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| import java.text.DecimalFormat;
import java.text.NumberFormat;
public class Afficheur {
int[][] patterns = {
{ 1, 1, 1, 1, 1, 1, 0 }, //0
{ 0, 1, 1, 0, 0, 0, 0 }, //1
{ 1, 1, 0, 1, 1, 0, 1 }, //2
{ 1, 1, 1, 1, 0, 0, 1 }, //3
{ 0, 1, 1, 0, 0, 1, 1 }, //4
{ 1, 0, 1, 1, 0, 1, 1 }, //5
{ 1, 0, 1, 1, 1, 1, 1 }, //6
{ 1, 1, 1, 0, 0, 0, 0 }, //7
{ 1, 1, 1, 1, 1, 1, 1 }, //8
{ 1, 1, 1, 1, 0, 1, 1 } };//9
int[][] teachingOutput = {
{ 0 },
{ 1 },
{ 0 },
{ 1 },
{ 0 },
{ 1 },
{ 0 },
{ 1 },
{ 0 },
{ 1 } };
//{ 0 , 1 , 0 , 1 , 0 , 1 , 0 , 1 , 0, 1 } };
int numberOfInputNeurons = patterns[0].length;
int numberOfOutputNeurons = teachingOutput[0].length;
int numberOfPatterns = patterns.length;
double[][] weights;
public Afficheur() {
System.out.println("numberOfInputNeurons : "+numberOfInputNeurons);
System.out.println("numberOfOutputNeurons : "+numberOfOutputNeurons);
System.out.println("numberOfPatterns : "+numberOfPatterns);
weights = new double[numberOfInputNeurons][numberOfOutputNeurons];
}
public void deltaRule()
{
//System.out.println("delatarule");
boolean allCorrect = false;
boolean error = false;
double learningFactor = 0.9;
while (!allCorrect)
{
error = false;
for (int i = 0; i < numberOfPatterns; i++)
{
int[] output = setOutputValues(i);
for (int j = 0; j < numberOfOutputNeurons; j++)
{
if (teachingOutput[i][j] != output[j])
{
for (int k = 0; k < numberOfInputNeurons; k++)
{
weights[k][j] = weights[k][j] + learningFactor
* patterns[i][k]
* (teachingOutput[i][j] - output[j]);
}
}
}
for (int z = 0; z < output.length; z++)
{
if (output[z] != teachingOutput[i][z])
error = true;
}
}
if (!error)
{
allCorrect = true;
}
}
}
int[] setOutputValues(int patternNo)
{
double seuil = 0.7;
double net=0;
int[] result = new int[numberOfOutputNeurons];
int[] toImpress = patterns[patternNo];
for (int i = 0; i < toImpress.length; i++)
{
for (int j = 0; j < result.length; j++)
{
/*net = weights[0][j] * toImpress[0] + weights[1][j]
* toImpress[1] + weights[2][j] * toImpress[2]
+ weights[3][j] * toImpress[3];*/
for (int k = 0; k < numberOfInputNeurons; k++)
{
net+=weights[k][j] * toImpress[k];
}
if (net > seuil)
result[j] = 1;
else
result[j] = 0;
//net=0;
}
}
return result;
}
public void printMatrix(double[][] matrix) {
for (int i = 0; i < matrix.length; i++) {
for (int j = 0; j < matrix[i].length; j++) {
NumberFormat f = NumberFormat.getInstance();
if (f instanceof DecimalFormat) {
DecimalFormat decimalFormat = ((DecimalFormat) f);
decimalFormat.setMaximumFractionDigits(1);
decimalFormat.setMinimumFractionDigits(1);
System.out.print("(" + f.format(matrix[i][j]) + ")");
}
}
System.out.println();
}
}
public void verif()
{
double net=0;
for (int i = 0; i < numberOfPatterns; i++)
{
for (int k = 0; k < numberOfInputNeurons; k++)
{
net+=weights[k][0] * patterns[i][k];
//System.out.println(weights[k][0]+" * "+patterns[i][k]+" calcul "+net);
}
System.out.println(i+" : "+net);
net=0;
}
}
public static void main(String[] args)
{
Afficheur p = new Afficheur();
System.out.println("Poids de depart : ");
p.printMatrix(p.weights);
p.deltaRule();
System.out.println("Poids apres apprentissage: ");
p.printMatrix(p.weights);
System.out.println("Resultat : ");
p.verif();
}
} |
