Training a classifier and classifying new sequences

//create a Sample for each class from the input data, using the DNA alphabet
Sample[] data = new Sample[2];
data[0] = new DNASample( args[0] );

//the length of our input sequences
int length = data[0].getElementLength();

data[1] = new Sample( new DNASample( args[1] ), length );


//sequences that will be classified
Sample toClassify = new DNASample( args[2] );
 
//create a new PWM
BayesianNetworkModel pwm = new BayesianNetworkModel( new BayesianNetworkModelParameterSet(
		//the alphabet and the length of the model:
		data[0].getAlphabetContainer(), 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 ) );