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;
}
}
public static void evolve_nn_2(
TrainingSet trainer,
TrainingSet testSet,
int numInputs,
int populationSize,
int numGenerations,
int numToSelect,
int largeMutationsPerSelected,
double largeMutationFactor,
int smallMutationsPerSelected,
double smallMutationFactor,
int numChildrenToBreed,
int numToChoose,
String fileOutput,
int featureToClassify,
int[] hiddenLayerSizes,
boolean[] toggleFeatures,
double maxThreshold,
int maxOverfitGenerations,
int num_possible)
throws NumberFormatException, IOException {
Vector<StrokeNetwork> netpopulation = new Vector<StrokeNetwork>();
System.out.println("training set size: " + trainer.inputs.length);
StrokeNetwork thisnet;
for (int i = 0; i < populationSize; i++) {
thisnet = FullyConnectedNNBuilder.networkBuilder(true, numInputs, 0.5, 1, hiddenLayerSizes);
netpopulation.add(thisnet);
}
double[] fitnesses = getFitnesses(trainer, netpopulation, featureToClassify, num_possible);
long time;
long time2;
System.out.println("testing set size: " + testSet.inputs.length);
StrokeNetwork bestNetModel = (StrokeNetwork) netpopulation.get(0);
int bestNet = 0;
double bestNetFitness = 0;
double bestNetTestedFitness = Integer.MIN_VALUE;
StrokeNetwork bestNetTestedModel = null;
int numGenerationsSinceImproved = 0;
for (int i = 0; i < numGenerations; i++) {
System.out.println("generation " + i);
time = System.currentTimeMillis();
netpopulation =
evolvePopulation(
netpopulation,
fitnesses,
numToSelect,
largeMutationsPerSelected,
largeMutationFactor,
smallMutationsPerSelected,
smallMutationFactor,
numChildrenToBreed,
numGenerations,
numGenerationsSinceImproved);
time2 = System.currentTimeMillis();
System.out.println("mutation took: " + (time2 - time));
fitnesses = getFitnesses(trainer, netpopulation, featureToClassify, num_possible);
System.out.println("getting fitness took: " + (System.currentTimeMillis() - time2));
System.out.println(
"took: " + (System.currentTimeMillis() - time) + ", fitness: " + fitnesses[0]);
bestNet = findMax(fitnesses);
bestNetModel = (StrokeNetwork) netpopulation.get(bestNet);
for (int j = 0; j < testSet.inputs.length; j++) {
bestNetFitness +=
get1FitnessFromTestSet(
testSet.inputs[j], testSet.classifications[j], bestNetModel, featureToClassify);
// System.out.println(bestNetFitness);
}
System.out.println("test fitness: " + bestNetFitness);
System.out.println(bestNetTestedFitness);
System.out.println(numGenerationsSinceImproved);
if (bestNetFitness > (bestNetTestedFitness + 0.01)) {
numGenerationsSinceImproved = 0;
bestNetTestedModel = bestNetModel;
bestNetTestedFitness = bestNetFitness;
} else {
numGenerationsSinceImproved++;
}
if (numGenerationsSinceImproved > maxOverfitGenerations) {
break;
}
bestNetFitness = 0;
}
double trainFit = 0;
for (int j = 0; j < trainer.inputs.length; j++) {
trainFit +=
get1Fitness(
trainer.inputs[j],
trainer.classifications[j],
bestNetTestedModel,
featureToClassify,
num_possible);
}
double testFit = 0;
for (int j = 0; j < testSet.inputs.length; j++) {
testFit +=
get1FitnessFromTestSet(
testSet.inputs[j], testSet.classifications[j], bestNetTestedModel, featureToClassify);
}
System.out.println("most fit (training): " + trainFit);
System.out.println("most fit (testing): " + testFit);
FullyConnectedNNBuilder.networkToFile(fileOutput, bestNetTestedModel);
}
