This is an automated email from the ASF dual-hosted git repository.
erans pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/commons-math.git
commit 693d560427fb756bb553c663affa1a3d5c8575f9
Author: Gilles Sadowski <gillese...@gmail.com>
AuthorDate: Sun Jul 11 16:46:11 2021 +0200
Code style and Javadoc nits.
---
.../legacy/distribution/EmpiricalDistribution.java | 144 +++++++++------------
.../distribution/EmpiricalDistributionTest.java | 22 ++--
2 files changed, 78 insertions(+), 88 deletions(-)
diff --git
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistribution.java
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistribution.java
index c8f5021..d1d38e9 100644
---
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistribution.java
+++
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistribution.java
@@ -46,40 +46,46 @@ import
org.apache.commons.math4.legacy.core.jdkmath.AccurateMath;
/**
* <p>Represents an <a
href="http://en.wikipedia.org/wiki/Empirical_distribution_function";>
- * empirical probability distribution</a> -- a probability distribution derived
+ * empirical probability distribution</a>: Probability distribution derived
* from observed data without making any assumptions about the functional form
* of the population distribution that the data come from.</p>
*
- * <p>An <code>EmpiricalDistribution</code> maintains data structures, called
- * <i>distribution digests</i>, that describe empirical distributions and
- * support the following operations: <ul>
- * <li>loading the distribution from a file of observed data values</li>
- * <li>dividing the input data into "bin ranges" and reporting bin frequency
- * counts (data for histogram)</li>
- * <li>reporting univariate statistics describing the full set of data values
- * as well as the observations within each bin</li>
- * <li>generating random values from the distribution</li>
+ * <p>An {@code EmpiricalDistribution} maintains data structures called
+ * <i>distribution digests</i> that describe empirical distributions and
+ * support the following operations:
+ * <ul>
+ * <li>loading the distribution from a file of observed data values</li>
+ * <li>dividing the input data into "bin ranges" and reporting bin frequency
+ * counts (data for histogram)</li>
+ * <li>reporting univariate statistics describing the full set of data values
+ * as well as the observations within each bin</li>
+ * <li>generating random values from the distribution</li>
* </ul>
- * Applications can use <code>EmpiricalDistribution</code> to build grouped
+ *
+ * Applications can use {@code EmpiricalDistribution} to build grouped
* frequency histograms representing the input data or to generate random
values
- * "like" those in the input file -- i.e., the values generated will follow the
+ * "like" those in the input file, i.e. the values generated will follow the
* distribution of the values in the file.
*
* <p>The implementation uses what amounts to the
* <a
href="http://nedwww.ipac.caltech.edu/level5/March02/Silverman/Silver2_6.html";>
* Variable Kernel Method</a> with Gaussian smoothing:<p>
* <strong>Digesting the input file</strong>
- * <ol><li>Pass the file once to compute min and max.</li>
- * <li>Divide the range from min-max into <code>binCount</code> "bins."</li>
- * <li>Pass the data file again, computing bin counts and univariate
- * statistics (mean, std dev.) for each of the bins </li>
- * <li>Divide the interval (0,1) into subintervals associated with the bins,
- * with the length of a bin's subinterval proportional to its
count.</li></ol>
- * <strong>Generating random values from the distribution</strong><ol>
- * <li>Generate a uniformly distributed value in (0,1) </li>
- * <li>Select the subinterval to which the value belongs.
- * <li>Generate a random Gaussian value with mean = mean of the associated
- * bin and std dev = std dev of associated bin.</li></ol>
+ * <ol>
+ * <li>Pass the file once to compute min and max.</li>
+ * <li>Divide the range from min to max into {@code binCount} bins.</li>
+ * <li>Pass the data file again, computing bin counts and univariate
+ * statistics (mean and std dev.) for each bin.</li>
+ * <li>Divide the interval (0,1) into subintervals associated with the bins,
+ * with the length of a bin's subinterval proportional to its count.</li>
+ * </ol>
+ * <strong>Generating random values from the distribution</strong>
+ * <ol>
+ * <li>Generate a uniformly distributed value in (0,1) </li>
+ * <li>Select the subinterval to which the value belongs.
+ * <li>Generate a random Gaussian value with mean = mean of the associated
+ * bin and std dev = std dev of associated bin.</li>
+ * </ol>
*
* <p>EmpiricalDistribution implements the {@link ContinuousDistribution}
interface
* as follows. Given x within the range of values in the dataset, let B
@@ -91,49 +97,38 @@ import
org.apache.commons.math4.legacy.core.jdkmath.AccurateMath;
* grouped frequency distribution at the bin endpoints and interpolates within
* bins using within-bin kernels.</p>
*
- *<strong>USAGE NOTES:</strong><ul>
- *<li>The <code>binCount</code> is set by default to 1000. A good rule of
thumb
- * is to set the bin count to approximately the length of the input file
divided
- * by 10. </li>
- *<li>The input file <i>must</i> be a plain text file containing one valid
numeric
- * entry per line.</li>
+ * <strong>USAGE NOTES:</strong>
+ * <ul>
+ * <li>The {@code binCount} is set by default to 1000. A good rule of thumb
+ * is to set the bin count to approximately the length of the input file
divided
+ * by 10. </li>
+ * <li>The input file <i>must</i> be a plain text file containing one valid
numeric
+ * entry per line.</li>
* </ul>
- *
*/
public class EmpiricalDistribution extends AbstractRealDistribution
implements ContinuousDistribution {
-
/** Default bin count. */
public static final int DEFAULT_BIN_COUNT = 1000;
-
/** Character set for file input. */
private static final String FILE_CHARSET = "US-ASCII";
-
/** Serializable version identifier. */
private static final long serialVersionUID = 5729073523949762654L;
-
- /** List of SummaryStatistics objects characterizing the bins. */
+ /** Bins' characteristics. */
private final List<SummaryStatistics> binStats;
-
/** Sample statistics. */
private SummaryStatistics sampleStats;
-
/** Max loaded value. */
private double max = Double.NEGATIVE_INFINITY;
-
/** Min loaded value. */
private double min = Double.POSITIVE_INFINITY;
-
/** Grid size. */
private double delta;
-
- /** number of bins. */
+ /** Number of bins. */
private final int binCount;
-
- /** is the distribution loaded? */
+ /** Whether the distribution is loaded. */
private boolean loaded;
-
- /** upper bounds of subintervals in (0,1) "belonging" to the bins. */
+ /** Upper bounds of subintervals in (0,1) belonging to the bins. */
private double[] upperBounds;
/**
@@ -247,11 +242,10 @@ public class EmpiricalDistribution extends
AbstractRealDistribution
}
/**
- * Provides methods for computing <code>sampleStats</code> and
- * <code>beanStats</code> abstracting the source of data.
+ * Provides methods for computing {@code sampleStats} and
+ * {@code beanStats} abstracting the source of data.
*/
- private abstract class DataAdapter{
-
+ private abstract class DataAdapter {
/**
* Compute bin stats.
*
@@ -265,16 +259,14 @@ public class EmpiricalDistribution extends
AbstractRealDistribution
* @throws IOException if an error occurs computing sample stats
*/
public abstract void computeStats() throws IOException;
-
}
/**
- * <code>DataAdapter</code> for data provided through some input stream.
+ * {@code DataAdapter} for data provided through some input stream.
*/
- private class StreamDataAdapter extends DataAdapter{
-
+ private class StreamDataAdapter extends DataAdapter {
/** Input stream providing access to the data. */
- private BufferedReader inputStream;
+ private final BufferedReader inputStream;
/**
* Create a StreamDataAdapter from a BufferedReader.
@@ -282,7 +274,6 @@ public class EmpiricalDistribution extends
AbstractRealDistribution
* @param in BufferedReader input stream
*/
StreamDataAdapter(BufferedReader in){
- super();
inputStream = in;
}
@@ -298,7 +289,6 @@ public class EmpiricalDistribution extends
AbstractRealDistribution
}
inputStream.close();
- inputStream = null;
}
/** {@inheritDoc} */
@@ -312,15 +302,13 @@ public class EmpiricalDistribution extends
AbstractRealDistribution
sampleStats.addValue(val);
}
inputStream.close();
- inputStream = null;
}
}
/**
- * <code>DataAdapter</code> for data provided as array of doubles.
+ * {@code DataAdapter} for data provided as array of doubles.
*/
private class ArrayDataAdapter extends DataAdapter {
-
/** Array of input data values. */
private final double[] inputArray;
@@ -331,7 +319,6 @@ public class EmpiricalDistribution extends
AbstractRealDistribution
* @throws NullArgumentException if in is null
*/
ArrayDataAdapter(double[] in) {
- super();
NullArgumentException.check(in);
inputArray = in;
}
@@ -349,8 +336,7 @@ public class EmpiricalDistribution extends
AbstractRealDistribution
@Override
public void computeBinStats() throws IOException {
for (int i = 0; i < inputArray.length; i++) {
- SummaryStatistics stats =
- binStats.get(findBin(inputArray[i]));
+ SummaryStatistics stats = binStats.get(findBin(inputArray[i]));
stats.addValue(inputArray[i]);
}
}
@@ -362,12 +348,11 @@ public class EmpiricalDistribution extends
AbstractRealDistribution
* @param da object providing access to the data
* @throws IOException if an IO error occurs
*/
- private void fillBinStats(final DataAdapter da)
- throws IOException {
+ private void fillBinStats(final DataAdapter da) throws IOException {
// Set up grid
min = sampleStats.getMin();
max = sampleStats.getMax();
- delta = (max - min)/binCount;
+ delta = (max - min) / binCount;
// Initialize binStats ArrayList
if (!binStats.isEmpty()) {
@@ -375,7 +360,7 @@ public class EmpiricalDistribution extends
AbstractRealDistribution
}
for (int i = 0; i < binCount; i++) {
SummaryStatistics stats = new SummaryStatistics();
- binStats.add(i,stats);
+ binStats.add(i, stats);
}
// Filling data in binStats Array
@@ -383,13 +368,12 @@ public class EmpiricalDistribution extends
AbstractRealDistribution
// Assign upperBounds based on bin counts
upperBounds = new double[binCount];
- upperBounds[0] =
- ((double) binStats.get(0).getN()) / (double) sampleStats.getN();
- for (int i = 1; i < binCount-1; i++) {
- upperBounds[i] = upperBounds[i-1] +
- ((double) binStats.get(i).getN()) / (double) sampleStats.getN();
+ upperBounds[0] = binStats.get(0).getN() / (double) sampleStats.getN();
+ for (int i = 1; i < binCount - 1; i++) {
+ upperBounds[i] = upperBounds[i - 1] +
+ binStats.get(i).getN() / (double) sampleStats.getN();
}
- upperBounds[binCount-1] = 1.0d;
+ upperBounds[binCount - 1] = 1d;
}
/**
@@ -399,9 +383,8 @@ public class EmpiricalDistribution extends
AbstractRealDistribution
* @return the index of the bin containing the value
*/
private int findBin(double value) {
- return AccurateMath.min(
- AccurateMath.max((int) AccurateMath.ceil((value - min) /
delta) - 1, 0),
- binCount - 1);
+ return AccurateMath.min(AccurateMath.max((int)
AccurateMath.ceil((value - min) / delta) - 1, 0),
+ binCount - 1);
}
/**
@@ -490,7 +473,7 @@ public class EmpiricalDistribution extends
AbstractRealDistribution
return loaded;
}
- // Distribution methods ---------------------------
+ // Distribution methods.
/**
* {@inheritDoc}
@@ -588,21 +571,22 @@ public class EmpiricalDistribution extends
AbstractRealDistribution
*/
@Override
public double inverseCumulativeProbability(final double p) {
- if (p < 0.0 || p > 1.0) {
+ if (p < 0 ||
+ p > 1) {
throw new OutOfRangeException(p, 0, 1);
}
- if (p == 0.0) {
+ if (p == 0) {
return getSupportLowerBound();
}
- if (p == 1.0) {
+ if (p == 1) {
return getSupportUpperBound();
}
int i = 0;
while (cumBinP(i) < p) {
- i++;
+ ++i;
}
final ContinuousDistribution kernel = getKernel(binStats.get(i));
diff --git
a/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistributionTest.java
b/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistributionTest.java
index 70b9f1d..3705d6e 100644
---
a/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistributionTest.java
+++
b/commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistributionTest.java
@@ -667,8 +667,7 @@ public final class EmpiricalDistributionTest extends
RealDistributionAbstractTes
}
}
- @Ignore
- @Test
+ @Ignore@Test
public void testMath1462() {
final double[] data = {
6464.0205, 6449.1328, 6489.4569, 6497.5533, 6251.6487,
@@ -689,13 +688,20 @@ public final class EmpiricalDistributionTest extends
RealDistributionAbstractTes
final EmpiricalDistribution ed = new
EmpiricalDistribution(data.length);
ed.load(data);
- final double p50 = ed.inverseCumulativeProbability(0.5);
- final double p51 = ed.inverseCumulativeProbability(0.51111);
- final double p49 = ed.inverseCumulativeProbability(0.49999);
+ double v;
+ double p;
+
+ p = 0.49999;
+ v = ed.inverseCumulativeProbability(p);
+ Assert.assertTrue("p=" + p + " => v=" + v, v < 6341);
+
+ p = 0.5;
+ v = ed.inverseCumulativeProbability(p);
+ Assert.assertTrue("p=" + p + " => v=" + v, v < 7000);
- Assert.assertTrue(p51 < 6350);
- Assert.assertTrue(p49 < 6341);
- Assert.assertTrue(p50 < 7000);
+ p = 0.51111;
+ v = ed.inverseCumulativeProbability(p);
+ Assert.assertTrue("p=" + p + " => v=" + v, v < 6350);
}
/**
