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.


        /** {@inheritDoc} */
        @Override
        public void computeBinStats() throws IOException {
            for (int i = 0; i < inputArray.length; i++) {
                SummaryStatistics stats =
                    binStats.get(findBin(inputArray[i]));
                stats.addValue(inputArray[i]);
            }
        }

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     * @return an array containing the initializers for the x, y and
     * z coordinates of the features array of the neurons.
     */
    private FeatureInitializer[] makeInitializers() {
        final SummaryStatistics[] centre = new SummaryStatistics[] {
            new SummaryStatistics(),
            new SummaryStatistics(),
            new SummaryStatistics()
        };
        for (Vector3D p : rings.getPoints()) {
            centre[0].addValue(p.getX());
            centre[1].addValue(p.getY());
            centre[2].addValue(p.getZ());

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     * 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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     * 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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    }


    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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    }


    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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    @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,
                TestUtils.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,
                TestUtils.t(0d, oneSidedP), 10E-10);
        Assert.assertEquals("one sample t stat", 3.86485535541,

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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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