Example usage for org.apache.commons.math3.analysis.integration RombergIntegrator RombergIntegrator

Introduction

In this page you can find the example usage for org.apache.commons.math3.analysis.integration RombergIntegrator RombergIntegrator.

Prototype

public RombergIntegrator() 

Source Link

Document

Construct a Romberg integrator with default settings (max iteration count set to #ROMBERG_MAX_ITERATIONS_COUNT )

Usage

From source file:io.yields.math.concepts.operator.Volume.java

public Volume() {
    this.integrator = new RombergIntegrator();
}

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From source file:io.yields.math.concepts.operator.CurveLength.java

protected CurveLength() {
    this.integrator = new RombergIntegrator();
}

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From source file:gamlss.distributions.GT.java

/**
 * This is the Skew t type3 distribution with supplied link 
 * function for each of the distribution parameters.
 * @param muLink - link function for mu distribution parameter
 * @param sigmaLink - link function for sigma distribution parameter
 * @param nuLink - link function for nu distribution parameter
 * @param tauLink - link function for tau distribution parameter
 */// ww w  . ja v a 2s .  c  om
public GT(final int muLink, final int sigmaLink, final int nuLink, final int tauLink) {

    distributionParameterLink.put(DistributionSettings.MU,
            MakeLinkFunction.checkLink(DistributionSettings.GT, muLink));
    distributionParameterLink.put(DistributionSettings.SIGMA,
            MakeLinkFunction.checkLink(DistributionSettings.GT, sigmaLink));
    distributionParameterLink.put(DistributionSettings.NU,
            MakeLinkFunction.checkLink(DistributionSettings.GT, nuLink));
    distributionParameterLink.put(DistributionSettings.TAU,
            MakeLinkFunction.checkLink(DistributionSettings.GT, tauLink));

    integrator = new RombergIntegrator();
    function = new IntegratingFunction();
    interval = new double[2];
    uniRootSolver = new BrentSolver(1.0e-12, 1.0e-8);
    uniRootObj = new UniRootObjFunction();
}

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From source file:org.apache.mahout.math.jet.random.DistributionChecks.java

public static void checkDistribution(final AbstractContinousDistribution dist, double[] x, double offset,
        double scale, int n) {
    double[] xs = Arrays.copyOf(x, x.length);
    for (int i = 0; i < xs.length; i++) {
        xs[i] = xs[i] * scale + offset;/*  w  ww .j  a  v  a2  s.c  o m*/
    }
    Arrays.sort(xs);

    // collect samples
    double[] y = new double[n];
    for (int i = 0; i < n; i++) {
        y[i] = dist.nextDouble();
    }
    Arrays.sort(y);

    // compute probabilities for bins
    double[] p = new double[xs.length + 1];
    double lastP = 0;
    for (int i = 0; i < xs.length; i++) {
        double thisP = dist.cdf(xs[i]);
        p[i] = thisP - lastP;
        lastP = thisP;
    }
    p[p.length - 1] = 1 - lastP;

    // count samples in each bin
    int[] k = new int[xs.length + 1];
    int lastJ = 0;
    for (int i = 0; i < k.length - 1; i++) {
        int j = 0;
        while (j < n && y[j] < xs[i]) {
            j++;
        }
        k[i] = j - lastJ;
        lastJ = j;
    }
    k[k.length - 1] = n - lastJ;

    // now verify probabilities by comparing to integral of pdf
    UnivariateIntegrator integrator = new RombergIntegrator();
    for (int i = 0; i < xs.length - 1; i++) {
        double delta = integrator.integrate(1000000, new UnivariateFunction() {
            @Override
            public double value(double v) {
                return dist.pdf(v);
            }
        }, xs[i], xs[i + 1]);
        Assert.assertEquals(delta, p[i + 1], 1.0e-6);
    }

    // finally compute G^2 of observed versus predicted.  See http://en.wikipedia.org/wiki/G-test
    double sum = 0;
    for (int i = 0; i < k.length; i++) {
        if (k[i] != 0) {
            sum += k[i] * Math.log(k[i] / p[i] / n);
        }
    }
    sum *= 2;

    // sum is chi^2 distributed with degrees of freedom equal to number of partitions - 1
    int dof = k.length - 1;
    // fisher's approximation is that sqrt(2*x) is approximately unit normal with mean sqrt(2*dof-1)
    double z = Math.sqrt(2 * sum) - Math.sqrt(2 * dof - 1);
    Assert.assertTrue(String.format("offset=%.3f scale=%.3f Z = %.1f", offset, scale, z), Math.abs(z) < 3);
}

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From source file:org.apache.solr.client.solrj.io.eval.IntegrateEvaluator.java

@Override
public Object doWork(Object... values) throws IOException {

    if (values.length != 3) {
        throw new IOException("The integrate function requires 3 parameters");
    }/*from w w w. j  a  v  a  2 s.c o  m*/

    if (!(values[0] instanceof VectorFunction)) {
        throw new IOException(String.format(Locale.ROOT,
                "Invalid expression %s - found type %s for the first value, expecting a FunctionVector",
                toExpression(constructingFactory), values[0].getClass().getSimpleName()));
    }

    VectorFunction vectorFunction = (VectorFunction) values[0];
    if (!(vectorFunction.getFunction() instanceof UnivariateFunction)) {
        throw new IOException("Cannot evaluate integral from parameter.");
    }

    Number min = null;
    Number max = null;

    if (values[1] instanceof Number) {
        min = (Number) values[1];
    } else {
        throw new IOException("The second parameter of the integrate function must be a number");
    }

    if (values[2] instanceof Number) {
        max = (Number) values[2];
    } else {
        throw new IOException("The third parameter of the integrate function must be a number");
    }

    UnivariateFunction func = (UnivariateFunction) vectorFunction.getFunction();

    RombergIntegrator rombergIntegrator = new RombergIntegrator();
    return rombergIntegrator.integrate(5000, func, min.doubleValue(), max.doubleValue());
}

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