001 /*
002 * Licensed to the Apache Software Foundation (ASF) under one or more
003 * contributor license agreements. See the NOTICE file distributed with
004 * this work for additional information regarding copyright ownership.
005 * The ASF licenses this file to You under the Apache License, Version 2.0
006 * (the "License"); you may not use this file except in compliance with
007 * the License. You may obtain a copy of the License at
008 *
009 * http://www.apache.org/licenses/LICENSE-2.0
010 *
011 * Unless required by applicable law or agreed to in writing, software
012 * distributed under the License is distributed on an "AS IS" BASIS,
013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
014 * See the License for the specific language governing permissions and
015 * limitations under the License.
016 */
017
018 package org.apache.commons.math3.random;
019
020 import java.io.BufferedReader;
021 import java.io.File;
022 import java.io.FileInputStream;
023 import java.io.IOException;
024 import java.io.InputStream;
025 import java.io.InputStreamReader;
026 import java.net.URL;
027 import java.nio.charset.Charset;
028 import java.util.ArrayList;
029 import java.util.List;
030
031 import org.apache.commons.math3.distribution.AbstractRealDistribution;
032 import org.apache.commons.math3.distribution.NormalDistribution;
033 import org.apache.commons.math3.distribution.RealDistribution;
034 import org.apache.commons.math3.exception.MathIllegalStateException;
035 import org.apache.commons.math3.exception.MathInternalError;
036 import org.apache.commons.math3.exception.NullArgumentException;
037 import org.apache.commons.math3.exception.OutOfRangeException;
038 import org.apache.commons.math3.exception.ZeroException;
039 import org.apache.commons.math3.exception.util.LocalizedFormats;
040 import org.apache.commons.math3.stat.descriptive.StatisticalSummary;
041 import org.apache.commons.math3.stat.descriptive.SummaryStatistics;
042 import org.apache.commons.math3.util.FastMath;
043 import org.apache.commons.math3.util.MathUtils;
044
045 /**
046 * <p>Represents an <a href="http://http://en.wikipedia.org/wiki/Empirical_distribution_function">
047 * empirical probability distribution</a> -- a probability distribution derived
048 * from observed data without making any assumptions about the functional form
049 * of the population distribution that the data come from.</p>
050 *
051 * <p>An <code>EmpiricalDistribution</code> maintains data structures, called
052 * <i>distribution digests</i>, that describe empirical distributions and
053 * support the following operations: <ul>
054 * <li>loading the distribution from a file of observed data values</li>
055 * <li>dividing the input data into "bin ranges" and reporting bin frequency
056 * counts (data for histogram)</li>
057 * <li>reporting univariate statistics describing the full set of data values
058 * as well as the observations within each bin</li>
059 * <li>generating random values from the distribution</li>
060 * </ul>
061 * Applications can use <code>EmpiricalDistribution</code> to build grouped
062 * frequency histograms representing the input data or to generate random values
063 * "like" those in the input file -- i.e., the values generated will follow the
064 * distribution of the values in the file.</p>
065 *
066 * <p>The implementation uses what amounts to the
067 * <a href="http://nedwww.ipac.caltech.edu/level5/March02/Silverman/Silver2_6.html">
068 * Variable Kernel Method</a> with Gaussian smoothing:<p>
069 * <strong>Digesting the input file</strong>
070 * <ol><li>Pass the file once to compute min and max.</li>
071 * <li>Divide the range from min-max into <code>binCount</code> "bins."</li>
072 * <li>Pass the data file again, computing bin counts and univariate
073 * statistics (mean, std dev.) for each of the bins </li>
074 * <li>Divide the interval (0,1) into subintervals associated with the bins,
075 * with the length of a bin's subinterval proportional to its count.</li></ol>
076 * <strong>Generating random values from the distribution</strong><ol>
077 * <li>Generate a uniformly distributed value in (0,1) </li>
078 * <li>Select the subinterval to which the value belongs.
079 * <li>Generate a random Gaussian value with mean = mean of the associated
080 * bin and std dev = std dev of associated bin.</li></ol></p>
081 *
082 * <p>EmpiricalDistribution implements the {@link RealDistribution} interface
083 * as follows. Given x within the range of values in the dataset, let B
084 * be the bin containing x and let K be the within-bin kernel for B. Let P(B-)
085 * be the sum of the probabilities of the bins below B and let K(B) be the
086 * mass of B under K (i.e., the integral of the kernel density over B). Then
087 * set P(X < x) = P(B-) + P(B) * K(x) / K(B) where K(x) is the kernel distribution
088 * evaluated at x. This results in a cdf that matches the grouped frequency
089 * distribution at the bin endpoints and interpolates within bins using
090 * within-bin kernels.</p>
091 *
092 *<strong>USAGE NOTES:</strong><ul>
093 *<li>The <code>binCount</code> is set by default to 1000. A good rule of thumb
094 * is to set the bin count to approximately the length of the input file divided
095 * by 10. </li>
096 *<li>The input file <i>must</i> be a plain text file containing one valid numeric
097 * entry per line.</li>
098 * </ul></p>
099 *
100 * @version $Id: EmpiricalDistribution.java 1422350 2012-12-15 20:47:47Z psteitz $
101 */
102 public class EmpiricalDistribution extends AbstractRealDistribution {
103
104 /** Default bin count */
105 public static final int DEFAULT_BIN_COUNT = 1000;
106
107 /** Character set for file input */
108 private static final String FILE_CHARSET = "US-ASCII";
109
110 /** Serializable version identifier */
111 private static final long serialVersionUID = 5729073523949762654L;
112
113 /** List of SummaryStatistics objects characterizing the bins */
114 private final List<SummaryStatistics> binStats;
115
116 /** Sample statistics */
117 private SummaryStatistics sampleStats = null;
118
119 /** Max loaded value */
120 private double max = Double.NEGATIVE_INFINITY;
121
122 /** Min loaded value */
123 private double min = Double.POSITIVE_INFINITY;
124
125 /** Grid size */
126 private double delta = 0d;
127
128 /** number of bins */
129 private final int binCount;
130
131 /** is the distribution loaded? */
132 private boolean loaded = false;
133
134 /** upper bounds of subintervals in (0,1) "belonging" to the bins */
135 private double[] upperBounds = null;
136
137 /** RandomDataGenerator instance to use in repeated calls to getNext() */
138 private final RandomDataGenerator randomData;
139
140 /**
141 * Creates a new EmpiricalDistribution with the default bin count.
142 */
143 public EmpiricalDistribution() {
144 this(DEFAULT_BIN_COUNT);
145 }
146
147 /**
148 * Creates a new EmpiricalDistribution with the specified bin count.
149 *
150 * @param binCount number of bins
151 */
152 public EmpiricalDistribution(int binCount) {
153 this(binCount, new RandomDataGenerator());
154 }
155
156 /**
157 * Creates a new EmpiricalDistribution with the specified bin count using the
158 * provided {@link RandomGenerator} as the source of random data.
159 *
160 * @param binCount number of bins
161 * @param generator random data generator (may be null, resulting in default JDK generator)
162 * @since 3.0
163 */
164 public EmpiricalDistribution(int binCount, RandomGenerator generator) {
165 this(binCount, new RandomDataGenerator(generator));
166 }
167
168 /**
169 * Creates a new EmpiricalDistribution with default bin count using the
170 * provided {@link RandomGenerator} as the source of random data.
171 *
172 * @param generator random data generator (may be null, resulting in default JDK generator)
173 * @since 3.0
174 */
175 public EmpiricalDistribution(RandomGenerator generator) {
176 this(DEFAULT_BIN_COUNT, generator);
177 }
178
179 /**
180 * Creates a new EmpiricalDistribution with the specified bin count using the
181 * provided {@link RandomDataImpl} instance as the source of random data.
182 *
183 * @param binCount number of bins
184 * @param randomData random data generator (may be null, resulting in default JDK generator)
185 * @since 3.0
186 * @deprecated As of 3.1. Please use {@link #EmpiricalDistribution(int,RandomGenerator)} instead.
187 */
188 @Deprecated
189 public EmpiricalDistribution(int binCount, RandomDataImpl randomData) {
190 this(binCount, randomData.getDelegate());
191 }
192
193 /**
194 * Creates a new EmpiricalDistribution with default bin count using the
195 * provided {@link RandomDataImpl} as the source of random data.
196 *
197 * @param randomData random data generator (may be null, resulting in default JDK generator)
198 * @since 3.0
199 * @deprecated As of 3.1. Please use {@link #EmpiricalDistribution(RandomGenerator)} instead.
200 */
201 @Deprecated
202 public EmpiricalDistribution(RandomDataImpl randomData) {
203 this(DEFAULT_BIN_COUNT, randomData);
204 }
205
206 /**
207 * Private constructor to allow lazy initialisation of the RNG contained
208 * in the {@link #randomData} instance variable.
209 *
210 * @param binCount number of bins
211 * @param randomData Random data generator.
212 */
213 private EmpiricalDistribution(int binCount,
214 RandomDataGenerator randomData) {
215 super(null);
216 this.binCount = binCount;
217 this.randomData = randomData;
218 binStats = new ArrayList<SummaryStatistics>();
219 }
220
221 /**
222 * Computes the empirical distribution from the provided
223 * array of numbers.
224 *
225 * @param in the input data array
226 * @exception NullArgumentException if in is null
227 */
228 public void load(double[] in) throws NullArgumentException {
229 DataAdapter da = new ArrayDataAdapter(in);
230 try {
231 da.computeStats();
232 // new adapter for the second pass
233 fillBinStats(new ArrayDataAdapter(in));
234 } catch (IOException ex) {
235 // Can't happen
236 throw new MathInternalError();
237 }
238 loaded = true;
239
240 }
241
242 /**
243 * Computes the empirical distribution using data read from a URL.
244 *
245 * <p>The input file <i>must</i> be an ASCII text file containing one
246 * valid numeric entry per line.</p>
247 *
248 * @param url url of the input file
249 *
250 * @throws IOException if an IO error occurs
251 * @throws NullArgumentException if url is null
252 * @throws ZeroException if URL contains no data
253 */
254 public void load(URL url) throws IOException, NullArgumentException, ZeroException {
255 MathUtils.checkNotNull(url);
256 Charset charset = Charset.forName(FILE_CHARSET);
257 BufferedReader in =
258 new BufferedReader(new InputStreamReader(url.openStream(), charset));
259 try {
260 DataAdapter da = new StreamDataAdapter(in);
261 da.computeStats();
262 if (sampleStats.getN() == 0) {
263 throw new ZeroException(LocalizedFormats.URL_CONTAINS_NO_DATA, url);
264 }
265 // new adapter for the second pass
266 in = new BufferedReader(new InputStreamReader(url.openStream(), charset));
267 fillBinStats(new StreamDataAdapter(in));
268 loaded = true;
269 } finally {
270 try {
271 in.close();
272 } catch (IOException ex) { //NOPMD
273 // ignore
274 }
275 }
276 }
277
278 /**
279 * Computes the empirical distribution from the input file.
280 *
281 * <p>The input file <i>must</i> be an ASCII text file containing one
282 * valid numeric entry per line.</p>
283 *
284 * @param file the input file
285 * @throws IOException if an IO error occurs
286 * @throws NullArgumentException if file is null
287 */
288 public void load(File file) throws IOException, NullArgumentException {
289 MathUtils.checkNotNull(file);
290 Charset charset = Charset.forName(FILE_CHARSET);
291 InputStream is = new FileInputStream(file);
292 BufferedReader in = new BufferedReader(new InputStreamReader(is, charset));
293 try {
294 DataAdapter da = new StreamDataAdapter(in);
295 da.computeStats();
296 // new adapter for second pass
297 is = new FileInputStream(file);
298 in = new BufferedReader(new InputStreamReader(is, charset));
299 fillBinStats(new StreamDataAdapter(in));
300 loaded = true;
301 } finally {
302 try {
303 in.close();
304 } catch (IOException ex) { //NOPMD
305 // ignore
306 }
307 }
308 }
309
310 /**
311 * Provides methods for computing <code>sampleStats</code> and
312 * <code>beanStats</code> abstracting the source of data.
313 */
314 private abstract class DataAdapter{
315
316 /**
317 * Compute bin stats.
318 *
319 * @throws IOException if an error occurs computing bin stats
320 */
321 public abstract void computeBinStats() throws IOException;
322
323 /**
324 * Compute sample statistics.
325 *
326 * @throws IOException if an error occurs computing sample stats
327 */
328 public abstract void computeStats() throws IOException;
329
330 }
331
332 /**
333 * <code>DataAdapter</code> for data provided through some input stream
334 */
335 private class StreamDataAdapter extends DataAdapter{
336
337 /** Input stream providing access to the data */
338 private BufferedReader inputStream;
339
340 /**
341 * Create a StreamDataAdapter from a BufferedReader
342 *
343 * @param in BufferedReader input stream
344 */
345 public StreamDataAdapter(BufferedReader in){
346 super();
347 inputStream = in;
348 }
349
350 /** {@inheritDoc} */
351 @Override
352 public void computeBinStats() throws IOException {
353 String str = null;
354 double val = 0.0d;
355 while ((str = inputStream.readLine()) != null) {
356 val = Double.parseDouble(str);
357 SummaryStatistics stats = binStats.get(findBin(val));
358 stats.addValue(val);
359 }
360
361 inputStream.close();
362 inputStream = null;
363 }
364
365 /** {@inheritDoc} */
366 @Override
367 public void computeStats() throws IOException {
368 String str = null;
369 double val = 0.0;
370 sampleStats = new SummaryStatistics();
371 while ((str = inputStream.readLine()) != null) {
372 val = Double.valueOf(str).doubleValue();
373 sampleStats.addValue(val);
374 }
375 inputStream.close();
376 inputStream = null;
377 }
378 }
379
380 /**
381 * <code>DataAdapter</code> for data provided as array of doubles.
382 */
383 private class ArrayDataAdapter extends DataAdapter {
384
385 /** Array of input data values */
386 private double[] inputArray;
387
388 /**
389 * Construct an ArrayDataAdapter from a double[] array
390 *
391 * @param in double[] array holding the data
392 * @throws NullArgumentException if in is null
393 */
394 public ArrayDataAdapter(double[] in) throws NullArgumentException {
395 super();
396 MathUtils.checkNotNull(in);
397 inputArray = in;
398 }
399
400 /** {@inheritDoc} */
401 @Override
402 public void computeStats() throws IOException {
403 sampleStats = new SummaryStatistics();
404 for (int i = 0; i < inputArray.length; i++) {
405 sampleStats.addValue(inputArray[i]);
406 }
407 }
408
409 /** {@inheritDoc} */
410 @Override
411 public void computeBinStats() throws IOException {
412 for (int i = 0; i < inputArray.length; i++) {
413 SummaryStatistics stats =
414 binStats.get(findBin(inputArray[i]));
415 stats.addValue(inputArray[i]);
416 }
417 }
418 }
419
420 /**
421 * Fills binStats array (second pass through data file).
422 *
423 * @param da object providing access to the data
424 * @throws IOException if an IO error occurs
425 */
426 private void fillBinStats(final DataAdapter da)
427 throws IOException {
428 // Set up grid
429 min = sampleStats.getMin();
430 max = sampleStats.getMax();
431 delta = (max - min)/(Double.valueOf(binCount)).doubleValue();
432
433 // Initialize binStats ArrayList
434 if (!binStats.isEmpty()) {
435 binStats.clear();
436 }
437 for (int i = 0; i < binCount; i++) {
438 SummaryStatistics stats = new SummaryStatistics();
439 binStats.add(i,stats);
440 }
441
442 // Filling data in binStats Array
443 da.computeBinStats();
444
445 // Assign upperBounds based on bin counts
446 upperBounds = new double[binCount];
447 upperBounds[0] =
448 ((double) binStats.get(0).getN()) / (double) sampleStats.getN();
449 for (int i = 1; i < binCount-1; i++) {
450 upperBounds[i] = upperBounds[i-1] +
451 ((double) binStats.get(i).getN()) / (double) sampleStats.getN();
452 }
453 upperBounds[binCount-1] = 1.0d;
454 }
455
456 /**
457 * Returns the index of the bin to which the given value belongs
458 *
459 * @param value the value whose bin we are trying to find
460 * @return the index of the bin containing the value
461 */
462 private int findBin(double value) {
463 return FastMath.min(
464 FastMath.max((int) FastMath.ceil((value - min) / delta) - 1, 0),
465 binCount - 1);
466 }
467
468 /**
469 * Generates a random value from this distribution.
470 * <strong>Preconditions:</strong><ul>
471 * <li>the distribution must be loaded before invoking this method</li></ul>
472 * @return the random value.
473 * @throws MathIllegalStateException if the distribution has not been loaded
474 */
475 public double getNextValue() throws MathIllegalStateException {
476
477 if (!loaded) {
478 throw new MathIllegalStateException(LocalizedFormats.DISTRIBUTION_NOT_LOADED);
479 }
480
481 // Start with a uniformly distributed random number in (0,1)
482 final double x = randomData.nextUniform(0,1);
483
484 // Use this to select the bin and generate a Gaussian within the bin
485 for (int i = 0; i < binCount; i++) {
486 if (x <= upperBounds[i]) {
487 SummaryStatistics stats = binStats.get(i);
488 if (stats.getN() > 0) {
489 if (stats.getStandardDeviation() > 0) { // more than one obs
490 return randomData.nextGaussian(stats.getMean(),
491 stats.getStandardDeviation());
492 } else {
493 return stats.getMean(); // only one obs in bin
494 }
495 }
496 }
497 }
498 throw new MathIllegalStateException(LocalizedFormats.NO_BIN_SELECTED);
499 }
500
501 /**
502 * Returns a {@link StatisticalSummary} describing this distribution.
503 * <strong>Preconditions:</strong><ul>
504 * <li>the distribution must be loaded before invoking this method</li></ul>
505 *
506 * @return the sample statistics
507 * @throws IllegalStateException if the distribution has not been loaded
508 */
509 public StatisticalSummary getSampleStats() {
510 return sampleStats;
511 }
512
513 /**
514 * Returns the number of bins.
515 *
516 * @return the number of bins.
517 */
518 public int getBinCount() {
519 return binCount;
520 }
521
522 /**
523 * Returns a List of {@link SummaryStatistics} instances containing
524 * statistics describing the values in each of the bins. The list is
525 * indexed on the bin number.
526 *
527 * @return List of bin statistics.
528 */
529 public List<SummaryStatistics> getBinStats() {
530 return binStats;
531 }
532
533 /**
534 * <p>Returns a fresh copy of the array of upper bounds for the bins.
535 * Bins are: <br/>
536 * [min,upperBounds[0]],(upperBounds[0],upperBounds[1]],...,
537 * (upperBounds[binCount-2], upperBounds[binCount-1] = max].</p>
538 *
539 * <p>Note: In versions 1.0-2.0 of commons-math, this method
540 * incorrectly returned the array of probability generator upper
541 * bounds now returned by {@link #getGeneratorUpperBounds()}.</p>
542 *
543 * @return array of bin upper bounds
544 * @since 2.1
545 */
546 public double[] getUpperBounds() {
547 double[] binUpperBounds = new double[binCount];
548 for (int i = 0; i < binCount - 1; i++) {
549 binUpperBounds[i] = min + delta * (i + 1);
550 }
551 binUpperBounds[binCount - 1] = max;
552 return binUpperBounds;
553 }
554
555 /**
556 * <p>Returns a fresh copy of the array of upper bounds of the subintervals
557 * of [0,1] used in generating data from the empirical distribution.
558 * Subintervals correspond to bins with lengths proportional to bin counts.</p>
559 *
560 * <p>In versions 1.0-2.0 of commons-math, this array was (incorrectly) returned
561 * by {@link #getUpperBounds()}.</p>
562 *
563 * @since 2.1
564 * @return array of upper bounds of subintervals used in data generation
565 */
566 public double[] getGeneratorUpperBounds() {
567 int len = upperBounds.length;
568 double[] out = new double[len];
569 System.arraycopy(upperBounds, 0, out, 0, len);
570 return out;
571 }
572
573 /**
574 * Property indicating whether or not the distribution has been loaded.
575 *
576 * @return true if the distribution has been loaded
577 */
578 public boolean isLoaded() {
579 return loaded;
580 }
581
582 /**
583 * Reseeds the random number generator used by {@link #getNextValue()}.
584 *
585 * @param seed random generator seed
586 * @since 3.0
587 */
588 public void reSeed(long seed) {
589 randomData.reSeed(seed);
590 }
591
592 // Distribution methods ---------------------------
593
594 /**
595 * {@inheritDoc}
596 * @since 3.1
597 */
598 public double probability(double x) {
599 return 0;
600 }
601
602 /**
603 * {@inheritDoc}
604 *
605 * <p>Returns the kernel density normalized so that its integral over each bin
606 * equals the bin mass.</p>
607 *
608 * <p>Algorithm description: <ol>
609 * <li>Find the bin B that x belongs to.</li>
610 * <li>Compute K(B) = the mass of B with respect to the within-bin kernel (i.e., the
611 * integral of the kernel density over B).</li>
612 * <li>Return k(x) * P(B) / K(B), where k is the within-bin kernel density
613 * and P(B) is the mass of B.</li></ol></p>
614 * @since 3.1
615 */
616 public double density(double x) {
617 if (x < min || x > max) {
618 return 0d;
619 }
620 final int binIndex = findBin(x);
621 final RealDistribution kernel = getKernel(binStats.get(binIndex));
622 return kernel.density(x) * pB(binIndex) / kB(binIndex);
623 }
624
625 /**
626 * {@inheritDoc}
627 *
628 * <p>Algorithm description:<ol>
629 * <li>Find the bin B that x belongs to.</li>
630 * <li>Compute P(B) = the mass of B and P(B-) = the combined mass of the bins below B.</li>
631 * <li>Compute K(B) = the probability mass of B with respect to the within-bin kernel
632 * and K(B-) = the kernel distribution evaluated at the lower endpoint of B</li>
633 * <li>Return P(B-) + P(B) * [K(x) - K(B-)] / K(B) where
634 * K(x) is the within-bin kernel distribution function evaluated at x.</li></ol></p>
635 *
636 * @since 3.1
637 */
638 public double cumulativeProbability(double x) {
639 if (x < min) {
640 return 0d;
641 } else if (x >= max) {
642 return 1d;
643 }
644 final int binIndex = findBin(x);
645 final double pBminus = pBminus(binIndex);
646 final double pB = pB(binIndex);
647 final double[] binBounds = getUpperBounds();
648 final double kB = kB(binIndex);
649 final double lower = binIndex == 0 ? min : binBounds[binIndex - 1];
650 final RealDistribution kernel = k(x);
651 final double withinBinCum =
652 (kernel.cumulativeProbability(x) - kernel.cumulativeProbability(lower)) / kB;
653 return pBminus + pB * withinBinCum;
654 }
655
656 /**
657 * {@inheritDoc}
658 *
659 * <p>Algorithm description:<ol>
660 * <li>Find the smallest i such that the sum of the masses of the bins
661 * through i is at least p.</li>
662 * <li>
663 * Let K be the within-bin kernel distribution for bin i.</br>
664 * Let K(B) be the mass of B under K. <br/>
665 * Let K(B-) be K evaluated at the lower endpoint of B (the combined
666 * mass of the bins below B under K).<br/>
667 * Let P(B) be the probability of bin i.<br/>
668 * Let P(B-) be the sum of the bin masses below bin i. <br/>
669 * Let pCrit = p - P(B-)<br/>
670 * <li>Return the inverse of K evaluated at <br/>
671 * K(B-) + pCrit * K(B) / P(B) </li>
672 * </ol></p>
673 *
674 * @since 3.1
675 */
676 public double inverseCumulativeProbability(final double p) throws OutOfRangeException {
677 if (p < 0.0 || p > 1.0) {
678 throw new OutOfRangeException(p, 0, 1);
679 }
680
681 if (p == 0.0) {
682 return getSupportLowerBound();
683 }
684
685 if (p == 1.0) {
686 return getSupportUpperBound();
687 }
688
689 int i = 0;
690 while (cumBinP(i) < p) {
691 i++;
692 }
693
694 final RealDistribution kernel = getKernel(binStats.get(i));
695 final double kB = kB(i);
696 final double[] binBounds = getUpperBounds();
697 final double lower = i == 0 ? min : binBounds[i - 1];
698 final double kBminus = kernel.cumulativeProbability(lower);
699 final double pB = pB(i);
700 final double pBminus = pBminus(i);
701 final double pCrit = p - pBminus;
702 if (pCrit <= 0) {
703 return lower;
704 }
705 return kernel.inverseCumulativeProbability(kBminus + pCrit * kB / pB);
706 }
707
708 /**
709 * {@inheritDoc}
710 * @since 3.1
711 */
712 public double getNumericalMean() {
713 return sampleStats.getMean();
714 }
715
716 /**
717 * {@inheritDoc}
718 * @since 3.1
719 */
720 public double getNumericalVariance() {
721 return sampleStats.getVariance();
722 }
723
724 /**
725 * {@inheritDoc}
726 * @since 3.1
727 */
728 public double getSupportLowerBound() {
729 return min;
730 }
731
732 /**
733 * {@inheritDoc}
734 * @since 3.1
735 */
736 public double getSupportUpperBound() {
737 return max;
738 }
739
740 /**
741 * {@inheritDoc}
742 * @since 3.1
743 */
744 public boolean isSupportLowerBoundInclusive() {
745 return true;
746 }
747
748 /**
749 * {@inheritDoc}
750 * @since 3.1
751 */
752 public boolean isSupportUpperBoundInclusive() {
753 return true;
754 }
755
756 /**
757 * {@inheritDoc}
758 * @since 3.1
759 */
760 public boolean isSupportConnected() {
761 return true;
762 }
763
764 /**
765 * {@inheritDoc}
766 * @since 3.1
767 */
768 @Override
769 public double sample() {
770 return getNextValue();
771 }
772
773 /**
774 * {@inheritDoc}
775 * @since 3.1
776 */
777 @Override
778 public void reseedRandomGenerator(long seed) {
779 randomData.reSeed(seed);
780 }
781
782 /**
783 * The probability of bin i.
784 *
785 * @param i the index of the bin
786 * @return the probability that selection begins in bin i
787 */
788 private double pB(int i) {
789 return i == 0 ? upperBounds[0] :
790 upperBounds[i] - upperBounds[i - 1];
791 }
792
793 /**
794 * The combined probability of the bins up to but not including bin i.
795 *
796 * @param i the index of the bin
797 * @return the probability that selection begins in a bin below bin i.
798 */
799 private double pBminus(int i) {
800 return i == 0 ? 0 : upperBounds[i - 1];
801 }
802
803 /**
804 * Mass of bin i under the within-bin kernel of the bin.
805 *
806 * @param i index of the bin
807 * @return the difference in the within-bin kernel cdf between the
808 * upper and lower endpoints of bin i
809 */
810 @SuppressWarnings("deprecation")
811 private double kB(int i) {
812 final double[] binBounds = getUpperBounds();
813 final RealDistribution kernel = getKernel(binStats.get(i));
814 return i == 0 ? kernel.cumulativeProbability(min, binBounds[0]) :
815 kernel.cumulativeProbability(binBounds[i - 1], binBounds[i]);
816 }
817
818 /**
819 * The within-bin kernel of the bin that x belongs to.
820 *
821 * @param x the value to locate within a bin
822 * @return the within-bin kernel of the bin containing x
823 */
824 private RealDistribution k(double x) {
825 final int binIndex = findBin(x);
826 return getKernel(binStats.get(binIndex));
827 }
828
829 /**
830 * The combined probability of the bins up to and including binIndex.
831 *
832 * @param binIndex maximum bin index
833 * @return sum of the probabilities of bins through binIndex
834 */
835 private double cumBinP(int binIndex) {
836 return upperBounds[binIndex];
837 }
838
839 /**
840 * The within-bin smoothing kernel.
841 *
842 * @param bStats summary statistics for the bin
843 * @return within-bin kernel parameterized by bStats
844 */
845 private RealDistribution getKernel(SummaryStatistics bStats) {
846 // For now, hard-code Gaussian (only kernel supported)
847 return new NormalDistribution(
848 bStats.getMean(), bStats.getStandardDeviation());
849 }
850 }