Examples of org.apache.commons.math3.stat.descriptive.SummaryStatistics

Package org.apache.commons.math3.stat.descriptive

Examples of org.apache.commons.math3.stat.descriptive.SummaryStatistics

org.apache.commons.math3.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.

        // covariance matrix), for each Monte-Carlo run.
        final SummaryStatistics[] sigmaEstimate = new SummaryStatistics[numParams];


        // Initialize statistics accumulators.
        for (int i = 0; i < numParams; i++) {
            paramsFoundByDirectSolution[i] = new SummaryStatistics();
            sigmaEstimate[i] = new SummaryStatistics();
        }


        // Dummy optimizer (to compute the covariance matrix).
        final AbstractLeastSquaresOptimizer optim = new DummyOptimizer();
        final double[] init = { slope, offset };

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                testStatistic.chiSquare(expected,observed) < 16.27);
    }


    @Test
    public void testDoubleDirect() {
        SummaryStatistics sample = new SummaryStatistics();
        final int N = 10000;
        for (int i = 0; i < N; ++i) {
            sample.addValue(generator.nextDouble());
        }
        Assert.assertEquals("Note: This test will fail randomly about 1 in 100 times.",
                0.5, sample.getMean(), FastMath.sqrt(N/12.0) * 2.576);
        Assert.assertEquals(1.0 / (2.0 * FastMath.sqrt(3.0)),
                     sample.getStandardDeviation(), 0.01);
    }

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                     sample.getStandardDeviation(), 0.01);
    }


    @Test
    public void testFloatDirect() {
        SummaryStatistics sample = new SummaryStatistics();
        final int N = 1000;
        for (int i = 0; i < N; ++i) {
            sample.addValue(generator.nextFloat());
        }
        Assert.assertEquals("Note: This test will fail randomly about 1 in 100 times.",
                0.5, sample.getMean(), FastMath.sqrt(N/12.0) * 2.576);
        Assert.assertEquals(1.0 / (2.0 * FastMath.sqrt(3.0)),
                     sample.getStandardDeviation(), 0.01);
    }

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    @Test
    public void testAnovaPValueSummaryStatistics() {
        // Target comparison values computed using R version 2.6.0 (Linux version)
        List<SummaryStatistics> threeClasses = new ArrayList<SummaryStatistics>();
        SummaryStatistics statsA = new SummaryStatistics();
        for (double a : classA) {
            statsA.addValue(a);
        }
        threeClasses.add(statsA);
        SummaryStatistics statsB = new SummaryStatistics();
        for (double b : classB) {
            statsB.addValue(b);
        }
        threeClasses.add(statsB);
        SummaryStatistics statsC = new SummaryStatistics();
        for (double c : classC) {
            statsC.addValue(c);
        }
        threeClasses.add(statsC);


        Assert.assertEquals("ANOVA P-value", 6.959446E-06,
                 testStatistic.anovaPValue(threeClasses, true), 1E-12);

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    private SummaryStatistics emptyStats = new SummaryStatistics();
   SummaryStatistics tooShortStats = null;


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

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    @Test
    public void testOneSampleT() {
        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)
        Assert.assertEquals("t statistic",  -2.81976445346,
                testStatistic.t(mu, observed), 10E-10);

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    @Test
    public void testOneSampleTTest() {
        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)
        Assert.assertEquals("one sample t stat", 3.86485535541,
                testStatistic.t(0d, oneSidedP), 10E-10);
        Assert.assertEquals("one sample t stat", 3.86485535541,

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    @Test
    public void testTwoSampleTHeterscedastic() {
        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)
        Assert.assertEquals("two sample heteroscedastic t stat", 1.60371728768,
                testStatistic.t(sample1, sample2), 1E-10);

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        // Initialize binStats ArrayList
        if (!binStats.isEmpty()) {
            binStats.clear();
        }
        for (int i = 0; i < binCount; i++) {
            SummaryStatistics stats = new SummaryStatistics();
            binStats.add(i,stats);
        }


        // Filling data in binStats Array
        DataAdapterFactory aFactory = new DataAdapterFactory();

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        double x = randomData.nextUniform(0,1);


        // Use this to select the bin and generate a Gaussian within the bin
        for (int i = 0; i < binCount; i++) {
           if (x <= upperBounds[i]) {
               SummaryStatistics stats = binStats.get(i);
               if (stats.getN() > 0) {
                   if (stats.getStandardDeviation() > 0) {  // more than one obs
                        return randomData.nextGaussian
                            (stats.getMean(),stats.getStandardDeviation());
                   } else {
                       return stats.getMean(); // only one obs in bin
                   }
               }
           }
        }
        throw new MathIllegalStateException(LocalizedFormats.NO_BIN_SELECTED);

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org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction

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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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