public static double get1FitnessFromTestSet(
double[] inputs, int classification, StrokeNetwork network, int featureToClassify) {
double output = FullyConnectedNNBuilder.getOutput(inputs, network);
if (classification == featureToClassify) {
/*
}
if (classification == 0 || classification == 1 || classification == 3 || classification == 7 || classification == 8 || classification == 9
|| classification == 10 || classification == 11 || classification == 19 || classification == 20 || classification == 21
|| classification == 22 || classification == 23) {
*/
/*
if (output > 0) {
return 1.0;
} else {
return -1.0;
}*/
return output;
} else {
/*
if (output < 0) {
return 1.0;
} else {
return -1.0;
}
*/
return -1 * output;
}
}
// return difference between output of desired classification, and output of highest
// classification (not desired).
public static double get1Fitness(
double[] inputs,
int classification,
StrokeNetwork network,
int featureToClassify,
int num_possible) {
double output = FullyConnectedNNBuilder.getOutput(inputs, network);
if (classification == featureToClassify) {
/*if (classification == 0 || classification == 1 || classification == 3 || classification == 7 || classification == 8 || classification == 9
|| classification == 10 || classification == 11 || classification == 19 || classification == 20 || classification == 21
|| classification == 22 || classification == 23) {
*/
// System.out.println(output);
return output;
} else {
// return -0.0416666667*output;
// return -1.08333333333*output;
// System.out.println(-1.0*(1.0/(num_possible-1.0))*output);
return -1.0 * (1.0 / (num_possible - 1.0)) * output;
}
}
