fi.smaa.libror.RejectionValueFunctionSampler.java Source code

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Here is the source code for fi.smaa.libror.RejectionValueFunctionSampler.java

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/*
 * This file is part of libror.
 * libror is distributed from http://smaa.fi/libror
 * Copyright (C) 2011-15 Tommi Tervonen.
 *
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

package fi.smaa.libror;

import java.util.Arrays;

import org.apache.commons.math.linear.RealVector;

public class RejectionValueFunctionSampler extends MCValueFunctionSampler {

    public static final int DEFAULT_MAX_ITERS = 10000000;
    private int maxTries;

    /**
     * Construct a new sampler with the given performance matrix. The alternatives are in rows, and evaluations in columns.
     *  
     * @param perfMatrix the performanceMatrix to use.
     * @param count the amount of functions to sample, > 0
     */
    public RejectionValueFunctionSampler(RORModel model, int count) {
        this(model, count, DEFAULT_MAX_ITERS);
    }

    public RejectionValueFunctionSampler(RORModel model, int count, int maxIters) {
        super(model, count);
        maxTries = maxIters;
    }

    public int getMaxIters() {
        return maxTries;
    }

    public FullValueFunction[] getValueFunctions() {
        if (vfs == null) {
            throw new IllegalStateException("sample() not called");
        }
        return vfs;
    }

    private FullValueFunction sampleValueFunction() throws SamplingException {
        int curIter = 1;

        do {
            FullValueFunction vf = new FullValueFunction();
            for (int i = 0; i < model.getNrCriteria(); i++) {
                RealVector lvls = model.getPerfMatrix().getLevels()[i];
                PartialValueFunction pvf = new PartialValueFunction(lvls.getDimension());
                vf.addValueFunction(pvf);
                sampleRandomPartialVF(pvf);
            }

            double[] w = new double[model.getNrCriteria()];
            RandomUtil.createSumToOneRand(w);
            vf.setWeights(w);
            if (acceptance.check(vf)) {
                return vf;
            }
            misses++;
            curIter++;
        } while (curIter <= maxTries);
        throw new SamplingException("Cannot sample a VF within " + maxTries + " iterations");
    }

    private void sampleRandomPartialVF(PartialValueFunction pvf) {
        double[] vals = pvf.getValues();
        for (int i = 1; i < vals.length - 1; i++) {
            vals[i] = RandomUtil.createUnif01();
        }
        Arrays.sort(vals);
    }

    public void doSample() throws SamplingException {
        for (int i = 0; i < vfs.length; i++) {
            vfs[i] = sampleValueFunction();
        }
    }

}