Java Code Examples for org.apache.commons.math.stat.descriptive.SummaryStatistics#addValue()

/** test failure modes and distribution of nextGaussian() */
public void testNextGaussian() {
	try {
		randomData.nextGaussian(0, 0);
		fail("zero sigma -- IllegalArgumentException expected");
	} catch (IllegalArgumentException ex) {
		// ignored
	}
	SummaryStatistics u = new SummaryStatistics();
	for (int i = 0; i < largeSampleSize; i++) {
		u.addValue(randomData.nextGaussian(0, 1));
	}
	double xbar = u.getMean();
	double s = u.getStandardDeviation();
	double n = 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);
}
 

/** test failure modes and distribution of nextGaussian() */
public void testNextGaussian() {
	try {
		randomData.nextGaussian(0, 0);
		fail("zero sigma -- IllegalArgumentException expected");
	} catch (IllegalArgumentException ex) {
		// ignored
	}
	SummaryStatistics u = new SummaryStatistics();
	for (int i = 0; i < largeSampleSize; i++) {
		u.addValue(randomData.nextGaussian(0, 1));
	}
	double xbar = u.getMean();
	double s = u.getStandardDeviation();
	double n = 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);
}
 

/**
 * Timing.
 *
 * @param repeatChunk Each timing measurement will done done for that
 * number of repeats of the code.
 * @param repeatStat Timing will be averaged over that number of runs.
 * @param runGC Call {@code System.gc()} between each timed block. When
 * set to {@code true}, the test will run much slower.
 * @param methods Codes being timed.
 * @return for each of the given {@code methods}, a
 * {@link StatisticalSummary} of the average times (in milliseconds)
 * taken by a single call to the {@code call} method (i.e. the time
 * taken by each timed block divided by {@code repeatChunk}).
 */
public static StatisticalSummary[] time(int repeatChunk,
                                        int repeatStat,
                                        boolean runGC,
                                        Callable<Double> ... methods) {
    final double[][][] times = timesAndResults(repeatChunk,
                                               repeatStat,
                                               runGC,
                                               methods);

    final int len = methods.length;
    final StatisticalSummary[] stats = new StatisticalSummary[len];
    for (int j = 0; j < len; j++) {
        final SummaryStatistics s = new SummaryStatistics();
        for (int k = 0; k < repeatStat; k++) {
            s.addValue(times[j][k][0]);
        }
        stats[j] = s.getSummary();
    }

    return stats;
}
 

private void tstDoubleGen(double tolerance)throws Exception {
    empiricalDistribution2.load(dataArray);
    SummaryStatistics stats = new SummaryStatistics();
    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);
}
 

/**
 * Computes binStats
 * 
 * @param min  minimum value
 * @param delta  grid size
 * @throws IOException if an IO error occurs
 */
public void computeBinStats(double min, double delta)
        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;
}
 

/** {@inheritDoc} */
@Override
public void computeStats() throws IOException {
    sampleStats = new SummaryStatistics();
    for (int i = 0; i < inputArray.length; i++) {
        sampleStats.addValue(inputArray[i]);
    }
}
 

private void tstDoubleGen(double tolerance)throws Exception {
    empiricalDistribution2.load(dataArray);
    SummaryStatistics stats = new SummaryStatistics();
    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);
}
 

@Test
public void testFloatDirect() {
    SummaryStatistics sample = new SummaryStatistics();
    for (int i = 0; i < 1000; ++i) {
        sample.addValue(generator.nextFloat());
    }
    Assert.assertEquals(0.5, sample.getMean(), 0.01);
    Assert.assertEquals(1.0 / (2.0 * FastMath.sqrt(3.0)),
                 sample.getStandardDeviation(),
                 0.01);
}
 

@Test
public void testFloat() {
    MersenneTwister mt = new MersenneTwister(4442733263l);
    SummaryStatistics sample = new SummaryStatistics();
    for (int i = 0; i < 1000; ++i) {
        sample.addValue(mt.nextFloat());
    }
    assertEquals(0.5, sample.getMean(), 0.01);
    assertEquals(1.0 / (2.0 * FastMath.sqrt(3.0)),
                 sample.getStandardDeviation(),
                 0.006);
}
 

@Test
public void testFloat() {
    MersenneTwister mt = new MersenneTwister(4442733263l);
    SummaryStatistics sample = new SummaryStatistics();
    for (int i = 0; i < 1000; ++i) {
        sample.addValue(mt.nextFloat());
    }
    assertEquals(0.5, sample.getMean(), 0.01);
    assertEquals(1.0 / (2.0 * Math.sqrt(3.0)),
                 sample.getStandardDeviation(),
                 0.006);
}
 

@Test
public void testGaussian() {
    MersenneTwister mt = new MersenneTwister(42853252100l);
    SummaryStatistics sample = new SummaryStatistics();
    for (int i = 0; i < 1000; ++i) {
        sample.addValue(mt.nextGaussian());
    }
    assertEquals(0.0, sample.getMean(), 0.005);
    assertEquals(1.0, sample.getStandardDeviation(), 0.025);
}
 

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

@Test
public void testGaussian() {
    MersenneTwister mt = new MersenneTwister(42853252100l);
    SummaryStatistics sample = new SummaryStatistics();
    for (int i = 0; i < 1000; ++i) {
        sample.addValue(mt.nextGaussian());
    }
    assertEquals(0.0, sample.getMean(), 0.005);
    assertEquals(1.0, sample.getStandardDeviation(), 0.025);
}
 

public void setUp() {
    tooShortStats = new SummaryStatistics();
    tooShortStats.addValue(0d);
}
 

@Override
public void setUp() {
    tooShortStats = new SummaryStatistics();
    tooShortStats.addValue(0d);
}