View source for Performing a 10-fold cross validation

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//create a DNA-alphabet
AlphabetContainer container = new AlphabetContainer( new DNAAlphabet() );
//the length of our input sequences
int length = 16;

//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 )
};

//create a new PWM
BayesianNetworkModel pwm = new BayesianNetworkModel( new BayesianNetworkModelParameterSet(
		//the alphabet:
		container, 
		//the length of the model
		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 new mixture model using 2 PWMs
MixtureModel mixPwms = new MixtureModel(
		//the length of the mixture model
		length, 
		//the two components, which are PWMs
		new Model[]{pwm,pwm},
		//the number of starts of the EM
		10,
		//the equivalent sample sizes
		new double[]{pwm.getESS(),pwm.getESS()},
		//the hyper-parameters to draw the initial component weights (hidden variables)
		1,
		//stopping criterion
		1E-6,
		//parameterization of the model, THETA complies with the
		//parameterization by probabilities
		Parameterization.THETA);

//create a new inhomogeneous Markov model of order 3
BayesianNetworkModel mm = new BayesianNetworkModel( new BayesianNetworkModelParameterSet( container, length, 256, "my PWM", ModelType.IMM, (byte) 3, LearningType.ML_OR_MAP ) );

//create a new PWM scoring function
BayesianNetworkScoringFunction dPwm = new BayesianNetworkScoringFunction(
		//the alphabet
		container, 
		//the length of the scoring function
		length, 
		//the equivalent sample size for the plug-in parameters
		4, 
		//we use plug-in parameters
		true, 
		//a PWM is an inhomogeneous Markov model of order 0
		new InhomogeneousMarkov(0));

//create a new mixture scoring function
MixtureScoringFunction dMixPwms = new MixtureScoringFunction(
		//the number of starts
		10,
		//we use plug-in parameters
		true,
		//the two components, which are PWMs
		dPwm,dPwm);

//create a new scoring function that is an inhomogeneous Markov model of order 3
BayesianNetworkScoringFunction dMm = new BayesianNetworkScoringFunction(container, length, 4, true, new InhomogeneousMarkov(3));

//create the classifiers
AbstractScoreBasedClassifier[] classifiers = new AbstractScoreBasedClassifier[]{
                               //model based with mixture model and Markov model
                               new ModelBasedClassifier( mixPwms, mm ),
                               //conditional likelihood based classifier
                               new CLLClassifier( new CLLClassifierParameterSet(container, length, 
                            		   //method for optimizing the conditional likelihood
                            		   Optimizer.QUASI_NEWTON_BFGS, 
                            		   //parameters of the numerical optimization
                            		   1E-6, 1E-6, 1E-2, true, true, false),
                            		   //mixture scoring function and Markov model scoring function
                            		   dMixPwms,dMm )
};

//create an new k-fold cross validation using above classifiers
KFoldCrossValidation cv = new KFoldCrossValidation( classifiers );

//we use a specificity of 0.999 to compute the sensitivity and a sensitivity of 0.95 to compute FPR and PPV
MeasureParameters mp = new MeasureParameters(false, 0.999, 0.95, 0.95);
//we do a 10-fold cross validation and partition the data by means of the number of symbols
KFoldCVAssessParameterSet cvpars = new KFoldCVAssessParameterSet(PartitionMethod.PARTITION_BY_NUMBER_OF_SYMBOLS, length, true, 10);

//compute the result of the cross validation and print them to System.out
System.out.println( cv.assess( mp, cvpars, data ) );
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