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

Package org.apache.commons.math3.distribution

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

org.apache.commons.math3.linear.Array2DRowRealMatrix
orld.wolfram.com/NegativeBinomialDistribution.html">MathWorld, but not in Wikipedia.

For a random variable {@code X} whose values are distributed according to thisdistribution, the probability mass function is given by
{@code P(X = k) = C(k + r - 1, r - 1) * p^r * (1 - p)^k,}
where {@code r} is the number of successes, {@code p} is the probability ofsuccess, and {@code X} is the total number of failures. {@code C(n, k)} isthe binomial coefficient ( {@code n} choose {@code k}). The mean and variance of {@code X} are
{@code E(X) = (1 - p) * r / p, var(X) = (1 - p) * r / p^2.}
Finally, the cumulative distribution function is given by
{@code P(X <= k) = I(p, r, k + 1)}, where I is the regularized incomplete Beta function.
@see Negative binomial distribution (Wikipedia) @see Negative binomial distribution (MathWorld) @since 1.2 (changed to concrete class in 3.0)

    @Override
    public double inverseCumulativeProbability(double p) throws OutOfRangeException {
        double ret;


        if (p < 0.0 || p > 1.0) {
            throw new OutOfRangeException(p, 0.0, 1.0);
        } else if (p == 1.0) {
            ret = Double.POSITIVE_INFINITY;
        } else {
            ret = -mean * FastMath.log(1.0 - p);
        }

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        if (lo >= hi) {
            throw new NumberIsTooLargeException(lo, hi, false);
        }
        if (init < lo ||
            init > hi) {
            throw new OutOfRangeException(init, lo, hi);
        }


        lower = lo;
        upper = hi;
        start = init;

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    @Override
    public double inverseCumulativeProbability(double p)
        throws OutOfRangeException {
        if (p < 0 || p > 1) {
            throw new OutOfRangeException(p, 0, 1);
        }
        if (p == 0) {
            return a;
        }
        if (p == 1) {

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        /**
         * {@inheritDoc}
         * @throws TooManyEvaluationsException.
         */
        public void trigger(int max) {
            throw new TooManyEvaluationsException(max);
        }

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        /**
         * {@inheritDoc}
         * @throws TooManyIterationsException.
         */
        public void trigger(int max) {
            throw new TooManyIterationsException(max);
        }

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                                              double threshold) {
        super(wrong, threshold, false);
        this.index = index;
        this.threshold = threshold;


        final ExceptionContext context = getContext();
        context.addMessage(LocalizedFormats.NOT_POSITIVE_DEFINITE_MATRIX);
        context.addMessage(LocalizedFormats.ARRAY_ELEMENT, wrong, index);
    }

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            // build the P matrix elements from Taylor series formulas
            final BigFraction[] pI = pData[i];
            final int factor = -(i + 1);
            int aj = factor;
            for (int j = 0; j < pI.length; ++j) {
                pI[j] = new BigFraction(aj * (j + 2));
                aj *= factor;
            }
        }


        return new Array2DRowFieldMatrix<BigFraction>(pData, false);

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      List<SiteWithPolynomial> nearestSites =
          nearestSiteMap.get(site);
      
      RealVector vector = new ArrayRealVector(SITES_FOR_APPROX);
      RealMatrix matrix = new Array2DRowRealMatrix(
          SITES_FOR_APPROX, DefaultPolynomial.NUM_COEFFS);
      
      for (int row = 0; row < SITES_FOR_APPROX; row++) {
        SiteWithPolynomial nearSite = nearestSites.get(row);
        DefaultPolynomial.populateMatrix(matrix, row, nearSite.pos.x, nearSite.pos.z);

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        }


        // solve the rectangular system in the least square sense
        // to get the best estimate of the Nordsieck vector [s2 ... sk]
        QRDecomposition decomposition;
        decomposition = new QRDecomposition(new Array2DRowRealMatrix(a, false));
        RealMatrix x = decomposition.getSolver().solve(new Array2DRowRealMatrix(b, false));
        return new Array2DRowRealMatrix(x.getData(), false);
    }

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            // update Nordsieck vector
            final double[] predictedScaled = new double[y0.length];
            for (int j = 0; j < y0.length; ++j) {
                predictedScaled[j] = stepSize * yDot[j];
            }
            final Array2DRowRealMatrix nordsieckTmp = updateHighOrderDerivativesPhase1(nordsieck);
            updateHighOrderDerivativesPhase2(scaled, predictedScaled, nordsieckTmp);
            interpolator.reinitialize(stepEnd, stepSize, predictedScaled, nordsieckTmp);


            // discrete events handling
            interpolator.storeTime(stepEnd);

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org.apache.commons.math3.analysis.function.Sin

org.apache.commons.math3.analysis.function.Sinc

org.apache.commons.math3.analysis.MultivariateFunction

org.apache.commons.math3.analysis.QuinticFunction

org.apache.commons.math3.analysis.solvers.BrentSolver

org.apache.commons.math3.analysis.UnivariateFunction

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