org.apache.commons.math.genetics.RandomKey Maven / Gradle / Ivy

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.apache.commons.math.genetics;

import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.Comparator;
import java.util.List;

/**
 * 
 * Random Key chromosome is used for permutation representation. It is a vector
 * of a fixed length of real numbers in [0,1] interval. The index of the i-th
 * smallest value in the vector represents an i-th member of the permutation.
 * 
 *
 * 
 * For example, the random key [0.2, 0.3, 0.8, 0.1] corresponds to the
 * permutation of indices (3,0,1,2). If the original (unpermuted) sequence would
 * be (a,b,c,d), this would mean the sequence (d,a,b,c).
 * 
 *
 * 
 * With this representation, common operators like n-point crossover can be
 * used, because any such chromosome represents a valid permutation.
 * 
 *
 * 
 * Since the chromosome (and thus its arrayRepresentation) is immutable, the
 * array representation is sorted only once in the constructor.
 * 
 *
 * 
 * For details, see:
 * 

 * Bean, J.C.: Genetic algorithms and random keys for sequencing and
 * optimization. ORSA Journal on Computing 6 (1994) 154–160
 * Rothlauf, F.: Representations for Genetic and Evolutionary Algorithms.
 * Volume 104 of Studies in Fuzziness and Soft Computing. Physica-Verlag,
 * Heidelberg (2002)
 * 
 * 
 *
 * @param 
 *            type of the permuted objects
 * @since 2.0
 * @version $Revision: 811685 $ $Date: 2009-09-05 19:36:48 +0200 (sam. 05 sept. 2009) $
 */
public abstract class RandomKey extends AbstractListChromosome implements PermutationChromosome {

    /**
     * Cache of sorted representation (unmodifiable).
     */
    private final List sortedRepresentation;

    /**
     * Base sequence [0,1,...,n-1], permuted accorting to the representation (unmodifiable).
     */
    private final List baseSeqPermutation;

    /**
     * Constructor.
     *
     * @param representation list of [0,1] values representing the permutation
     */
    public RandomKey(List representation) {
        super(representation);
        // store the sorted representation
        List sortedRepr = new ArrayList (getRepresentation());
        Collections.sort(sortedRepr);
        sortedRepresentation = Collections.unmodifiableList(sortedRepr);
        // store the permutation of [0,1,...,n-1] list for toString() and isSame() methods
        baseSeqPermutation = Collections.unmodifiableList(
            decodeGeneric(baseSequence(getLength()), getRepresentation(), sortedRepresentation)
        );
    }

    /**
     * Constructor.
     *
     * @param representation array of [0,1] values representing the permutation
     */
    public RandomKey(Double[] representation) {
        this(Arrays.asList(representation));
    }

    /**
     * {@inheritDoc}
     */
    public List decode(List sequence) {
        return decodeGeneric(sequence, getRepresentation(), sortedRepresentation);
    }

    /**
     * Decodes a permutation represented by representation and
     * returns a (generic) list with the permuted values.
     *
     * @param  generic type of the sequence values
     * @param sequence the unpermuted sequence
     * @param representation representation of the permutation ([0,1] vector)
     * @param sortedRepr sorted representation
     * @return list with the sequence values permuted according to the representation
     */
    private static  List decodeGeneric(List sequence, List representation, List sortedRepr) {
        int l = sequence.size();

        if (representation.size() != l) {
            throw new IllegalArgumentException(String.format("Length of sequence for decoding (%s) has to be equal to the length of the RandomKey (%s)", l, representation.size()));
        }
        if (representation.size() != sortedRepr.size()) {
            throw new IllegalArgumentException(String.format("Representation and sortedRepr must have same sizes, %d != %d", representation.size(), sortedRepr.size()));
        }

        List reprCopy = new ArrayList (representation);// do not modify the orig. representation

        // now find the indices in the original repr and use them for permuting
        List res = new ArrayList (l);
        for (int i=0; itrue iff another is a RandomKey and
     * encodes the same permutation.
     *
     * @param another chromosome to compare
     * @return true iff chromosomes encode the same permutation
     */
    @Override
    protected boolean isSame(Chromosome another) {
        // type check
        if (! (another instanceof RandomKey))
            return false;
        RandomKey anotherRk = (RandomKey) another;
        // size check
        if (getLength() != anotherRk.getLength())
            return false;

        // two different representations can still encode the same permutation
        // the ordering is what counts
        List thisPerm = this.baseSeqPermutation;
        List anotherPerm = anotherRk.baseSeqPermutation;

        for (int i=0; i chromosomeRepresentation) throws InvalidRepresentationException {
        for (double val : chromosomeRepresentation) {
            if (val < 0 || val > 1) {
                throw new InvalidRepresentationException("Values of representation must be in [0,1] interval");
            }
        }
    }


    /**
     * Generates a representation corresponding to a random permutation of
     * length l which can be passed to the RandomKey constructor.
     *
     * @param l
     *            length of the permutation
     * @return representation of a random permutation
     */
    public static final List randomPermutation(int l) {
        List repr = new ArrayList(l);
        for (int i=0; i identityPermutation(int l) {
        List repr = new ArrayList(l);
        for (int i=0; idata sorted by comparator. The
     * data is not modified during the process.
     *
     * This is useful if you want to inject some permutations to the initial
     * population.
     *
     * @param  type of the data
     * @param data list of data determining the order
     * @param comparator how the data will be compared
     * @return list representation of the permutation corresponding to the parameters
     */
    public static  List comparatorPermutation(List data, Comparator comparator) {
        List sortedData = new ArrayList (data);
        Collections.sort(sortedData, comparator);

        return inducedPermutation(data, sortedData);
    }

    /**
     * Generates a representation of a permutation corresponding to a
     * permutation which yields permutedData when applied to
     * originalData.
     *
     * This method can be viewed as an inverse to {@link #decode(List)}.
     *
     * @param  type of the data
     * @param originalData the original, unpermuted data
     * @param permutedData the data, somehow permuted
     * @return representation of a permutation corresponding to the permutation originalData -> permutedData
     * @throws IllegalArgumentException iff the permutedData and originalData contains different data
     */
    public static  List inducedPermutation(List originalData, List permutedData) throws IllegalArgumentException {
        if (originalData.size() != permutedData.size()) {
            throw new IllegalArgumentException("originalData and permutedData must have same length");
        }
        int l = originalData.size();

        List origDataCopy = new ArrayList (originalData);

        Double[] res = new Double[l];
        for (int i=0; i baseSequence(int l) {
        List baseSequence = new ArrayList (l);
        for (int i=0; i