public weka.classifiers.Classifier getClassifier() throws Exception {
StringToWordVector stwv = new StringToWordVector();
stwv.setTFTransform(hasParam(Constant.RUNTIME_PARAMS.USE_TFIDF));
stwv.setIDFTransform(hasParam(Constant.RUNTIME_PARAMS.USE_TFIDF));
stwv.setLowerCaseTokens(hasParam(Constant.RUNTIME_PARAMS.CONV_LOWERCASE));
stwv.setUseStoplist(hasParam(Constant.RUNTIME_PARAMS.REM_STOP_WORDS));
stwv.setOutputWordCounts(hasParam(Constant.RUNTIME_PARAMS.USE_WORD_FREQ));
if (hasParam(Constant.RUNTIME_PARAMS.TRAIN_AND_TEST)) stwv.setInputFormat(getTrainData());
if (hasParam(Constant.RUNTIME_PARAMS.USE_BIGRAM)) {
NGramTokenizer tokenizer = new NGramTokenizer();
tokenizer.setNGramMinSize(2);
stwv.setTokenizer(tokenizer);
} else if (hasParam(Constant.RUNTIME_PARAMS.USE_TRIGRAM)) {
NGramTokenizer tokenizer = new NGramTokenizer();
tokenizer.setNGramMinSize(3);
stwv.setTokenizer(tokenizer);
}
if (hasParam(Constant.RUNTIME_PARAMS.USE_STEMMER)) {
SnowballStemmer stemmer = new SnowballStemmer("porter");
stwv.setStemmer(stemmer);
}
Logistic l = new Logistic();
FilteredClassifier cls = new FilteredClassifier();
cls.setClassifier(l);
cls.setFilter(stwv);
if (hasParam(Constant.RUNTIME_PARAMS.TRAIN_AND_TEST)) cls.buildClassifier(getTrainData());
return cls;
}
public static void classify(Instances train, File file) throws Exception {
FastVector atts = new FastVector();
String[] classes = {"classical", "hiphop", "pop", "rock"};
double[] val;
FastVector attValsRel = new FastVector();
// Setting attributes for the test data
Attribute attributeZero = new Attribute("Zero_Crossings");
atts.addElement(attributeZero);
Attribute attributeLPC = new Attribute("LPC");
atts.addElement(attributeLPC);
Attribute attributeCentroid = new Attribute("Spectral_Centroid");
atts.addElement(attributeCentroid);
Attribute attributeRollOff = new Attribute("Spectral_Rolloff_Point");
atts.addElement(attributeRollOff);
Attribute attributePeakDetection = new Attribute("Peak_Detection");
atts.addElement(attributePeakDetection);
Attribute attributeStrongestBeat = new Attribute("Strongest_Beat");
atts.addElement(attributeStrongestBeat);
Attribute attributeBeatSum = new Attribute("Beat_Sum");
atts.addElement(attributeBeatSum);
Attribute attributeRMS = new Attribute("RMS");
atts.addElement(attributeRMS);
Attribute attributeConstantQ = new Attribute("ConstantQ");
atts.addElement(attributeConstantQ);
Attribute attributeMFT = new Attribute("MagnitudeFFT");
atts.addElement(attributeMFT);
Attribute attributeMFCC = new Attribute("MFCC");
atts.addElement(attributeMFCC);
for (int i = 0; i < classes.length; i++) attValsRel.addElement(classes[i]);
atts.addElement(new Attribute("class", attValsRel));
// Adding instances to the test dataset
Instances test = new Instances("AudioSamples", atts, 0);
val = makeData(file, null, attValsRel, test.numAttributes());
Instance instance = new Instance(test.numAttributes());
instance.setValue(attributeZero, val[0]);
instance.setValue(attributeLPC, val[1]);
instance.setValue(attributeCentroid, val[2]);
instance.setValue(attributeRollOff, val[3]);
instance.setValue(attributePeakDetection, val[4]);
instance.setValue(attributeStrongestBeat, val[5]);
instance.setValue(attributeBeatSum, val[6]);
instance.setValue(attributeRMS, val[7]);
instance.setValue(attributeConstantQ, val[8]);
instance.setValue(attributeMFT, val[9]);
instance.setValue(attributeMFCC, val[10]);
test.add(instance);
// Setting the class attribute
test.setClassIndex(test.numAttributes() - 1);
System.out.println(test);
// Trainging the classifier with train dataset
classifier = new FilteredClassifier();
classifier.buildClassifier(train);
// Classifying the test data with the train data
for (int i = 0; i < test.numInstances(); i++) {
double pred = classifier.classifyInstance(test.instance(i));
System.out.println("===== Classified instance =====");
System.out.println("Class predicted: " + test.classAttribute().value((int) pred));
}
}