public static void main(String[] args) throws Exception {
MnistDatabase.loadImages();
Level level = new Level();
for (int indexImage = 0; indexImage < 60000; indexImage++) {
Image image =
ImageUtils.gradientImage(
ImageUtils.scaleImage(MnistDatabase.trainImages.get(indexImage), 0.25));
level.addFeature(
image.getDataOneDimensional(), 0, 0, MnistDatabase.trainLabels.get(indexImage));
}
for (int indexImage = 0; indexImage < 60000; indexImage++) {
Image image =
ImageUtils.gradientImage(
ImageUtils.scaleImage(MnistDatabase.trainImages.get(indexImage), 0.25));
level.processFeature(
image.getDataOneDimensional(), 0, 0, MnistDatabase.trainLabels.get(indexImage));
}
System.out.println(level.statistic.size());
for (PlacedFeature pFeature : level.statistic.keySet()) {
System.out.println(level.statistic.get(pFeature).correlations);
}
testSet(level, MnistDatabase.trainImages, MnistDatabase.trainLabels);
testSet(level, MnistDatabase.testImages, MnistDatabase.testLabels);
}
private static void testSet(Level level, List<Image> images, List<Integer> labels) {
int errCount = 0;
int testSet = 1000;
for (int indexImage = 0; indexImage < testSet; indexImage++) {
Image image = ImageUtils.gradientImage(ImageUtils.scaleImage(images.get(indexImage), 0.25));
Integer label = labels.get(indexImage);
int prediction = level.getCorrelation(image.getDataOneDimensional());
if (prediction != label) {
// System.out.println(label +": "+prediction);
errCount++;
}
}
System.out.println("Error rate: " + errCount * 100. / testSet + "%");
}
