public void predictOutput() {
network.load(Settings.networkFile);
for (int ntestSample = 0; ntestSample < networkInput.rows(); ntestSample++) {
Mat testSample = networkInput.row(ntestSample);
Mat classificationResult = new Mat(1, Settings.CLASSES, CvType.CV_32F);
// System.out.println(testSample.dump());
network.predict(testSample, classificationResult);
// System.out.println(classificationResult.dump());
int maxIndex = 0;
double value = 0;
double maxValue = classificationResult.get(0, 0)[0];
for (int index = 1; index < Settings.CLASSES; index++) {
value = classificationResult.get(0, index)[0];
if (value > maxValue) {
maxValue = value;
maxIndex = index;
}
}
if (networkOutput.get(ntestSample, maxIndex)[0] != 1) {
for (int classIndex = 0; classIndex < Settings.CLASSES; classIndex++) {
if (networkOutput.get(ntestSample, classIndex)[0] == 1) {
results[classIndex][1]++;
}
}
} else {
results[maxIndex][0]++;
results[maxIndex][1]++;
}
}
}