 59, 67, 58, 66, 60, 72, 79, 74, 75, 71, 70, 72, 66, 70, 65, 77, 74, 67, 76, 74, 81, 57, 56, 42, 42, 50, 59, 62, 66, 70, 73, 59, 62, 67, 66, 71, 76, 70, 69, 67, 59, 60, 55
1, 62, 80, 54, 68, 66, 70, 68, 69, 68, 70, 63, 70, 68, 58, 57, 50, 68, 65, 66, 70, 68, 65, 65, 74, 53, 61, 59, 67, 68, 72, 74, 72, 61, 61, 67, 69, 64, 72, 68, 70, 61, 60, 55, 63
1, 54, 42, 57, 60, 61, 44, 39, 54, 12, 22, 47, 52, 58, 63, 51, 35, 47, 31, 57, 50, 69, 60, 68, 60, 48, 38, 37, 36, 29, 22, 47, 54, 72, 62, 42, 18, 33, 30, 23, 28, 49, 40, 76, 45
1, 65, 70, 68, 71, 63, 59, 75, 75, 65, 63, 75, 78, 72, 74, 58, 59, 68, 71, 75, 68, 83, 81, 80, 82, 71, 57, 68, 67, 62, 69, 72, 75, 60, 61, 68, 69, 73, 73, 71, 77, 74, 64, 61, 50
1, 59, 75, 77, 80, 60, 64, 70, 67, 54, 68, 78, 77, 69, 64, 57, 56, 59, 57, 71, 70, 79, 77, 67, 65, 54, 54, 61, 58, 61, 64, 79, 74, 67, 66, 60, 58, 70, 72, 60, 65, 61, 60, 51, 52
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1, 58, 65, 78, 76, 64, 58, 74, 70, 63, 57, 81, 72, 62, 59, 41, 41, 64, 57, 75, 58, 75, 70, 64, 66, 32, 29, 66, 56, 57, 63, 71, 72, 61, 58, 71, 62, 77, 62, 72, 62, 49, 35, 30, 20
1, 60, 64, 61, 74, 57, 63, 67, 70, 59, 65, 62, 74, 58, 68, 51, 41, 63, 67, 60, 70, 73, 85, 62, 46, 31, 35, 56, 79, 58, 64, 70, 75, 59, 58, 57, 66, 62, 71, 69, 61, 41, 31, 24, 18
1, 51, 43, 66, 63, 54, 56, 71, 75, 65, 63, 69, 68, 61, 65, 60, 55, 65, 69, 65, 66, 77, 74, 65, 69, 62, 55, 67, 59, 61, 48, 71, 66, 63, 64, 60, 55, 70, 71, 78, 67, 72, 65, 70, 65
1, 57, 55, 56, 53, 51, 44, 68, 56, 68, 66, 64, 58, 58, 68, 66, 68, 72, 70, 67, 61, 70, 63, 70, 62, 44, 41, 63, 45, 42, 61, 64, 51, 68, 55, 71, 69, 62, 54, 57, 52, 38, 27, 39, 32
1, 68, 70, 70, 69, 65, 63, 71, 74, 73, 70, 79, 78, 77, 74, 61, 60, 69, 73, 70, 69, 76, 73, 69, 71, 62, 56, 67, 63, 65, 65, 67, 75, 59, 60, 67, 69, 69, 66, 62, 64, 68, 60, 56, 51
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1, 75, 75, 64, 63, 58, 58, 75, 77, 73, 74, 73, 76, 69, 65, 67, 68, 73, 76, 79, 78, 76, 74, 72, 66, 60, 61, 57, 58, 47, 40, 61, 63, 58, 47, 65, 66, 59, 63, 60, 57, 63, 47, 42, 40
1, 71, 66, 66, 71, 50, 41, 77, 60, 57, 58, 68, 69, 40, 61, 50, 34, 69, 51, 60, 60, 79, 63, 40, 61, 30, 14, 72, 41, 38, 36, 56, 65, 44, 47, 56, 40, 70, 63, 56, 58, 20, 25, 23, 16
1, 73, 60, 68, 54, 75, 63, 77, 72, 60, 54, 66, 69, 69, 66, 66, 59, 73, 59, 64, 62, 76, 76, 77, 68, 66, 57, 80, 62, 66, 60, 71, 66, 61, 59, 68, 61, 69, 63, 79, 67, 74, 73, 62, 58
1, 67, 70, 63, 48, 63, 60, 76, 75, 65, 52, 63, 48, 64, 52, 57, 62, 64, 76, 65, 65, 74, 62, 69, 64, 54, 62, 63, 78, 70, 59, 63, 49, 59, 53, 68, 65, 76, 62, 72, 61, 68, 61, 54, 53
1, 68, 73, 78, 83, 70, 71, 77, 74, 65, 61, 75, 74, 70, 69, 54, 56, 61, 60, 86, 79, 86, 86, 82, 79, 72, 58, 75, 57, 65, 53, 77, 75, 58, 61, 64, 56, 72, 68, 69, 65, 65, 62, 58, 58
1, 62, 54, 68, 70, 51, 28, 54, 23, 54, 54, 63, 65, 63, 70, 23, 11, 47, 31, 50, 59, 68, 67, 70, 75, 17, 16, 49, 44, 36, 19, 70, 72, 62, 68, 40, 29, 63, 58, 48, 44, 28, 10, 15, 28
1, 75, 74, 69, 73, 64, 61, 71, 71, 61, 58, 76, 78, 70, 68, 54, 52, 64, 66, 73, 75, 82, 82, 70, 80, 55, 55, 60, 70, 65, 67, 76, 77, 61, 62, 62, 65, 70, 67, 67, 68, 59, 56, 50, 46
1, 79, 77, 78, 70, 69, 65, 75, 75, 69, 66, 61, 60, 65, 59, 60, 59, 73, 70, 65, 69, 66, 69, 55, 53, 61, 56, 66, 69, 75, 66, 76, 71, 65, 51, 72, 75, 72, 73, 68, 63, 41, 36, 25, 27
1, 68, 62, 64, 65, 70, 69, 76, 75, 63, 59, 70, 59, 72, 66, 69, 68, 74, 74, 79, 70, 83, 83, 72, 69, 73, 78, 80, 83, 71, 70, 71, 63, 54, 51, 71, 70, 61, 56, 77, 76, 66, 67, 49, 54
1, 68, 74, 76, 84, 72, 72, 85, 75, 73, 70, 77, 78, 70, 66, 59, 58, 75, 70, 57, 64, 81, 76, 70, 64, 48, 47, 49, 54, 62, 64, 72, 76, 65, 62, 67, 68, 70, 70, 67, 58, 58, 58, 51, 52
1, 70, 69, 66, 66, 66, 67, 78, 80, 70, 65, 75, 71, 74, 72, 63, 66, 74, 75, 72, 71, 76, 74, 78, 76, 60, 52, 60, 63, 63, 61, 69, 71, 60, 59, 68, 67, 72, 69, 73, 68, 66, 67, 56, 56
1, 73, 80, 82, 84, 71, 71, 77, 75, 61, 58, 69, 72, 63, 62, 56, 54, 58, 52, 70, 69, 78, 76, 72, 76, 58, 63, 62, 62, 58, 57, 74, 77, 57, 62, 60, 53, 68, 69, 55, 49, 63, 63, 56, 48
1, 71, 67, 77, 74, 67, 61, 75, 71, 74, 69, 73, 73, 73, 66, 62, 64, 75, 71, 71, 65, 78, 68, 73, 70, 61, 59, 68, 67, 69, 66, 71, 70, 64, 64, 66, 68, 70, 68, 66, 51, 61, 43, 44, 37
1, 73, 70, 77, 79, 61, 59, 72, 66, 65, 66, 74, 79, 64, 59, 53, 54, 70, 69, 81, 80, 75, 74, 73, 62, 66, 63, 63, 70, 63, 60, 71, 65, 56, 49, 68, 62, 67, 70, 53, 62, 54, 43, 55, 37
1, 63, 63, 72, 75, 60, 61, 62, 63, 61, 63, 76, 75, 72, 63, 55, 56, 61, 69, 60, 63, 74, 78, 72, 73, 55, 54, 62, 68, 63, 64, 77, 77, 64, 62, 59, 65, 63, 67, 72, 78, 68, 68, 82, 66
1, 75, 67, 74, 75, 75, 69, 72, 72, 67, 66, 73, 70, 76, 73, 73, 69, 72, 76, 75, 67, 74, 75, 76, 71, 74, 68, 80, 78, 71, 67, 73, 68, 60, 58, 68, 68, 66, 61, 79, 71, 73, 65, 58, 53
1, 64, 60, 55, 51, 59, 59, 81, 79, 66, 59, 62, 61, 67, 61, 67, 61, 78, 73, 64, 61, 80, 67, 78, 73, 53, 44, 62, 53, 65, 61, 68, 64, 55, 60, 66, 68, 75, 70, 73, 77, 71, 67, 62, 62
1, 74, 77, 80, 77, 69, 64, 79, 76, 70, 68, 77, 78, 71, 69, 58, 55, 70, 65, 67, 65, 77, 77, 64, 61, 48, 45, 61, 54, 66, 65, 75, 78, 61, 69, 65, 58, 71, 71, 55, 60, 28, 39, 36, 35
1, 69, 66, 67, 67, 75, 78, 78, 69, 60, 62, 68, 73, 60, 66, 69, 75, 64, 67, 67, 60, 72, 67, 60, 63, 65, 64, 70, 64, 62, 63, 77, 72, 69, 58, 64, 64, 70, 54, 62, 47, 70, 46, 56, 46
1, 75, 79, 65, 74, 63, 64, 74, 73, 73, 71, 76, 79, 70, 69, 55, 64, 69, 71, 79, 76, 77, 78, 65, 70, 60, 60, 65, 61, 70, 65, 74, 71, 62, 62, 69, 67, 72, 72, 72, 66, 61, 64, 55, 54
1, 69, 65, 72, 68, 70, 71, 76, 78, 64, 62, 72, 68, 76, 78, 68, 73, 68, 72, 59, 53, 66, 66, 67, 75, 65, 67, 64, 55, 71, 72, 78, 75, 62, 61, 67, 66, 66, 64, 79, 75, 73, 75, 63, 62
1, 70, 69, 72, 70, 72, 76, 78, 72, 62, 57, 68, 73, 64, 65, 67, 60, 73, 67, 63, 55, 77, 74, 62, 60, 56, 57, 60, 60, 61, 61, 75, 76, 59, 63, 67, 63, 74, 66, 76, 77, 74, 73, 65, 67
1, 61, 66, 70, 74, 59, 64, 72, 73, 64, 65, 72, 72, 70, 72, 64, 62, 72, 74, 65, 69, 75, 75, 74, 76, 65, 64, 74, 73, 65, 70, 67, 72, 53, 56, 65, 68, 66, 68, 75, 80, 71, 75, 61, 64
1, 70, 60, 75, 78, 69, 69, 68, 71, 68, 59, 76, 69, 73, 71, 67, 65, 66, 63, 75, 65, 75, 72, 77, 79, 75, 75, 70, 64, 73, 75, 76, 74, 63, 61, 64, 63, 66, 64, 72, 75, 73, 72, 64, 61
1, 76, 75, 75, 77, 75, 72, 75, 75, 68, 75, 72, 71, 62, 65, 59, 64, 65, 67, 77, 81, 76, 77, 68, 72, 66, 63, 65, 68, 69, 65, 75, 76, 58, 63, 72, 75, 65, 70, 73, 71, 64, 63, 50, 50
1, 75, 67, 73, 75, 60, 58, 69, 71, 65, 61, 74, 78, 67, 64, 59, 57, 68, 68, 60, 61, 74, 72, 64, 66, 51, 51, 58, 53, 69, 63, 74, 76, 57, 60, 59, 64, 55, 60, 61, 59, 45, 56, 43, 50
1, 56, 68, 58, 67, 52, 61, 63, 76, 44, 61, 54, 68, 54, 70, 49, 70, 46, 69, 41, 59, 53, 70, 53, 67, 48, 57, 52, 68, 58, 68, 66, 75, 55, 60, 57, 68, 58, 71, 63, 71, 52, 64, 52, 54
1, 63, 70, 64, 72, 56, 64, 58, 69, 68, 70, 68, 78, 75, 64, 67, 67, 68, 72, 67, 67, 78, 75, 68, 66, 65, 62, 68, 67, 64, 68, 74, 75, 58, 58, 66, 69, 59, 58, 53, 64, 59, 54, 43, 49
1, 65, 60, 71, 72, 67, 72, 68, 72, 54, 55, 75, 70, 75, 75, 75, 70, 69, 68, 61, 62, 74, 71, 74, 79, 72, 76, 62, 61, 71, 68, 76, 75, 63, 60, 65, 70, 49, 48, 73, 68, 75, 74, 66, 67
1, 68, 66, 70, 70, 62, 67, 73, 74, 69, 70, 70, 75, 69, 70, 65, 68, 70, 67, 64, 64, 74, 75, 68, 68, 66, 66, 64, 56, 71, 68, 69, 75, 63, 64, 62, 62, 74, 68, 81, 75, 74, 70, 65, 58
1, 65, 62, 69, 62, 74, 72, 73, 69, 68, 59, 74, 67, 71, 69, 71, 70, 71, 67, 70, 66, 78, 77, 76, 79, 74, 77, 70, 74, 67, 72, 67, 67, 61, 58, 66, 64, 58, 47, 73, 67, 73, 66, 58, 52
1, 52, 49, 74, 72, 72, 76, 77, 77, 67, 66, 66, 64, 75, 70, 74, 77, 72, 73, 82, 80, 78, 78, 73, 70, 79, 75, 73, 79, 79, 78, 74, 71, 63, 63, 71, 73, 50, 58, 66, 68, 66, 58, 37, 38
1, 65, 77, 64, 65, 56, 62, 61, 69, 71, 74, 70, 74, 63, 64, 57, 64, 66, 68, 66, 75, 65, 74, 63, 65, 54, 59, 64, 68, 65, 74, 72, 78, 64, 67, 69, 71, 65, 70, 59, 68, 56, 63, 50, 51
1, 61, 58, 66, 74, 68, 68, 72, 71, 40, 42, 52, 54, 70, 75, 63, 67, 54, 59, 67, 65, 75, 78, 71, 79, 69, 75, 72, 69, 67, 69, 63, 69, 52, 58, 64, 65, 59, 59, 75, 76, 67, 69, 58, 63
1, 67, 59, 60, 65, 69, 69, 73, 70, 66, 63, 70, 70, 79, 76, 73, 70, 71, 73, 73, 68, 74, 75, 80, 74, 73, 71, 67, 64, 71, 70, 72, 70, 63, 64, 70, 71, 67, 61, 66, 69, 70, 74, 66, 66
1, 64, 55, 65, 57, 64, 63, 75, 76, 59, 55, 70, 59, 73, 69, 71, 70, 75, 72, 57, 52, 70, 56, 76, 67, 64, 60, 69, 60, 64, 63, 69, 66, 62, 62, 67, 70, 65, 60, 79, 77, 77, 75, 63, 60
1, 66, 65, 69, 68, 75, 63, 77, 77, 68, 68, 69, 68, 69, 67, 71, 65, 70, 69, 70, 63, 77, 71, 63, 63, 65, 56, 66, 65, 72, 70, 78, 74, 64, 63, 70, 71, 73, 69, 77, 70, 70, 62, 57, 52
1, 61, 57, 71, 71, 73, 70, 76, 74, 66, 63, 76, 79, 65, 66, 71, 71, 74, 70, 71, 67, 74, 80, 64, 68, 65, 62, 68, 67, 64, 67, 74, 75, 63, 64, 68, 68, 76, 72, 72, 70, 77, 78, 67, 65
1, 67, 65, 73, 78, 60, 57, 66, 62, 63, 63, 74, 74, 72, 66, 56, 54, 65, 62, 67, 69, 79, 81, 72, 72, 55, 55, 51, 57, 65, 66, 72, 74, 64, 64, 64, 60, 74, 73, 69, 73, 73, 66, 62, 58
1, 71, 71, 71, 72, 77, 72, 73, 65, 69, 65, 81, 79, 68, 72, 67, 71, 70, 70, 64, 61, 72, 67, 56, 73, 67, 75, 62, 58, 58, 58, 73, 69, 66, 61, 55, 56, 52, 53, 65, 58, 71, 76, 68, 70
1, 68, 49, 62, 59, 67, 64, 68, 67, 71, 62, 73, 63, 64, 62, 61, 56, 74, 75, 70, 69, 76, 75, 66, 64, 62, 64, 63, 67, 65, 66, 70, 67, 61, 57, 66, 68, 72, 64, 69, 57, 68, 66, 59, 59
1, 74, 76, 72, 74, 70, 71, 73, 77, 70, 73, 69, 73, 74, 75, 62, 68, 75, 73, 69, 71, 73, 74, 72, 73, 67, 62, 73, 69, 73, 75, 75, 76, 60, 62, 69, 72, 62, 67, 71, 75, 61, 62, 49, 51
1, 67, 68, 70, 72, 70, 72, 70, 76, 68, 71, 74, 70, 75, 76, 71, 75, 77, 78, 69, 72, 82, 74, 69, 75, 70, 71, 70, 69, 67, 66, 68, 63, 57, 61, 65, 64, 51, 52, 76, 76, 73, 74, 55, 56
1, 70, 76, 62, 67, 74, 72, 80, 76, 55, 59, 62, 64, 64, 58, 73, 70, 74, 71, 72, 68, 73, 69, 72, 63, 69, 63, 60, 57, 47, 52, 67, 64, 66, 62, 73, 69, 71, 70, 60, 62, 67, 67, 60, 56
1, 58, 74, 69, 74, 45, 44, 56, 49, 57, 62, 66, 69, 65, 59, 37, 41, 37, 43, 64, 66, 78, 75, 66, 59, 57, 57, 65, 67, 46, 42, 72, 68, 68, 69, 46, 43, 69, 73, 47, 52, 39, 47, 46, 50
1, 65, 77, 72, 73, 57, 60, 59, 68, 65, 70, 72, 73, 61, 71, 51, 57, 60, 63, 69, 68, 75, 80, 70, 70, 58, 61, 64, 69, 65, 72, 71, 74, 60, 67, 63, 63, 50, 59, 45, 66, 44, 52, 40, 44
1, 57, 47, 64, 71, 63, 69, 64, 71, 61, 60, 67, 65, 69, 69, 65, 68, 73, 68, 71, 65, 81, 69, 78, 73, 74, 73, 74, 62, 64, 70, 76, 77, 65, 62, 75, 74, 58, 57, 66, 68, 68, 72, 55, 56
1, 42, 51, 72, 56, 72, 67, 58, 54, 56, 56, 65, 68, 55, 58, 57, 62, 69, 75, 49, 56, 47, 61, 43, 44, 38, 47, 60, 68, 42, 45, 67, 62, 47, 48, 63, 64, 58, 72, 51, 54, 60, 55, 39, 45
1, 73, 62, 69, 70, 73, 67, 76, 77, 72, 66, 75, 73, 78, 69, 73, 61, 66, 58, 74, 64, 75, 73, 76, 66, 70, 59, 63, 58, 67, 66, 76, 71, 66, 65, 65, 66, 71, 64, 70, 61, 72, 67, 58, 59
1, 61, 66, 70, 75, 60, 60, 74, 75, 64, 67, 68, 75, 65, 64, 58, 57, 68, 70, 62, 68, 66, 75, 60, 65, 55, 51, 61, 67, 61, 64, 72, 78, 62, 69, 66, 70, 66, 73, 50, 59, 48, 55, 47, 45
1, 23, 60, 54, 35, 43, 64, 32, 46, 29, 28, 72, 72, 63, 64, 47, 44, 36, 33, 56, 52, 72, 74, 63, 60, 53, 56, 57, 59, 28, 25, 59, 67, 59, 64, 40, 28, 57, 56, 39, 39, 76, 60, 62, 53
1, 59, 57, 70, 72, 62, 44, 76, 72, 53, 56, 69, 68, 68, 66, 55, 51, 67, 69, 59, 55, 76, 69, 60, 58, 56, 54, 66, 53, 51, 38, 70, 67, 71, 65, 65, 60, 66, 71, 68, 46, 76, 51, 66, 61
1, 72, 73, 62, 66, 66, 64, 70, 75, 62, 67, 72, 74, 70, 70, 69, 62, 69, 69, 73, 70, 67, 64, 63, 53, 47, 35, 70, 71, 66, 67, 70, 73, 57, 57, 68, 68, 61, 60, 70, 61, 66, 53, 50, 46
1, 68, 60, 63, 60, 45, 36, 72, 55, 50, 56, 68, 56, 32, 51, 27, 8, 66, 50, 50, 77, 76, 48, 51, 61, 18, 32, 67, 26, 39, 44, 67, 64, 44, 55, 50, 28, 60, 60, 63, 59, 13, 9, 13, 11
1, 62, 63, 66, 62, 67, 66, 80, 79, 55, 55, 61, 58, 52, 62, 49, 48, 78, 83, 59, 68, 73, 66, 59, 58, 42, 49, 67, 86, 59, 52, 72, 74, 58, 63, 70, 70, 76, 66, 67, 58, 54, 65, 46, 42
1, 37, 23, 70, 67, 56, 49, 26, 20, 36, 32, 65, 62, 73, 72, 60, 52, 37, 21, 61, 58, 75, 72, 75, 71, 75, 76, 68, 59, 22, 15, 76, 72, 64, 61, 41, 31, 51, 45, 21, 22, 75, 78, 56, 49
1, 75, 67, 79, 72, 64, 58, 72, 68, 60, 62, 66, 69, 68, 74, 58, 54, 63, 58, 63, 61, 74, 70, 63, 70, 46, 43, 64, 51, 71, 65, 73, 77, 67, 68, 67, 59, 70, 65, 73, 66, 60, 67, 55, 58
1, 65, 62, 68, 74, 57, 56, 62, 60, 65, 63, 74, 71, 69, 67, 56, 59, 66, 63, 76, 75, 79, 77, 78, 77, 61, 53, 66, 69, 59, 62, 76, 76, 62, 64, 63, 61, 75, 74, 67, 69, 66, 68, 50, 44
1, 72, 64, 69, 75, 67, 59, 79, 68, 68, 56, 72, 76, 64, 61, 59, 59, 69, 69, 70, 61, 73, 74, 69, 73, 62, 46, 67, 65, 52, 36, 76, 73, 58, 55, 59, 57, 72, 63, 65, 48, 56, 42, 35, 31
1, 69, 65, 70, 74, 64, 71, 70, 74, 66, 67, 71, 75, 65, 69, 63, 66, 70, 75, 68, 70, 76, 82, 67, 69, 66, 64, 69, 71, 68, 69, 73, 72, 59, 67, 65, 66, 65, 68, 68, 76, 67, 75, 63, 61
1, 62, 54, 46, 44, 72, 66, 56, 67, 56, 56, 67, 62, 60, 59, 62, 63, 75, 69, 69, 69, 76, 77, 69, 68, 64, 64, 69, 68, 38, 40, 58, 64, 53, 63, 59, 57, 66, 77, 47, 48, 35, 44, 35, 43
1, 47, 50, 64, 71, 71, 71, 53, 79, 39, 39, 55, 62, 61, 66, 75, 64, 54, 50, 62, 59, 67, 64, 75, 73, 75, 74, 68, 64, 37, 40, 67, 75, 65, 69, 56, 53, 43, 36, 41, 43, 73, 74, 59, 70
1, 64, 69, 72, 82, 36, 43, 72, 73, 57, 63, 80, 82, 56, 63, 35, 33, 70, 75, 60, 67, 70, 78, 51, 61, 24, 17, 61, 61, 63, 67, 69, 74, 57, 61, 68, 71, 59, 73, 48, 74, 33, 38, 13, 9
1, 69, 69, 73, 72, 66, 63, 76, 72, 69, 66, 69, 79, 72, 74, 58, 62, 73, 70, 69, 71, 78, 76, 71, 76, 65, 63, 66, 70, 60, 63, 74, 75, 62, 64, 66, 68, 69, 72, 73, 69, 71, 71, 57, 62
1, 55, 62, 64, 62, 64, 74, 71, 67, 63, 66, 70, 70, 65, 64, 65, 65, 68, 72, 66, 70, 74, 77, 65, 68, 67, 75, 66, 73, 65, 67, 74, 72, 64, 68, 70, 70, 54, 61, 63, 66, 68, 78, 54, 61
1, 64, 51, 64, 54, 70, 71, 70, 72, 64, 52, 72, 59, 70, 67, 70, 68, 76, 70, 60, 49, 71, 64, 73, 64, 76, 71, 73, 64, 68, 63, 71, 62, 56, 56, 71, 70, 57, 53, 72, 70, 67, 66, 56, 52
1, 54, 52, 72, 69, 73, 56, 69, 67, 67, 61, 70, 71, 72, 62, 70, 60, 69, 70, 73, 71, 79, 72, 78, 66, 72, 67, 63, 58, 73, 70, 70, 72, 61, 60, 67, 72, 67, 63, 76, 66, 78, 63, 63, 59
1, 65, 57, 58, 51, 70, 63, 71, 76, 58, 58, 63, 58, 69, 62, 77, 68, 72, 74, 69, 61, 72, 63, 77, 63, 76, 67, 67, 66, 67, 63, 61, 54, 56, 50, 63, 65, 59, 55, 75, 71, 73, 70, 60, 58
1, 69, 75, 65, 67, 59, 60, 77, 76, 70, 68, 76, 71, 75, 68, 66, 60, 76, 70, 69, 70, 75, 78, 72, 68, 61, 63, 68, 70, 67, 65, 70, 69, 55, 61, 66, 64, 69, 66, 68, 65, 67, 50, 53, 41
1, 73, 73, 75, 79, 64, 66, 68, 61, 71, 68, 77, 75, 63, 73, 62, 55, 70, 64, 77, 75, 75, 79, 70, 75, 65, 59, 68, 56, 63, 67, 74, 73, 60, 60, 62, 60, 73, 77, 70, 72, 73, 67, 64, 61
1, 68, 58, 76, 71, 64, 70, 73, 75, 61, 58, 68, 64, 69, 71, 66, 73, 75, 69, 78, 74, 81, 78, 73, 74, 72, 80, 69, 69, 67, 67, 74, 74, 58, 59, 69, 70, 58, 53, 70, 67, 66, 78, 53, 64
1, 32, 41, 76, 34, 65, 53, 30, 54, 16, 51, 61, 43, 74, 70, 62, 21, 34, 42, 61, 37, 58, 66, 54, 49, 17, 19, 11, 42, 53, 30, 71, 9, 61, 16, 31, 43, 67, 61, 29, 31, 17, 8, 18, 11
1, 76, 65, 60, 40, 32, 34, 65, 50, 53, 37, 66, 53, 33, 31, 30, 30, 69, 75, 64, 45, 68, 65, 57, 43, 23, 23, 53, 77, 48, 32, 55, 32, 48, 23, 62, 51, 65, 59, 43, 39, 35, 30, 24, 21
1, 60, 51, 75, 60, 65, 45, 64, 55, 55, 61, 66, 74, 61, 50, 62, 41, 70, 63, 60, 62, 76, 69, 70, 54, 51, 47, 77, 80, 69, 48, 74, 59, 72, 57, 76, 68, 69, 63, 62, 53, 57, 31, 46, 30
1, 64, 60, 71, 69, 71, 65, 66, 64, 68, 59, 63, 67, 68, 64, 73, 59, 73, 60, 72, 56, 77, 68, 69, 69, 66, 59, 57, 40, 55, 53, 69, 63, 71, 63, 66, 55, 66, 58, 65, 65, 75, 64, 61, 56
1, 65, 69, 66, 76, 58, 67, 65, 72, 66, 64, 77, 75, 64, 62, 63, 59, 71, 61, 67, 45, 74, 45, 66, 46, 64, 48, 62, 27, 65, 67, 72, 72, 62, 66, 71, 64, 71, 70, 72, 69, 70, 65, 63, 61
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================================================
FILE: Data/ionosphere.csv
================================================
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1,0,0.5984,0.40332,0.82809,0.80521,0.76001,0.70709,0.8401,-0.10984,0.97311,0.07981,0.95824,-0.85727,0.91962,0.88444,0.95452,-0.05206,0.88673,0.18135,0.98484,-0.69594,0.8667,-0.85755,0.28604,-0.30063,1,0.17076,0.62958,0.42677,0.87757,0.81007,0.81979,0.68822
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1,0,0.64122,0.01403,0.34146,-0.02439,0.52751,0.03466,0.19512,0.12195,0.43313,0.04755,0.21951,0.04878,0.29268,0,0.36585,0,0.31707,0.07317,0.26829,0.12195,0.23698,0.05813,0.21951,0.09756,0.19304,0.05641,0.1741,0.05504,0.19512,0,0.17073,0.07317
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1,0,0.34694,0.20408,0.46939,0.2449,0.40816,0.20408,0.46939,0.44898,0.30612,0.59184,0.12245,0.55102,0,0.5102,-0.06122,0.55102,-0.20408,0.55102,-0.28571,0.44898,-0.28571,0.32653,-0.61224,0.22449,-0.46579,0.14895,-0.59184,0.18367,-0.34694,0,-0.26531,-0.2449
1,0,0,0,1,-1,0,0,0,0,1,1,1,-0.25342,1,0.23288,1,-1,0,0,0,0,1,1,0,0,1,-1,0,0,1,-1,0,0
1,0,0.89706,0.38235,0.91176,0.375,0.74265,0.67647,0.45588,0.77941,0.19118,0.88971,-0.02206,0.86029,-0.20588,0.82353,-0.375,0.67647,-0.5,0.47794,-0.73529,0.38235,-0.86029,0.08824,-0.74265,-0.125,-0.67925,-0.24131,-0.55147,-0.42647,-0.44118,-0.50735,-0.28676,-0.56618
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1,0,0.26667,-0.1,0.53333,0,0.33333,-0.13333,0.36667,0.11667,0.56667,0.01667,0.71667,0.08333,0.7,-0.06667,0.53333,0.2,0.41667,-0.01667,0.31667,0.2,0.7,0,0.25,0.13333,0.46214,0.05439,0.4,0.03333,0.46667,0.03333,0.41667,-0.05
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1,0,0.95202,0.02254,0.93757,-0.01272,0.93526,0.01214,0.96705,-0.01734,0.96936,0.0052,0.95665,-0.03064,0.9526,-0.00405,0.9948,-0.02659,0.99769,0.01792,0.93584,-0.04971,0.93815,-0.0237,0.97052,-0.04451,0.96215,-0.01647,0.97399,0.01908,0.95434,-0.0341,0.95838,0.00809
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1,0,0.62745,-0.07843,0.72549,0,0.60784,-0.07843,0.62745,-0.11765,0.68627,-0.11765,0.66667,-0.13725,0.64706,-0.09804,0.54902,-0.11765,0.54902,-0.21569,0.58824,-0.19608,0.66667,-0.23529,0.45098,-0.2549,0.52409,-0.24668,0.56863,-0.31373,0.43137,-0.21569,0.47059,-0.27451
1,0,0.25,0.16667,0.46667,0.26667,0.19036,0.23966,0.07766,0.19939,0.0107,0.14922,-0.02367,0.10188,-0.03685,0.06317,-0.03766,0.03458,-0.0323,0.01532,-0.02474,0.00357,-0.01726,-0.00273,-0.01097,-0.00539,-0.00621,-0.00586,-0.00294,-0.0052,-0.00089,-0.00408,0.00025,-0.00291
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1,0,0.85271,0.05426,1,0.08069,1,1,0.91473,-0.00775,0.83721,0.03876,1,0.27153,1,1,0.81395,0.04651,0.90698,0.11628,1,0.5067,1,-1,0.8062,0.03876,1,0.71613,0.84496,0.06977,1,0.87317,1,1
1,0,0.90374,-0.01604,1,0.08021,1,0.01604,0.93048,0.00535,0.93583,-0.01604,1,0,1,0.06417,1,0.04813,0.91444,0.04278,0.96791,0.02139,0.9893,-0.01604,0.96257,0.05348,0.96974,0.04452,0.87701,0.0107,1,0.09091,0.97861,0.06417
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1,0,0.6,0.03333,0.63333,0.06667,0.7,0.06667,0.7,0,0.63333,0,0.8,0,0.73333,0,0.7,0.1,0.66667,0.1,0.73333,-0.03333,0.76667,0,0.63333,0.13333,0.65932,0.10168,0.6,0.13333,0.6,0.16667,0.63333,0.16667
1,0,0.05866,-0.00838,0.06704,0.00838,0,-0.01117,0.00559,-0.03911,0.01676,-0.07542,-0.00559,0.05307,0.06425,-0.03352,0,0.09497,-0.06425,0.07542,-0.04749,0.02514,0.02793,-0.00559,0.00838,0.00559,0.10335,-0.00838,0.03073,-0.00279,0.04469,0,0.04749,-0.03352
1,0,0.94653,0.28713,0.72554,0.67248,0.47564,0.82455,0.01267,0.89109,-0.24871,0.84475,-0.47644,0.56079,-0.75881,0.41743,-0.66455,0.07208,-0.65426,-0.19525,-0.52475,-0.44,-0.30851,-0.55089,-0.04119,-0.64792,0.16085,-0.5642,0.36752,-0.41901,0.46059,-0.22535,0.50376,-0.0598
1,0,0.0546,0.01437,-0.02586,0.04598,0.01437,0.04598,-0.07759,0.00862,0.01724,-0.06609,-0.03736,0.0431,-0.08333,-0.04598,-0.09483,0.08046,-0.04023,0.05172,0.02011,0.02299,-0.03736,-0.01149,0.03161,-0.00862,0.00862,0.01724,0.02586,0.01149,0.02586,0.01149,-0.04598,-0.00575
1,0,0.72414,-0.01084,0.79704,0.01084,0.8,0.00197,0.79015,0.01084,0.78424,-0.00985,0.8335,0.03251,0.85123,0.01675,0.80099,-0.00788,0.79113,-0.02956,0.75961,0.0335,0.74778,0.05517,0.72611,-0.01478,0.78041,0.00612,0.74089,-0.05025,0.82956,0.02956,0.79015,0.00788
1,0,0.03852,0.02568,0.00428,0,0.01997,-0.01997,0.0214,-0.04993,-0.0485,-0.01284,0.01427,-0.02282,0,-0.03281,-0.04708,-0.02853,-0.01712,0.03566,0.0214,0.00428,0.05136,-0.02282,0.05136,0.01854,0.03994,0.01569,0.01997,0.00713,-0.02568,-0.01854,-0.01427,0.01997
1,0,0.4709,0.22751,0.42328,0.33598,0.25661,0.47619,0.01852,0.49471,-0.02116,0.53968,-0.34127,0.31217,-0.4127,0.3254,-0.51587,0.06878,-0.5,-0.1164,-0.14815,-0.1455,-0.14815,-0.38095,-0.2328,0.00265,0.03574,-0.31739,0.15873,-0.21693,0.24868,-0.24339,0.2672,0.04233
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0,0,0,0,1,1,1,1,0,0,0,0,-1,-1,0,0,-1,-1,0,0,0,0,1,1,0,0,1,1,0,0,-1,1,0,0
1,0,0.88804,0.38138,0.65926,0.69431,0.29148,0.87892,-0.06726,0.90135,-0.39597,0.80441,-0.64574,0.56502,-0.8296,0.26906,-0.7894,-0.08205,-0.6278,-0.30942,-0.46637,-0.55605,-0.16449,-0.64338,0.09562,-0.61055,0.30406,-0.48392,0.43227,-0.29838,0.47029,-0.09461,0.42152,0.12556
0,0,1,-1,1,1,1,1,1,1,1,1,1,-1,1,1,1,1,1,-1,1,-1,1,-1,1,-1,1,1,1,-1,1,1,1,1
1,0,0.73523,-0.38293,0.80151,0.10278,0.78826,0.15266,0.5558,0.05252,1,0.21225,0.71947,0.28954,0.68798,0.32925,0.49672,0.17287,0.64333,-0.02845,0.57399,0.42528,0.5312,0.44872,0.9453,0.57549,0.44174,0.482,0.12473,1,0.3507,0.49721,0.30588,0.49831
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0
1,0,0.94649,0.00892,0.97287,-0.0026,0.98922,0.00372,0.95801,0.01598,0.94054,0.0353,0.97213,0.04719,0.98625,0.01858,0.94277,0.07135,0.98551,-0.00706,0.9777,0.0498,0.96358,0.07098,0.93274,0.08101,0.95243,0.04356,0.97473,0.00818,0.97845,0.07061,1,-0.0026
0,0,1,1,-1,-1,-1,-1,0,0,0,0,-1,-1,0,0,0,0,0,0,-1,1,1,1,0,0,1,-1,0,0,-1,-1,-1,-1
1,0,0.50466,-0.169,0.71442,0.01513,0.71063,0.02258,0.68065,0.01282,0.34615,0.05594,0.6905,0.04393,0.68101,0.05058,0.67023,0.05692,0.63403,-0.04662,0.64503,0.06856,0.63077,0.07381,0.84033,0.18065,0.59935,0.08304,0.38228,0.0676,0.56466,0.09046,0.54632,0.09346
1,0,0.68729,1,0.91973,-0.76087,0.81773,0.04348,0.76087,0.10702,0.86789,0.73746,0.70067,0.18227,0.7592,0.13712,0.93478,-0.25084,0.70736,0.18729,0.64883,0.24582,0.60201,0.77425,1,-0.53846,0.89262,0.22216,0.7107,0.53846,1,-0.06522,0.56522,0.23913
1,0,0.76296,-0.07778,1,-0.2963,1,-0.85741,0.8,0.06111,0.45556,-0.42778,1,-0.12581,1,-0.83519,0.49259,0.01852,0.82222,-0.05926,0.98215,-0.19938,1,0.22037,0.6963,-0.26481,0.92148,-0.24549,0.78889,0.02037,0.87492,-0.27105,1,-0.57037
1,0,0.38521,0.15564,0.41245,0.07393,0.26459,0.24125,0.23346,0.1323,0.19455,0.25292,0.24514,0.36965,0.08949,0.22957,-0.03891,0.36965,0.05058,0.24903,0.24903,0.09728,0.07782,0.29961,-0.02494,0.28482,-0.06024,0.26256,-0.14786,0.14786,-0.09339,0.31128,-0.19066,0.28794
1,0,0.5754,-0.03175,0.75198,-0.05357,0.61508,-0.0119,0.53968,0.03373,0.61706,0.09921,0.59127,-0.02381,0.62698,0.0119,0.70833,0.02579,0.60317,0.01587,0.47817,-0.02778,0.59127,0.0377,0.5,0.03968,0.61291,-0.01237,0.61706,-0.13492,0.68849,-0.01389,0.625,-0.03175
1,0,0.06404,-0.15271,-0.04433,0.05911,0.08374,-0.02463,-0.01478,0.18719,0.06404,0,0.12315,-0.09852,0.05911,0,0.0197,-0.02956,-0.12808,-0.2069,0.06897,0.01478,0.06897,0.02956,0.07882,0.16256,0.28079,-0.04926,-0.05911,-0.0936,0.04433,0.05419,0.07389,-0.10837
1,0,0.61857,0.1085,0.70694,-0.06935,0.70358,0.01678,0.74273,0.00224,0.71029,0.15772,0.71588,-0.00224,0.79754,0.066,0.83669,-0.16555,0.6868,-0.0906,0.62528,-0.01342,0.60962,0.11745,0.71253,-0.09508,0.69845,-0.01673,0.63311,0.0481,0.78859,-0.05145,0.65213,-0.04698
1,0,0.25316,0.35949,0,0,-0.2962,-1,0,0,0.07595,-0.07342,0,0,0,0,0,0,0,0,0.00759,0.68101,-0.2,0.33671,-0.1038,0.35696,0.0557,-1,0,0,0.06329,-1,0,0
1,0,0.88103,-0.00857,0.89818,-0.02465,0.94105,-0.01822,0.89175,-0.12755,0.82208,-0.10932,0.88853,0.01179,0.90782,-0.13719,0.87138,-0.06109,0.90782,-0.02358,0.87996,-0.14577,0.82851,-0.12433,0.90139,-0.19507,0.88245,-0.14903,0.84352,-0.12862,0.88424,-0.18542,0.91747,-0.16827
1,0,0.42708,-0.5,0,0,0,0,0.46458,0.51042,0.58958,0.02083,0,0,0,0,0.16458,-0.45417,0.59167,-0.18333,0,0,0,0,0.9875,-0.40833,-1,-1,-0.27917,-0.75625,0,0,0,0
1,0,0.88853,0.01631,0.92007,0.01305,0.92442,0.01359,0.89179,-0.10223,0.90103,-0.08428,0.9304,-0.01033,0.93094,-0.08918,0.86025,-0.05057,0.89451,-0.04024,0.88418,-0.12126,0.88907,-0.11909,0.8298,-0.14138,0.86453,-0.11808,0.85536,-0.13051,0.83524,-0.12452,0.86786,-0.12235
1,0,0,0,1,0.12889,0.88444,-0.02,0,0,1,-0.42444,1,0.19556,1,-0.05333,1,-0.81556,0,0,1,-0.04,1,-0.18667,0,0,1,-1,0,0,1,0.11778,0.90667,-0.09556
1,0,0.81143,0.03714,0.85143,-0.00143,0.79,0.00714,0.79571,-0.04286,0.87571,0,0.85571,-0.06714,0.86429,0.00286,0.82857,-0.05429,0.81,-0.11857,0.76857,-0.08429,0.84286,-0.05,0.77,-0.06857,0.81598,-0.08669,0.82571,-0.10429,0.81429,-0.05,0.82143,-0.15143
1,0,0,0,0,0,0,0,0,0,0,0,-1,1,1,0.55172,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0
1,0,0.4987,0.01818,0.43117,-0.0961,0.50649,-0.04156,0.5013,0.0961,0.44675,0.05974,0.55844,-0.11948,0.51688,-0.03636,0.52727,-0.05974,0.55325,-0.01039,0.48571,-0.03377,0.49091,-0.01039,0.59221,0,0.53215,-0.0328,0.43117,0.03377,0.54545,-0.05455,0.58961,-0.08571
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,-1,0,0,0,0,0,0
1,0,1,0.5,1,0.25,0.25,1,0.16851,0.9118,-0.13336,0.80454,-0.34107,0.60793,-0.4382,0.37856,-0.43663,0.16709,-0.36676,0.00678,-0.26477,-0.09025,-0.16178,-0.12964,-0.07782,-0.12744,-0.02089,-0.10242,0.01033,-0.07036,0.02224,-0.04142,0.02249,-0.02017
1,0,0,0,0,0,1,1,-1,-1,0,0,1,-0.11111,0,0,0,0,-1,1,1,1,1,-1,0,0,1,-1,0,0,0,0,1,1
1,0,0.87048,0.38027,0.64099,0.69212,0.31347,0.86625,-0.03933,0.9074,-0.42173,0.79346,-0.70561,0.5156,-0.81049,0.22735,-0.81136,-0.12539,-0.67474,-0.38102,-0.38334,-0.62861,-0.13013,-0.70762,0.15552,-0.66421,0.38544,-0.51568,0.52573,-0.29897,0.56239,-0.05938,0.5146,0.16645
1,0,0,0,0,0,0,0,-1,1,0,0,1,0.37333,-0.12,-0.12,0,0,-1,-1,0,0,1,-1,0,0,1,0.22667,0,0,0,0,0,0
1,0,0.88179,0.43491,0.59573,0.77655,0.19672,0.94537,-0.24103,0.92544,-0.62526,0.71257,-0.86443,0.33652,-0.92384,-0.05338,-0.77356,-0.44707,-0.4695,-0.73285,-0.10237,-0.82217,0.26384,-0.7757,0.55984,-0.5591,0.72147,-0.24433,0.72478,0.09599,0.58137,0.38915,0.34749,0.57656
1,0,0.32834,0.0252,0.15236,0.21278,0.14919,0.74003,-0.25706,0.92324,-0.10312,0.1938,-0.61352,0.25786,-0.94053,-0.05409,-0.13117,-0.14329,-0.30315,-0.44615,-0.11409,-0.85597,0.02668,-0.22786,0.27942,-0.06295,0.33737,-0.11876,0.27657,-0.11409,0.15078,0.13296,0.12197,0.20468
1,0,0.83427,0.39121,0.5404,0.78579,0.12326,0.89402,-0.33221,0.83578,-0.70086,0.59564,-0.86622,0.21909,-0.84442,-0.24164,-0.59714,-0.61894,-0.19354,-0.87787,0.12439,-0.89064,0.51109,-0.72454,0.79143,-0.27734,0.83008,0.08718,0.66592,0.49079,0.37542,0.70011,-0.03983,0.79444
1,0,0.62335,-0.0349,0.59085,0.00481,0.60409,-0.07461,0.63177,0.00963,0.62455,-0.07461,0.67028,0.0722,0.62936,-0.08424,0.67509,0.09146,0.67148,0,0.58965,0.10108,0.5006,0.03129,0.65945,0.14079,0.60463,0.02019,0.51384,0.04452,0.61733,-0.00963,0.61372,-0.09146
1,0,0.74449,-0.0239,0.70772,0.03309,0.72243,0.16912,0.79228,0.07721,0.81434,0.43934,0.63787,0.00551,0.70772,0.21691,1,0.06066,0.61029,0.05147,0.67463,0.04228,0.52022,-0.25,0.72978,-0.15809,0.61727,0.07124,0.30882,0.0864,0.55916,0.07458,0.60294,0.21691
1,0,0.61538,0.18923,0.78157,0.0178,0.77486,0.02647,0.65077,-0.10308,0.77538,0.08,0.73961,0.0506,0.72322,0.05776,0.68615,-0.08923,0.61692,0.16308,0.66233,0.07573,0.63878,0.08041,0.60154,-0.07231,0.58803,0.08767,0.55077,0.25692,0.53389,0.09207,0.50609,0.09322
1,0,0.68317,0.05375,0.84803,0.00202,0.84341,0.00301,0.843,0.09901,0.75813,0.04102,0.81892,0.00585,0.80738,0.00673,0.80622,-0.12447,0.77935,-0.03536,0.76365,0.00909,0.74635,0.00978,0.79632,-0.04243,0.70824,0.01096,0.62235,0.11598,0.66624,0.0119,0.64407,0.01227
1,0,0.5,0,0.38696,0.10435,0.4913,0.06522,0.46957,-0.03913,0.35652,-0.12609,0.45652,0.04783,0.50435,0.02609,0.35652,0.19565,0.42174,0.14783,0.42174,-0.02609,0.32174,-0.11304,0.47391,-0.0087,0.41789,0.06908,0.38696,0.03913,0.35217,0.14783,0.44783,0.17391
1,0,0.7983,0.09417,0.78129,0.20656,0.71628,0.28068,0.6932,0.41252,0.65917,0.50122,0.57898,0.60814,0.4921,0.58445,0.33354,0.67861,0.29587,0.63548,0.09599,0.68104,0.02066,0.72236,-0.08748,0.63183,-0.11925,0.60696,-0.18226,0.56015,-0.25516,0.51701,-0.27339,0.42467
1,0,1,0.09802,1,0.25101,0.9839,0.33044,0.80365,0.5302,0.74977,0.60297,0.56937,0.71942,0.55311,0.74079,0.29452,0.82193,0.21137,0.79777,0.09709,0.82162,-0.01734,0.7987,-0.15144,0.75596,-0.22839,0.69187,-0.31713,0.60948,-0.40291,0.54522,-0.42815,0.44534
1,0,0.8941,0.13425,0.87001,0.31543,0.78896,0.43388,0.63388,0.59975,0.54003,0.71016,0.39699,0.76161,0.24266,0.79523,0.09134,0.79598,-0.09159,0.76261,-0.20201,0.66926,-0.30263,0.6261,-0.40552,0.50489,-0.46215,0.40753,-0.50314,0.27252,-0.52823,0.19172,-0.48808,0.05972
1,0,0.94631,0.17498,0.90946,0.33143,0.85096,0.4996,0.73678,0.63842,0.59215,0.73838,0.48698,0.83614,0.30459,0.90665,0.17959,0.93429,-0.00701,0.93109,-0.1888,0.89383,-0.33023,0.82492,-0.46534,0.76482,-0.58563,0.66335,-0.67929,0.52564,-0.75321,0.42488,-0.8121,0.26092
1,0,0.91767,0.18198,0.8609,0.35543,0.72873,0.45747,0.60425,0.69865,0.50376,0.74922,0.361,0.81795,0.15664,0.83558,0.00396,0.8521,-0.1639,0.77853,-0.35996,0.76193,-0.43087,0.65385,-0.5314,0.53886,-0.60328,0.40972,-0.64511,0.27338,-0.6571,0.13667,-0.64056,0.05394
1,0,0.76627,0.21106,0.63935,0.38112,0.48409,0.525,0.15,0.22273,0.13753,0.59565,-0.07727,0.44545,0,0.48636,-0.27491,0.42014,-0.56136,0.36818,-0.36591,0.18864,-0.40533,0.07588,-0.38483,-0.03229,-0.33942,-0.12486,-0.2754,-0.19714,-0.19962,-0.24648,-0.11894,-0.27218
1,0,0.5894,-0.60927,0.8543,0.55298,0.81126,0.07285,0.56623,0.16225,0.32781,0.24172,0.50331,0.12252,0.63907,0.19868,0.71854,0.42715,0.54305,0.13907,0.65232,0.27815,0.68874,0.07285,0.51872,0.26653,0.49013,0.27687,0.46216,0.28574,0.43484,0.29324,0.40821,0.29942
1,0,1,0.11385,0.70019,-0.12144,0.81594,0.09677,0.71157,0.01139,0.56167,-0.0778,0.6907,0.12524,0.58634,0.03985,0.53131,-0.03416,0.6945,0.16888,0.72676,0.07211,0.32068,0.05882,0.53321,0.37381,0.4909,0.17951,0.1518,0.32448,0.44141,0.18897,0.56167,0.1518
1,0,0.84843,0.06794,0.80562,-0.02299,0.77031,-0.03299,0.66725,-0.0662,0.59582,-0.07666,0.6726,-0.05771,0.6426,-0.06438,0.39199,0.0453,0.71254,0.01394,0.5597,-0.08039,0.5343,-0.08453,0.47038,-0.22822,0.48659,-0.09128,0.52613,-0.08537,0.44277,-0.09621,0.42223,-0.09808
1,0,1,0.08013,0.96775,-0.00482,0.96683,-0.00722,0.8798,-0.03923,1,0.01419,0.96186,-0.01436,0.95947,-0.01671,0.98497,0.01002,0.91152,-0.08848,0.95016,-0.02364,0.94636,-0.02591,0.98164,0.02003,0.93772,-0.03034,1,-0.05843,0.92774,-0.03464,0.92226,-0.03673
1,0,0.47938,-0.12371,0.42784,-0.12371,0.70103,-0.39175,0.73196,0.07216,0.26289,-0.21649,0.49485,0.15979,0.45361,-0.11856,0.42268,0.06186,0.5,-0.2732,0.54639,0.18557,0.42268,0.08247,0.70619,0.19588,0.53396,-0.12447,0.15464,-0.26289,0.47423,0.04124,0.45361,-0.51546
1,0,0.6351,-0.04388,0.7653,0.02968,0.61432,0.36028,0.65358,-0.00462,0.64203,0.08314,0.79446,-0.43418,0.72517,0.54965,0.59584,0.13857,0.6351,0.2194,0.63279,-0.25404,0.70951,0.15359,0.64665,0.23095,0.68775,0.17704,0.61663,0.07621,0.66316,0.19841,0.69053,0.36721
1,0,0.50112,-0.03596,0.61124,0.01348,0.58876,0.01573,0.58876,0.02472,0.66742,-0.00449,0.71685,-0.04719,0.66517,0.00899,0.57303,0.02472,0.64719,-0.07416,0.56854,0.14157,0.57528,-0.03596,0.46517,0.04944,0.56588,0.00824,0.4764,-0.03596,0.54607,0.10562,0.60674,-0.0809
1,0,0.71521,-0.00647,0.66667,-0.04207,0.63107,-0.05178,0.77994,0.08091,0.67314,0.09709,0.64725,0.15858,0.60194,-0.01942,0.54369,-0.04531,0.46926,-0.10032,0.64725,0.14887,0.39159,0.21683,0.52427,-0.05502,0.45105,0.0004,0.31392,-0.06796,0.49191,-0.1068,0.30421,-0.05178
1,0,0.68148,0.1037,0.77037,0.03457,0.65185,0.08148,0.60988,-0.00494,0.79012,0.11852,0.59753,0.04938,0.62469,0.0963,0.78272,-0.17531,0.73827,-0.10864,0.48642,0.00988,0.60988,0.08148,0.66667,-0.1284,0.63773,-0.02451,0.76543,0.02222,0.61235,-0.0716,0.51358,-0.04691
1,0,0.60678,-0.02712,0.67119,0.04068,0.52881,-0.04407,0.50508,0.03729,0.70508,-0.07797,0.57966,-0.02034,0.5322,0.07797,0.64068,0.11864,0.56949,-0.02373,0.5322,0.00678,0.71525,-0.0339,0.52881,-0.0339,0.57262,0.0075,0.58644,-0.00339,0.58983,-0.02712,0.50169,0.0678
1,0,0.49515,0.09709,0.29612,0.05825,0.34951,0,0.57282,-0.02427,0.58252,0.02427,0.33495,0.04854,0.52427,0.00485,0.47087,-0.1068,0.43204,0.00485,0.34951,0.05825,0.18932,0.25728,0.31068,-0.15049,0.36547,0.03815,0.3932,0.17476,0.26214,0,0.37379,-0.01942
1,0,0.98822,0.02187,0.93102,0.341,0.83904,0.35222,0.74706,0.48906,0.73584,0.51879,0.55076,0.60179,0.4313,0.66237,0.318,0.70443,0.28379,0.68873,0.07515,0.73696,0.06338,0.71284,-0.16489,0.69714,-0.16556,0.6051,-0.16209,0.55805,-0.34717,0.44195,-0.33483,0.37465
1,0,0.97905,0.1581,0.90112,0.35237,0.82039,0.48561,0.7176,0.64888,0.58827,0.73743,0.40349,0.83156,0.2514,0.84804,0.047,0.85475,-0.12193,0.79749,-0.2618,0.80754,-0.37835,0.71676,-0.51034,0.58324,-0.57587,0.4604,-0.61899,0.30796,-0.65754,0.18345,-0.64134,0.02968
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1,0,0.89589,0.39286,0.66129,0.71804,0.29521,0.90824,-0.04787,0.94415,-0.45725,0.84605,-0.7766,0.58511,-0.92819,0.25133,-0.92282,-0.15315,-0.76064,-0.48404,-0.50931,-0.76197,-0.14895,-0.88591,0.21581,-0.85703,0.53229,-0.68593,0.74846,-0.40656,0.83142,-0.07029,0.76862,0.27926
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1,0,0.7479,0.0084,0.83312,0.01659,0.82638,0.02469,0.86555,0.01681,0.60504,0.05882,0.79093,0.04731,0.77441,0.05407,0.64706,0.19328,0.84034,0.04202,0.71285,0.07122,0.68895,0.07577,0.66387,0.08403,0.63728,0.08296,0.61345,0.01681,0.58187,0.08757,0.5533,0.08891
1,0,0.85013,0.01809,0.92211,0.01456,0.92046,0.0218,0.92765,0.0801,0.87597,0.1137,0.91161,0.0432,0.90738,0.05018,0.87339,0.02842,0.95866,0,0.89097,0.07047,0.8843,0.07697,0.83721,0.10853,0.86923,0.0895,0.87597,0.08786,0.85198,0.10134,0.84258,0.10698
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1,0,0.39394,-0.24242,0.62655,0.0127,0.45455,0.09091,0.63636,0.09091,0.21212,-0.21212,0.57576,0.15152,0.39394,0,0.56156,0.04561,0.51515,0.0303,0.78788,0.18182,0.30303,-0.15152,0.48526,0.05929,0.46362,0.06142,0.33333,-0.0303,0.41856,0.0641,0.39394,0.24242
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1,0,1,0.51515,0.45455,0.33333,0.06061,0.36364,-0.32104,0.73062,-0.45455,0.48485,-0.57576,0,-0.57576,-0.12121,-0.33333,-0.48485,-0.09091,-0.84848,0.48485,-0.57576,0.57576,-0.42424,1,-0.39394,0.72961,0.12331,0.9697,0.57576,0.24242,0.36364,0.09091,0.33333
1,0,0.8811,0,0.94817,-0.02744,0.93598,-0.0122,0.90244,0.01829,0.90244,0.01829,0.93902,0.00915,0.95732,0.00305,1,0.02744,0.94207,-0.0122,0.90854,0.02439,0.91463,0.05488,0.99695,0.04878,0.89666,0.02226,0.90854,0.00915,1,0.05488,0.97561,-0.0122
1,0,0.82624,0.08156,0.79078,-0.08156,0.90426,-0.01773,0.92908,0.01064,0.80142,0.08865,0.94681,-0.00709,0.94326,0,0.93262,0.20213,0.95035,-0.00709,0.91489,0.00709,0.80496,0.07092,0.91135,0.15957,0.89527,0.08165,0.7766,0.06738,0.92553,0.18085,0.92553,0
1,0,0.74468,0.10638,0.88706,0.00982,0.88542,0.01471,0.87234,-0.01418,0.7305,0.10638,0.87657,0.02912,0.87235,0.03382,0.95745,0.07801,0.95035,0.04255,0.85597,0.04743,0.84931,0.05178,0.87234,0.11348,0.83429,0.06014,0.74468,-0.03546,0.8171,0.068,0.80774,0.07173
1,0,0.87578,0.03727,0.89951,0.00343,0.8921,0.0051,0.86335,0,0.95031,0.07453,0.87021,0.00994,0.86303,0.01151,0.83851,-0.06211,0.85714,0.02484,0.84182,0.01603,0.83486,0.01749,0.79503,-0.04348,0.82111,0.02033,0.81988,0.08696,0.80757,0.02308,0.80088,0.02441
1,0,0.97513,0.0071,0.98579,0.01954,1,0.01954,0.9929,0.01599,0.95737,0.02309,0.97158,0.03552,1,0.0373,0.97869,0.02131,0.98579,0.05684,0.97158,0.04796,0.94494,0.05506,0.98401,0.03552,0.9754,0.06477,0.94849,0.08171,0.99112,0.06217,0.98934,0.09947
1,0,1,0.01105,1,0.01105,1,0.0232,0.99448,-0.01436,0.99448,-0.00221,0.98343,0.0232,1,0.00884,0.97569,0.00773,0.97901,0.01657,0.98011,0.00663,0.98122,0.02099,0.97127,-0.00663,0.98033,0.016,0.97901,0.01547,0.98564,0.02099,0.98674,0.02762
1,0,1,-0.01342,1,0.01566,1,-0.00224,1,0.06264,0.97763,0.04474,0.95973,0.02908,1,0.06488,0.98881,0.03356,1,0.03579,0.99776,0.09396,0.95749,0.07383,1,0.10067,0.99989,0.08763,0.99105,0.08501,1,0.10067,1,0.10067
1,0,0.8842,0.36724,0.67123,0.67382,0.39613,0.86399,0.02424,0.93182,-0.35148,0.83713,-0.60316,0.58842,-0.78658,0.38778,-0.83285,-0.00642,-0.69318,-0.32963,-0.52504,-0.53924,-0.27377,-0.68126,0.00806,-0.69774,0.26028,-0.60678,0.44569,-0.43383,0.54209,-0.21542,0.56286,0.02823
1,0,0.90147,0.41786,0.64131,0.75725,0.3044,0.95148,-0.20449,0.96534,-0.55483,0.81191,-0.81857,0.50949,-0.96986,0.10345,-0.91456,-0.31412,-0.70163,-0.65461,-0.32354,-0.88999,0.05865,-0.94172,0.44483,-0.82154,0.74105,-0.55231,0.89415,-0.18725,0.87893,0.20359,0.70555,0.54852
1,0,0.32789,0.11042,0.1597,0.29308,0.1402,0.74485,-0.25131,0.91993,-0.16503,0.26664,-0.63714,0.24865,-0.9765,-0.00337,-0.23227,-0.19909,-0.30522,-0.48886,-0.14426,-0.89991,0.09345,-0.28916,0.28307,-0.1856,0.39599,-0.11498,0.31005,0.05614,0.21443,0.2054,0.13376,0.26422
1,0,0.65845,0.43617,0.44681,0.74804,0.05319,0.85106,-0.32027,0.82139,-0.68253,0.52408,-0.84211,0.07111,-0.82811,-0.28723,-0.47032,-0.71725,-0.04759,-0.86002,0.23292,-0.76316,0.56663,-0.52128,0.743,-0.18645,0.74758,0.23713,0.45185,0.59071,0.20549,0.76764,-0.18533,0.74356
1,0,0.19466,0.05725,0.04198,0.25191,-0.10557,0.48866,-0.18321,-0.18321,-0.41985,0.06107,-0.4542,0.0916,-0.16412,-0.30534,-0.10305,-0.39695,0.18702,-0.17557,0.34012,-0.11953,0.28626,-0.16031,0.216
gitextract_r00bh6ch/ ├── .idea/ │ ├── .name │ ├── encodings.xml │ ├── inspectionProfiles/ │ │ ├── Project_Default.xml │ │ └── profiles_settings.xml │ ├── misc.xml │ ├── ml.iml │ ├── modules.xml │ ├── scopes/ │ │ └── scope_settings.xml │ ├── vcs.xml │ └── workspace.xml ├── 1_Perceptron.ipynb ├── Data/ │ ├── denver.csv │ ├── denver_data_description.txt │ ├── ex1data1.txt │ ├── ex1data2.txt │ ├── heart.txt │ ├── heart_test.txt │ ├── ionosphere.csv │ ├── sklearn_digits.csv │ ├── stackloss.csv │ └── train_binary.csv ├── LinearRegression.py ├── LogisticClassifier.py ├── MultiLayerPerceptron.py ├── Old/ │ ├── BackPropagationNN.py │ └── README.md └── README.md
SYMBOL INDEX (36 symbols across 4 files)
FILE: LinearRegression.py
class LinReg (line 3) | class LinReg(object):
method __init__ (line 7) | def __init__(self, learning_rate = 0.01, iterations = 50, verbose = Tr...
method fit (line 27) | def fit(self, X, y):
method predict (line 62) | def predict(self, X):
function demo (line 82) | def demo():
FILE: LogisticClassifier.py
class Logit (line 5) | class Logit(object):
method __init__ (line 9) | def __init__(self, learning_rate = 0.01, iterations = 100, verbose = T...
method sigmoid (line 29) | def sigmoid(self, x):
method fit (line 38) | def fit(self, X, y):
method predict (line 75) | def predict(self, X, labels):
function demo (line 107) | def demo():
FILE: MultiLayerPerceptron.py
function sigmoid (line 7) | def sigmoid(x):
function dsigmoid (line 11) | def dsigmoid(y):
function softmax (line 15) | def softmax(w):
function tanh (line 21) | def tanh(x):
function dtanh (line 25) | def dtanh(y):
class MLP_Classifier (line 28) | class MLP_Classifier(object):
method __init__ (line 41) | def __init__(self, input, hidden, output, iterations = 50, learning_ra...
method feedForward (line 88) | def feedForward(self, inputs):
method backPropagate (line 119) | def backPropagate(self, targets):
method test (line 171) | def test(self, patterns):
method fit (line 179) | def fit(self, patterns):
method predict (line 208) | def predict(self, X):
function demo (line 217) | def demo():
FILE: Old/BackPropagationNN.py
function sigmoid (line 10) | def sigmoid(x):
function dsigmoid (line 14) | def dsigmoid(y):
function tanh (line 18) | def tanh(x):
function dtanh (line 22) | def dtanh(y):
class MLP_NeuralNetwork (line 25) | class MLP_NeuralNetwork(object):
method __init__ (line 38) | def __init__(self, input, hidden, output, iterations, learning_rate, m...
method feedForward (line 73) | def feedForward(self, inputs):
method backPropagate (line 105) | def backPropagate(self, targets):
method test (line 159) | def test(self, patterns):
method train (line 167) | def train(self, patterns):
method predict (line 185) | def predict(self, X):
function demo (line 194) | def demo():
Condensed preview — 27 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (539K chars).
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{
"path": ".idea/.name",
"chars": 2,
"preview": "ml"
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{
"path": ".idea/encodings.xml",
"chars": 164,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n <component name=\"Encoding\" useUTFGuessing=\"true\" native2A"
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{
"path": ".idea/inspectionProfiles/Project_Default.xml",
"chars": 567,
"preview": "<component name=\"InspectionProjectProfileManager\">\n <profile version=\"1.0\" is_locked=\"false\">\n <option name=\"myName\""
},
{
"path": ".idea/inspectionProfiles/profiles_settings.xml",
"chars": 235,
"preview": "<component name=\"InspectionProjectProfileManager\">\n <settings>\n <option name=\"PROJECT_PROFILE\" value=\"Project Defaul"
},
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"path": ".idea/misc.xml",
"chars": 277,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n <component name=\"ProjectRootManager\" version=\"2\" project-"
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"path": ".idea/ml.iml",
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"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<module type=\"PYTHON_MODULE\" version=\"4\">\n <component name=\"NewModuleRootManager"
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"preview": "<component name=\"DependencyValidationManager\">\n <state>\n <option name=\"SKIP_IMPORT_STATEMENTS\" value=\"false\" />\n </"
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{
"path": ".idea/vcs.xml",
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"chars": 29081,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n <component name=\"ChangeListManager\">\n <list default=\"t"
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"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Perceptron Tutorial\\n\",\n \"\\n\","
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{
"path": "Data/denver_data_description.txt",
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"preview": "X1 = total population (in thousands)\nX2 = % change in population over past several years\nX3 = % of children (under 18) i"
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"path": "Data/ex1data1.txt",
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"path": "Data/heart.txt",
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{
"path": "LinearRegression.py",
"chars": 3685,
"preview": "import numpy as np\n\nclass LinReg(object):\n \"\"\"\n multivariate linear regression using gradient descent\n \"\"\"\n "
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{
"path": "LogisticClassifier.py",
"chars": 4444,
"preview": "import math\nimport numpy as np\n\n\nclass Logit(object):\n \"\"\"\n logistic regression using gradient descent\n \"\"\"\n "
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{
"path": "MultiLayerPerceptron.py",
"chars": 9912,
"preview": "import time\nimport random\nimport numpy as np\nnp.seterr(all = 'ignore')\n\n# transfer functions\ndef sigmoid(x):\n return "
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{
"path": "Old/BackPropagationNN.py",
"chars": 8917,
"preview": "import math\nimport random\nimport numpy as np\nnp.seterr(all = 'ignore')\n\n# sigmoid transfer function\n# IMPORTANT: when us"
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{
"path": "Old/README.md",
"chars": 471,
"preview": "* BackPropagationNN.py - Basic MultiLayer Perceptron (MLP) network, adapted and from the book ['Programming Collective I"
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{
"path": "README.md",
"chars": 4705,
"preview": "# Machine-Learning\nVarious machine learning algorithms broken down in basic and readable python code. Useful for studyin"
}
]
// ... and 1 more files (download for full content)
About this extraction
This page contains the full source code of the FlorianMuellerklein/Machine-Learning GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 27 files (15.7 MB), approximately 392.5k tokens, and a symbol index with 36 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.