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

org.apache.commons.math.stat.StorelessDescriptiveStatisticsImpl
Accumulates univariate statistics for values fed in through the addValue() method. Does not store raw data values. All data are represented internally as doubles. Integers, floats and longs can be added, but they will be converted to doubles by addValue(). @version $Revision: 1.2 $ $Date: 2003/11/19 03:28:23 $

        return suite;
    }
    
    /** test stats */
    public void testStats() {
        StorelessDescriptiveStatisticsImpl u = new StorelessDescriptiveStatisticsImpl(); 
        assertEquals("total count",0,u.getN(),tolerance);
        u.addValue(one);
        u.addValue(twoF);
        u.addValue(twoL);
        u.addValue(three);
        assertEquals("N",n,u.getN(),tolerance);
        assertEquals("sum",sum,u.getSum(),tolerance);
        assertEquals("sumsq",sumSq,u.getSumsq(),tolerance);
        assertEquals("var",var,u.getVariance(),tolerance);
        assertEquals("std",std,u.getStandardDeviation(),tolerance);
        assertEquals("mean",mean,u.getMean(),tolerance);
        assertEquals("min",min,u.getMin(),tolerance);
        assertEquals("max",max,u.getMax(),tolerance);
        u.clear();
        assertEquals("total count",0,u.getN(),tolerance);    
    }

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        u.clear();
        assertEquals("total count",0,u.getN(),tolerance);    
    }     
    
    public void testN0andN1Conditions() throws Exception {
      StorelessDescriptiveStatisticsImpl u = new StorelessDescriptiveStatisticsImpl();
        assertTrue("Mean of n = 0 set should be NaN", 
            Double.isNaN( u.getMean() ) );
    assertTrue("Standard Deviation of n = 0 set should be NaN", 
            Double.isNaN( u.getStandardDeviation() ) );
    assertTrue("Variance of n = 0 set should be NaN", 
            Double.isNaN(u.getVariance() ) );
    assertTrue("skew of n = 0 set should be NaN",
      Double.isNaN(u.getSkewness() ) );  
    assertTrue("kurtosis of n = 0 set should be NaN", 
      Double.isNaN(u.getKurtosis() ) );    
    
  
    /* n=1 */
    u.addValue(one);
    assertTrue("mean should be one (n = 1)", 
      u.getMean() == one);
    assertTrue("geometric should be one (n = 1) instead it is " + u.getGeometricMean(), 
      u.getGeometricMean() == one);
    assertTrue("Std should be zero (n = 1)", 
      u.getStandardDeviation() == 0.0);
    assertTrue("variance should be zero (n = 1)", 
      u.getVariance() == 0.0);
    assertTrue("skew should be zero (n = 1)", 
      u.getSkewness() == 0.0);
    assertTrue("kurtosis should be zero (n = 1)", 
      u.getKurtosis() == 0.0);    
          
    /* n=2 */        
    u.addValue(twoF);
    assertTrue("Std should not be zero (n = 2)", 
      u.getStandardDeviation() != 0.0);
    assertTrue("variance should not be zero (n = 2)", 
      u.getVariance() != 0.0);
    assertTrue("skew should not be zero (n = 2)", 
      u.getSkewness() == 0.0);
    assertTrue("kurtosis should be zero (n = 2)", 
      u.getKurtosis() == 0.0);


    /* n=3 */
    u.addValue(twoL);
    assertTrue("skew should not be zero (n = 3)", 
      u.getSkewness() != 0.0);
    assertTrue("kurtosis should be zero (n = 3)", 
      u.getKurtosis() == 0.0);
        
    /* n=4 */
    u.addValue(three);
    assertTrue("kurtosis should not be zero (n = 4)", 
      u.getKurtosis() != 0.0);        
            
    }

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      u.getKurtosis() != 0.0);        
            
    }


    public void testProductAndGeometricMean() throws Exception {
      StorelessDescriptiveStatisticsImpl u = new StorelessDescriptiveStatisticsImpl(10);
            
        u.addValue( 1.0 );
        u.addValue( 2.0 );
        u.addValue( 3.0 );
        u.addValue( 4.0 );


        assertEquals( "Geometric mean not expected", 2.213364, 
            u.getGeometricMean(), 0.00001 );


        // Now test rolling - StorelessDescriptiveStatistics should discount the contribution
        // of a discarded element
        for( int i = 0; i < 10; i++ ) {
            u.addValue( i + 2 );
        }
        // Values should be (2,3,4,5,6,7,8,9,10,11)
        
        assertEquals( "Geometric mean not expected", 5.755931, 
            u.getGeometricMean(), 0.00001 );
    }

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        assertEquals( "Geometric mean not expected", 5.755931, 
            u.getGeometricMean(), 0.00001 );
    }
    
    public void testRollingMinMax() {
        StorelessDescriptiveStatisticsImpl u = new StorelessDescriptiveStatisticsImpl(3);
        u.addValue( 1.0 );
        u.addValue( 5.0 );
        u.addValue( 3.0 );
        u.addValue( 4.0 ); // discarding min
        assertEquals( "min not expected", 3.0, 
            u.getMin(), Double.MIN_VALUE);
        u.addValue(1.0);  // discarding max
        assertEquals( "max not expected", 4.0, 
            u.getMax(), Double.MIN_VALUE);
    }

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        assertEquals( "max not expected", 4.0, 
            u.getMax(), Double.MIN_VALUE);
    }
    
    public void testNaNContracts() {
        StorelessDescriptiveStatisticsImpl u = new StorelessDescriptiveStatisticsImpl();
        double nan = Double.NaN;
        assertTrue("mean not NaN",Double.isNaN(u.getMean())); 
        assertTrue("min not NaN",Double.isNaN(u.getMin())); 
        assertTrue("std dev not NaN",Double.isNaN(u.getStandardDeviation())); 
        assertTrue("var not NaN",Double.isNaN(u.getVariance())); 
        assertTrue("geom mean not NaN",Double.isNaN(u.getGeometricMean()));
        
        u.addValue(1.0);
        
        assertEquals( "mean not expected", 1.0, 
            u.getMean(), Double.MIN_VALUE);
        assertEquals( "variance not expected", 0.0, 
            u.getVariance(), Double.MIN_VALUE);
        assertEquals( "geometric mean not expected", 1.0, 
            u.getGeometricMean(), Double.MIN_VALUE);
        
        u.addValue(-1.0);
        
        assertTrue("geom mean not NaN",Double.isNaN(u.getGeometricMean()));
        
        u.addValue(0.0);
        
        assertTrue("geom mean not NaN",Double.isNaN(u.getGeometricMean()));
        
        //FiXME: test all other NaN contract specs
    }

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        //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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     * Computes sampleStats (first pass through data file).
     */
    private void computeStats(BufferedReader in) throws IOException {
        String str = null;
        double val = 0.0;
        sampleStats = new StorelessDescriptiveStatisticsImpl();
        while ((str = in.readLine()) != null) {
            val = new Double(str).doubleValue();
            sampleStats.addValue(val);
        }
        in.close();

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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 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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