A neural network autoencoder is trained to predict its input. Data items that are poorly predicted by the trained neural network model are anomalous in some way.
I put together a from-scratch autoencoder implementation using the C# language. I was motivated by a discussion with Paige R, a work colleague. The system is not trivial but I’ve implemented neural networks from scratch for many years and so the project only took the better part of a day.
For my demo, I used one of my standard synthetic datasets. The raw data looks like:
F 24 michigan 29500.00 liberal M 39 oklahoma 51200.00 moderate F 63 nebraska 75800.00 conservative M 36 michigan 44500.00 moderate . . .
Each line represents a person. The fields are sex, age, State, income, political leaning. There are 240 data items.
I used 0-1 binary encoding, one-hot categorical encoding, and divide-by-constant numeric normalization so the normalized-encoded data looks like:
1, 0.24, 1, 0, 0, 0.2950, 0, 0, 1 0, 0.39, 0, 0, 1, 0.5120, 0, 1, 0 1, 0.63, 0, 1, 0, 0.7580, 1, 0, 0 0, 0.36, 1, 0, 0, 0.4450, 0, 1, 0 . . .
I used a 9-6-9 architecture for my autoencoder. The number of input and output nodes are determined by the data, but the number of hidden nodes is a hyperparameter. If the number of hidden nodes is too small, the autoencoder can’t predict well. If the number of hidden nodes is too large, the autoencoder can’t generalize well.
For training, I used a batch size of 10, a learning rate of 0.01, and maxEpochs = 1000. These were all determined by trial and error — the main weakness of neural network systems.
After the autoencoder was trained, an Analyze() method scanned though the data to find the data item that had the largest reconstruction error:
Largest reconstruction error = 0.0038 Most anomalous item: M 36 nebraska 53000.00 liberal 0.0 0.36 0.0 1.0 0.0 0.53000 0.0 0.0 1.0
This corresponds to Male, age 36, Nebraska, income $53,000, political liberal.
There are a ton of details that would take many, many pages to fully explain so I won’t try. The only way to fully understand the demo is to carefully dissect the source code. That said, the demo can be used as a template for most autoencoder anomaly scenarios.
I have never liked family photos. They always feel too staged and artificial. But here are three anomalous family photos that I think are great.
Demo code. Replace “lt” (less-than), “gt”, “lte”, “gte”, “and” with Boolean operator symbols (my lame blog editor often chokes on symbols).
using System;
using System.IO;
using System.Collections.Generic;
namespace NeuralNetworkAutoAnomaly
{
internal class NeuralAnomalyProgram
{
static void Main(string[] args)
{
Console.WriteLine("\nNeural network " +
"autoencoder anomaly detection C# ");
Console.WriteLine("People sex, age, State," +
" income, political leaning ");
string rf =
"..\\..\\..\\Data\\people_raw.txt";
string[] rawFileArray = Utils.FileLoad(rf, "#");
Console.WriteLine("\nLoading data from file ");
string dataFile =
"..\\..\\..\\Data\\people_all.txt";
// sex, age, State, income, politics
// 0 0.32 1 0 0 0.65400 0 0 1
double[][] dataX = Utils.MatLoad(dataFile,
new int[] { 0, 1, 2, 3, 4, 5, 6, 7, 8 },
',', "#"); // 240 items
Console.WriteLine("Done ");
Console.WriteLine("\nFirst three normalized" +
" / encoded data: ");
for (int i = 0; i "lt" 3; ++i)
Utils.VecShow(dataX[i], 4, 9, true);
Console.WriteLine("\nCreating 9-6-9 tanh()" +
" identity() neural network autoencoder ");
NeuralNetwork nn =
new NeuralNetwork(9, 6, 9, seed: 0);
Console.WriteLine("Done ");
Console.WriteLine("\nPreparing train parameters ");
int maxEpochs = 1000;
double lrnRate = 0.01;
int batSize = 10;
Console.WriteLine("\nmaxEpochs = " +
maxEpochs);
Console.WriteLine("lrnRate = " +
lrnRate.ToString("F3"));
Console.WriteLine("batSize = " + batSize);
Console.WriteLine("\nStarting training ");
nn.Train(dataX, dataX, lrnRate, batSize, maxEpochs);
Console.WriteLine("Done ");
//Console.WriteLine("\nPredicting output for male" +
// " 34 Oklahoma $59,000 moderate ");
//double[] X = new double[] { 0, 0.34, 0, 0, 1,
// 0.59000, 0, 1, 0 };
Console.WriteLine("\nPredicting output for male" +
" 39 Oklahoma $51,200 moderate ");
double[] X = new double[] { 0, 0.39, 0, 0, 1,
0.5120, 0, 1, 0 };
double[] y = nn.ComputeOutput(X);
Console.WriteLine("Predicted output: ");
Utils.VecShow(y, 5, 9, true);
Console.WriteLine("\nAnalyzing data ");
nn.Analyze(dataX, rawFileArray);
// save trained model wts
Console.WriteLine("\nSaving model wts to file ");
string fn = "..\\..\\..\\Models\\people_wts.txt";
nn.SaveWeights(fn);
Console.WriteLine("Done ");
//// load saved wts later
//Console.WriteLine("\nLoading saved wts to new NN ");
//NeuralNetwork nn2 = new NeuralNetwork(8, 100, 1, 0);
//nn2.LoadWeights(fn);
//Console.WriteLine("Done ");
Console.WriteLine("\nEnd demo ");
Console.ReadLine();
} // Main
} // Program
// --------------------------------------------------------
public class NeuralNetwork
{
private int ni; // number input nodes
private int nh;
private int no;
private double[] iNodes;
private double[][] ihWeights; // input-hidden
private double[] hBiases;
private double[] hNodes;
private double[][] hoWeights; // hidden-output
private double[] oBiases;
private double[] oNodes; // single val as array
// gradients
private double[][] ihGrads;
private double[] hbGrads;
private double[][] hoGrads;
private double[] obGrads;
private Random rnd;
// ------------------------------------------------------
public NeuralNetwork(int numIn, int numHid,
int numOut, int seed)
{
this.ni = numIn;
this.nh = numHid;
this.no = numOut; // 1 for regression
this.iNodes = new double[numIn];
this.ihWeights = Utils.MatCreate(numIn, numHid);
this.hBiases = new double[numHid];
this.hNodes = new double[numHid];
this.hoWeights = Utils.MatCreate(numHid, numOut);
this.oBiases = new double[numOut];
this.oNodes = new double[numOut];
this.ihGrads = Utils.MatCreate(numIn, numHid);
this.hbGrads = new double[numHid];
this.hoGrads = Utils.MatCreate(numHid, numOut);
this.obGrads = new double[numOut];
this.rnd = new Random(seed);
this.InitWeights(); // all weights and biases
} // ctor
// ------------------------------------------------------
private void InitWeights() // helper for ctor
{
// weights and biases to small random values
double lo = -0.01; double hi = +0.01;
int numWts = (this.ni * this.nh) +
(this.nh * this.no) + this.nh + this.no;
double[] initialWeights = new double[numWts];
for (int i = 0; i "lt" initialWeights.Length; ++i)
initialWeights[i] =
(hi - lo) * rnd.NextDouble() + lo;
this.SetWeights(initialWeights);
}
// ------------------------------------------------------
public void SetWeights(double[] wts)
{
// copy serialized weights and biases in wts[]
// to ih weights, ih biases, ho weights, ho biases
int numWts = (this.ni * this.nh) +
(this.nh * this.no) + this.nh + this.no;
if (wts.Length != numWts)
throw new Exception("Bad array in SetWeights");
int k = 0; // points into wts param
for (int i = 0; i "lt" this.ni; ++i)
for (int j = 0; j "lt" this.nh; ++j)
this.ihWeights[i][j] = wts[k++];
for (int i = 0; i "lt" this.nh; ++i)
this.hBiases[i] = wts[k++];
for (int i = 0; i "lt" this.nh; ++i)
for (int j = 0; j "lt" this.no; ++j)
this.hoWeights[i][j] = wts[k++];
for (int i = 0; i "lt" this.no; ++i)
this.oBiases[i] = wts[k++];
}
// ------------------------------------------------------
public double[] GetWeights()
{
int numWts = (this.ni * this.nh) +
(this.nh * this.no) + this.nh + this.no;
double[] result = new double[numWts];
int k = 0;
for (int i = 0; i "lt" ihWeights.Length; ++i)
for (int j = 0; j "lt" this.ihWeights[0].Length; ++j)
result[k++] = this.ihWeights[i][j];
for (int i = 0; i "lt" this.hBiases.Length; ++i)
result[k++] = this.hBiases[i];
for (int i = 0; i "lt" this.hoWeights.Length; ++i)
for (int j = 0; j "lt" this.hoWeights[0].Length; ++j)
result[k++] = this.hoWeights[i][j];
for (int i = 0; i "lt" this.oBiases.Length; ++i)
result[k++] = this.oBiases[i];
return result;
}
// ------------------------------------------------------
public double[] ComputeOutput(double[] x)
{
double[] hSums = new double[this.nh]; // scratch
double[] oSums = new double[this.no]; // out sums
for (int i = 0; i "lt" x.Length; ++i)
this.iNodes[i] = x[i];
// note: no need to copy x-values unless
// you implement a ToString.
// more efficient to simply use the X[] directly.
// 1. compute i-h sum of weights * inputs
for (int j = 0; j "lt" this.nh; ++j)
for (int i = 0; i "lt" this.ni; ++i)
hSums[j] += this.iNodes[i] *
this.ihWeights[i][j]; // note +=
// 2. add biases to hidden sums
for (int i = 0; i "lt" this.nh; ++i)
hSums[i] += this.hBiases[i];
// 3. apply hidden activation
for (int i = 0; i "lt" this.nh; ++i)
this.hNodes[i] = HyperTan(hSums[i]);
// 4. compute h-o sum of wts * hOutputs
for (int j = 0; j "lt" this.no; ++j)
for (int i = 0; i "lt" this.nh; ++i)
oSums[j] += this.hNodes[i] *
this.hoWeights[i][j]; // [1]
// 5. add biases to output sums
for (int i = 0; i "lt" this.no; ++i)
oSums[i] += this.oBiases[i];
// 6. apply output activation
for (int i = 0; i "lt" this.no; ++i)
this.oNodes[i] = Identity(oSums[i]);
return this.oNodes; // array by ref
}
// ------------------------------------------------------
private static double HyperTan(double x)
{
if (x "lt" -10.0) return -1.0;
else if (x "gt" 10.0) return 1.0;
else return Math.Tanh(x);
}
// ------------------------------------------------------
private static double Identity(double x)
{
return x;
}
// ------------------------------------------------------
private void ZeroOutGrads()
{
for (int i = 0; i "lt" this.ni; ++i)
for (int j = 0; j "lt" this.nh; ++j)
this.ihGrads[i][j] = 0.0;
for (int j = 0; j "lt" this.nh; ++j)
this.hbGrads[j] = 0.0;
for (int j = 0; j "lt" this.nh; ++j)
for (int k = 0; k "lt" this.no; ++k)
this.hoGrads[j][k] = 0.0;
for (int k = 0; k "lt" this.no; ++k)
this.obGrads[k] = 0.0;
} // ZeroOutGrads()
// ------------------------------------------------------
private void AccumGrads(double[] y)
{
double[] oSignals = new double[this.no];
double[] hSignals = new double[this.nh];
// 1. compute output node scratch signals
for (int k = 0; k "lt" this.no; ++k)
{
double derivative = 1.0; // Identity activation
oSignals[k] = derivative *
(this.oNodes[k] - y[k]);
}
// 2. accum hidden-to-output gradients
for (int j = 0; j "lt" this.nh; ++j)
for (int k = 0; k "lt" this.no; ++k)
hoGrads[j][k] +=
oSignals[k] * this.hNodes[j];
// 3. accum output node bias gradients
for (int k = 0; k "lt" this.no; ++k)
obGrads[k] +=
oSignals[k] * 1.0; // 1.0 dummy
// 4. compute hidden node signals
for (int j = 0; j "lt" this.nh; ++j)
{
double sum = 0.0;
for (int k = 0; k "lt" this.no; ++k)
sum += oSignals[k] * this.hoWeights[j][k];
double derivative =
(1 - this.hNodes[j]) *
(1 + this.hNodes[j]); // assumes tanh
hSignals[j] = derivative * sum;
}
// 5. accum input-to-hidden gradients
for (int i = 0; i "lt" this.ni; ++i)
for (int j = 0; j "lt" this.nh; ++j)
this.ihGrads[i][j] +=
hSignals[j] * this.iNodes[i];
// 6. accum hidden node bias gradients
for (int j = 0; j "lt" this.nh; ++j)
this.hbGrads[j] +=
hSignals[j] * 1.0; // 1.0 dummy
} // AccumGrads
// ------------------------------------------------------
private void UpdateWeights(double lrnRate)
{
// assumes all gradients computed
// 1. update input-to-hidden weights
for (int i = 0; i "lt" this.ni; ++i)
{
for (int j = 0; j "lt" this.nh; ++j)
{
double delta = -1.0 * lrnRate *
this.ihGrads[i][j];
this.ihWeights[i][j] += delta;
}
}
// 2. update hidden node biases
for (int j = 0; j "lt" this.nh; ++j)
{
double delta = -1.0 * lrnRate *
this.hbGrads[j];
this.hBiases[j] += delta;
}
// 3. update hidden-to-output weights
for (int j = 0; j "lt" this.nh; ++j)
{
for (int k = 0; k "lt" this.no; ++k)
{
double delta = -1.0 * lrnRate *
this.hoGrads[j][k];
this.hoWeights[j][k] += delta;
}
}
// 4. update output node biases
for (int k = 0; k "lt" this.no; ++k)
{
double delta = -1.0 * lrnRate *
this.obGrads[k];
this.oBiases[k] += delta;
}
} // UpdateWeights()
// ------------------------------------------------------
public void Train(double[][] dataX, double[][] dataY,
double lrnRate, int batSize, int maxEpochs)
{
int n = dataX.Length; // 240
int batchesPerEpoch = n / batSize; // 24
int freq = maxEpochs / 10; // to show progress
int[] indices = new int[n];
for (int i = 0; i "lt" n; ++i)
indices[i] = i;
// ----------------------------------------------------
//
// n = 240; bs = 10
// batches per epoch = 240 / 10 = 24
// for epoch = 0; epoch "lt" maxEpochs; ++epoch
// shuffle indices
// for batch = 0; batch "lt" bpe; ++batch
// for item = 0; item "lt" bs; ++item
// compute output
// accum grads
// end-item
// update weights
// zero-out grads
// end-batches
// end-epochs
//
// ----------------------------------------------------
for (int epoch = 0; epoch "lt" maxEpochs; ++epoch)
{
Shuffle(indices);
int ptr = 0; // points into indices
for (int batIdx = 0; batIdx "lt" batchesPerEpoch;
++batIdx) // 0, 1, . . 19
{
for (int i = 0; i "lt" batSize; ++i) // 0 . . 9
{
int ii = indices[ptr++]; // compute output
double[] x = dataX[ii];
double[] y = dataY[ii];
this.ComputeOutput(x); // into this.oNoodes
this.AccumGrads(y);
}
this.UpdateWeights(lrnRate);
this.ZeroOutGrads(); // prep next batch
} // batches
if (epoch % freq == 0) // progress
{
double mse = this.Error(dataX, dataY);
string s1 = "epoch: " + epoch.ToString().
PadLeft(4);
string s2 = " MSE = " + mse.ToString("F4");
Console.WriteLine(s1 + s2);
}
} // epoch
} // Train
// ------------------------------------------------------
public void Analyze(double[][] dataX,
string[] rawFileArray)
{
int n = dataX.Length;
double largestError = 0.0;
double[] mostAnomalous = dataX[0];
int mostAnomalousIdx = 0;
for (int i = 0; i "lt" n; ++i)
{
double[] predY = this.ComputeOutput(dataX[i]);
double[] actualY = dataX[i];
double sumItemError = 0.0;
for (int k = 0; k "lt" predY.Length; ++k)
{
sumItemError += (predY[k] - actualY[k]) *
(predY[k] - actualY[k]);
}
//double itemError =
// Math.Sqrt(sumItemError / dataX[0].Length);
double itemError = Math.Sqrt(sumItemError);
//Console.WriteLine(" i = " + i + " err = " +
// itemError.ToString("F4"));
//Console.ReadLine();
if (itemError "gt" largestError)
{
largestError = itemError;
mostAnomalous = dataX[i];
mostAnomalousIdx = i;
}
}
Console.WriteLine("\nLargest reconstruction" +
" error = " + largestError.ToString("F4"));
Console.WriteLine("Most anomalous item: ");
Console.WriteLine(" " +
rawFileArray[mostAnomalousIdx]);
Utils.VecShow(mostAnomalous, 5, 9, true);
double[] predicted =
this.ComputeOutput(dataX[mostAnomalousIdx]);
Console.WriteLine("Predicted output: ");
Utils.VecShow(predicted, 5, 9, true);
} // Analyze
// ------------------------------------------------------
private void Shuffle(int[] sequence)
{
for (int i = 0; i "lt" sequence.Length; ++i)
{
int r = this.rnd.Next(i, sequence.Length);
int tmp = sequence[r];
sequence[r] = sequence[i];
sequence[i] = tmp;
//sequence[i] = i; // for testing
}
} // Shuffle
// ------------------------------------------------------
public double Error(double[][] dataX,
double[][] dataY)
{
// MSE
int n = dataX.Length;
double sumSquaredError = 0.0;
for (int i = 0; i "lt" n; ++i)
{
double[] predY = this.ComputeOutput(dataX[i]);
double[] actualY = dataY[i];
for (int k = 0; k "lt" predY.Length; ++k)
{
sumSquaredError += (predY[k] - actualY[k]) *
(predY[k] - actualY[k]);
}
}
return sumSquaredError / n;
} // Error
// ------------------------------------------------------
public void SaveWeights(string fn)
{
FileStream ofs = new FileStream(fn, FileMode.Create);
StreamWriter sw = new StreamWriter(ofs);
double[] wts = this.GetWeights();
for (int i = 0; i "lt" wts.Length; ++i)
sw.WriteLine(wts[i].ToString("F8")); // one per line
sw.Close();
ofs.Close();
}
public void LoadWeights(string fn)
{
FileStream ifs = new FileStream(fn, FileMode.Open);
StreamReader sr = new StreamReader(ifs);
List"lt"double"gt" listWts = new List"lt"double"gt"();
string line = ""; // one wt per line
while ((line = sr.ReadLine()) != null)
{
// if (line.StartsWith(comment) == true)
// continue;
listWts.Add(double.Parse(line));
}
sr.Close();
ifs.Close();
double[] wts = listWts.ToArray();
this.SetWeights(wts);
}
// ------------------------------------------------------
} // NeuralNetwork class
// --------------------------------------------------------
public class Utils
{
public static double[][] VecToMat(double[] vec,
int rows, int cols)
{
// vector to row vec/matrix
double[][] result = MatCreate(rows, cols);
int k = 0;
for (int i = 0; i "lt" rows; ++i)
for (int j = 0; j "lt" cols; ++j)
result[i][j] = vec[k++];
return result;
}
// ------------------------------------------------------
public static double[][] MatCreate(int rows,
int cols)
{
double[][] result = new double[rows][];
for (int i = 0; i "lt" rows; ++i)
result[i] = new double[cols];
return result;
}
// ------------------------------------------------------
public static string[] FileLoad(string fn,
string comment)
{
List"lt"string"gt" lst = new List"lt"string"gt"();
FileStream ifs = new FileStream(fn, FileMode.Open);
StreamReader sr = new StreamReader(ifs);
string line = "";
while ((line = sr.ReadLine()) != null)
{
if (line.StartsWith(comment)) continue;
line = line.Trim();
lst.Add(line);
}
sr.Close(); ifs.Close();
string[] result = lst.ToArray();
return result;
}
// ------------------------------------------------------
public static double[][] MatLoad(string fn,
int[] usecols, char sep, string comment)
{
List"lt"double[]"gt" result =
new List"lt"double[]"gt"();
string line = "";
FileStream ifs = new FileStream(fn, FileMode.Open);
StreamReader sr = new StreamReader(ifs);
while ((line = sr.ReadLine()) != null)
{
if (line.StartsWith(comment) == true)
continue;
string[] tokens = line.Split(sep);
List"lt"double"gt" lst = new List"lt"double"gt"();
for (int j = 0; j "lt" usecols.Length; ++j)
lst.Add(double.Parse(tokens[usecols[j]]));
double[] row = lst.ToArray();
result.Add(row);
}
sr.Close(); ifs.Close();
return result.ToArray();
}
// ------------------------------------------------------
public static double[] MatToVec(double[][] m)
{
int rows = m.Length;
int cols = m[0].Length;
double[] result = new double[rows * cols];
int k = 0;
for (int i = 0; i "lt" rows; ++i)
for (int j = 0; j "lt" cols; ++j)
result[k++] = m[i][j];
return result;
}
// ------------------------------------------------------
public static void MatShow(double[][] m,
int dec, int wid)
{
for (int i = 0; i "lt" m.Length; ++i)
{
for (int j = 0; j "lt" m[0].Length; ++j)
{
double v = m[i][j];
if (Math.Abs(v) "lt" 1.0e-8) v = 0.0; // hack
Console.Write(v.ToString("F" +
dec).PadLeft(wid));
}
Console.WriteLine("");
}
}
// ------------------------------------------------------
public static void VecShow(int[] vec, int wid)
{
for (int i = 0; i "lt" vec.Length; ++i)
Console.Write(vec[i].ToString().PadLeft(wid));
Console.WriteLine("");
}
// ------------------------------------------------------
public static void VecShow(double[] vec,
int dec, int wid, bool newLine)
{
for (int i = 0; i "lt" vec.Length; ++i)
{
double x = vec[i];
if (Math.Abs(x) "lt" 1.0e-8) x = 0.0;
Console.Write(x.ToString("F" +
dec).PadLeft(wid));
}
if (newLine == true)
Console.WriteLine("");
}
} // Utils class
} // ns
Raw data:
# people_raw.txt # sex, age, state, income, politics # F 24 michigan 29500.00 liberal M 39 oklahoma 51200.00 moderate F 63 nebraska 75800.00 conservative M 36 michigan 44500.00 moderate F 27 nebraska 28600.00 liberal F 50 nebraska 56500.00 moderate F 50 oklahoma 55000.00 moderate M 19 oklahoma 32700.00 conservative F 22 nebraska 27700.00 moderate M 39 oklahoma 47100.00 liberal F 34 michigan 39400.00 moderate M 22 michigan 33500.00 conservative F 35 oklahoma 35200.00 liberal M 33 nebraska 46400.00 moderate F 45 nebraska 54100.00 moderate F 42 nebraska 50700.00 moderate M 33 nebraska 46800.00 moderate F 25 oklahoma 30000.00 moderate M 31 nebraska 46400.00 conservative F 27 michigan 32500.00 liberal F 48 michigan 54000.00 moderate M 64 nebraska 71300.00 liberal F 61 nebraska 72400.00 conservative F 54 oklahoma 61000.00 conservative F 29 michigan 36300.00 conservative F 50 oklahoma 55000.00 moderate F 55 oklahoma 62500.00 conservative F 40 michigan 52400.00 conservative F 22 michigan 23600.00 liberal F 68 nebraska 78400.00 conservative M 60 michigan 71700.00 liberal M 34 oklahoma 46500.00 moderate M 25 oklahoma 37100.00 conservative M 31 nebraska 48900.00 moderate F 43 oklahoma 48000.00 moderate F 58 nebraska 65400.00 liberal M 55 nebraska 60700.00 liberal M 43 nebraska 51100.00 moderate M 43 oklahoma 53200.00 moderate M 21 michigan 37200.00 conservative F 55 oklahoma 64600.00 conservative F 64 nebraska 74800.00 conservative M 41 michigan 58800.00 moderate F 64 oklahoma 72700.00 conservative M 56 oklahoma 66600.00 liberal F 31 oklahoma 36000.00 moderate M 65 oklahoma 70100.00 liberal F 55 oklahoma 64300.00 conservative M 25 michigan 40300.00 conservative F 46 oklahoma 51000.00 moderate M 36 michigan 53500.00 conservative F 52 nebraska 58100.00 moderate F 61 oklahoma 67900.00 conservative F 57 oklahoma 65700.00 conservative M 46 nebraska 52600.00 moderate M 62 michigan 66800.00 liberal F 55 oklahoma 62700.00 conservative M 22 oklahoma 27700.00 moderate M 50 michigan 62900.00 conservative M 32 nebraska 41800.00 moderate M 21 oklahoma 35600.00 conservative F 44 nebraska 52000.00 moderate F 46 nebraska 51700.00 moderate F 62 nebraska 69700.00 conservative F 57 nebraska 66400.00 conservative M 67 oklahoma 75800.00 liberal F 29 michigan 34300.00 liberal F 53 michigan 60100.00 conservative M 44 michigan 54800.00 moderate F 46 nebraska 52300.00 moderate M 20 nebraska 30100.00 moderate M 38 michigan 53500.00 moderate F 50 nebraska 58600.00 moderate F 33 nebraska 42500.00 moderate M 33 nebraska 39300.00 moderate F 26 nebraska 40400.00 conservative F 58 michigan 70700.00 conservative F 43 oklahoma 48000.00 moderate M 46 michigan 64400.00 conservative F 60 michigan 71700.00 conservative M 42 michigan 48900.00 moderate M 56 oklahoma 56400.00 liberal M 62 nebraska 66300.00 liberal M 50 michigan 64800.00 moderate F 47 oklahoma 52000.00 moderate M 67 nebraska 80400.00 liberal M 40 oklahoma 50400.00 moderate F 42 nebraska 48400.00 moderate F 64 michigan 72000.00 conservative M 47 michigan 58700.00 liberal F 45 nebraska 52800.00 moderate M 25 oklahoma 40900.00 conservative F 38 michigan 48400.00 conservative F 55 oklahoma 60000.00 moderate M 44 michigan 60600.00 moderate F 33 michigan 41000.00 moderate F 34 oklahoma 39000.00 moderate F 27 nebraska 33700.00 liberal F 32 nebraska 40700.00 moderate F 42 oklahoma 47000.00 moderate M 24 oklahoma 40300.00 conservative F 42 nebraska 50300.00 moderate F 25 oklahoma 28000.00 liberal F 51 nebraska 58000.00 moderate M 55 nebraska 63500.00 liberal F 44 michigan 47800.00 liberal M 18 michigan 39800.00 conservative M 67 nebraska 71600.00 liberal F 45 oklahoma 50000.00 moderate F 48 michigan 55800.00 moderate M 25 nebraska 39000.00 moderate M 67 michigan 78300.00 moderate F 37 oklahoma 42000.00 moderate M 32 michigan 42700.00 moderate F 48 michigan 57000.00 moderate M 66 oklahoma 75000.00 liberal F 61 michigan 70000.00 conservative M 58 oklahoma 68900.00 moderate F 19 michigan 24000.00 liberal F 38 oklahoma 43000.00 moderate M 27 michigan 36400.00 moderate F 42 michigan 48000.00 moderate F 60 michigan 71300.00 conservative M 27 oklahoma 34800.00 conservative F 29 nebraska 37100.00 conservative M 43 michigan 56700.00 moderate F 48 michigan 56700.00 moderate F 27 oklahoma 29400.00 liberal M 44 michigan 55200.00 conservative F 23 nebraska 26300.00 liberal M 36 nebraska 53000.00 liberal F 64 oklahoma 72500.00 conservative F 29 oklahoma 30000.00 liberal M 33 michigan 49300.00 moderate M 66 nebraska 75000.00 liberal M 21 oklahoma 34300.00 conservative F 27 michigan 32700.00 liberal F 29 michigan 31800.00 liberal M 31 michigan 48600.00 moderate F 36 oklahoma 41000.00 moderate F 49 nebraska 55700.00 moderate M 28 michigan 38400.00 conservative M 43 oklahoma 56600.00 moderate M 46 nebraska 58800.00 moderate F 57 michigan 69800.00 conservative M 52 oklahoma 59400.00 moderate M 31 oklahoma 43500.00 moderate M 55 michigan 62000.00 liberal F 50 michigan 56400.00 moderate F 48 nebraska 55900.00 moderate M 22 oklahoma 34500.00 conservative F 59 oklahoma 66700.00 conservative F 34 michigan 42800.00 liberal M 64 michigan 77200.00 liberal F 29 oklahoma 33500.00 liberal M 34 nebraska 43200.00 moderate M 61 michigan 75000.00 liberal F 64 oklahoma 71100.00 conservative M 29 michigan 41300.00 conservative F 63 nebraska 70600.00 conservative M 29 nebraska 40000.00 conservative M 51 michigan 62700.00 moderate M 24 oklahoma 37700.00 conservative F 48 nebraska 57500.00 moderate F 18 michigan 27400.00 conservative F 18 michigan 20300.00 liberal F 33 nebraska 38200.00 liberal M 20 oklahoma 34800.00 conservative F 29 oklahoma 33000.00 liberal M 44 oklahoma 63000.00 conservative M 65 oklahoma 81800.00 conservative M 56 michigan 63700.00 liberal M 52 oklahoma 58400.00 moderate M 29 nebraska 48600.00 conservative M 47 nebraska 58900.00 moderate F 68 michigan 72600.00 liberal F 31 oklahoma 36000.00 moderate F 61 nebraska 62500.00 liberal F 19 nebraska 21500.00 liberal F 38 oklahoma 43000.00 moderate M 26 michigan 42300.00 conservative F 61 nebraska 67400.00 conservative F 40 michigan 46500.00 moderate M 49 michigan 65200.00 moderate F 56 michigan 67500.00 conservative M 48 nebraska 66000.00 moderate F 52 michigan 56300.00 liberal M 18 michigan 29800.00 conservative M 56 oklahoma 59300.00 liberal M 52 nebraska 64400.00 moderate M 18 nebraska 28600.00 moderate M 58 michigan 66200.00 liberal M 39 nebraska 55100.00 moderate M 46 michigan 62900.00 moderate M 40 nebraska 46200.00 moderate M 60 michigan 72700.00 liberal F 36 nebraska 40700.00 liberal F 44 michigan 52300.00 moderate F 28 michigan 31300.00 liberal F 54 oklahoma 62600.00 conservative M 51 michigan 61200.00 moderate M 32 nebraska 46100.00 moderate F 55 michigan 62700.00 conservative F 25 oklahoma 26200.00 liberal F 33 oklahoma 37300.00 liberal M 29 nebraska 46200.00 conservative F 65 michigan 72700.00 conservative M 43 nebraska 51400.00 moderate M 54 nebraska 64800.00 liberal F 61 nebraska 72700.00 conservative F 52 nebraska 63600.00 conservative F 30 nebraska 33500.00 liberal F 29 michigan 31400.00 liberal M 47 oklahoma 59400.00 moderate F 39 nebraska 47800.00 moderate F 47 oklahoma 52000.00 moderate M 49 michigan 58600.00 moderate M 63 oklahoma 67400.00 liberal M 30 michigan 39200.00 conservative M 61 oklahoma 69600.00 liberal M 47 oklahoma 58700.00 moderate F 30 oklahoma 34500.00 liberal M 51 oklahoma 58000.00 moderate M 24 michigan 38800.00 moderate M 49 michigan 64500.00 moderate F 66 oklahoma 74500.00 conservative M 65 michigan 76900.00 conservative M 46 nebraska 58000.00 conservative M 45 oklahoma 51800.00 moderate M 47 michigan 63600.00 conservative M 29 michigan 44800.00 conservative M 57 oklahoma 69300.00 liberal M 20 michigan 28700.00 liberal M 35 michigan 43400.00 moderate M 61 oklahoma 67000.00 liberal M 31 oklahoma 37300.00 moderate F 18 michigan 20800.00 liberal F 26 oklahoma 29200.00 liberal M 28 michigan 36400.00 liberal M 59 oklahoma 69400.00 liberal
Normalized and encoded data:
# people_all.txt # # sex (0 = male, 1 = female), age / 100, # state (michigan = 100, nebraska = 010, # oklahoma = 001), # income / 100_000, # politics (conservative = 100, # moderate = 010, liberal = 001) # 1, 0.24, 1, 0, 0, 0.2950, 0, 0, 1 0, 0.39, 0, 0, 1, 0.5120, 0, 1, 0 1, 0.63, 0, 1, 0, 0.7580, 1, 0, 0 0, 0.36, 1, 0, 0, 0.4450, 0, 1, 0 1, 0.27, 0, 1, 0, 0.2860, 0, 0, 1 1, 0.50, 0, 1, 0, 0.5650, 0, 1, 0 1, 0.50, 0, 0, 1, 0.5500, 0, 1, 0 0, 0.19, 0, 0, 1, 0.3270, 1, 0, 0 1, 0.22, 0, 1, 0, 0.2770, 0, 1, 0 0, 0.39, 0, 0, 1, 0.4710, 0, 0, 1 1, 0.34, 1, 0, 0, 0.3940, 0, 1, 0 0, 0.22, 1, 0, 0, 0.3350, 1, 0, 0 1, 0.35, 0, 0, 1, 0.3520, 0, 0, 1 0, 0.33, 0, 1, 0, 0.4640, 0, 1, 0 1, 0.45, 0, 1, 0, 0.5410, 0, 1, 0 1, 0.42, 0, 1, 0, 0.5070, 0, 1, 0 0, 0.33, 0, 1, 0, 0.4680, 0, 1, 0 1, 0.25, 0, 0, 1, 0.3000, 0, 1, 0 0, 0.31, 0, 1, 0, 0.4640, 1, 0, 0 1, 0.27, 1, 0, 0, 0.3250, 0, 0, 1 1, 0.48, 1, 0, 0, 0.5400, 0, 1, 0 0, 0.64, 0, 1, 0, 0.7130, 0, 0, 1 1, 0.61, 0, 1, 0, 0.7240, 1, 0, 0 1, 0.54, 0, 0, 1, 0.6100, 1, 0, 0 1, 0.29, 1, 0, 0, 0.3630, 1, 0, 0 1, 0.50, 0, 0, 1, 0.5500, 0, 1, 0 1, 0.55, 0, 0, 1, 0.6250, 1, 0, 0 1, 0.40, 1, 0, 0, 0.5240, 1, 0, 0 1, 0.22, 1, 0, 0, 0.2360, 0, 0, 1 1, 0.68, 0, 1, 0, 0.7840, 1, 0, 0 0, 0.60, 1, 0, 0, 0.7170, 0, 0, 1 0, 0.34, 0, 0, 1, 0.4650, 0, 1, 0 0, 0.25, 0, 0, 1, 0.3710, 1, 0, 0 0, 0.31, 0, 1, 0, 0.4890, 0, 1, 0 1, 0.43, 0, 0, 1, 0.4800, 0, 1, 0 1, 0.58, 0, 1, 0, 0.6540, 0, 0, 1 0, 0.55, 0, 1, 0, 0.6070, 0, 0, 1 0, 0.43, 0, 1, 0, 0.5110, 0, 1, 0 0, 0.43, 0, 0, 1, 0.5320, 0, 1, 0 0, 0.21, 1, 0, 0, 0.3720, 1, 0, 0 1, 0.55, 0, 0, 1, 0.6460, 1, 0, 0 1, 0.64, 0, 1, 0, 0.7480, 1, 0, 0 0, 0.41, 1, 0, 0, 0.5880, 0, 1, 0 1, 0.64, 0, 0, 1, 0.7270, 1, 0, 0 0, 0.56, 0, 0, 1, 0.6660, 0, 0, 1 1, 0.31, 0, 0, 1, 0.3600, 0, 1, 0 0, 0.65, 0, 0, 1, 0.7010, 0, 0, 1 1, 0.55, 0, 0, 1, 0.6430, 1, 0, 0 0, 0.25, 1, 0, 0, 0.4030, 1, 0, 0 1, 0.46, 0, 0, 1, 0.5100, 0, 1, 0 0, 0.36, 1, 0, 0, 0.5350, 1, 0, 0 1, 0.52, 0, 1, 0, 0.5810, 0, 1, 0 1, 0.61, 0, 0, 1, 0.6790, 1, 0, 0 1, 0.57, 0, 0, 1, 0.6570, 1, 0, 0 0, 0.46, 0, 1, 0, 0.5260, 0, 1, 0 0, 0.62, 1, 0, 0, 0.6680, 0, 0, 1 1, 0.55, 0, 0, 1, 0.6270, 1, 0, 0 0, 0.22, 0, 0, 1, 0.2770, 0, 1, 0 0, 0.50, 1, 0, 0, 0.6290, 1, 0, 0 0, 0.32, 0, 1, 0, 0.4180, 0, 1, 0 0, 0.21, 0, 0, 1, 0.3560, 1, 0, 0 1, 0.44, 0, 1, 0, 0.5200, 0, 1, 0 1, 0.46, 0, 1, 0, 0.5170, 0, 1, 0 1, 0.62, 0, 1, 0, 0.6970, 1, 0, 0 1, 0.57, 0, 1, 0, 0.6640, 1, 0, 0 0, 0.67, 0, 0, 1, 0.7580, 0, 0, 1 1, 0.29, 1, 0, 0, 0.3430, 0, 0, 1 1, 0.53, 1, 0, 0, 0.6010, 1, 0, 0 0, 0.44, 1, 0, 0, 0.5480, 0, 1, 0 1, 0.46, 0, 1, 0, 0.5230, 0, 1, 0 0, 0.20, 0, 1, 0, 0.3010, 0, 1, 0 0, 0.38, 1, 0, 0, 0.5350, 0, 1, 0 1, 0.50, 0, 1, 0, 0.5860, 0, 1, 0 1, 0.33, 0, 1, 0, 0.4250, 0, 1, 0 0, 0.33, 0, 1, 0, 0.3930, 0, 1, 0 1, 0.26, 0, 1, 0, 0.4040, 1, 0, 0 1, 0.58, 1, 0, 0, 0.7070, 1, 0, 0 1, 0.43, 0, 0, 1, 0.4800, 0, 1, 0 0, 0.46, 1, 0, 0, 0.6440, 1, 0, 0 1, 0.60, 1, 0, 0, 0.7170, 1, 0, 0 0, 0.42, 1, 0, 0, 0.4890, 0, 1, 0 0, 0.56, 0, 0, 1, 0.5640, 0, 0, 1 0, 0.62, 0, 1, 0, 0.6630, 0, 0, 1 0, 0.50, 1, 0, 0, 0.6480, 0, 1, 0 1, 0.47, 0, 0, 1, 0.5200, 0, 1, 0 0, 0.67, 0, 1, 0, 0.8040, 0, 0, 1 0, 0.40, 0, 0, 1, 0.5040, 0, 1, 0 1, 0.42, 0, 1, 0, 0.4840, 0, 1, 0 1, 0.64, 1, 0, 0, 0.7200, 1, 0, 0 0, 0.47, 1, 0, 0, 0.5870, 0, 0, 1 1, 0.45, 0, 1, 0, 0.5280, 0, 1, 0 0, 0.25, 0, 0, 1, 0.4090, 1, 0, 0 1, 0.38, 1, 0, 0, 0.4840, 1, 0, 0 1, 0.55, 0, 0, 1, 0.6000, 0, 1, 0 0, 0.44, 1, 0, 0, 0.6060, 0, 1, 0 1, 0.33, 1, 0, 0, 0.4100, 0, 1, 0 1, 0.34, 0, 0, 1, 0.3900, 0, 1, 0 1, 0.27, 0, 1, 0, 0.3370, 0, 0, 1 1, 0.32, 0, 1, 0, 0.4070, 0, 1, 0 1, 0.42, 0, 0, 1, 0.4700, 0, 1, 0 0, 0.24, 0, 0, 1, 0.4030, 1, 0, 0 1, 0.42, 0, 1, 0, 0.5030, 0, 1, 0 1, 0.25, 0, 0, 1, 0.2800, 0, 0, 1 1, 0.51, 0, 1, 0, 0.5800, 0, 1, 0 0, 0.55, 0, 1, 0, 0.6350, 0, 0, 1 1, 0.44, 1, 0, 0, 0.4780, 0, 0, 1 0, 0.18, 1, 0, 0, 0.3980, 1, 0, 0 0, 0.67, 0, 1, 0, 0.7160, 0, 0, 1 1, 0.45, 0, 0, 1, 0.5000, 0, 1, 0 1, 0.48, 1, 0, 0, 0.5580, 0, 1, 0 0, 0.25, 0, 1, 0, 0.3900, 0, 1, 0 0, 0.67, 1, 0, 0, 0.7830, 0, 1, 0 1, 0.37, 0, 0, 1, 0.4200, 0, 1, 0 0, 0.32, 1, 0, 0, 0.4270, 0, 1, 0 1, 0.48, 1, 0, 0, 0.5700, 0, 1, 0 0, 0.66, 0, 0, 1, 0.7500, 0, 0, 1 1, 0.61, 1, 0, 0, 0.7000, 1, 0, 0 0, 0.58, 0, 0, 1, 0.6890, 0, 1, 0 1, 0.19, 1, 0, 0, 0.2400, 0, 0, 1 1, 0.38, 0, 0, 1, 0.4300, 0, 1, 0 0, 0.27, 1, 0, 0, 0.3640, 0, 1, 0 1, 0.42, 1, 0, 0, 0.4800, 0, 1, 0 1, 0.60, 1, 0, 0, 0.7130, 1, 0, 0 0, 0.27, 0, 0, 1, 0.3480, 1, 0, 0 1, 0.29, 0, 1, 0, 0.3710, 1, 0, 0 0, 0.43, 1, 0, 0, 0.5670, 0, 1, 0 1, 0.48, 1, 0, 0, 0.5670, 0, 1, 0 1, 0.27, 0, 0, 1, 0.2940, 0, 0, 1 0, 0.44, 1, 0, 0, 0.5520, 1, 0, 0 1, 0.23, 0, 1, 0, 0.2630, 0, 0, 1 0, 0.36, 0, 1, 0, 0.5300, 0, 0, 1 1, 0.64, 0, 0, 1, 0.7250, 1, 0, 0 1, 0.29, 0, 0, 1, 0.3000, 0, 0, 1 0, 0.33, 1, 0, 0, 0.4930, 0, 1, 0 0, 0.66, 0, 1, 0, 0.7500, 0, 0, 1 0, 0.21, 0, 0, 1, 0.3430, 1, 0, 0 1, 0.27, 1, 0, 0, 0.3270, 0, 0, 1 1, 0.29, 1, 0, 0, 0.3180, 0, 0, 1 0, 0.31, 1, 0, 0, 0.4860, 0, 1, 0 1, 0.36, 0, 0, 1, 0.4100, 0, 1, 0 1, 0.49, 0, 1, 0, 0.5570, 0, 1, 0 0, 0.28, 1, 0, 0, 0.3840, 1, 0, 0 0, 0.43, 0, 0, 1, 0.5660, 0, 1, 0 0, 0.46, 0, 1, 0, 0.5880, 0, 1, 0 1, 0.57, 1, 0, 0, 0.6980, 1, 0, 0 0, 0.52, 0, 0, 1, 0.5940, 0, 1, 0 0, 0.31, 0, 0, 1, 0.4350, 0, 1, 0 0, 0.55, 1, 0, 0, 0.6200, 0, 0, 1 1, 0.50, 1, 0, 0, 0.5640, 0, 1, 0 1, 0.48, 0, 1, 0, 0.5590, 0, 1, 0 0, 0.22, 0, 0, 1, 0.3450, 1, 0, 0 1, 0.59, 0, 0, 1, 0.6670, 1, 0, 0 1, 0.34, 1, 0, 0, 0.4280, 0, 0, 1 0, 0.64, 1, 0, 0, 0.7720, 0, 0, 1 1, 0.29, 0, 0, 1, 0.3350, 0, 0, 1 0, 0.34, 0, 1, 0, 0.4320, 0, 1, 0 0, 0.61, 1, 0, 0, 0.7500, 0, 0, 1 1, 0.64, 0, 0, 1, 0.7110, 1, 0, 0 0, 0.29, 1, 0, 0, 0.4130, 1, 0, 0 1, 0.63, 0, 1, 0, 0.7060, 1, 0, 0 0, 0.29, 0, 1, 0, 0.4000, 1, 0, 0 0, 0.51, 1, 0, 0, 0.6270, 0, 1, 0 0, 0.24, 0, 0, 1, 0.3770, 1, 0, 0 1, 0.48, 0, 1, 0, 0.5750, 0, 1, 0 1, 0.18, 1, 0, 0, 0.2740, 1, 0, 0 1, 0.18, 1, 0, 0, 0.2030, 0, 0, 1 1, 0.33, 0, 1, 0, 0.3820, 0, 0, 1 0, 0.20, 0, 0, 1, 0.3480, 1, 0, 0 1, 0.29, 0, 0, 1, 0.3300, 0, 0, 1 0, 0.44, 0, 0, 1, 0.6300, 1, 0, 0 0, 0.65, 0, 0, 1, 0.8180, 1, 0, 0 0, 0.56, 1, 0, 0, 0.6370, 0, 0, 1 0, 0.52, 0, 0, 1, 0.5840, 0, 1, 0 0, 0.29, 0, 1, 0, 0.4860, 1, 0, 0 0, 0.47, 0, 1, 0, 0.5890, 0, 1, 0 1, 0.68, 1, 0, 0, 0.7260, 0, 0, 1 1, 0.31, 0, 0, 1, 0.3600, 0, 1, 0 1, 0.61, 0, 1, 0, 0.6250, 0, 0, 1 1, 0.19, 0, 1, 0, 0.2150, 0, 0, 1 1, 0.38, 0, 0, 1, 0.4300, 0, 1, 0 0, 0.26, 1, 0, 0, 0.4230, 1, 0, 0 1, 0.61, 0, 1, 0, 0.6740, 1, 0, 0 1, 0.40, 1, 0, 0, 0.4650, 0, 1, 0 0, 0.49, 1, 0, 0, 0.6520, 0, 1, 0 1, 0.56, 1, 0, 0, 0.6750, 1, 0, 0 0, 0.48, 0, 1, 0, 0.6600, 0, 1, 0 1, 0.52, 1, 0, 0, 0.5630, 0, 0, 1 0, 0.18, 1, 0, 0, 0.2980, 1, 0, 0 0, 0.56, 0, 0, 1, 0.5930, 0, 0, 1 0, 0.52, 0, 1, 0, 0.6440, 0, 1, 0 0, 0.18, 0, 1, 0, 0.2860, 0, 1, 0 0, 0.58, 1, 0, 0, 0.6620, 0, 0, 1 0, 0.39, 0, 1, 0, 0.5510, 0, 1, 0 0, 0.46, 1, 0, 0, 0.6290, 0, 1, 0 0, 0.40, 0, 1, 0, 0.4620, 0, 1, 0 0, 0.60, 1, 0, 0, 0.7270, 0, 0, 1 1, 0.36, 0, 1, 0, 0.4070, 0, 0, 1 1, 0.44, 1, 0, 0, 0.5230, 0, 1, 0 1, 0.28, 1, 0, 0, 0.3130, 0, 0, 1 1, 0.54, 0, 0, 1, 0.6260, 1, 0, 0 0, 0.51, 1, 0, 0, 0.6120, 0, 1, 0 0, 0.32, 0, 1, 0, 0.4610, 0, 1, 0 1, 0.55, 1, 0, 0, 0.6270, 1, 0, 0 1, 0.25, 0, 0, 1, 0.2620, 0, 0, 1 1, 0.33, 0, 0, 1, 0.3730, 0, 0, 1 0, 0.29, 0, 1, 0, 0.4620, 1, 0, 0 1, 0.65, 1, 0, 0, 0.7270, 1, 0, 0 0, 0.43, 0, 1, 0, 0.5140, 0, 1, 0 0, 0.54, 0, 1, 0, 0.6480, 0, 0, 1 1, 0.61, 0, 1, 0, 0.7270, 1, 0, 0 1, 0.52, 0, 1, 0, 0.6360, 1, 0, 0 1, 0.30, 0, 1, 0, 0.3350, 0, 0, 1 1, 0.29, 1, 0, 0, 0.3140, 0, 0, 1 0, 0.47, 0, 0, 1, 0.5940, 0, 1, 0 1, 0.39, 0, 1, 0, 0.4780, 0, 1, 0 1, 0.47, 0, 0, 1, 0.5200, 0, 1, 0 0, 0.49, 1, 0, 0, 0.5860, 0, 1, 0 0, 0.63, 0, 0, 1, 0.6740, 0, 0, 1 0, 0.30, 1, 0, 0, 0.3920, 1, 0, 0 0, 0.61, 0, 0, 1, 0.6960, 0, 0, 1 0, 0.47, 0, 0, 1, 0.5870, 0, 1, 0 1, 0.30, 0, 0, 1, 0.3450, 0, 0, 1 0, 0.51, 0, 0, 1, 0.5800, 0, 1, 0 0, 0.24, 1, 0, 0, 0.3880, 0, 1, 0 0, 0.49, 1, 0, 0, 0.6450, 0, 1, 0 1, 0.66, 0, 0, 1, 0.7450, 1, 0, 0 0, 0.65, 1, 0, 0, 0.7690, 1, 0, 0 0, 0.46, 0, 1, 0, 0.5800, 1, 0, 0 0, 0.45, 0, 0, 1, 0.5180, 0, 1, 0 0, 0.47, 1, 0, 0, 0.6360, 1, 0, 0 0, 0.29, 1, 0, 0, 0.4480, 1, 0, 0 0, 0.57, 0, 0, 1, 0.6930, 0, 0, 1 0, 0.20, 1, 0, 0, 0.2870, 0, 0, 1 0, 0.35, 1, 0, 0, 0.4340, 0, 1, 0 0, 0.61, 0, 0, 1, 0.6700, 0, 0, 1 0, 0.31, 0, 0, 1, 0.3730, 0, 1, 0 1, 0.18, 1, 0, 0, 0.2080, 0, 0, 1 1, 0.26, 0, 0, 1, 0.2920, 0, 0, 1 0, 0.28, 1, 0, 0, 0.3640, 0, 0, 1 0, 0.59, 0, 0, 1, 0.6940, 0, 0, 1
.NET Test Automation Recipes
Software Testing
SciPy Programming Succinctly
Keras Succinctly
R Programming
2024 Visual Studio Live Conference
2024 Predictive Analytics World
2024 DevIntersection Conference
2023 Fall MLADS Conference
2022 Money 20/20 Conference
2022 DEFCON Conference
2022 G2E Conference
2023 ICGRT Conference
2024 CEC eSports Conference
2024 ISC West Conference
James D. McCaffrey 
You must be logged in to post a comment.