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) $

        super(name);
    }


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

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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,
                testStatistic.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, 
                testStatistic.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, 
                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 = Math.random();


        // 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 MathRuntimeException("no bin selected");

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                throws IOException {
            String str = null;
            double val = 0.0d;
            while ((str = inputStream.readLine()) != null) {
                val = Double.parseDouble(str);
                SummaryStatistics stats = binStats.get(findBin(min, val, delta));
                stats.addValue(val);
            }


            inputStream.close();
            inputStream = null;
        }

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         */
        @Override
        public void computeStats() throws IOException {
            String str = null;
            double val = 0.0;
            sampleStats = new SummaryStatistics();
            while ((str = inputStream.readLine()) != null) {
                val = Double.valueOf(str).doubleValue();
                sampleStats.addValue(val);
            }
            inputStream.close();

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         * 
         * @throws IOException if an IO error occurs
         */
        @Override
        public void computeStats() throws IOException {
            sampleStats = new SummaryStatistics();
            for (int i = 0; i < inputArray.length; i++) {
                sampleStats.addValue(inputArray[i]);
            }
        }

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

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