source: anac2020/BlingBling/src/main/java/geniusweb/blingbling/Ranknet4j/CrossEntropyError.java

package geniusweb.blingbling.Ranknet4j;

import org.neuroph.core.learning.error.ErrorFunction;
import org.neuroph.core.transfer.Sigmoid;

import java.io.Serializable;

/**
 * Special error function which is recommended to be used in classification models
 */
public class CrossEntropyError implements ErrorFunction, Serializable {

//    private double[] errorDerivative;
    private transient double totalError;
    private transient double n;
    Sigmoid sigfunc = new Sigmoid();

    @Override
    public double getTotalError() {
        return -totalError / n ;
    }

    @Override
    public void reset() {
        totalError = 0;
        n = 0;
    }

    @Override
    public double[] addPatternError(double[] predictedOutput, double[] targetOutput) {
        double[] errorDerivative = new double[targetOutput.length];

        if (predictedOutput.length != targetOutput.length)
            throw new IllegalArgumentException("Output array length and desired output array length must be the same size!");

        for (int i = 0; i < predictedOutput.length; i++) {
                Double sigm = sigfunc.getOutput(predictedOutput[i]);
            errorDerivative[i] =  -1.0/sigm * sigm * (1-sigm);
            totalError += targetOutput[i] * Math.log(sigm);

        }
        n++;
        
        return errorDerivative;
    }
    
    public double[] addPatternError2(double[] predictedOutput, double[] targetOutput) {
        double[] errorDerivative = new double[targetOutput.length];

        if (predictedOutput.length != targetOutput.length)
            throw new IllegalArgumentException("Output array length and desired output array length must be the same size!");

        for (int i = 0; i < predictedOutput.length; i++) {
                Double sigm = sigfunc.getOutput(predictedOutput[i]);
            errorDerivative[i] =  1.0/sigm * sigm * (1-sigm);
//            totalError += targetOutput[i] * Math.log(sigm);

        }
//        n++;
        
        return errorDerivative;
    }

}