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) $

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

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    /**
     * Test StorelessDescriptiveStatistics
    */
    public void testUnivariateImpl() throws Exception {
        SummaryStatistics u = SummaryStatistics.newInstance(SummaryStatisticsImpl.class);
        loadStats("data/PiDigits.txt", u);
        assertEquals("PiDigits: std", std, u.getStandardDeviation(), .0000000000001);
        assertEquals("PiDigits: mean", mean, u.getMean(), .0000000000001);  


        loadStats("data/Mavro.txt", u);
        assertEquals("Mavro: std", std, u.getStandardDeviation(), .00000000000001);
        assertEquals("Mavro: mean", mean, u.getMean(), .00000000000001);
        
        //loadStats("data/Michelso.txt");
        //assertEquals("Michelso: std", std, u.getStandardDeviation(), .00000000000001);
        //assertEquals("Michelso: mean", mean, u.getMean(), .00000000000001);   
                                        
        loadStats("data/NumAcc1.txt", u);
        assertEquals("NumAcc1: std", std, u.getStandardDeviation(), .00000000000001);
        assertEquals("NumAcc1: mean", mean, u.getMean(), .00000000000001);
        
        //loadStats("data/NumAcc2.txt");
        //assertEquals("NumAcc2: std", std, u.getStandardDeviation(), .000000001);
        //assertEquals("NumAcc2: mean", mean, u.getMean(), .00000000000001);
    }

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


            inputStream.close();
            inputStream = null;
        }

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

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

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         ("std dev", stats.getStandardDeviation(),1.0173699343977738,tolerance);
    }


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

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        // Initialize binStats ArrayList
        if (!binStats.isEmpty()) {
            binStats.clear();
        }
        for (int i = 0; i < binCount; i++) {
            SummaryStatistics stats = SummaryStatistics.newInstance();
            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 = (SummaryStatistics)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 RuntimeException("No bin selected");

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                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
            CloseableUtils.closeQuietly(sharder);

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