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NeuralNetwork.java
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NeuralNetwork.java
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import javax.sql.rowset.spi.SyncFactory;
public class NeuralNetwork {
private double synapticWeights[][];
public NeuralNetwork(int numberOfFeatures)
{
for(int i=0; i< numberOfFeatures; i++)
for(int j=0; j<=1; j++)
synapticWeights = new double[i][j];
}
private double[][] addMatrices(double x[][], double y[][])
{
double z[][] = null;
for(int i=0; i<=x.length; i++)
for(int j=0; j<=x[0].length; j++)
z = new double[i][j];
for(int i=0; i<z.length; i++)
for(int j=0; j<z[0].length; j++)
z[i][j] = x[i][j] + y[i][j];
return z;
}
private double[][] subtractMatrices(double x[][], double y[][])
{
double z[][] = null;
for(int i=0; i<=x.length; i++)
for(int j=0; j<=x[0].length; j++)
z = new double[i][j];
for(int i=0; i<z.length; i++)
for(int j=0; j<z[0].length; j++)
z[i][j] = x[i][j] - y[i][j];
return z;
}
private double[][] transpose(double x[][])
{
double z[][] = null;
for(int i=0; i<=x[0].length; i++)
for(int j=0; j<=x.length; j++)
z = new double[i][j];
for(int i=0; i<x.length; i++)
for(int j=0; j<x[0].length; j++)
z[j][i] = x[i][j];
return z;
}
private double[][] dotProduct(double x[][], double y[][])
{
double z[][] = null;
for(int i=0; i<=x.length; i++)
for(int j=0; j<=y[0].length; j++)
z = new double[i][j];
double sum = 0.0;
for (int i = 0 ; i < x.length ; i++ )
{
for ( int j=0; j < y[0].length; j++)
{
for (int k=0; k < x[0].length; k++ )
sum += x[i][k]*y[k][j];
z[i][j] = sum;
sum = 0;
}
}
return z;
}
private double[][] sigmoid(double z[][])
{
for(int i=0; i<z.length; i++)
for(int j=0; j<z[0].length; j++)
z[i][j] = (1/(1 + Math.exp(-z[i][j])));
return z;
}
void print(double x[][])
{
for(int i=0; i<x.length; i++)
{
for(int j=0; j<x[0].length; j++)
System.out.print(x[i][j] + " ");
System.out.println();
}
System.out.println();
}
public void train(double inputFeatures[][], double inputLabels[][], int iterations)
{
for(int i=0; i< inputFeatures[0].length; i++)
for(int j=0; j<1; j++)
synapticWeights[i][j] = 1.0;
// synapticWeights[0][0] = -0.16595599;
// synapticWeights[1][0] = 0.44064899;
// synapticWeights[2][0] = -0.99977125;
double output[][] = null;
double error[][] = null;
double adjustments[][] = null;
for(int i=0; i<iterations; i++)
{
output = predict(inputFeatures);
error = subtractMatrices(inputLabels, output);
adjustments = dotProduct(transpose(inputFeatures), error);
synapticWeights = addMatrices(synapticWeights, adjustments);
}
}
public double[][] predict(double inputs[][])
{
return (sigmoid(dotProduct(inputs, synapticWeights)));
}
public void printWeights()
{
System.out.println("New synaptic weights:");
print(synapticWeights);
}
}