/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.commons.math.random;
import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.io.Serializable;
import java.net.URL;
import java.util.ArrayList;
import java.util.List;
import org.apache.commons.math.MathRuntimeException;
import org.apache.commons.math.exception.util.LocalizedFormats;
import org.apache.commons.math.stat.descriptive.StatisticalSummary;
import org.apache.commons.math.stat.descriptive.SummaryStatistics;
import org.apache.commons.math.util.FastMath;
/**
* Implements <code>EmpiricalDistribution</code> interface. This 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></p><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>
* </ul></p>
*
* @version $Id: EmpiricalDistributionImpl.java 1131229 2011-06-03 20:49:25Z luc $
*/
public class EmpiricalDistributionImpl implements Serializable, EmpiricalDistribution {
/** Serializable version identifier */
private static final long serialVersionUID = 5729073523949762654L;
/** List of SummaryStatistics objects characterizing the bins */
private final List<SummaryStatistics> binStats;
/** Sample statistics */
private SummaryStatistics sampleStats = null;
/** Max loaded value */
private double max = Double.NEGATIVE_INFINITY;
/** Min loaded value */
private double min = Double.POSITIVE_INFINITY;
/** Grid size */
private double delta = 0d;
/** number of bins */
private final int binCount;
/** is the distribution loaded? */
private boolean loaded = false;
/** upper bounds of subintervals in (0,1) "belonging" to the bins */
private double[] upperBounds = null;
/** RandomData instance to use in repeated calls to getNext() */
private final RandomData randomData = new RandomDataImpl();
/**
* Creates a new EmpiricalDistribution with the default bin count.
*/
public EmpiricalDistributionImpl() {
binCount = 1000;
binStats = new ArrayList<SummaryStatistics>();
}
/**
* Creates a new EmpiricalDistribution with the specified bin count.
*
* @param binCount number of bins
*/
public EmpiricalDistributionImpl(int binCount) {
this.binCount = binCount;
binStats = new ArrayList<SummaryStatistics>();
}
/**
* Computes the empirical distribution from the provided
* array of numbers.
*
* @param in the input data array
*/
public void load(double[] in) {
DataAdapter da = new ArrayDataAdapter(in);
try {
da.computeStats();
fillBinStats(in);
} catch (IOException e) {
throw new MathRuntimeException(e);
}
loaded = true;
}
/**
* Computes the empirical distribution using data read from a URL.
* @param url url of the input file
*
* @throws IOException if an IO error occurs
*/
public void load(URL url) throws IOException {
BufferedReader in =
new BufferedReader(new InputStreamReader(url.openStream()));
try {
DataAdapter da = new StreamDataAdapter(in);
da.computeStats();
if (sampleStats.getN() == 0) {
throw MathRuntimeException.createEOFException(LocalizedFormats.URL_CONTAINS_NO_DATA,
url);
}
in = new BufferedReader(new InputStreamReader(url.openStream()));
fillBinStats(in);
loaded = true;
} finally {
try {
in.close();
} catch (IOException ex) {
// ignore
}
}
}
/**
* Computes the empirical distribution from the input file.
*
* @param file the input file
* @throws IOException if an IO error occurs
*/
public void load(File file) throws IOException {
BufferedReader in = new BufferedReader(new FileReader(file));
try {
DataAdapter da = new StreamDataAdapter(in);
da.computeStats();
in = new BufferedReader(new FileReader(file));
fillBinStats(in);
loaded = true;
} finally {
try {
in.close();
} catch (IOException ex) {
// ignore
}
}
}
/**
* Provides methods for computing <code>sampleStats</code> and
* <code>beanStats</code> abstracting the source of data.
*/
private abstract class DataAdapter{
/**
* Compute bin stats.
*
* @throws IOException if an error occurs computing bin stats
*/
public abstract void computeBinStats() throws IOException;
/**
* Compute sample statistics.
*
* @throws IOException if an error occurs computing sample stats
*/
public abstract void computeStats() throws IOException;
}
/**
* Factory of <code>DataAdapter</code> objects. For every supported source
* of data (array of doubles, file, etc.) an instance of the proper object
* is returned.
*/
private class DataAdapterFactory{
/**
* Creates a DataAdapter from a data object
*
* @param in object providing access to the data
* @return DataAdapter instance
*/
public DataAdapter getAdapter(Object in) {
if (in instanceof BufferedReader) {
BufferedReader inputStream = (BufferedReader) in;
return new StreamDataAdapter(inputStream);
} else if (in instanceof double[]) {
double[] inputArray = (double[]) in;
return new ArrayDataAdapter(inputArray);
} else {
throw MathRuntimeException.createIllegalArgumentException(
LocalizedFormats.INPUT_DATA_FROM_UNSUPPORTED_DATASOURCE,
in.getClass().getName(),
BufferedReader.class.getName(), double[].class.getName());
}
}
}
/**
* <code>DataAdapter</code> for data provided through some input stream
*/
private class StreamDataAdapter extends DataAdapter{
/** Input stream providing access to the data */
private BufferedReader inputStream;
/**
* Create a StreamDataAdapter from a BufferedReader
*
* @param in BufferedReader input stream
*/
public StreamDataAdapter(BufferedReader in){
super();
inputStream = in;
}
/** {@inheritDoc} */
@Override
public void computeBinStats() throws IOException {
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;
}
/** {@inheritDoc} */
@Override
public void computeStats() throws IOException {
String str = null;
double val = 0.0;
sampleStats = new SummaryStatistics();
while ((str = inputStream.readLine()) != null) {
val = Double.valueOf(str).doubleValue();
sampleStats.addValue(val);
}
inputStream.close();
inputStream = null;
}
}
/**
* <code>DataAdapter</code> for data provided as array of doubles.
*/
private class ArrayDataAdapter extends DataAdapter {
/** Array of input data values */
private double[] inputArray;
/**
* Construct an ArrayDataAdapter from a double[] array
*
* @param in double[] array holding the data
*/
public ArrayDataAdapter(double[] in){
super();
inputArray = in;
}
/** {@inheritDoc} */
@Override
public void computeStats() throws IOException {
sampleStats = new SummaryStatistics();
for (int i = 0; i < inputArray.length; i++) {
sampleStats.addValue(inputArray[i]);
}
}
/** {@inheritDoc} */
@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]);
}
}
}
/**
* Fills binStats array (second pass through data file).
*
* @param in object providing access to the data
* @throws IOException if an IO error occurs
*/
private void fillBinStats(Object in) throws IOException {
// Set up grid
min = sampleStats.getMin();
max = sampleStats.getMax();
delta = (max - min)/(Double.valueOf(binCount)).doubleValue();
// Initialize binStats ArrayList
if (!binStats.isEmpty()) {
binStats.clear();
}
for (int i = 0; i < binCount; i++) {
SummaryStatistics stats = new SummaryStatistics();
binStats.add(i,stats);
}
// Filling data in binStats Array
DataAdapterFactory aFactory = new DataAdapterFactory();
DataAdapter da = aFactory.getAdapter(in);
da.computeBinStats();
// 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[binCount-1] = 1.0d;
}
/**
* Returns the index of the bin to which the given value belongs
*
* @param value the value whose bin we are trying to find
* @return the index of the bin containing the value
*/
private int findBin(double value) {
return FastMath.min(
FastMath.max((int) FastMath.ceil((value- min) / delta) - 1, 0),
binCount - 1);
}
/**
* Generates a random value from this distribution.
*
* @return the random value.
* @throws IllegalStateException if the distribution has not been loaded
*/
public double getNextValue() throws IllegalStateException {
if (!loaded) {
throw MathRuntimeException.createIllegalStateException(LocalizedFormats.DISTRIBUTION_NOT_LOADED);
}
// Start with a uniformly distributed random number in (0,1)
double x = FastMath.random();
// Use this to select the bin and generate a Gaussian within the bin
for (int i = 0; i < binCount; i++) {
if (x <= upperBounds[i]) {
SummaryStatistics stats = binStats.get(i);
if (stats.getN() > 0) {
if (stats.getStandardDeviation() > 0) { // more than one obs
return randomData.nextGaussian
(stats.getMean(),stats.getStandardDeviation());
} else {
return stats.getMean(); // only one obs in bin
}
}
}
}
throw new MathRuntimeException(LocalizedFormats.NO_BIN_SELECTED);
}
/**
* Returns a {@link StatisticalSummary} describing this distribution.
* <strong>Preconditions:</strong><ul>
* <li>the distribution must be loaded before invoking this method</li></ul>
*
* @return the sample statistics
* @throws IllegalStateException if the distribution has not been loaded
*/
public StatisticalSummary getSampleStats() {
return sampleStats;
}
/**
* Returns the number of bins.
*
* @return the number of bins.
*/
public int getBinCount() {
return binCount;
}
/**
* Returns a List of {@link SummaryStatistics} instances containing
* statistics describing the values in each of the bins. The list is
* indexed on the bin number.
*
* @return List of bin statistics.
*/
public List<SummaryStatistics> getBinStats() {
return binStats;
}
/**
* <p>Returns a fresh copy of the array of upper bounds for the bins.
* Bins are: <br/>
* [min,upperBounds[0]],(upperBounds[0],upperBounds[1]],...,
* (upperBounds[binCount-2], upperBounds[binCount-1] = max].</p>
*
* <p>Note: In versions 1.0-2.0 of commons-math, this method
* incorrectly returned the array of probability generator upper
* bounds now returned by {@link #getGeneratorUpperBounds()}.</p>
*
* @return array of bin upper bounds
* @since 2.1
*/
public double[] getUpperBounds() {
double[] binUpperBounds = new double[binCount];
binUpperBounds[0] = min + delta;
for (int i = 1; i < binCount - 1; i++) {
binUpperBounds[i] = binUpperBounds[i-1] + delta;
}
binUpperBounds[binCount - 1] = max;
return binUpperBounds;
}
/**
* <p>Returns a fresh copy of the array of upper bounds of the subintervals
* of [0,1] used in generating data from the empirical distribution.
* Subintervals correspond to bins with lengths proportional to bin counts.</p>
*
* <p>In versions 1.0-2.0 of commons-math, this array was (incorrectly) returned
* by {@link #getUpperBounds()}.</p>
*
* @since 2.1
* @return array of upper bounds of subintervals used in data generation
*/
public double[] getGeneratorUpperBounds() {
int len = upperBounds.length;
double[] out = new double[len];
System.arraycopy(upperBounds, 0, out, 0, len);
return out;
}
/**
* Property indicating whether or not the distribution has been loaded.
*
* @return true if the distribution has been loaded
*/
public boolean isLoaded() {
return loaded;
}
}