Examples of UnivariateStatistic

org.apache.commons.math.stat.descriptive.UnivariateStatistic
Base interface implemented by all statistics. @version $Revision: 811685 $ $Date: 2009-09-05 19:36:48 +0200 (sam. 05 sept. 2009) $
org.apache.commons.math3.stat.descriptive.UnivariateStatistic
Base interface implemented by all statistics.

Examples of org.apache.commons.math.stat.descriptive.UnivariateStatistic

    //System.out.println(ut.getColumnDimension() + "\t" + ut.getRowDimension());
    //System.out.println(vt.getColumnDimension() + "\t" + vt.getRowDimension());
    //System.out.println(wordNo);
    //System.out.println(featureList.size());
    
    UnivariateStatistic mean = new Mean();
    UnivariateStatistic stdev = new StandardDeviation();
    List<Double> cmeans = new ArrayList<Double>();
    List<Double> cstdevs = new ArrayList<Double>();
    List<Double> ncmeans = new ArrayList<Double>();
    List<Double> ncstdevs = new ArrayList<Double>();
    int ccount = 0;
    int nccount = 0;
    for(int i=0;i<values;i++) {
      Map<String,Double> results = new HashMap<String,Double>();
      for(int j=0;j<wordNo;j++) {
        results.put(terms.get(j), ut.get(i,j));
      }
      List<String> resultsList = StringTools.getSortedList(results);
      for(String s : resultsList.subList(0, 20)) {
        System.out.println(s + "\t" + results.get(s));
      }
      System.out.println("...");
      for(String s : resultsList.subList(resultsList.size()-20, resultsList.size())) {
        System.out.println(s + "\t" + results.get(s));
      }
      List<Double> chemScores = new ArrayList<Double>();
      List<Double> nonChemScores = new ArrayList<Double>();
      List<Double> allScores = new ArrayList<Double>();
      for(int j=500;j<1000;j++) {
        boolean isChem = chemSet.contains(terms.get(j));
        //System.out.println(isChem);
        List<Double> list = isChem ? chemScores : nonChemScores;
        list.add(ut.get(i,j));
        allScores.add(ut.get(i,j));
      }
      System.out.println(chemScores.size() + "\t" + nonChemScores.size());
      double [] cs = new double[chemScores.size()];
      for(int j=0;j<chemScores.size();j++) cs[j] = chemScores.get(j);
      double [] ncs = new double[nonChemScores.size()];
      for(int j=0;j<nonChemScores.size();j++) ncs[j] = nonChemScores.get(j);
      double [] as = new double[allScores.size()];
      for(int j=0;j<allScores.size();j++) as[j] = allScores.get(j);
      System.out.println(mean.evaluate(cs) + "\t" + stdev.evaluate(cs));
      System.out.println(mean.evaluate(ncs) + "\t" + stdev.evaluate(ncs));
      cmeans.add(mean.evaluate(cs));
      cstdevs.add(stdev.evaluate(cs));
      ncmeans.add(mean.evaluate(ncs));
      ncstdevs.add(stdev.evaluate(ncs));
      System.out.println();
      ccount = cs.length;
      nccount = ncs.length;
    }
    double cp = ccount / (0.0 + ccount + nccount);

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Examples of org.apache.commons.math3.stat.descriptive.UnivariateStatistic


    @Test
    public void testAllTechniquesSingleton() {
        double[] singletonArray = new double[] { 1d };
        for (EstimationType e : EstimationType.values()) {
            UnivariateStatistic percentile = getTestMedian(e);
            Assert.assertEquals(1d, percentile.evaluate(singletonArray), 0);
            Assert.assertEquals(1d, percentile.evaluate(singletonArray, 0, 1),
                    0);
            Assert.assertEquals(1d,
                    new Median().evaluate(singletonArray, 0, 1, 5), 0);
            Assert.assertEquals(1d,
                    new Median().evaluate(singletonArray, 0, 1, 100), 0);
            Assert.assertTrue(Double.isNaN(percentile.evaluate(singletonArray,
                    0, 0)));
        }
    }

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Examples of org.apache.commons.math3.stat.descriptive.UnivariateStatistic

    }


    @Test
    public void testAllTechniques5() {
        reset(5, Percentile.EstimationType.LEGACY);
        final UnivariateStatistic percentile = getUnivariateStatistic();
        Assert.assertEquals(this.percentile5, percentile.evaluate(testArray),
                getTolerance());
        testAssertMappedValues(testArray,
                new Object[][] { { Percentile.EstimationType.LEGACY, percentile5 }, { Percentile.EstimationType.R_1, 8.8000 },
                        { Percentile.EstimationType.R_2, 8.8000 }, { Percentile.EstimationType.R_3, 8.2000 }, { Percentile.EstimationType.R_4, 8.2600 },
                        { Percentile.EstimationType.R_5, 8.5600 }, { Percentile.EstimationType.R_6, 8.2900 },

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Examples of org.apache.commons.math3.stat.descriptive.UnivariateStatistic


        final double[] nullArray = null;
        final double[] emptyArray = new double[] {};
        for (final Percentile.EstimationType e : Percentile.EstimationType.values()) {
            reset (50, e);
            final UnivariateStatistic percentile = getUnivariateStatistic();
            try {
                percentile.evaluate(nullArray);
                Assert.fail("Expecting MathIllegalArgumentException "
                        + "for null array");
            } catch (final MathIllegalArgumentException ex) {
                // expected
            }
            Assert.assertTrue(Double.isNaN(percentile.evaluate(emptyArray)));
        }


    }

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Examples of org.apache.commons.math3.stat.descriptive.UnivariateStatistic

    @Test
    public void testAllTechniquesSingleton() {
        final double[] singletonArray = new double[] { 1d };
        for (final Percentile.EstimationType e : Percentile.EstimationType.values()) {
            reset (50, e);
            final UnivariateStatistic percentile = getUnivariateStatistic();
            Assert.assertEquals(1d, percentile.evaluate(singletonArray), 0);
            Assert.assertEquals(1d, percentile.evaluate(singletonArray, 0, 1),
                    0);
            Assert.assertEquals(1d,
                    new Percentile().evaluate(singletonArray, 0, 1, 5), 0);
            Assert.assertEquals(1d,
                    new Percentile().evaluate(singletonArray, 0, 1, 100), 0);
            Assert.assertTrue(Double.isNaN(percentile.evaluate(singletonArray,
                    0, 0)));
        }
    }

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Examples of org.apache.commons.math3.stat.descriptive.UnivariateStatistic

    @Test
    public void testAllTechniquesEmpty() {
        final double[] singletonArray = new double[] { };
        for (final Percentile.EstimationType e : Percentile.EstimationType.values()) {
            reset (50, e);
            final UnivariateStatistic percentile = getUnivariateStatistic();
            Assert.assertEquals(Double.NaN, percentile.evaluate(singletonArray),
                    0);
            Assert.assertEquals(Double.NaN, percentile.evaluate(singletonArray,
                    0, 0),
                    0);
            Assert.assertEquals(Double.NaN,
                    new Percentile().evaluate(singletonArray, 0, 0, 5), 0);
            Assert.assertEquals(Double.NaN,
                    new Percentile().evaluate(singletonArray, 0, 0, 100), 0);
            Assert.assertTrue(Double.isNaN(percentile.evaluate(singletonArray,
                    0, 0)));
        }
    }

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Examples of org.apache.commons.math3.stat.descriptive.UnivariateStatistic

    }


    @Test
    public void testAllTechniquesSpecialValues() {
        reset(50d, Percentile.EstimationType.LEGACY);
        final UnivariateStatistic percentile = getUnivariateStatistic();
        double[] specialValues =
                new double[] { 0d, 1d, 2d, 3d, 4d, Double.NaN };
        Assert.assertEquals(2.5d, percentile.evaluate(specialValues), 0);


        testAssertMappedValues(specialValues, new Object[][] {
                { Percentile.EstimationType.LEGACY, 2.5d }, { Percentile.EstimationType.R_1, 2.0 }, { Percentile.EstimationType.R_2, 2.0 }, { Percentile.EstimationType.R_3, 1.0 },
                { Percentile.EstimationType.R_4, 1.5 }, { Percentile.EstimationType.R_5, 2.0 }, { Percentile.EstimationType.R_6, 2.0 },
                { Percentile.EstimationType.R_7, 2.0 }, { Percentile.EstimationType.R_8, 2.0 }, { Percentile.EstimationType.R_9, 2.0 }}, 50d, 0d);


        specialValues =
                new double[] { Double.NEGATIVE_INFINITY, 1d, 2d, 3d,
                        Double.NaN, Double.POSITIVE_INFINITY };
        Assert.assertEquals(2.5d, percentile.evaluate(specialValues), 0);


        testAssertMappedValues(specialValues, new Object[][] {
                { Percentile.EstimationType.LEGACY, 2.5d }, { Percentile.EstimationType.R_1, 2.0 }, { Percentile.EstimationType.R_2, 2.0 }, { Percentile.EstimationType.R_3, 1.0 },
                { Percentile.EstimationType.R_4, 1.5 }, { Percentile.EstimationType.R_5, 2.0 }, { Percentile.EstimationType.R_7, 2.0 }, { Percentile.EstimationType.R_7, 2.0 },
                { Percentile.EstimationType.R_8, 2.0 }, { Percentile.EstimationType.R_9, 2.0 } }, 50d, 0d);


        specialValues =
                new double[] { 1d, 1d, Double.POSITIVE_INFINITY,
                        Double.POSITIVE_INFINITY };
        Assert.assertTrue(Double.isInfinite(percentile.evaluate(specialValues)));


        testAssertMappedValues(specialValues, new Object[][] {
                // This is one test not matching with R results.
                { Percentile.EstimationType.LEGACY, Double.POSITIVE_INFINITY },
                { Percentile.EstimationType.R_1,/* 1.0 */Double.NaN },
                { Percentile.EstimationType.R_2, /* Double.POSITIVE_INFINITY */Double.NaN },
                { Percentile.EstimationType.R_3, /* 1.0 */Double.NaN }, { Percentile.EstimationType.R_4, /* 1.0 */Double.NaN },
                { Percentile.EstimationType.R_5, Double.POSITIVE_INFINITY },
                { Percentile.EstimationType.R_6, Double.POSITIVE_INFINITY },
                { Percentile.EstimationType.R_7, Double.POSITIVE_INFINITY },
                { Percentile.EstimationType.R_8, Double.POSITIVE_INFINITY },
                { Percentile.EstimationType.R_9, Double.POSITIVE_INFINITY }, }, 50d, 0d);


        specialValues = new double[] { 1d, 1d, Double.NaN, Double.NaN };
        Assert.assertTrue(Double.isNaN(percentile.evaluate(specialValues)));
        testAssertMappedValues(specialValues, new Object[][] {
                { Percentile.EstimationType.LEGACY, Double.NaN }, { Percentile.EstimationType.R_1, 1.0 }, { Percentile.EstimationType.R_2, 1.0 }, { Percentile.EstimationType.R_3, 1.0 },
                { Percentile.EstimationType.R_4, 1.0 }, { Percentile.EstimationType.R_5, 1.0 },{ Percentile.EstimationType.R_6, 1.0 },{ Percentile.EstimationType.R_7, 1.0 },
                { Percentile.EstimationType.R_8, 1.0 }, { Percentile.EstimationType.R_9, 1.0 },}, 50d, 0d);

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