Examples of SummaryStatistics

nz.co.abrahams.asithappens.storage.SummaryStatistics
Summary statistics for a set of data. @author mark
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) $
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.

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

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

    }


    private void tstGen(double tolerance)throws Exception {
        empiricalDistribution.load(url);
        empiricalDistribution.reSeed(1000);
        SummaryStatistics stats = new SummaryStatistics();
        for (int i = 1; i < 1000; i++) {
            stats.addValue(empiricalDistribution.getNextValue());
        }
        Assert.assertEquals("mean", 5.069831575018909, stats.getMean(),tolerance);
        Assert.assertEquals("std dev", 1.0173699343977738, stats.getStandardDeviation(),tolerance);
    }

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

    }


    private void tstDoubleGen(double tolerance)throws Exception {
        empiricalDistribution2.load(dataArray);
        empiricalDistribution2.reSeed(1000);
        SummaryStatistics stats = new SummaryStatistics();
        for (int i = 1; i < 1000; i++) {
            stats.addValue(empiricalDistribution2.getNextValue());
        }
        Assert.assertEquals("mean", 5.069831575018909, stats.getMean(), tolerance);
        Assert.assertEquals("std dev", 1.0173699343977738, stats.getStandardDeviation(), tolerance);
    }

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

     * Test SummaryStatistics - implementations that do not store the data
     * and use single pass algorithms to compute statistics
    */
    @Test
    public void testSummaryStatistics() throws Exception {
        SummaryStatistics u = new SummaryStatistics();
        loadStats("data/PiDigits.txt", u);
        Assert.assertEquals("PiDigits: std", std, u.getStandardDeviation(), 1E-13);
        Assert.assertEquals("PiDigits: mean", mean, u.getMean(), 1E-13);


        loadStats("data/Mavro.txt", u);
        Assert.assertEquals("Mavro: std", std, u.getStandardDeviation(), 1E-14);
        Assert.assertEquals("Mavro: mean", mean, u.getMean(), 1E-14);


        loadStats("data/Michelso.txt", u);
        Assert.assertEquals("Michelso: std", std, u.getStandardDeviation(), 1E-13);
        Assert.assertEquals("Michelso: mean", mean, u.getMean(), 1E-13);


        loadStats("data/NumAcc1.txt", u);
        Assert.assertEquals("NumAcc1: std", std, u.getStandardDeviation(), 1E-14);
        Assert.assertEquals("NumAcc1: mean", mean, u.getMean(), 1E-14);


        loadStats("data/NumAcc2.txt", u);
        Assert.assertEquals("NumAcc2: std", std, u.getStandardDeviation(), 1E-14);
        Assert.assertEquals("NumAcc2: mean", mean, u.getMean(), 1E-14);
    }

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

     * loads a DescriptiveStatistics off of a test file
     */
    private void loadStats(String resource, Object u) throws Exception {


        DescriptiveStatistics d = null;
        SummaryStatistics s = null;
        if (u instanceof DescriptiveStatistics) {
            d = (DescriptiveStatistics) u;
        } else {
            s = (SummaryStatistics) u;
        }
        u.getClass().getDeclaredMethod(
                "clear", new Class[]{}).invoke(u, new Object[]{});
        mean = Double.NaN;
        std = Double.NaN;


        InputStream resourceAsStream = CertifiedDataTest.class.getResourceAsStream(resource);
        Assert.assertNotNull("Could not find resource "+resource,resourceAsStream);
        BufferedReader in =
            new BufferedReader(
                    new InputStreamReader(
                            resourceAsStream));


        String line = null;


        for (int j = 0; j < 60; j++) {
            line = in.readLine();
            if (j == 40) {
                mean =
                    Double.parseDouble(
                            line.substring(line.lastIndexOf(":") + 1).trim());
            }
            if (j == 41) {
                std =
                    Double.parseDouble(
                            line.substring(line.lastIndexOf(":") + 1).trim());
            }
        }


        line = in.readLine();


        while (line != null) {
            if (d != null) {
                d.addValue(Double.parseDouble(line.trim()));
            }  else {
                s.addValue(Double.parseDouble(line.trim()));
            }
            line = in.readLine();
        }


        resourceAsStream.close();

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

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

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

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


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

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


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

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