Examples of org.apache.commons.math.stat.DescriptiveStatistics

org.apache.commons.math.stat.DescriptiveStatistics
DescriptiveStatistics implements the Univariate interface but maintains the set of values which contribute to the values being returned. This implementation of Univariate provides additional percentile functionality such as. This additional functionality comes with a price of increased storage costs. @version $Revision: 1.2 $ $Date: 2003/11/19 03:28:23 $

        
        //FiXME: test all other NaN contract specs
    }


    public void testSkewAndKurtosis() {
        DescriptiveStatistics u = new StorelessDescriptiveStatisticsImpl();
        
        double[] testArray = 
        { 12.5, 12, 11.8, 14.2, 14.9, 14.5, 21, 8.2, 10.3, 11.3, 14.1,
          9.9, 12.2, 12, 12.1, 11, 19.8, 11, 10, 8.8, 9, 12.3 };
        for( int i = 0; i < testArray.length; i++) {
            u.addValue( testArray[i]);
        }
        
        assertEquals("mean", 12.40455, u.getMean(), 0.0001);
        assertEquals("variance", 10.00236, u.getVariance(), 0.0001);
        assertEquals("skewness", 1.437424, u.getSkewness(), 0.0001);
        assertEquals("kurtosis", 2.37719, u.getKurtosis(), 0.0001);
    }

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        // Initialize binStats ArrayList
        if (!binStats.isEmpty()) {
            binStats.clear();
        }
        for (int i = 0; i < binCount; i++) {
            DescriptiveStatistics stats = new StorelessDescriptiveStatisticsImpl();
            binStats.add(i,stats);
        }
        
        // Pass the data again, filling data in binStats Array
        String str = null;
        double val = 0.0d;
        while ((str = in.readLine()) != null) {
            val = new Double(str).doubleValue();
            
            // Find bin and add value to binStats for the bin
            boolean found = false;
            int i = 0;
            while (!found) {
                if (i >= binCount) {
                    throw new RuntimeException("bin alignment error");
                }
                if (val <= binUpperBounds[i]) {
                    found = true;
                    DescriptiveStatistics stats = (DescriptiveStatistics)binStats.get(i);
                    stats.addValue(val);
                }
                i++;
            }
        }
        in.close();

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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]) {
               DescriptiveStatistics stats = (DescriptiveStatistics)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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        double next = 0.0;
        double tolerance = 0.1;
        vs.computeDistribution();
        assertTrue("empirical distribution property", 
            vs.getEmpiricalDistribution() != null);
        DescriptiveStatistics stats = new StorelessDescriptiveStatisticsImpl();
        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 = new StorelessDescriptiveStatisticsImpl();
        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);
        
    }

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                   // really just checking to make sure we do not bomb
    }
    
    private void tstGen(double tolerance)throws Exception {
        empiricalDistribution.load(file);   
        DescriptiveStatistics stats = new StorelessDescriptiveStatisticsImpl();
        for (int i = 1; i < 1000; i++) {
            stats.addValue(empiricalDistribution.getNextValue());
        }
        //TODO: replace these with statistical tests -- refactor as necessary
        assertEquals("mean", stats.getMean(),5.069831575018909,tolerance);
        assertEquals
         ("std dev", stats.getStandardDeviation(),1.0173699343977738,tolerance);
    }

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            double x = randomData.nextGaussian(0,0);
            fail("zero sigma -- IllegalArgumentException expected");
        } catch (IllegalArgumentException ex) {
            ;
        }
        DescriptiveStatistics u = new StorelessDescriptiveStatisticsImpl();
        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);
    }

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Related Classes of org.apache.commons.math.stat.DescriptiveStatistics

org.apache.commons.discovery.tools.DiscoverClass

org.apache.commons.math.random.EmpiricalDistributionImpl

org.apache.commons.math.random.EmpiricalDistributionTest

org.apache.commons.math.random.RandomDataTest

org.apache.commons.math.random.ValueServerTest

org.apache.commons.math.stat.univariate.UnivariateImplTest

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