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

Introduction

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

Prototype

JDKRandomGenerator

Source Link

Usage

From source file:com.tomgibara.cluster.CreateGaussianMouse.java

public static void main(String[] args) throws IOException {
    GaussianRandomGenerator gen = new GaussianRandomGenerator(new JDKRandomGenerator());
    FileWriter writer = new FileWriter("R/gmouse.txt");
    try {/*from ww  w. j  a v a  2 s . c  om*/
        writeCluster(gen, new double[] { 0, 0 }, new double[] { 4, 4 }, 100, writer);
        writeCluster(gen, new double[] { -4, 4 }, new double[] { 2, 2 }, 50, writer);
        writeCluster(gen, new double[] { 4, 4 }, new double[] { 2, 2 }, 50, writer);
    } finally {
        writer.close();
    }
}

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From source file:com.tomgibara.cluster.CreateGaussianCross.java

public static void main(String[] args) throws IOException {
    GaussianRandomGenerator gen = new GaussianRandomGenerator(new JDKRandomGenerator());
    final double[] center = new double[] { 0, 0 };
    int clusterSize = 300;
    FileWriter writer = new FileWriter("R/cross.txt");
    try {//from  w ww . java2s  .c  o m
        writeCluster(gen, center, new double[] { 6, 1 }, clusterSize, writer);
        writeCluster(gen, center, new double[] { 1, 6 }, clusterSize, writer);
    } finally {
        writer.close();
    }
}

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From source file:com.tomgibara.cluster.CreateUniformMouse.java

public static void main(String[] args) throws IOException {
    UniformRandomGenerator gen = new UniformRandomGenerator(new JDKRandomGenerator());
    FileWriter writer = new FileWriter("R/umouse.txt");
    try {/*  w  ww  .jav  a  2s .c  o  m*/
        writeCluster(gen, new double[] { 0, 0 }, new double[] { 4, 4 }, 300, writer);
        writeCluster(gen, new double[] { -4, 4 }, new double[] { 2, 2 }, 100, writer);
        writeCluster(gen, new double[] { 4, 4 }, new double[] { 2, 2 }, 100, writer);
    } finally {
        writer.close();
    }
}

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From source file:com.tomgibara.cluster.CreateUniformCircles.java

public static void main(String[] args) throws IOException {
    UniformRandomGenerator gen = new UniformRandomGenerator(new JDKRandomGenerator());
    FileWriter writer = new FileWriter("R/circles.txt");
    int size = 400;
    try {/* ww  w  . java2s  .  c  o m*/
        for (int y = 4; y >= -4; y--) {
            for (int x = -4; x <= 4; x++) {
                writeCluster(gen, new double[] { x, y }, new double[] { 0.3, 0.3 }, size, writer);
            }
        }
    } finally {
        writer.close();
    }
}

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From source file:datafu.pig.hash.lsh.interfaces.LSHCreator.java

public RandomGenerator createGenerator() {
    RandomGenerator rg = new JDKRandomGenerator();
    rg.setSeed(seed);
    return rg;
}

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From source file:lfsom.visualization.clustering.LFSKMeans.java

/**
 * Calculate random centroids for each cluster.
 */// w w  w  .  j  av a2s .c  o  m
private void initClustersRandomly() {
    RandomGenerator rg = new JDKRandomGenerator();
    // FIXME: this is for testing purposes only
    rg.setSeed(RANDOM_SEED);
    // for each cluster
    for (int clusterIndex = 0; clusterIndex < k; clusterIndex++) {
        // for each of its attributes
        double[] centroid = new double[numberOfAttributes];
        for (int attributeIndex = 0; attributeIndex < numberOfAttributes; attributeIndex++) {
            centroid[attributeIndex] = differences[attributeIndex] * rg.nextDouble()
                    + minValues[attributeIndex];
        }
        clusters[clusterIndex] = new LFSCluster(centroid);
    }
    System.out.println("initial centroids: ");
    // printCentroids();
}

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From source file:lfsom.visualization.clustering.LFSKMeans.java

/**
 * cluster centres are initialised by equally sized random chunks of the
 * input data when there's 150 instances, we assign 50 chosen randomly to
 * each cluster and calculate its centre from these (the last cluster might
 * be larger if numInstances mod k < 0)
 *//*from w w w. j a v a  2 s  .c  om*/
private void initClustersEqualNumbers() {
    HashSet<Integer> usedIndices = new HashSet<Integer>();
    int limit = numberOfInstances / k;
    // FIXME: Test clustering with new permutation generator!
    // int[] randPermIndices = RandomTools.permutation(new
    // Random(RANDOM_SEED), this.numberOfInstances);
    JDKRandomGenerator rg = new JDKRandomGenerator();
    rg.setSeed(RANDOM_SEED);
    int[] randPermIndices = new RandomDataImpl(rg).nextPermutation(this.numberOfInstances,
            this.numberOfInstances);
    for (int clusterIndex = 0; clusterIndex < k; clusterIndex++) {
        LFSCluster c = new LFSCluster(new double[data[0].length]);
        // System.out.println("cluster: " + clusterIndex);
        for (int randPermIndice : randPermIndices) {
            int currentIndex = randPermIndice;
            if ((c.getNumberOfInstances() < limit || clusterIndex == k - 1)
                    && !usedIndices.contains(currentIndex)) {
                c.addIndex(currentIndex);
                usedIndices.add(currentIndex);
                // System.out.print(" " + currentIndex);
            }
        }
        // System.out.println();
        c.calculateCentroid(data);
        // clusters[clusterIndex] = c;
        clusters[clusterIndex] = new LFSCluster(c.getCentroid());

    }
}

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From source file:lfsom.visualization.clustering.LFSKMeans.java

/** Take random points from the input data as centroids. */
private void initClustersRandomlyOnInstances() {
    ArrayList<double[]> usedInstances = new ArrayList<double[]>();
    RandomGenerator rg = new JDKRandomGenerator();
    // FIXME: this is for testing purposes only
    rg.setSeed(RANDOM_SEED);/* w  w  w.  ja  v  a 2 s . c o m*/
    // for each cluster
    for (int clusterIndex = 0; clusterIndex < k; clusterIndex++) {
        // draw a random input
        double[] centroid = data[rg.nextInt(data.length - 1)].clone();
        while (usedInstances.contains(centroid)) {
            centroid = data[rg.nextInt(data.length - 1)].clone();
        }
        usedInstances.add(centroid);
        clusters[clusterIndex] = new LFSCluster(centroid);
    }
}

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From source file:org.dishevelled.analysis.GraphGenerators.java

/**
 * Connect the specified graph randomly with the specified value on all added edges.
 *
 * @param <N> graph node type//from ww w .  ja  v  a  2  s. co m
 * @param <E> graph edge type
 * @param graph graph to connect, must not be null
 * @param edgeCount edge count, must be at least zero
 * @param edgeValues edge values, must not be null
 * @return the specified graph connected randomly with the specified value on all added edges
 */
public static <N, E> Graph<N, E> connectRandomly(final Graph<N, E> graph, final int edgeCount,
        final BinaryFunction<N, N, E> edgeValues) {
    return connectRandomly(graph, edgeCount, edgeValues, new JDKRandomGenerator());
}

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From source file:org.dishevelled.analysis.GraphGenerators.java

/**
 * Create and return a new randomly connected graph with the specified nodes values
 * on nodes and values provided by the specified function on edges.
 *
 * @param <N> graph node type//w w  w. j a  va  2s  .  co m
 * @param <E> graph edge type
 * @param nodeValues list of node values, must not be null
 * @param edgeCount edge count, must be at least zero
 * @param edgeValues edge values, must not be null
 * @return a new randomly connected graph with the specified nodes values
 *    on nodes and the specified edge value on all added edges
 */
public static <N, E> Graph<N, E> connectRandomly(final List<N> nodeValues, final int edgeCount,
        final BinaryFunction<N, N, E> edgeValues) {
    return connectRandomly(nodeValues, edgeCount, edgeValues, new JDKRandomGenerator());
}

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