expshell.Neural.java Source code

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Here is the source code for expshell.Neural.java

Source

/*
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package expshell;

import static java.lang.Math.pow;
import java.util.ArrayList;
import java.util.List;
import weka.core.Debug.Random;
import weka.core.Instance;

/**
 *
 * @author YH Jonathan Kwok
 */
public class Neural {

    List<Double> values = new ArrayList<Double>();
    List<Double> weights = new ArrayList<Double>();
    double randMin = 0.1;
    double randMax = 5.0;

    public Neural(int numInputs) {
        //values = vals;
        //add the bias input at the end
        //values.add(-1.0);
        //set the weights according to the size of the values we have
        setWeights(numInputs + 1);
    }

    public void setWeights(int numAtt) {
        for (int i = 0; i < numAtt; i++) {
            Random r = new Random();
            double randomValue = randMin + (randMax - randMin) * r.nextDouble();
            weights.add(randomValue);
        }
    }

    public double cal(Instance ins) {
        double sum = 0;
        sum += -1 * weights.get(0);
        for (int i = 1; i < weights.size(); i++) {
            sum += ins.value(i - 1) * weights.get(i);
        }

        List<Double> temp = new ArrayList<Double>();
        for (int i = 0; i < ins.numValues(); i++) {
            temp.add(ins.value(i));
        }
        values = temp;
        //if (sum > 0)
        //    return 1.0;
        return threshold(sum);
    }

    public double calDouble(List<Double> lD) {
        values = lD;
        double sum = 0;
        sum += -1 * weights.get(0);
        for (int i = 1; i < weights.size(); i++) {
            sum += lD.get(i - 1) * weights.get(i);
        }
        return threshold(sum);
    }

    public double threshold(double sum) {
        return 1 / (1 + pow(Math.E, -sum));
    }
}