singlesamplemultiplierexperiment.java~

Java遗传算法库

/*
 * SingleSampleMultiplierExperiment.java
 *
 * Created on 30 August 2003, 10:11
 */

package es.pj.circuits.experiment;

import es.SampleData;
import es.ESLib;

/** One output sample per input sample taken as average of values after tsetup.
 *
 * @author  mmg20
 */
public class SingleSampleMultiplierExperiment extends MultiplierExperiment 
{    
    /** Creates a new instance of SingleSampleMultiplierExperiment */
    public SingleSampleMultiplierExperiment( int width, double tSetup, boolean alwaysRandom ) {
        super( width, tSetup, alwaysRandom );
    }

    public double getFitness(SampleData[] in,SampleData[] out)
    {
        // calculate what output should be,
        // allow for a tsetup time for the latch output.  This will be measured
        // as a fraction of the separation of input samples.
        int inputSampleSeparation = in[ 0 ].getSampleSeparation();
        int inputSamples = in[ 0 ].length();
        int outputSamples = inputSamples;

        int numInputs = getNumOfInputs();

        SampleData[] desQ = new SampleData[ out.length ];
        SampleData[] samQ = new SampleData[ out.length ];
        for( int ql = 0; ql < out.length; ql++ )
        {
            desQ[ ql ] = new SampleData( 1, outputSamples );
            samQ[ ql ] = new SampleData( 1, outputSamples );
        }

        // calculate what Q should have been and sample Q
        for( int idl = 0; idl < inputSamples; idl++ )
        {
            int a = ESLib.getLine( in, idl, 0, width - 1 );
            int b = ESLib.getLine( in, idl, width, 2 * width - 1 );
            int desiredQ = a * b;
            ESLib.setLine( desQ, idl, desiredQ );
            
            int qHighCount[] = new int[ out.length ];
            for( int odl = ( int ) ( tSetup * inputSampleSeparation ); odl < inputSampleSeparation; odl++ )
            {
                for( int ql = 0; ql < out.length; ql++ )
                {
                    if( out[ ql ].get( idl * inputSampleSeparation + odl ) )
                    {
                        qHighCount[ ql ]++;
                    }
                }
            }
            for( int ql = 0; ql < out.length; ql++ )
            {
                if( qHighCount[ ql ] > ( ( inputSampleSeparation - ( int ) ( tSetup * inputSampleSeparation ) ) / 2 ) )
                {
                    samQ[ ql ].set( idl );
                }
            }
        }

        double fitness = 0;

        //DebugLib.trcLogger.text( com.ibm.logging.IRecordType.TYPE_DEFAULT_TRACE, this.getClass().getName(), "getFitness", ESLib.sampleDatasToString( desQ, out ) );

        for( int ql = 0; ql < out.length; ql++ )
        {
            final int funIss = 1;
            final double funTSetup = 0;
            double currFit = getFitnessForOutput( funTSetup, inputSamples, funIss, desQ[ ql ], samQ[ ql ] );
            //DebugLib.trcLogger.text( com.ibm.logging.IRecordType.TYPE_DEFAULT_TRACE, this.getClass().getName(), "getFitness", currCorr + "" );
            //DebugLib.trcLogger.text( com.ibm.logging.IRecordType.TYPE_DEFAULT_TRACE, this.getClass().getName(), "getFitness", "Corr " + ql + " = " + currCorr );
            fitness += currFit;
        }
        return fitness / out.length;
    }
}