Examples of org.apache.commons.math3.distribution.UniformIntegerDistribution

Package org.apache.commons.math3.distribution

Examples of org.apache.commons.math3.distribution.UniformIntegerDistribution

org.apache.commons.math3.exception.DimensionMismatchException
pedia.org/wiki/Uniform_distribution_(discrete)" >Uniform distribution (discrete), at Wikipedia @since 3.0

                final int target;
                if (i == start) {
                    target = start;
                } else {
                    // NumberIsTooLargeException cannot occur.
                    target = new UniformIntegerDistribution(rng, start, i).sample();
                }
                final int temp = list[target];
                list[target] = list[i];
                list[i] = temp;
            }
        }
            break;
        case HEAD: {
            for (int i = 0; i <= start; i++) {
                final int target;
                if (i == start) {
                    target = start;
                } else {
                    // NumberIsTooLargeException cannot occur.
                    target = new UniformIntegerDistribution(rng, i, start).sample();
                }
                final int temp = list[target];
                list[target] = list[i];
                list[i] = temp;
            }

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    public void testWithInitialCapacity() {


        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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    public void testWithInitialCapacityAndExpansionFactor() {


        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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     * uniformly distributed over [-100, 100].
     *
     * @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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            }
            final int next;
            if (i == 4 || cur == length - 1) {
                next = length - 1;
            } else {
                next = (new UniformIntegerDistribution(cur, length - 1)).sample();
            }
            final int subLength = next - cur + 1;
            out[i] = new double[subLength];
            System.arraycopy(sample, offset, out[i], 0, subLength);
            cur = next + 1;

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        double[] values = new double[len];
        double[] weights = new double[len];


        // 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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                // tests for termination and stringent tolerances
                if (FastMath.abs(actRed) <= TWO_EPS &&
                    preRed <= TWO_EPS &&
                    ratio <= 2.0) {
                    throw new ConvergenceException(LocalizedFormats.TOO_SMALL_COST_RELATIVE_TOLERANCE,
                                                   costRelativeTolerance);
                } else if (delta <= TWO_EPS * xNorm) {
                    throw new ConvergenceException(LocalizedFormats.TOO_SMALL_PARAMETERS_RELATIVE_TOLERANCE,
                                                   parRelativeTolerance);
                } else if (maxCosine <= TWO_EPS) {
                    throw new ConvergenceException(LocalizedFormats.TOO_SMALL_ORTHOGONALITY_TOLERANCE,
                                                   orthoTolerance);
                }
            }
        }
    }

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                for (int j = k; j < nR; ++j) {
                    double aki = weightedJacobian[j][permutation[i]];
                    norm2 += aki * aki;
                }
                if (Double.isInfinite(norm2) || Double.isNaN(norm2)) {
                    throw new ConvergenceException(LocalizedFormats.UNABLE_TO_PERFORM_QR_DECOMPOSITION_ON_JACOBIAN,
                                                   nR, nC);
                }
                if (norm2 > ak2) {
                    nextColumn = i;
                    ak2        = norm2;

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     * length.
     */
    protected double[] computeResiduals(double[] objectiveValue) {
        final double[] target = getTarget();
        if (objectiveValue.length != target.length) {
            throw new DimensionMismatchException(target.length,
                                                 objectiveValue.length);
        }


        final double[] residuals = new double[target.length];
        for (int i = 0; i < target.length; i++) {

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        /** {@inheritDoc} */
        public RealVector solve(final RealVector b) {
            final int m = lTData.length;
            if (b.getDimension() != m) {
                throw new DimensionMismatchException(b.getDimension(), m);
            }


            final double[] x = b.toArray();


            // Solve LY = b

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