Training a classifier and classifying new sequences

//create a DNA-alphabet
AlphabetContainer container = new AlphabetContainer( new DNAAlphabet() );
//the length of our input sequences
int length = 7;

//create a Sample for each class from the input data, using the alphabet from above
Sample[] data = new Sample[]{	new Sample( container, new StringExtractor( new File(args[0]), 100) ),
                             	new Sample( container, new StringExtractor( new File(args[1]), 100 ), length ) };

//sequences that will be classified
Sample toClassify = new Sample(container, new StringExtractor( new File(args[2]), 100 ) );

//create a new PWM
BayesianNetworkModel pwm = new BayesianNetworkModel( new BayesianNetworkModelParameterSet(
		//the alphabet and the length of the model:
		container, length, 
		//the equivalent sample size to compute hyper-parameters
		4, 
		//some identifier for the model
		"my PWM", 
		//we want a PWM, which is an inhomogeneous Markov model (IMM) of order 0
		ModelType.IMM, (byte) 0, 
		//we want to estimate the MAP-parameters
		LearningType.ML_OR_MAP ) );

//create a classifier with a PWM in the foreground and a PWM in the background
ModelBasedClassifier classifier = new ModelBasedClassifier( pwm, pwm );

//train the classifier
classifier.train( data );

//use the trained classifier to classify new sequences
byte[] result = classifier.classify( toClassify );

System.out.println( Arrays.toString( result ) );