Examples of org.apache.commons.math.stat.descriptive.SummaryStatistics

Package org.apache.commons.math.stat.descriptive

Examples of org.apache.commons.math.stat.descriptive.SummaryStatistics

org.apache.commons.math.stat.descriptive.SummaryStatistics

Computes summary statistics for a stream of data values added using the {@link #addValue(double) addValue} method. The data values are not stored inmemory, so this class can be used to compute statistics for very large data streams.

The {@link StorelessUnivariateStatistic} instances used to maintain summarystate and compute statistics are configurable via setters. For example, the default implementation for the variance can be overridden by calling {@link #setVarianceImpl(StorelessUnivariateStatistic)}. Actual parameters to these methods must implement the {@link StorelessUnivariateStatistic}interface and configuration must be completed before addValue is called. No configuration is necessary to use the default, commons-math provided implementations.

Note: This class is not thread-safe. Use {@link SynchronizedSummaryStatistics} if concurrent access from multiplethreads is required.
@version $Revision: 1042376 $ $Date: 2010-12-05 16:54:55 +0100 (dim. 05 déc. 2010) $

    public void testInternalDifferentiation()
        throws IntegratorException, DerivativeException {
        FirstOrderIntegrator integ =
            new DormandPrince54Integrator(1.0e-8, 100.0, 1.0e-4, 1.0e-4);
        double hP = 1.0e-12;
        SummaryStatistics residualsP0 = new SummaryStatistics();
        SummaryStatistics residualsP1 = new SummaryStatistics();
        for (double b = 2.88; b < 3.08; b += 0.001) {
            Brusselator brusselator = new Brusselator(b);
            brusselator.setParameter(0, b);
            double[] z = { 1.3, b };
            double[][] dZdZ0 = new double[2][2];
            double[][] dZdP  = new double[2][1];
            double hY = 1.0e-12;
            FirstOrderIntegratorWithJacobians extInt =
                new FirstOrderIntegratorWithJacobians(integ, brusselator, new double[] { b },
                                                      new double[] { hY, hY }, new double[] { hP });
            extInt.setMaxEvaluations(5000);
            extInt.integrate(0, z, new double[][] { { 0.0 }, { 1.0 } }, 20.0, z, dZdZ0, dZdP);
            Assert.assertEquals(5000, extInt.getMaxEvaluations());
            Assert.assertTrue(extInt.getEvaluations() > 2000);
            Assert.assertTrue(extInt.getEvaluations() < 2500);
            Assert.assertEquals(4 * integ.getEvaluations(), extInt.getEvaluations());
            residualsP0.addValue(dZdP[0][0] - brusselator.dYdP0());
            residualsP1.addValue(dZdP[1][0] - brusselator.dYdP1());
        }
        Assert.assertTrue((residualsP0.getMax() - residualsP0.getMin()) < 0.006);
        Assert.assertTrue(residualsP0.getStandardDeviation() < 0.0009);
        Assert.assertTrue((residualsP1.getMax() - residualsP1.getMin()) < 0.009);
        Assert.assertTrue(residualsP1.getStandardDeviation() < 0.0014);
    }

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    @Test
    public void testAnalyticalDifferentiation()
        throws IntegratorException, DerivativeException {
        FirstOrderIntegrator integ =
            new DormandPrince54Integrator(1.0e-8, 100.0, 1.0e-4, 1.0e-4);
        SummaryStatistics residualsP0 = new SummaryStatistics();
        SummaryStatistics residualsP1 = new SummaryStatistics();
        for (double b = 2.88; b < 3.08; b += 0.001) {
            Brusselator brusselator = new Brusselator(b);
            brusselator.setParameter(0, b);
            double[] z = { 1.3, b };
            double[][] dZdZ0 = new double[2][2];
            double[][] dZdP  = new double[2][1];
            FirstOrderIntegratorWithJacobians extInt =
                new FirstOrderIntegratorWithJacobians(integ, brusselator);
            extInt.setMaxEvaluations(5000);
            extInt.integrate(0, z, new double[][] { { 0.0 }, { 1.0 } }, 20.0, z, dZdZ0, dZdP);
            Assert.assertEquals(5000, extInt.getMaxEvaluations());
            Assert.assertTrue(extInt.getEvaluations() > 510);
            Assert.assertTrue(extInt.getEvaluations() < 610);
            Assert.assertEquals(integ.getEvaluations(), extInt.getEvaluations());
            residualsP0.addValue(dZdP[0][0] - brusselator.dYdP0());
            residualsP1.addValue(dZdP[1][0] - brusselator.dYdP1());
        }
        Assert.assertTrue((residualsP0.getMax() - residualsP0.getMin()) < 0.004);
        Assert.assertTrue(residualsP0.getStandardDeviation() < 0.0008);
        Assert.assertTrue((residualsP1.getMax() - residualsP1.getMin()) < 0.005);
        Assert.assertTrue(residualsP1.getStandardDeviation() < 0.0010);
    }

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        double next = 0.0;
        double tolerance = 0.1;
        vs.computeDistribution();
        assertTrue("empirical distribution property",
            vs.getEmpiricalDistribution() != null);
        SummaryStatistics stats = new SummaryStatistics();
        for (int i = 1; i < 1000; i++) {
            next = vs.getNext();
            stats.addValue(next);
        }
        assertEquals("mean", 5.069831575018909, stats.getMean(), tolerance);
        assertEquals
         ("std dev", 1.0173699343977738, stats.getStandardDeviation(),
            tolerance);


        vs.computeDistribution(500);
        stats = new SummaryStatistics();
        for (int i = 1; i < 1000; i++) {
            next = vs.getNext();
            stats.addValue(next);
        }
        assertEquals("mean", 5.069831575018909, stats.getMean(), tolerance);
        assertEquals
         ("std dev", 1.0173699343977738, stats.getStandardDeviation(),
            tolerance);


    }

View Full Code Here

    }


    public void testOneSampleTTest() throws Exception {
        double[] oneSidedP =
            {2d, 0d, 6d, 6d, 3d, 3d, 2d, 3d, -6d, 6d, 6d, 6d, 3d, 0d, 1d, 1d, 0d, 2d, 3d, 3d };
        SummaryStatistics oneSidedPStats = new SummaryStatistics();
        for (int i = 0; i < oneSidedP.length; i++) {
            oneSidedPStats.addValue(oneSidedP[i]);
        }
        // Target comparison values computed using R version 1.8.1 (Linux version)
        assertEquals("one sample t stat", 3.86485535541,
                TestUtils.t(0d, oneSidedP), 10E-10);
        assertEquals("one sample t stat", 3.86485535541,

View Full Code Here

    }


    public void testTwoSampleTHeterscedastic() throws Exception {
        double[] sample1 = { 7d, -4d, 18d, 17d, -3d, -5d, 1d, 10d, 11d, -2d };
        double[] sample2 = { -1d, 12d, -1d, -3d, 3d, -5d, 5d, 2d, -11d, -1d, -3d };
        SummaryStatistics sampleStats1 = new SummaryStatistics();
        for (int i = 0; i < sample1.length; i++) {
            sampleStats1.addValue(sample1[i]);
        }
        SummaryStatistics sampleStats2 = new SummaryStatistics();
        for (int i = 0; i < sample2.length; i++) {
            sampleStats2.addValue(sample2[i]);
        }


        // Target comparison values computed using R version 1.8.1 (Linux version)
        assertEquals("two sample heteroscedastic t stat", 1.60371728768,
                TestUtils.t(sample1, sample2), 1E-10);

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        }
    }
    public void testTwoSampleTHomoscedastic() throws Exception {
        double[] sample1 ={2, 4, 6, 8, 10, 97};
        double[] sample2 = {4, 6, 8, 10, 16};
        SummaryStatistics sampleStats1 = new SummaryStatistics();
        for (int i = 0; i < sample1.length; i++) {
            sampleStats1.addValue(sample1[i]);
        }
        SummaryStatistics sampleStats2 = new SummaryStatistics();
        for (int i = 0; i < sample2.length; i++) {
            sampleStats2.addValue(sample2[i]);
        }


        // Target comparison values computed using R version 1.8.1 (Linux version)
        assertEquals("two sample homoscedastic t stat", 0.73096310086,
                TestUtils.homoscedasticT(sample1, sample2), 10E-11);

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        super(name);
    }


    @Override
    public void setUp() {
        tooShortStats = new SummaryStatistics();
        tooShortStats.addValue(0d);
    }

View Full Code Here


    public void testOneSampleT() throws Exception {
        double[] observed =
            {93.0, 103.0, 95.0, 101.0, 91.0, 105.0, 96.0, 94.0, 101.0,  88.0, 98.0, 94.0, 101.0, 92.0, 95.0 };
        double mu = 100.0;
        SummaryStatistics sampleStats = null;
        sampleStats = new SummaryStatistics();
        for (int i = 0; i < observed.length; i++) {
            sampleStats.addValue(observed[i]);
        }


        // Target comparison values computed using R version 1.8.1 (Linux version)
        assertEquals("t statistic",  -2.81976445346,
                testStatistic.t(mu, observed), 10E-10);

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    }


    public void testOneSampleTTest() throws Exception {
        double[] oneSidedP =
            {2d, 0d, 6d, 6d, 3d, 3d, 2d, 3d, -6d, 6d, 6d, 6d, 3d, 0d, 1d, 1d, 0d, 2d, 3d, 3d };
        SummaryStatistics oneSidedPStats = new SummaryStatistics();
        for (int i = 0; i < oneSidedP.length; i++) {
            oneSidedPStats.addValue(oneSidedP[i]);
        }
        // Target comparison values computed using R version 1.8.1 (Linux version)
        assertEquals("one sample t stat", 3.86485535541,
                testStatistic.t(0d, oneSidedP), 10E-10);
        assertEquals("one sample t stat", 3.86485535541,

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    }


    public void testTwoSampleTHeterscedastic() throws Exception {
        double[] sample1 = { 7d, -4d, 18d, 17d, -3d, -5d, 1d, 10d, 11d, -2d };
        double[] sample2 = { -1d, 12d, -1d, -3d, 3d, -5d, 5d, 2d, -11d, -1d, -3d };
        SummaryStatistics sampleStats1 = new SummaryStatistics();
        for (int i = 0; i < sample1.length; i++) {
            sampleStats1.addValue(sample1[i]);
        }
        SummaryStatistics sampleStats2 = new SummaryStatistics();
        for (int i = 0; i < sample2.length; i++) {
            sampleStats2.addValue(sample2[i]);
        }


        // Target comparison values computed using R version 1.8.1 (Linux version)
        assertEquals("two sample heteroscedastic t stat", 1.60371728768,
                testStatistic.t(sample1, sample2), 1E-10);

View Full Code Here

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