Example usage for org.apache.commons.math.random MersenneTwister MersenneTwister

Introduction

In this page you can find the example usage for org.apache.commons.math.random MersenneTwister MersenneTwister.

Prototype

public MersenneTwister(long seed) 

Source Link

Document

Creates a new random number generator using a single long seed.

Usage

From source file:br.upe.ecomp.doss.algorithm.abc.ABC.java

/**
 * Each food source are initialized.//from w w  w .j a va 2s. c  o  m
 */
private void initializeFoodSource(int index) {

    MersenneTwister mt = new MersenneTwister(System.nanoTime());
    double randNumber;
    double fitness;
    double[] position = new double[dimensions];

    for (int i = 0; i < dimensions; i++) {
        randNumber = mt.nextDouble() * 32767 / ((double) 32767 + (double) 1);
        position[i] = randNumber
                * (getProblem().getUpperBound(dimensions) - getProblem().getLowerBound(dimensions))
                + getProblem().getLowerBound(dimensions);
    }

    fitness = getProblem().getFitness(position);
    foodSources[index] = new FoodSource(dimensions);
    foodSources[index].updateCurrentPosition(position, fitness);
    foodSources[index].updateBestPosition(position, fitness);
    foodSources[index].setCountStagnation(0);
}

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From source file:br.upe.ecomp.doss.algorithm.volitiveapso.WeightedAPSOParticle.java

/**
 * Updates the current position of the particle and the fitness variation.
 * //from  w w  w .  j  av  a 2 s .c o  m
 * @param problem The problem that we are trying to solve.
 */
public void updateCurrentPosition(Problem problem) {
    MersenneTwister random = new MersenneTwister(System.nanoTime());
    double[] position = getCurrentPosition();
    double[] nextPosition = new double[getDimensions()];
    double newPosition;
    for (int i = 0; i < getDimensions(); i++) {
        newPosition = position[i] + getVelocity()[i];

        // if a particle exceeds the search space limit, so inverts the particle velocity on
        // that dimension which the search space limit was exceeded
        if (newPosition >= problem.getUpperBound(i)) {
            position[i] = problem.getUpperBound(i) - getVelocity()[i] * random.nextDouble();
        } else if (newPosition <= problem.getLowerBound(i)) {
            position[i] = problem.getLowerBound(i) - getVelocity()[i] * random.nextDouble();
        } else {
            position[i] = newPosition;
        }
        nextPosition[i] = position[i];

    }

    deltaFitness = problem.getFitness(nextPosition) - getCurrentFitness();
    if (problem.isFitnessBetterThan(getCurrentFitness(), problem.getFitness(nextPosition))
            && deltaFitness < 0) {
        deltaFitness *= -1;
    }

    // The particle never reduces its weight
    if (!problem.isFitnessBetterThan(getCurrentFitness(), problem.getFitness(nextPosition))) {
        deltaFitness = 0;
    }

    previousPosition = getCurrentPosition().clone();
    updateCurrentPosition(nextPosition, problem.getFitness(nextPosition));

    if (problem.isFitnessBetterThan(getBestFitness(), getCurrentFitness())) {
        updateBestPosition(getCurrentPosition(), getCurrentFitness());
    }

}

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From source file:dr.evomodel.branchmodel.RandomBranchModel.java

public RandomBranchModel(TreeModel treeModel, //
        GY94CodonModel baseSubstitutionModel, //
        double rate, //
        boolean hasSeed, long seed) {

    super(RANDOM_BRANCH_MODEL);

    this.treeModel = treeModel;
    this.baseSubstitutionModel = baseSubstitutionModel;

    this.rate = rate;

    if (hasSeed) {

        // use fixed seed for e_i
        random = new MersenneTwister(seed);
    } else {//from  w ww . j  a v a  2  s.c  o m

        //use BEAST seed
        random = new MersenneTwister(MathUtils.nextLong());

    } //END: seed check

    setup();

}

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From source file:br.upe.ecomp.doss.algorithm.abc.ABC.java

private void sendEmployedBees() {

    MersenneTwister random = new MersenneTwister(System.nanoTime());
    double[] tempFood;
    double tempFitness;

    for (int i = 0; i < foodCount; i++) {
        double rand = random.nextDouble() * 32767 / ((double) (32767) + (double) (1));
        int param2change = (int) (rand * dimensions);

        rand = random.nextDouble() * 32767 / ((double) (32767) + (double) (1));
        int neighbour = (int) (rand * foodCount);

        tempFood = new double[dimensions];
        for (int j = 0; j < dimensions; j++) {
            tempFood[j] = foodSources[i].getCurrentPosition()[j];
        }// ww  w .  j  av a  2s .  c  o  m

        rand = random.nextDouble() * 32767 / ((double) (32767) + (double) (1));
        tempFood[param2change] = foodSources[i].getCurrentPosition()[param2change]
                + (foodSources[i].getCurrentPosition()[param2change]
                        - foodSources[neighbour].getCurrentPosition()[param2change]) * (rand - 0.5) * 2;

        if (tempFood[param2change] < getProblem().getLowerBound(dimensions))
            tempFood[param2change] = getProblem().getLowerBound(dimensions);
        if (tempFood[param2change] > getProblem().getUpperBound(dimensions))
            tempFood[param2change] = getProblem().getUpperBound(dimensions);

        tempFitness = getProblem().getFitness(tempFood);

        if (getProblem().isFitnessBetterThan(foodSources[i].getCurrentFitness(), tempFitness)) {
            foodSources[i].updateCurrentPosition(tempFood, tempFitness);
            foodSources[i].updateBestPosition(tempFood, tempFitness);
            foodSources[i].setCountStagnation(0);

            double bestSolutionFitness = getProblem().getFitness(solution);
            if (getProblem().isFitnessBetterThan(bestSolutionFitness, tempFitness)) {
                solution = tempFood;
            }
        } else {
            foodSources[i].setCountStagnation(foodSources[i].getCountStagnation() + 1);
        }
    }
}

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From source file:br.upe.ecomp.doss.algorithm.pso.PSOParticle.java

/**
 * Updates the current position of the particle.
 * //from   ww w .j a  va  2s.  co  m
 * @param problem The problem that we are trying to solve.
 */
public void updateCurrentPosition(Problem problem) {
    MersenneTwister random = new MersenneTwister(System.nanoTime());
    double[] position = getCurrentPosition().clone();
    double nextPosition;
    double positionLimiter = 0.5;
    for (int i = 0; i < getDimensions(); i++) {
        nextPosition = position[i] + velocity[i];

        // if a particle exceeds the search space limit, so inverts the particle velocity on
        // that dimension which the search space limit was exceeded
        if (nextPosition >= problem.getUpperBound(i)) {
            position[i] = problem.getUpperBound(i) - velocity[i] * random.nextDouble() * positionLimiter;
        } else if (nextPosition <= problem.getLowerBound(i)) {
            position[i] = problem.getLowerBound(i) - velocity[i] * random.nextDouble() * positionLimiter;
        } else {
            position[i] = nextPosition;
        }
    }
    updateCurrentPosition(position, problem.getFitness(position));
}

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From source file:br.upe.ecomp.doss.algorithm.bso.BSO.java

private void initializationPosition(int bee) {
    double[] position = new double[dimensions];
    MersenneTwister random = new MersenneTwister(System.nanoTime());
    double fitness;

    for (int i = 0; i < dimensions; i++) {
        double value = random.nextDouble();
        position[i] = (getProblem().getUpperBound(i) - getProblem().getLowerBound(i)) * value
                + getProblem().getLowerBound(i);

        if (position[i] > getProblem().getUpperBound(i)) {
            position[i] = getProblem().getUpperBound(i);
        } else if (position[i] < getProblem().getLowerBound(i)) {
            position[i] = getProblem().getLowerBound(i);
        }/*from   w w  w.j av a2  s . c o m*/
    }
    fitness = getProblem().getFitness(position);
    System.out.println("Current fitness in position: " + fitness);
    Bee beeAux = new Bee(dimensions);
    bees.add(bee, beeAux);
    bees.get(bee).updateCurrentPosition(position, fitness);
    bees.get(bee).updateBestPosition(position, fitness);

}

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From source file:br.upe.ecomp.doss.problem.movingpeaks.MovingPeaks.java

private double[] getMovingVector() {
    MersenneTwister random = new MersenneTwister(System.nanoTime());
    double[] vector = new double[dimensions];
    double movingLengthFraction = movingLength / dimensions;
    List<Integer> indexes = new ArrayList<Integer>();
    for (int i = 0; i < dimensions; i++) {
        indexes.add(i);/*from  ww w.ja v a 2  s  . c o  m*/
    }

    int index;
    while (indexes.size() > 1) {
        index = indexes.remove(random.nextInt(indexes.size() - 1));
        vector[index] = movingLengthFraction * random.nextDouble();
    }
    vector[indexes.remove(0)] = movingLength - getLength(vector);

    return vector;
}

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From source file:dr.app.beagle.tools.Partition.java

public Partition(TreeModel treeModel, //
        BranchModel branchModel, //
        GammaSiteRateModel siteModel, //
        BranchRateModel branchRateModel, //
        FrequencyModel freqModel, //
        int from, //
        int to, //
        int every //
) {/*ww w.  j  av  a2  s .  co  m*/

    this.treeModel = treeModel;
    this.siteRateModel = siteModel;
    this.freqModel = freqModel;
    this.branchModel = branchModel;
    this.branchRateModel = branchRateModel;

    this.from = from;
    this.to = to;
    this.every = every;

    dataType = freqModel.getDataType();
    partitionSiteCount = getPartitionSiteCount();

    setBufferHelpers();
    setSubstitutionModelDelegate();
    loadBeagleInstance();

    alignmentMap = new LinkedHashMap<Taxon, int[]>();
    random = new MersenneTwister(MathUtils.nextLong());

}

---

From source file:br.upe.ecomp.doss.algorithm.abc.ABC.java

private void sendOnlookerBees() {

    MersenneTwister random = new MersenneTwister(System.nanoTime());
    double[] tempFood;
    double tempFitness;

    int count = 0;
    int i = 0;//from www .ja  v  a 2s. com

    while (count < foodCount) {

        double rand = random.nextDouble() * 32767 / ((double) (32767) + (double) (1));

        if (rand < foodSources[i].getProbabilitySelection()) {
            count++;

            rand = random.nextDouble() * 32767 / ((double) (32767) + (double) (1));
            int param2change = (int) (rand * dimensions);

            rand = random.nextDouble() * 32767 / ((double) (32767) + (double) (1));
            int neighbour = (int) (rand * foodCount);

            while (neighbour == i) {
                rand = random.nextDouble() * 32767 / ((double) (32767) + (double) (1));
                neighbour = (int) (rand * foodCount);
            }

            tempFood = new double[dimensions];
            for (int j = 0; j < dimensions; j++) {
                tempFood[j] = foodSources[i].getCurrentPosition()[j];
            }

            rand = random.nextDouble() * 32767 / ((double) (32767) + (double) (1));
            tempFood[param2change] = foodSources[i].getCurrentPosition()[param2change]
                    + (foodSources[i].getCurrentPosition()[param2change]
                            - foodSources[neighbour].getCurrentPosition()[param2change]) * (rand - 0.5) * 2;

            if (tempFood[param2change] < getProblem().getLowerBound(dimensions))
                tempFood[param2change] = getProblem().getLowerBound(dimensions);
            if (tempFood[param2change] > getProblem().getUpperBound(dimensions))
                tempFood[param2change] = getProblem().getUpperBound(dimensions);

            tempFitness = getProblem().getFitness(tempFood);

            if (getProblem().isFitnessBetterThan(foodSources[i].getCurrentFitness(), tempFitness)) {
                foodSources[i].updateCurrentPosition(tempFood, tempFitness);
                foodSources[i].updateBestPosition(tempFood, tempFitness);
                foodSources[i].setCountStagnation(0);

                double bestSolutionFitness = getProblem().getFitness(solution);
                if (getProblem().isFitnessBetterThan(bestSolutionFitness, tempFitness)) {
                    solution = tempFood;
                }
            } else {
                foodSources[i].setCountStagnation(foodSources[i].getCountStagnation() + 1);
            }
        }
        i++;
        if (i == foodCount)
            i = 0;
    }
}

---

From source file:edu.cornell.med.icb.goby.modes.ReadQualityStatsMode.java

/**
 * Display the alignments as text files.
 *
 * @throws java.io.IOException error reading / writing
 *///  ww  w. ja  v  a 2 s. c  om
@Override
public void execute() throws IOException {
    PrintStream writer = null;
    try {
        numberOfSkippedReads = 0;
        numberOfObservedReads = 0;
        writer = outputFile == null ? System.out : new PrintStream(new FileOutputStream(outputFile));
        final Int2ObjectMap<ReadQualityStats> qualityStats = new Int2ObjectOpenHashMap<ReadQualityStats>();
        final ProgressLogger progress = new ProgressLogger(LOG);
        progress.start();
        final MersenneTwister random = new MersenneTwister(37);
        final boolean doSample = sampleFraction < 1.0d;
        writer.println("basename\treadIndex\t25%-percentile\tmedian\taverageQuality\t75%-percentile");
        for (final File filename : inputFiles) {
            final ReadsReader reader = new ReadsReader(filename);
            final String basename = ReadsReader.getBasename(filename.toString());
            for (final Reads.ReadEntry entry : reader) {
                if (!doSample || random.nextDouble() < sampleFraction) {
                    final ByteString qualityScores = entry.getQualityScores();
                    final int size = qualityScores.size();
                    for (int readIndex = 0; readIndex < size; readIndex++) {
                        final byte code = qualityScores.byteAt(readIndex);
                        ReadQualityStats stats = qualityStats.get(readIndex);
                        if (stats == null) {
                            stats = new ReadQualityStats(1.0d);
                            qualityStats.put(readIndex, stats);
                            stats.readIndex = readIndex;
                        }
                        stats.observe(code);
                    }
                    numberOfObservedReads++;
                } else {
                    numberOfSkippedReads++;
                }
                progress.lightUpdate();
            }

            for (final ReadQualityStats stat : qualityStats.values()) {
                if (!stat.sampleIsEmpty()) {
                    stat.evaluatePercentiles();
                    writer.printf("%s\t%d\t%d\t%d\t%f\t%d%n", basename, stat.readIndex, stat.percentile(25),
                            stat.percentile(50), stat.averageQuality / stat.observedCount, stat.percentile(75));
                }
            }
        }
        progress.updateAndDisplay();
        progress.stop();

    } finally {
        if (writer != System.out) {
            IOUtils.closeQuietly(writer);
        }
    }
}

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