public static void main(String[] args) throws IOException {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
System.out.println("Hello OpenCV " + Core.VERSION);
getTrainData();
List<Mat> traindataList = new ArrayList<Mat>();
List<Integer> trainLabelList = new ArrayList<Integer>();
readTrainData(outDataPath + "Arbys100Datas.csv", traindataList, trainLabelList);
Classifier classifier = new Classifier();
classifier.train(traindataList, trainLabelList);
classifier.test(traindataList, trainLabelList);
classifier.save(outDataPath + "svm.xml");
}
public static void trainAndValidate(
Map<String, List<FeatureVector>> songs, Map<String, List<FeatureVector>> testSongs)
throws Exception {
List<String> genres =
Arrays.asList(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
.stream()
.map((n) -> Classify.intToGenre(n))
.collect(Collectors.toList());
System.out.println("Binary logistics classifier on every genre:");
System.out.println("\t\tPositiv\tFalsePos\tFalseNeg\tAccuracy");
for (String genre : genres) {
Classifier c = new LogisticsRegressionClassifier(genre);
c.train(songs);
int positive = 0;
int total = 0;
int falsePositive = 0;
int falseNegative = 0;
for (String label : songs.keySet()) {
for (FeatureVector features : songs.get(label)) {
String result = c.classify(features);
if (result != null) {
if (genre.equals(label)) {
positive++;
} else {
falsePositive++;
}
} else {
if (label.equals(genre)) {
falseNegative++;
}
}
total++;
}
}
System.out.println(
Classify.shortenGenre(genre)
+ "\t"
+ positive
+ "\t\t"
+ falsePositive
+ "\t\t\t"
+ falseNegative
+ "\t\t\t"
+ (positive * 1.0) / (positive + falseNegative + falsePositive));
}
}
