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

Package org.apache.commons.math3.distribution

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

org.apache.commons.math3.exception.MathIllegalArgumentException
Abstract base class for {@link RealDistribution} tests.
To create a concrete test class for a continuous distribution implementation, first implement makeDistribution() to return a distribution instance to use in tests. Then implement each of the test data generation methods below. In each case, the test points and test values arrays returned represent parallel arrays of inputs and expected values for the distribution returned by makeDistribution(). Default implementations are provided for the makeInverseXxx methods that just invert the mapping defined by the arrays returned by the makeCumulativeXxx methods.
makeCumulativeTestPoints() -- arguments used to test cumulative probabilities makeCumulativeTestValues() -- expected cumulative probabilites makeDensityTestValues() -- expected density values at cumulativeTestPoints makeInverseCumulativeTestPoints() -- arguments used to test inverse cdf makeInverseCumulativeTestValues() -- expected inverse cdf values
To implement additional test cases with different distribution instances and test data, use the setXxx methods for the instance data in test cases and call the verifyXxx methods to verify results.
Error tolerance can be overriden by implementing getTolerance().
Test data should be validated against reference tables or other packages where possible, and the source of the reference data and/or validation should be documented in the test cases. A framework for validating distribution data against R is included in the /src/test/R source tree.
See {@link NormalDistributionTest} and {@link ChiSquaredDistributionTest}for examples.

     */
    public double correlation(final double[] xArray, final double[] yArray)
            throws DimensionMismatchException {


        if (xArray.length != yArray.length) {
            throw new DimensionMismatchException(xArray.length, yArray.length);
        }


        final int n = xArray.length;
        final long numPairs = sum(n - 1);

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     * @throws DimensionMismatchException if {@link #target} and
     * {@link #weightMatrix} have inconsistent dimensions.
     */
    private void checkParameters() {
        if (target.length != weightMatrix.getColumnDimension()) {
            throw new DimensionMismatchException(target.length,
                                                 weightMatrix.getColumnDimension());
        }
    }

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    public void increment(final double[] data)
        throws DimensionMismatchException {


        int length = data.length;
        if (length != dimension) {
            throw new DimensionMismatchException(length, dimension);
        }


        // only update the upper triangular part of the covariance matrix
        // as only these parts are actually stored
        for (int i = 0; i < length; i++){

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     * @throws DimensionMismatchException if the dimension of sc does not match this
     * @since 3.3
     */
    public void append(StorelessCovariance sc) throws DimensionMismatchException {
        if (sc.dimension != dimension) {
            throw new DimensionMismatchException(sc.dimension, dimension);
        }


        // only update the upper triangular part of the covariance matrix
        // as only these parts are actually stored
        for (int i = 0; i < dimension; i++) {

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            if (m.getColumnDimension() != m.getRowDimension()) {
                throw new NonSquareOperatorException(m.getColumnDimension(),
                                                     m.getRowDimension());
            }
            if (m.getRowDimension() != a.getRowDimension()) {
                throw new DimensionMismatchException(m.getRowDimension(),
                                                     a.getRowDimension());
            }
        }
    }

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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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   */
  public Rotation(Vector3D u, Vector3D v) throws MathArithmeticException {


    double normProduct = u.getNorm() * v.getNorm();
    if (normProduct == 0) {
        throw new MathArithmeticException(LocalizedFormats.ZERO_NORM_FOR_ROTATION_DEFINING_VECTOR);
    }


    double dot = u.dotProduct(v);


    if (dot < ((2.0e-15 - 1.0) * normProduct)) {

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     * @return the smallest prime greater than or equal to n.
     * @throws MathIllegalArgumentException if n &lt; 0.
     */
    public static int nextPrime(int n) {
        if (n < 0) {
            throw new MathIllegalArgumentException(LocalizedFormats.NUMBER_TOO_SMALL, n, 0);
        }
        if (n == 2) {
            return 2;
        }
        n |= 1;//make sure n is odd

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     * @throws MathIllegalArgumentException if n &lt; 2.
     */
    public static List<Integer> primeFactors(int n) {


        if (n < 2) {
            throw new MathIllegalArgumentException(LocalizedFormats.NUMBER_TOO_SMALL, n, 2);
        }
        // slower than trial div unless we do an awful lot of computation
        // (then it finally gets JIT-compiled efficiently
        // List<Integer> out = PollardRho.primeFactors(n);
        return SmallPrimes.trialDivision(n);

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   */
  public Rotation(Vector3D axis, double angle) throws MathIllegalArgumentException {


    double norm = axis.getNorm();
    if (norm == 0) {
      throw new MathIllegalArgumentException(LocalizedFormats.ZERO_NORM_FOR_ROTATION_AXIS);
    }


    double halfAngle = -0.5 * angle;
    double coeff = FastMath.sin(halfAngle) / norm;

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