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) $

      randomData.nextGaussian(0, 0);
      fail("zero sigma -- IllegalArgumentException expected");
    } catch (IllegalArgumentException ex) {
      // ignored
    }
    SummaryStatistics u = new SummaryStatistics();
    for (int i = 0; i < largeSampleSize; i++) {
      u.addValue(randomData.nextGaussian(0, 1));
    }
    double xbar = u.getMean();
    double s = u.getStandardDeviation();
    double n = u.getN();
    /*
     * t-test at .001-level TODO: replace with externalized t-test, with
     * test statistic defined in TestStatistic
     */
    assertTrue(Math.abs(xbar) / (s / Math.sqrt(n)) < 3.29);

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public class MersenneTwisterTest {


    @Test
    public void testGaussian() {
        MersenneTwister mt = new MersenneTwister(42853252100l);
        SummaryStatistics sample = new SummaryStatistics();
        for (int i = 0; i < 1000; ++i) {
            sample.addValue(mt.nextGaussian());
        }
        assertEquals(0.0, sample.getMean(), 0.005);
        assertEquals(1.0, sample.getStandardDeviation(), 0.025);
    }

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    }


    @Test
    public void testDouble() {
        MersenneTwister mt = new MersenneTwister(195357343514l);
        SummaryStatistics sample = new SummaryStatistics();
        for (int i = 0; i < 1000; ++i) {
            sample.addValue(mt.nextDouble());
        }
        assertEquals(0.5, sample.getMean(), 0.02);
        assertEquals(1.0 / (2.0 * Math.sqrt(3.0)),
                     sample.getStandardDeviation(),
                     0.002);
    }

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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, 
              testStatistic.homoscedasticT(sample1, sample2), 10E-11);

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    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,
                TestUtils.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, 
                TestUtils.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, 
                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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     * @param statistical summary
     */
    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;
        
        BufferedReader in =
            new BufferedReader(
                    new InputStreamReader(
                            CertifiedDataTest.class.getResourceAsStream(resource)));
        
        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();
        }
        
        in.close();

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    private Map<String, Double> certifiedValues;


    @Override
    protected void setUp() throws Exception {
        descriptives = new DescriptiveStatistics();
        summaries = new SummaryStatistics();
        certifiedValues = new HashMap<String, Double>();
        
        loadData();
    }

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