Training a classifier and classifying new sequences - Revision history

Grau: Reverted edits by 173.244.206.128 (talk) to last revision by Keilwagen

2011-05-11T16:40:25Z

Reverted edits by 173.244.206.128 (talk) to last revision by Keilwagen

Can't load revision 455

at 05:32, 11 May 2011

2011-05-11T05:32:28Z

pCxsJCQWuPWqrV

Can't load revision 397

Keilwagen at 14:25, 2 December 2009

2009-12-02T14:25:28Z

← Older revision		Revision as of 14:25, 2 December 2009
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	<source lang="java5">		<source lang="java5">
			//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] );

	~~//create a DNA-alphabet~~
	~~AlphabetContainer container = new AlphabetContainer( new DNAAlphabet() );~~
	//the length of our input sequences		//the length of our input sequences
	int length = 7;		int length = data[0].getElementLength();

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

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

	//create a new PWM		//create a new PWM
	BayesianNetworkModel pwm = new BayesianNetworkModel( new BayesianNetworkModelParameterSet(		BayesianNetworkModel pwm = new BayesianNetworkModel( new BayesianNetworkModelParameterSet(
	//the alphabet and the length of the model:		//the alphabet and the length of the model:
	~~container~~, length,		data[0].getAlphabetContainer(), length,
	//the equivalent sample size to compute hyper-parameters		//the equivalent sample size to compute hyper-parameters
	4,		4,
Line 25:		Line 25:
	//we want to estimate the MAP-parameters		//we want to estimate the MAP-parameters
	LearningType.ML_OR_MAP ) );		LearningType.ML_OR_MAP ) );

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

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

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

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

	</source>		</source>

Grau: New page: //create a DNA-alphabet AlphabetContainer container = new AlphabetContainer( new DNAAlphabet() ); //the length of our input sequences int length = 7; //create a Sam...

2008-09-07T12:31:56Z

New page: <source lang="java5"> //create a DNA-alphabet AlphabetContainer container = new AlphabetContainer( new DNAAlphabet() ); //the length of our input sequences int length = 7; //create a Sam...

New page

<source lang="java5">

//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 ) );

</source>