If other objects have already been added to this Frequency, v must be comparable to those that have already been added.
v
761762763764765766767768769770771
public static double[] normalize(final double[] sample) { DescriptiveStatistics stats = new DescriptiveStatistics(); // Add the data from the series to stats for (int i = 0; i < sample.length; i++) { stats.addValue(sample[i]); } // Compute mean and standard deviation double mean = stats.getMean(); double standardDeviation = stats.getStandardDeviation();
136137138139140141142143144145146
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(); }
496497498499500501502503504505506
double standardizedSample[] = StatUtils.normalize(sample); DescriptiveStatistics stats = new DescriptiveStatistics(); // Add the data from the array for (int i = 0; i < length; i++) { stats.addValue(standardizedSample[i]); } // the calculations do have a limited precision double distance = 1E-10; // check the mean an standard deviation Assert.assertEquals(0.0, stats.getMean(), distance);
353354355356357358359360361362363
String str = null; double val = 0.0d; while ((str = inputStream.readLine()) != null) { val = Double.parseDouble(str); SummaryStatistics stats = binStats.get(findBin(val)); stats.addValue(val); } inputStream.close(); inputStream = null; }
410411412413414415416417418419420
@Override public void computeBinStats() throws IOException { for (int i = 0; i < inputArray.length; i++) { SummaryStatistics stats = binStats.get(findBin(inputArray[i])); stats.addValue(inputArray[i]); } } } /**
200201202203204205206207208209210
// convert arrays to SummaryStatistics for (final double[] data : categoryData) { final SummaryStatistics dataSummaryStatistics = new SummaryStatistics(); categoryDataSummaryStatistics.add(dataSummaryStatistics); for (final double val : data) { dataSummaryStatistics.addValue(val); } } return anovaStats(categoryDataSummaryStatistics, false);
254255256257258259260261262263264
errNew = FastMath.abs((actualNew - expected) / ulp); if (Double.isNaN(actualOld) || Double.isInfinite(actualOld)) { Assert.assertFalse(msg, Double.isNaN(actualNew)); Assert.assertFalse(msg, Double.isInfinite(actualNew)); statNewOF.addValue(errNew); } else { statOld.addValue(errOld); statNewNoOF.addValue(errNew); } }
5859606162636465666768
Assert.assertTrue("empirical distribution property", vs.getEmpiricalDistribution() != null); SummaryStatistics stats = new SummaryStatistics(); for (int i = 1; i < 1000; i++) { next = vs.getNext(); stats.addValue(next); } Assert.assertEquals("mean", 5.069831575018909, stats.getMean(), tolerance); Assert.assertEquals("std dev", 1.0173699343977738, stats.getStandardDeviation(), tolerance);
6869707172737475767778
vs.computeDistribution(500); stats = new SummaryStatistics(); for (int i = 1; i < 1000; i++) { next = vs.getNext(); stats.addValue(next); } Assert.assertEquals("mean", 5.069831575018909, stats.getMean(), tolerance); Assert.assertEquals("std dev", 1.0173699343977738, stats.getStandardDeviation(), tolerance); }
166167168169170171172173174175176
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) Assert.assertEquals("two sample heteroscedastic t stat", 1.60371728768, testStatistic.t(sample1, sample2), 1E-10);