Examples of org.apache.commons.math3.distribution.WeibullDistribution.sample()

Package org.apache.commons.math3.distribution

Class org.apache.commons.math3.distribution.WeibullDistribution

Examples of org.apache.commons.math3.distribution.WeibullDistribution.sample()

org.apache.commons.math3.distribution.WeibullDistribution.sample()
esus.ox.ac.uk/~clifford/a5/chap1/node5.html"> Inversion Method to generate exponentially distributed random values from uniform deviates.
@return a random value. @since 2.2

        // and fill valuesList with values from values array with
        // values[i] repeated weights[i] times, each i
        final RealDistribution valueDist = new NormalDistribution(mu, sigma);
        List<Double> valuesList = new ArrayList<Double>();
        for (int i = 0; i < len; i++) {
            double value = valueDist.sample();
            values[i] = value;
            for (int j = 0; j < intWeights[i]; j++) {
                valuesList.add(new Double(value));
            }
        }

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  public static double rNorm(double mean, double sd) {
    RealDistribution dist = new NormalDistribution(RANDOM.getRandomGenerator(),
                                                   mean,
                                                   sd,
                                                   NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
    return dist.sample();
  }
  
  /**
   * Returns an integer sampled according to this distribution. Takes time proportional to np + 1. (Reference:
   * Non-Uniform Random Variate Generation, Devroye http://cgm.cs.mcgill.ca/~luc/rnbookindex.html) Second

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        ResizableDoubleArray eDA2 = new ResizableDoubleArray(2);
        Assert.assertEquals("Initial number of elements should be 0", 0, eDA2.getNumElements());


        final IntegerDistribution randomData = new UniformIntegerDistribution(100, 1000);
        final int iterations = randomData.sample();


        for( int i = 0; i < iterations; i++) {
            eDA2.addElement( i );
        }

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        ResizableDoubleArray eDA3 = new ResizableDoubleArray(3, 3.0, 3.5);
        Assert.assertEquals("Initial number of elements should be 0", 0, eDA3.getNumElements() );


        final IntegerDistribution randomData = new UniformIntegerDistribution(100, 3000);
        final int iterations = randomData.sample();


        for( int i = 0; i < iterations; i++) {
            eDA3.addElement( i );
        }

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        // Fill weights array with random int values between 1 and 5
        int[] intWeights = new int[len];
        final IntegerDistribution weightDist = new UniformIntegerDistribution(1, 5);
        for (int i = 0; i < len; i++) {
            intWeights[i] = weightDist.sample();
            weights[i] = intWeights[i];
        }


        // Fill values array with random data from N(mu, sigma)
        // and fill valuesList with values from values array with

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        ResizableDoubleArray eDA2 = new ResizableDoubleArray(2);
        Assert.assertEquals("Initial number of elements should be 0", 0, eDA2.getNumElements());


        final IntegerDistribution randomData = new UniformIntegerDistribution(100, 1000);
        final int iterations = randomData.sample();


        for( int i = 0; i < iterations; i++) {
            eDA2.addElement( i );
        }

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        ResizableDoubleArray eDA3 = new ResizableDoubleArray(3, 3.0f, 3.5f);
        Assert.assertEquals("Initial number of elements should be 0", 0, eDA3.getNumElements() );


        final IntegerDistribution randomData = new UniformIntegerDistribution(100, 3000);
        final int iterations = randomData.sample();


        for( int i = 0; i < iterations; i++) {
            eDA3.addElement( i );
        }

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     * @return array of random double values
     */
    private double[] generateSample() {
        final IntegerDistribution size = new UniformIntegerDistribution(10, 100);
        final RealDistribution randomData = new UniformRealDistribution(-100, 100);
        final int sampleSize = size.sample();
        final double[] out = randomData.sample(sampleSize);
        return out;
    }


    /**

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        // Fill weights array with random int values between 1 and 5
        int[] intWeights = new int[len];
        final IntegerDistribution weightDist = new UniformIntegerDistribution(1, 5);
        for (int i = 0; i < len; i++) {
            intWeights[i] = weightDist.sample();
            weights[i] = intWeights[i];
        }


        // Fill values array with random data from N(mu, sigma)
        // and fill valuesList with values from values array with

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        final int numSamples = 50;
        final double tol = 6;
        for (int i = 0; i < numSamples; i++) {
            x = distX.sample();
            for (int j = 0; j < numSamples; j++) {
                y = distY.sample();
//                 System.out.println(x + " " + y + " " + f.value(x, y) + " " + bcf.value(x, y));
                Assert.assertEquals(f.value(x, y),  bcf.value(x, y), tol);
            }
//             System.out.println();
        }

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