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The Math project is a library of lightweight, self-contained mathematics and statistics components addressing the most common practical problems not immediately available in the Java programming language or commons-lang.
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/*
* 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.IOException;
import java.io.InputStreamReader;
import java.net.MalformedURLException;
import java.net.URL;
import org.apache.commons.math.MathRuntimeException;
import org.apache.commons.math.exception.util.LocalizedFormats;
/**
* Generates values for use in simulation applications.
*
* How values are generated is determined by the mode
* property.
*
* Supported mode values are:
* - DIGEST_MODE -- uses an empirical distribution
* - REPLAY_MODE -- replays data from
valuesFileURL
* - UNIFORM_MODE -- generates uniformly distributed random values with
* mean =
mu
* - EXPONENTIAL_MODE -- generates exponentially distributed random values
* with mean =
mu
* - GAUSSIAN_MODE -- generates Gaussian distributed random values with
* mean =
mu and
* standard deviation = sigma
* - CONSTANT_MODE -- returns
mu every time.
*
* @version $Revision: 1003886 $ $Date: 2010-10-02 23:04:44 +0200 (sam. 02 oct. 2010) $
*
*/
public class ValueServer {
/** Use empirical distribution. */
public static final int DIGEST_MODE = 0;
/** Replay data from valuesFilePath. */
public static final int REPLAY_MODE = 1;
/** Uniform random deviates with mean = μ. */
public static final int UNIFORM_MODE = 2;
/** Exponential random deviates with mean = μ. */
public static final int EXPONENTIAL_MODE = 3;
/** Gaussian random deviates with mean = μ, std dev = σ. */
public static final int GAUSSIAN_MODE = 4;
/** Always return mu */
public static final int CONSTANT_MODE = 5;
/** mode determines how values are generated. */
private int mode = 5;
/** URI to raw data values. */
private URL valuesFileURL = null;
/** Mean for use with non-data-driven modes. */
private double mu = 0.0;
/** Standard deviation for use with GAUSSIAN_MODE. */
private double sigma = 0.0;
/** Empirical probability distribution for use with DIGEST_MODE. */
private EmpiricalDistribution empiricalDistribution = null;
/** File pointer for REPLAY_MODE. */
private BufferedReader filePointer = null;
/** RandomDataImpl to use for random data generation. */
private final RandomData randomData;
// Data generation modes ======================================
/** Creates new ValueServer */
public ValueServer() {
randomData = new RandomDataImpl();
}
/**
* Construct a ValueServer instance using a RandomData as its source
* of random data.
*
* @param randomData the RandomData instance used to source random data
* @since 1.1
*/
public ValueServer(RandomData randomData) {
this.randomData = randomData;
}
/**
* Returns the next generated value, generated according
* to the mode value (see MODE constants).
*
* @return generated value
* @throws IOException in REPLAY_MODE if a file I/O error occurs
*/
public double getNext() throws IOException {
switch (mode) {
case DIGEST_MODE: return getNextDigest();
case REPLAY_MODE: return getNextReplay();
case UNIFORM_MODE: return getNextUniform();
case EXPONENTIAL_MODE: return getNextExponential();
case GAUSSIAN_MODE: return getNextGaussian();
case CONSTANT_MODE: return mu;
default: throw MathRuntimeException.createIllegalStateException(
LocalizedFormats.UNKNOWN_MODE,
mode,
"DIGEST_MODE", DIGEST_MODE, "REPLAY_MODE", REPLAY_MODE,
"UNIFORM_MODE", UNIFORM_MODE, "EXPONENTIAL_MODE", EXPONENTIAL_MODE,
"GAUSSIAN_MODE", GAUSSIAN_MODE, "CONSTANT_MODE", CONSTANT_MODE);
}
}
/**
* Fills the input array with values generated using getNext() repeatedly.
*
* @param values array to be filled
* @throws IOException in REPLAY_MODE if a file I/O error occurs
*/
public void fill(double[] values) throws IOException {
for (int i = 0; i < values.length; i++) {
values[i] = getNext();
}
}
/**
* Returns an array of length length with values generated
* using getNext() repeatedly.
*
* @param length length of output array
* @return array of generated values
* @throws IOException in REPLAY_MODE if a file I/O error occurs
*/
public double[] fill(int length) throws IOException {
double[] out = new double[length];
for (int i = 0; i < length; i++) {
out[i] = getNext();
}
return out;
}
/**
* Computes the empirical distribution using values from the file
* in valuesFileURL, using the default number of bins.
*
* valuesFileURL must exist and be
* readable by *this at runtime.
*
* This method must be called before using getNext()
* with mode = DIGEST_MODE
*
* @throws IOException if an I/O error occurs reading the input file
*/
public void computeDistribution() throws IOException {
empiricalDistribution = new EmpiricalDistributionImpl();
empiricalDistribution.load(valuesFileURL);
}
/**
* Computes the empirical distribution using values from the file
* in valuesFileURL and binCount bins.
*
* valuesFileURL must exist and be readable by this process
* at runtime.
*
* This method must be called before using getNext()
* with mode = DIGEST_MODE
*
* @param binCount the number of bins used in computing the empirical
* distribution
* @throws IOException if an error occurs reading the input file
*/
public void computeDistribution(int binCount)
throws IOException {
empiricalDistribution = new EmpiricalDistributionImpl(binCount);
empiricalDistribution.load(valuesFileURL);
mu = empiricalDistribution.getSampleStats().getMean();
sigma = empiricalDistribution.getSampleStats().getStandardDeviation();
}
/** Getter for property mode.
* @return Value of property mode.
*/
public int getMode() {
return mode;
}
/** Setter for property mode.
* @param mode New value of property mode.
*/
public void setMode(int mode) {
this.mode = mode;
}
/**
* Getter for valuesFileURL
* @return Value of property valuesFileURL.
*/
public URL getValuesFileURL() {
return valuesFileURL;
}
/**
* Sets the valuesFileURL using a string URL representation
* @param url String representation for new valuesFileURL.
* @throws MalformedURLException if url is not well formed
*/
public void setValuesFileURL(String url) throws MalformedURLException {
this.valuesFileURL = new URL(url);
}
/**
* Sets the valuesFileURL
* @param url New value of property valuesFileURL.
*/
public void setValuesFileURL(URL url) {
this.valuesFileURL = url;
}
/** Getter for property empiricalDistribution.
* @return Value of property empiricalDistribution.
*/
public EmpiricalDistribution getEmpiricalDistribution() {
return empiricalDistribution;
}
/**
* Resets REPLAY_MODE file pointer to the beginning of the valuesFileURL.
*
* @throws IOException if an error occurs opening the file
*/
public void resetReplayFile() throws IOException {
if (filePointer != null) {
try {
filePointer.close();
filePointer = null;
} catch (IOException ex) {
// ignore
}
}
filePointer = new BufferedReader(new InputStreamReader(valuesFileURL.openStream()));
}
/**
* Closes valuesFileURL after use in REPLAY_MODE.
*
* @throws IOException if an error occurs closing the file
*/
public void closeReplayFile() throws IOException {
if (filePointer != null) {
filePointer.close();
filePointer = null;
}
}
/** Getter for property mu.
* @return Value of property mu.
*/
public double getMu() {
return mu;
}
/** Setter for property mu.
* @param mu New value of property mu.
*/
public void setMu(double mu) {
this.mu = mu;
}
/** Getter for property sigma.
* @return Value of property sigma.
*/
public double getSigma() {
return sigma;
}
/** Setter for property sigma.
* @param sigma New value of property sigma.
*/
public void setSigma(double sigma) {
this.sigma = sigma;
}
//------------- private methods ---------------------------------
/**
* Gets a random value in DIGEST_MODE.
*
* Preconditions:
* - Before this method is called,
computeDistribution()
* must have completed successfully; otherwise an
* IllegalStateException will be thrown
*
* @return next random value from the empirical distribution digest
*/
private double getNextDigest() {
if ((empiricalDistribution == null) ||
(empiricalDistribution.getBinStats().size() == 0)) {
throw MathRuntimeException.createIllegalStateException(LocalizedFormats.DIGEST_NOT_INITIALIZED);
}
return empiricalDistribution.getNextValue();
}
/**
* Gets next sequential value from the valuesFileURL.
*
* Throws an IOException if the read fails.
*
* This method will open the valuesFileURL if there is no
* replay file open.
*
* The valuesFileURL will be closed and reopened to wrap around
* from EOF to BOF if EOF is encountered. EOFException (which is a kind of
* IOException) may still be thrown if the valuesFileURL is
* empty.
*
* @return next value from the replay file
* @throws IOException if there is a problem reading from the file
* @throws NumberFormatException if an invalid numeric string is
* encountered in the file
*/
private double getNextReplay() throws IOException {
String str = null;
if (filePointer == null) {
resetReplayFile();
}
if ((str = filePointer.readLine()) == null) {
// we have probably reached end of file, wrap around from EOF to BOF
closeReplayFile();
resetReplayFile();
if ((str = filePointer.readLine()) == null) {
throw MathRuntimeException.createEOFException(LocalizedFormats.URL_CONTAINS_NO_DATA,
valuesFileURL);
}
}
return Double.valueOf(str).doubleValue();
}
/**
* Gets a uniformly distributed random value with mean = mu.
*
* @return random uniform value
*/
private double getNextUniform() {
return randomData.nextUniform(0, 2 * mu);
}
/**
* Gets an exponentially distributed random value with mean = mu.
*
* @return random exponential value
*/
private double getNextExponential() {
return randomData.nextExponential(mu);
}
/**
* Gets a Gaussian distributed random value with mean = mu
* and standard deviation = sigma.
*
* @return random Gaussian value
*/
private double getNextGaussian() {
return randomData.nextGaussian(mu, sigma);
}
}