Saving and loading a model - Revision history

Keilwagen at 14:26, 2 December 2009

2009-12-02T14:26:02Z

← Older revision		Revision as of 14:26, 2 December 2009
Line 1:		Line 1:
	<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();

	~~//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~~ ) };

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

	//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 22:		Line 21:
	//we want to estimate the MAP-parameters		//we want to estimate the MAP-parameters
	LearningType.ML_OR_MAP ) );		LearningType.ML_OR_MAP ) );

	//create a new classifier		//create a new classifier
	ModelBasedClassifier classifier = new ModelBasedClassifier( pwm, pwm );		ModelBasedClassifier classifier = new ModelBasedClassifier( pwm, pwm );

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

	//create the XML-representation of the classifier		//create the XML-representation of the classifier
	StringBuffer buf = classifier.toXML();		StringBuffer buf = classifier.toXML();

	//write it to disk		//write it to disk
	FileManager.writeFile( new File("myClassifier.xml"), buf );		FileManager.writeFile( new File(home+"myClassifier.xml"), buf );

	//read XML-representation from disk		//read XML-representation from disk
	StringBuffer buf2 = FileManager.readFile( new File("myClassifier.xml") );		StringBuffer buf2 = FileManager.readFile( new File(home+"myClassifier.xml") );

	//create new classifier from read StringBuffer containing XML-code		//create new classifier from read StringBuffer containing XML-code
	ModelBasedClassifier loaded = new ModelBasedClassifier(buf2);		ModelBasedClassifier loaded = new ModelBasedClassifier(buf2);

	</source>		</source>

Grau at 08:18, 29 October 2008

2008-10-29T08:18:18Z

← Older revision		Revision as of 08:18, 29 October 2008
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	Sample[] data = new Sample[]{ new Sample( container, new StringExtractor( new File(args[0]), 100) ),		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 ) };		new Sample( container, new StringExtractor( new File(args[1]), 100 ), length ) };

	~~Sample toClassify = new Sample(container, new StringExtractor( new File(args[2]), 100 ) );~~

	//create a new PWM		//create a new PWM

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...

2008-09-07T12:36:25Z

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 ) };

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 new classifier
ModelBasedClassifier classifier = new ModelBasedClassifier( pwm, pwm );

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

//create the XML-representation of the classifier
StringBuffer buf = classifier.toXML();

//write it to disk
FileManager.writeFile( new File("myClassifier.xml"), buf );

//read XML-representation from disk
StringBuffer buf2 = FileManager.readFile( new File("myClassifier.xml") );

//create new classifier from read StringBuffer containing XML-code
ModelBasedClassifier loaded = new ModelBasedClassifier(buf2);

</source>