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) $

                sharder.getQueue().put(Integer.toString(i));
                Thread.sleep(5);
            }
            timing.forWaiting().sleepABit();


            SummaryStatistics       statistics = new SummaryStatistics();
            for ( String path : sharder.getQueuePaths() )
            {
                int numChildren = client.checkExists().forPath(path).getNumChildren();
                Assert.assertTrue(numChildren > 0);
                Assert.assertTrue(numChildren >= (threshold * .1));
                statistics.addValue(numChildren);
            }
            latch.countDown();


            Assert.assertTrue(statistics.getMean() >= (threshold * .9));
        }
        finally
        {
            timing.sleepABit(); // let queue clear
            Closeables.closeQuietly(sharder);

View Full Code Here

            double x = randomData.nextGaussian(0,0);
            fail("zero sigma -- IllegalArgumentException expected");
        } catch (IllegalArgumentException ex) {
            ;
        }
        SummaryStatistics u = SummaryStatistics.newInstance();
        for (int i = 0; i<largeSampleSize; i++) {
            u.addValue(randomData.nextGaussian(0,1));
        }
        double xbar = u.getMean();
        double s = u.getStandardDeviation();
        double n = (double) 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);
    }

View Full Code Here

        double next = 0.0;
        double tolerance = 0.1;
        vs.computeDistribution();
        assertTrue("empirical distribution property", 
            vs.getEmpiricalDistribution() != null);
        SummaryStatistics stats = SummaryStatistics.newInstance();
        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 = SummaryStatistics.newInstance();
        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 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 = SummaryStatistics.newInstance();
        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);

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 = SummaryStatistics.newInstance();    
        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,

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 = SummaryStatistics.newInstance();  
        for (int i = 0; i < sample1.length; i++) {
            sampleStats1.addValue(sample1[i]);
        }
        SummaryStatistics sampleStats2 = SummaryStatistics.newInstance();    
        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

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

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 = SummaryStatistics.newInstance();
        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);

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 = SummaryStatistics.newInstance();    
        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 = SummaryStatistics.newInstance();  
        for (int i = 0; i < sample1.length; i++) {
            sampleStats1.addValue(sample1[i]);
        }
        SummaryStatistics sampleStats2 = SummaryStatistics.newInstance();    
        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);

View Full Code Here

0 1 2 3 4 5 6 7 8 9

TOP

Related Classes of org.apache.commons.math.stat.descriptive.SummaryStatistics

com.aldaviva.autorpg.game.QuestManager

com.thinkaurelius.titan.graphdb.TitanGraphPerformanceMemoryTest

com.thinkaurelius.titan.TestByteBuffer

org.apache.commons.discovery.tools.DiscoverClass

org.apache.commons.math.ode.jacobians.FirstOrderIntegratorWithJacobiansTest

org.apache.commons.math.random.EmpiricalDistributionImpl

org.apache.commons.math.random.EmpiricalDistributionImpl$ArrayDataAdapter

org.apache.commons.math.random.EmpiricalDistributionImpl$StreamDataAdapter

org.apache.commons.math.random.EmpiricalDistributionTest

org.apache.commons.math.random.MersenneTwisterTest

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.