https://www.jstacs.de/index.php?action=history&feed=atom&title=Train_classifiers_using_MCL_and_MSP 2026-04-04T16:19:00Z Revision history for this page on the wiki MediaWiki 1.38.2 https://www.jstacs.de/index.php?title=Train_classifiers_using_MCL_and_MSP&diff=244&oldid=prev 2009-12-02T16:30:47Z

<p>New page: &lt;source lang=&quot;java5&quot;&gt; //read FastA-files Sample[] data = { new DNASample( args[0] ), new DNASample( args[1] ) }; AlphabetContainer container = data[0].getAlphabetContaine...</p> <p><b>New page</b></p><div>&lt;source lang=&quot;java5&quot;&gt;<br /> //read FastA-files<br /> Sample[] data = {<br /> new DNASample( args[0] ),<br /> new DNASample( args[1] )<br /> };<br /> AlphabetContainer container = data[0].getAlphabetContainer();<br /> <br /> //equivalent sample size =^= ESS<br /> double essFg = 4, essBg = 4;<br /> //create ScoringFunction, here PWM<br /> NormalizableScoringFunction pwmFg = new BayesianNetworkScoringFunction( container, data[0].getElementLength(), essFg, true, new InhomogeneousMarkov(0) );<br /> NormalizableScoringFunction pwmBg = new BayesianNetworkScoringFunction( container, data[1].getElementLength(), essBg, true, new InhomogeneousMarkov(0) );<br /> <br /> //create parameters of the classifier<br /> int threads = AbstractMultiThreadedOptimizableFunction.getNumberOfAvailableProcessors();<br /> GenDisMixClassifierParameterSet cps = new GenDisMixClassifierParameterSet( container, data[0].getElementLength(),<br /> //optimization parameter<br /> Optimizer.QUASI_NEWTON_BFGS, 1E-9, 1E-11, 1, false, KindOfParameter.ZEROS, true, threads<br /> );<br /> //create classifiers<br /> MSPClassifier[] cl = {<br /> //MCL<br /> new MSPClassifier( cps, pwmFg, pwmBg ),<br /> //MSP with composite prior (here this equivalent to a transformed product-Dirichlet)<br /> new MSPClassifier( cps, new CompositeLogPrior(), pwmFg, pwmBg ),<br /> };<br /> <br /> //do what ever you like<br /> <br /> //e.g., train<br /> for( int i = 0; i &lt; cl.length; i++ ){<br /> cl[i].train( data );<br /> }<br /> <br /> //e.g., evaluate (normally done on a test data set)<br /> MeasureParameters mp = new MeasureParameters( false, 0.95, 0.999, 0.999 );<br /> for( int i = 0; i &lt; cl.length; i++ ){<br /> System.out.println( cl[i].evaluate( mp, true, data ) );<br /> }<br /> &lt;/source&gt;</div>

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