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The Apache Commons 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.
/*
* 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.math3.random;
import java.io.Serializable;
import java.security.MessageDigest;
import java.security.NoSuchAlgorithmException;
import java.security.NoSuchProviderException;
import java.security.SecureRandom;
import java.util.Collection;
import org.apache.commons.math3.distribution.BetaDistribution;
import org.apache.commons.math3.distribution.BinomialDistribution;
import org.apache.commons.math3.distribution.CauchyDistribution;
import org.apache.commons.math3.distribution.ChiSquaredDistribution;
import org.apache.commons.math3.distribution.RealDistribution;
import org.apache.commons.math3.distribution.FDistribution;
import org.apache.commons.math3.distribution.HypergeometricDistribution;
import org.apache.commons.math3.distribution.IntegerDistribution;
import org.apache.commons.math3.distribution.PascalDistribution;
import org.apache.commons.math3.distribution.TDistribution;
import org.apache.commons.math3.distribution.WeibullDistribution;
import org.apache.commons.math3.distribution.ZipfDistribution;
import org.apache.commons.math3.exception.MathIllegalArgumentException;
import org.apache.commons.math3.exception.MathInternalError;
import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.exception.NumberIsTooLargeException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.util.ArithmeticUtils;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.ResizableDoubleArray;
/**
* Implements the {@link RandomData} interface using a {@link RandomGenerator}
* instance to generate non-secure data and a {@link java.security.SecureRandom}
* instance to provide data for the nextSecureXxx methods. If no
* RandomGenerator is provided in the constructor, the default is
* to use a {@link Well19937c} generator. To plug in a different
* implementation, either implement RandomGenerator directly or
* extend {@link AbstractRandomGenerator}.
*
* Supports reseeding the underlying pseudo-random number generator (PRNG). The
* SecurityProvider and Algorithm used by the
* SecureRandom instance can also be reset.
*
*
* For details on the default PRNGs, see {@link java.util.Random} and
* {@link java.security.SecureRandom}.
*
*
* Usage Notes:
*
* -
* Instance variables are used to maintain
RandomGenerator and
* SecureRandom instances used in data generation. Therefore, to
* generate a random sequence of values or strings, you should use just
* one RandomDataImpl instance repeatedly.
* -
* The "secure" methods are *much* slower. These should be used only when a
* cryptographically secure random sequence is required. A secure random
* sequence is a sequence of pseudo-random values which, in addition to being
* well-dispersed (so no subsequence of values is an any more likely than other
* subsequence of the the same length), also has the additional property that
* knowledge of values generated up to any point in the sequence does not make
* it any easier to predict subsequent values.
* -
* When a new
RandomDataImpl is created, the underlying random
* number generators are not initialized. If you do not
* explicitly seed the default non-secure generator, it is seeded with the
* current time in milliseconds plus the system identity hash code on first use.
* The same holds for the secure generator. If you provide a RandomGenerator
* to the constructor, however, this generator is not reseeded by the constructor
* nor is it reseeded on first use.
* -
* The
reSeed and reSeedSecure methods delegate to the
* corresponding methods on the underlying RandomGenerator and
* SecureRandom instances. Therefore, reSeed(long)
* fully resets the initial state of the non-secure random number generator (so
* that reseeding with a specific value always results in the same subsequent
* random sequence); whereas reSeedSecure(long) does not
* reinitialize the secure random number generator (so secure sequences started
* with calls to reseedSecure(long) won't be identical).
* -
* This implementation is not synchronized.
*
*
*
* @version $Id: RandomDataImpl.java 1296517 2012-03-02 23:55:08Z sebb $
*/
public class RandomDataImpl implements RandomData, Serializable {
/** Serializable version identifier */
private static final long serialVersionUID = -626730818244969716L;
/**
* Used when generating Exponential samples.
* Table containing the constants
* q_i = sum_{j=1}^i (ln 2)^j/j! = ln 2 + (ln 2)^2/2 + ... + (ln 2)^i/i!
* until the largest representable fraction below 1 is exceeded.
*
* Note that
* 1 = 2 - 1 = exp(ln 2) - 1 = sum_{n=1}^infty (ln 2)^n / n!
* thus q_i -> 1 as i -> infty,
* so the higher i, the closer to one we get (the series is not alternating).
*
* By trying, n = 16 in Java is enough to reach 1.0.
*/
private static final double[] EXPONENTIAL_SA_QI;
/** underlying random number generator */
private RandomGenerator rand = null;
/** underlying secure random number generator */
private SecureRandom secRand = null;
/**
* Initialize tables
*/
static {
/**
* Filling EXPONENTIAL_SA_QI table.
* Note that we don't want qi = 0 in the table.
*/
final double LN2 = FastMath.log(2);
double qi = 0;
int i = 1;
/**
* MathUtils provides factorials up to 20, so let's use that limit together
* with Precision.EPSILON to generate the following code (a priori, we know that
* there will be 16 elements, but instead of hardcoding that, this is
* prettier):
*/
final ResizableDoubleArray ra = new ResizableDoubleArray(20);
while (qi < 1) {
qi += FastMath.pow(LN2, i) / ArithmeticUtils.factorial(i);
ra.addElement(qi);
++i;
}
EXPONENTIAL_SA_QI = ra.getElements();
}
/**
* Construct a RandomDataImpl, using a default random generator as the source
* of randomness.
*
* The default generator is a {@link Well19937c} seeded
* with {@code System.currentTimeMillis() + System.identityHashCode(this))}.
* The generator is initialized and seeded on first use.
*/
public RandomDataImpl() {
}
/**
* Construct a RandomDataImpl using the supplied {@link RandomGenerator} as
* the source of (non-secure) random data.
*
* @param rand the source of (non-secure) random data
* (may be null, resulting in the default generator)
* @since 1.1
*/
public RandomDataImpl(RandomGenerator rand) {
super();
this.rand = rand;
}
/**
* {@inheritDoc}
*
* Algorithm Description: hex strings are generated using a
* 2-step process.
*
* - {@code len / 2 + 1} binary bytes are generated using the underlying
* Random
* - Each binary byte is translated into 2 hex digits
*
*
*
* @param len the desired string length.
* @return the random string.
* @throws NotStrictlyPositiveException if {@code len <= 0}.
*/
public String nextHexString(int len) {
if (len <= 0) {
throw new NotStrictlyPositiveException(LocalizedFormats.LENGTH, len);
}
// Get a random number generator
RandomGenerator ran = getRan();
// Initialize output buffer
StringBuilder outBuffer = new StringBuilder();
// Get int(len/2)+1 random bytes
byte[] randomBytes = new byte[(len / 2) + 1];
ran.nextBytes(randomBytes);
// Convert each byte to 2 hex digits
for (int i = 0; i < randomBytes.length; i++) {
Integer c = Integer.valueOf(randomBytes[i]);
/*
* Add 128 to byte value to make interval 0-255 before doing hex
* conversion. This guarantees <= 2 hex digits from toHexString()
* toHexString would otherwise add 2^32 to negative arguments.
*/
String hex = Integer.toHexString(c.intValue() + 128);
// Make sure we add 2 hex digits for each byte
if (hex.length() == 1) {
hex = "0" + hex;
}
outBuffer.append(hex);
}
return outBuffer.toString().substring(0, len);
}
/** {@inheritDoc} */
public int nextInt(int lower, int upper) {
if (lower >= upper) {
throw new NumberIsTooLargeException(LocalizedFormats.LOWER_BOUND_NOT_BELOW_UPPER_BOUND,
lower, upper, false);
}
double r = getRan().nextDouble();
double scaled = r * upper + (1.0 - r) * lower + r;
return (int) FastMath.floor(scaled);
}
/** {@inheritDoc} */
public long nextLong(long lower, long upper) {
if (lower >= upper) {
throw new NumberIsTooLargeException(LocalizedFormats.LOWER_BOUND_NOT_BELOW_UPPER_BOUND,
lower, upper, false);
}
double r = getRan().nextDouble();
double scaled = r * upper + (1.0 - r) * lower + r;
return (long)FastMath.floor(scaled);
}
/**
* {@inheritDoc}
*
* Algorithm Description: hex strings are generated in
* 40-byte segments using a 3-step process.
*
* -
* 20 random bytes are generated using the underlying
*
SecureRandom.
* -
* SHA-1 hash is applied to yield a 20-byte binary digest.
* -
* Each byte of the binary digest is converted to 2 hex digits.
*
*
*/
public String nextSecureHexString(int len) {
if (len <= 0) {
throw new NotStrictlyPositiveException(LocalizedFormats.LENGTH, len);
}
// Get SecureRandom and setup Digest provider
SecureRandom secRan = getSecRan();
MessageDigest alg = null;
try {
alg = MessageDigest.getInstance("SHA-1");
} catch (NoSuchAlgorithmException ex) {
// this should never happen
throw new MathInternalError(ex);
}
alg.reset();
// Compute number of iterations required (40 bytes each)
int numIter = (len / 40) + 1;
StringBuilder outBuffer = new StringBuilder();
for (int iter = 1; iter < numIter + 1; iter++) {
byte[] randomBytes = new byte[40];
secRan.nextBytes(randomBytes);
alg.update(randomBytes);
// Compute hash -- will create 20-byte binary hash
byte[] hash = alg.digest();
// Loop over the hash, converting each byte to 2 hex digits
for (int i = 0; i < hash.length; i++) {
Integer c = Integer.valueOf(hash[i]);
/*
* Add 128 to byte value to make interval 0-255 This guarantees
* <= 2 hex digits from toHexString() toHexString would
* otherwise add 2^32 to negative arguments
*/
String hex = Integer.toHexString(c.intValue() + 128);
// Keep strings uniform length -- guarantees 40 bytes
if (hex.length() == 1) {
hex = "0" + hex;
}
outBuffer.append(hex);
}
}
return outBuffer.toString().substring(0, len);
}
/** {@inheritDoc} */
public int nextSecureInt(int lower, int upper) {
if (lower >= upper) {
throw new NumberIsTooLargeException(LocalizedFormats.LOWER_BOUND_NOT_BELOW_UPPER_BOUND,
lower, upper, false);
}
SecureRandom sec = getSecRan();
double r = sec.nextDouble();
double scaled = r * upper + (1.0 - r) * lower + r;
return (int)FastMath.floor(scaled);
}
/** {@inheritDoc} */
public long nextSecureLong(long lower, long upper) {
if (lower >= upper) {
throw new NumberIsTooLargeException(LocalizedFormats.LOWER_BOUND_NOT_BELOW_UPPER_BOUND,
lower, upper, false);
}
SecureRandom sec = getSecRan();
double r = sec.nextDouble();
double scaled = r * upper + (1.0 - r) * lower + r;
return (long)FastMath.floor(scaled);
}
/**
* {@inheritDoc}
*
* Algorithm Description:
*
- For small means, uses simulation of a Poisson process
* using Uniform deviates, as described
* here.
* The Poisson process (and hence value returned) is bounded by 1000 * mean.
*
* - For large means, uses the rejection algorithm described in
* Devroye, Luc. (1981).The Computer Generation of Poisson Random Variables
* Computing vol. 26 pp. 197-207.
*/
public long nextPoisson(double mean) {
if (mean <= 0) {
throw new NotStrictlyPositiveException(LocalizedFormats.MEAN, mean);
}
final double pivot = 40.0d;
if (mean < pivot) {
final RandomGenerator generator = getRan();
double p = FastMath.exp(-mean);
long n = 0;
double r = 1.0d;
double rnd = 1.0d;
while (n < 1000 * mean) {
rnd = generator.nextDouble();
r = r * rnd;
if (r >= p) {
n++;
} else {
return n;
}
}
return n;
} else {
final double lambda = FastMath.floor(mean);
final double lambdaFractional = mean - lambda;
final double logLambda = FastMath.log(lambda);
final double logLambdaFactorial = ArithmeticUtils.factorialLog((int) lambda);
final long y2 = lambdaFractional < Double.MIN_VALUE ? 0 : nextPoisson(lambdaFractional);
final double delta = FastMath.sqrt(lambda * FastMath.log(32 * lambda / FastMath.PI + 1));
final double halfDelta = delta / 2;
final double twolpd = 2 * lambda + delta;
final double a1 = FastMath.sqrt(FastMath.PI * twolpd) * FastMath.exp(1 / 8 * lambda);
final double a2 = (twolpd / delta) * FastMath.exp(-delta * (1 + delta) / twolpd);
final double aSum = a1 + a2 + 1;
final double p1 = a1 / aSum;
final double p2 = a2 / aSum;
final double c1 = 1 / (8 * lambda);
double x = 0;
double y = 0;
double v = 0;
int a = 0;
double t = 0;
double qr = 0;
double qa = 0;
for (;;) {
final double u = nextUniform(0.0, 1);
if (u <= p1) {
final double n = nextGaussian(0d, 1d);
x = n * FastMath.sqrt(lambda + halfDelta) - 0.5d;
if (x > delta || x < -lambda) {
continue;
}
y = x < 0 ? FastMath.floor(x) : FastMath.ceil(x);
final double e = nextExponential(1d);
v = -e - (n * n / 2) + c1;
} else {
if (u > p1 + p2) {
y = lambda;
break;
} else {
x = delta + (twolpd / delta) * nextExponential(1d);
y = FastMath.ceil(x);
v = -nextExponential(1d) - delta * (x + 1) / twolpd;
}
}
a = x < 0 ? 1 : 0;
t = y * (y + 1) / (2 * lambda);
if (v < -t && a == 0) {
y = lambda + y;
break;
}
qr = t * ((2 * y + 1) / (6 * lambda) - 1);
qa = qr - (t * t) / (3 * (lambda + a * (y + 1)));
if (v < qa) {
y = lambda + y;
break;
}
if (v > qr) {
continue;
}
if (v < y * logLambda - ArithmeticUtils.factorialLog((int) (y + lambda)) + logLambdaFactorial) {
y = lambda + y;
break;
}
}
return y2 + (long) y;
}
}
/** {@inheritDoc} */
public double nextGaussian(double mu, double sigma) {
if (sigma <= 0) {
throw new NotStrictlyPositiveException(LocalizedFormats.STANDARD_DEVIATION, sigma);
}
return sigma * getRan().nextGaussian() + mu;
}
/**
* {@inheritDoc}
*
*
* Algorithm Description: Uses the Algorithm SA (Ahrens)
* from p. 876 in:
* [1]: Ahrens, J. H. and Dieter, U. (1972). Computer methods for
* sampling from the exponential and normal distributions.
* Communications of the ACM, 15, 873-882.
*
*/
public double nextExponential(double mean) {
if (mean <= 0.0) {
throw new NotStrictlyPositiveException(LocalizedFormats.MEAN, mean);
}
// Step 1:
double a = 0;
double u = this.nextUniform(0, 1);
// Step 2 and 3:
while (u < 0.5) {
a += EXPONENTIAL_SA_QI[0];
u *= 2;
}
// Step 4 (now u >= 0.5):
u += u - 1;
// Step 5:
if (u <= EXPONENTIAL_SA_QI[0]) {
return mean * (a + u);
}
// Step 6:
int i = 0; // Should be 1, be we iterate before it in while using 0
double u2 = this.nextUniform(0, 1);
double umin = u2;
// Step 7 and 8:
do {
++i;
u2 = this.nextUniform(0, 1);
if (u2 < umin) {
umin = u2;
}
// Step 8:
} while (u > EXPONENTIAL_SA_QI[i]); // Ensured to exit since EXPONENTIAL_SA_QI[MAX] = 1
return mean * (a + umin * EXPONENTIAL_SA_QI[0]);
}
/**
* {@inheritDoc}
*
*
* Algorithm Description: scales the output of
* Random.nextDouble(), but rejects 0 values (i.e., will generate another
* random double if Random.nextDouble() returns 0). This is necessary to
* provide a symmetric output interval (both endpoints excluded).
*
*
* @throws MathIllegalArgumentException if one of the bounds is infinite or
* {@code NaN} or either bound is infinite or NaN
*/
public double nextUniform(double lower, double upper) {
return nextUniform(lower, upper, false);
}
/**
* {@inheritDoc}
*
*
* Algorithm Description: if the lower bound is excluded,
* scales the output of Random.nextDouble(), but rejects 0 values (i.e.,
* will generate another random double if Random.nextDouble() returns 0).
* This is necessary to provide a symmetric output interval (both
* endpoints excluded).
*
*
* @throws MathIllegalArgumentException if one of the bounds is infinite or
* {@code NaN}
* @since 3.0
*/
public double nextUniform(double lower, double upper,
boolean lowerInclusive) {
if (lower >= upper) {
throw new NumberIsTooLargeException(LocalizedFormats.LOWER_BOUND_NOT_BELOW_UPPER_BOUND,
lower, upper, false);
}
if (Double.isInfinite(lower) || Double.isInfinite(upper)) {
throw new MathIllegalArgumentException(LocalizedFormats.INFINITE_BOUND);
}
if (Double.isNaN(lower) || Double.isNaN(upper)) {
throw new MathIllegalArgumentException(LocalizedFormats.NAN_NOT_ALLOWED);
}
final RandomGenerator generator = getRan();
// ensure nextDouble() isn't 0.0
double u = generator.nextDouble();
while (!lowerInclusive && u <= 0.0) {
u = generator.nextDouble();
}
return u * upper + (1.0 - u) * lower;
}
/**
* Generates a random value from the {@link BetaDistribution Beta Distribution}.
* This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion}
* to generate random values.
*
* @param alpha first distribution shape parameter
* @param beta second distribution shape parameter
* @return random value sampled from the beta(alpha, beta) distribution
* @since 2.2
*/
public double nextBeta(double alpha, double beta) {
return nextInversionDeviate(new BetaDistribution(alpha, beta));
}
/**
* Generates a random value from the {@link BinomialDistribution Binomial Distribution}.
* This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion}
* to generate random values.
*
* @param numberOfTrials number of trials of the Binomial distribution
* @param probabilityOfSuccess probability of success of the Binomial distribution
* @return random value sampled from the Binomial(numberOfTrials, probabilityOfSuccess) distribution
* @since 2.2
*/
public int nextBinomial(int numberOfTrials, double probabilityOfSuccess) {
return nextInversionDeviate(new BinomialDistribution(numberOfTrials, probabilityOfSuccess));
}
/**
* Generates a random value from the {@link CauchyDistribution Cauchy Distribution}.
* This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion}
* to generate random values.
*
* @param median the median of the Cauchy distribution
* @param scale the scale parameter of the Cauchy distribution
* @return random value sampled from the Cauchy(median, scale) distribution
* @since 2.2
*/
public double nextCauchy(double median, double scale) {
return nextInversionDeviate(new CauchyDistribution(median, scale));
}
/**
* Generates a random value from the {@link ChiSquaredDistribution ChiSquare Distribution}.
* This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion}
* to generate random values.
*
* @param df the degrees of freedom of the ChiSquare distribution
* @return random value sampled from the ChiSquare(df) distribution
* @since 2.2
*/
public double nextChiSquare(double df) {
return nextInversionDeviate(new ChiSquaredDistribution(df));
}
/**
* Generates a random value from the {@link FDistribution F Distribution}.
* This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion}
* to generate random values.
*
* @param numeratorDf the numerator degrees of freedom of the F distribution
* @param denominatorDf the denominator degrees of freedom of the F distribution
* @return random value sampled from the F(numeratorDf, denominatorDf) distribution
* @since 2.2
*/
public double nextF(double numeratorDf, double denominatorDf) {
return nextInversionDeviate(new FDistribution(numeratorDf, denominatorDf));
}
/**
* Generates a random value from the
* {@link org.apache.commons.math3.distribution.GammaDistribution Gamma Distribution}.
*
* This implementation uses the following algorithms:
*
* For 0 < shape < 1:
* Ahrens, J. H. and Dieter, U., Computer methods for
* sampling from gamma, beta, Poisson and binomial distributions.
* Computing, 12, 223-246, 1974.
*
* For shape >= 1:
* Marsaglia and Tsang, A Simple Method for Generating
* Gamma Variables. ACM Transactions on Mathematical Software,
* Volume 26 Issue 3, September, 2000.
*
* @param shape the median of the Gamma distribution
* @param scale the scale parameter of the Gamma distribution
* @return random value sampled from the Gamma(shape, scale) distribution
* @since 2.2
*/
public double nextGamma(double shape, double scale) {
if (shape < 1) {
// [1]: p. 228, Algorithm GS
while (true) {
// Step 1:
final double u = this.nextUniform(0, 1);
final double bGS = 1 + shape/FastMath.E;
final double p = bGS*u;
if (p <= 1) {
// Step 2:
final double x = FastMath.pow(p, 1/shape);
final double u2 = this.nextUniform(0.0, 1);
if (u2 > FastMath.exp(-x)) {
// Reject
continue;
} else {
return scale*x;
}
} else {
// Step 3:
final double x = -1 * FastMath.log((bGS-p)/shape);
final double u2 = this.nextUniform(0, 1);
if (u2 > FastMath.pow(x, shape - 1)) {
// Reject
continue;
} else {
return scale*x;
}
}
}
}
// Now shape >= 1
final RandomGenerator generator = this.getRan();
final double d = shape - 0.333333333333333333;
final double c = 1.0 / (3*FastMath.sqrt(d));
while (true) {
final double x = generator.nextGaussian();
final double v = (1+c*x)*(1+c*x)*(1+c*x);
if (v <= 0) {
continue;
}
final double xx = x*x;
final double u = this.nextUniform(0, 1);
// Squeeze
if (u < 1 - 0.0331*xx*xx) {
return scale*d*v;
}
if (FastMath.log(u) < 0.5*xx + d*(1 - v + FastMath.log(v))) {
return scale*d*v;
}
}
}
/**
* Generates a random value from the {@link HypergeometricDistribution Hypergeometric Distribution}.
* This implementation uses {@link #nextInversionDeviate(IntegerDistribution) inversion}
* to generate random values.
*
* @param populationSize the population size of the Hypergeometric distribution
* @param numberOfSuccesses number of successes in the population of the Hypergeometric distribution
* @param sampleSize the sample size of the Hypergeometric distribution
* @return random value sampled from the Hypergeometric(numberOfSuccesses, sampleSize) distribution
* @since 2.2
*/
public int nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize) {
return nextInversionDeviate(new HypergeometricDistribution(populationSize, numberOfSuccesses, sampleSize));
}
/**
* Generates a random value from the {@link PascalDistribution Pascal Distribution}.
* This implementation uses {@link #nextInversionDeviate(IntegerDistribution) inversion}
* to generate random values.
*
* @param r the number of successes of the Pascal distribution
* @param p the probability of success of the Pascal distribution
* @return random value sampled from the Pascal(r, p) distribution
* @since 2.2
*/
public int nextPascal(int r, double p) {
return nextInversionDeviate(new PascalDistribution(r, p));
}
/**
* Generates a random value from the {@link TDistribution T Distribution}.
* This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion}
* to generate random values.
*
* @param df the degrees of freedom of the T distribution
* @return random value from the T(df) distribution
* @since 2.2
*/
public double nextT(double df) {
return nextInversionDeviate(new TDistribution(df));
}
/**
* Generates a random value from the {@link WeibullDistribution Weibull Distribution}.
* This implementation uses {@link #nextInversionDeviate(RealDistribution) inversion}
* to generate random values.
*
* @param shape the shape parameter of the Weibull distribution
* @param scale the scale parameter of the Weibull distribution
* @return random value sampled from the Weibull(shape, size) distribution
* @since 2.2
*/
public double nextWeibull(double shape, double scale) {
return nextInversionDeviate(new WeibullDistribution(shape, scale));
}
/**
* Generates a random value from the {@link ZipfDistribution Zipf Distribution}.
* This implementation uses {@link #nextInversionDeviate(IntegerDistribution) inversion}
* to generate random values.
*
* @param numberOfElements the number of elements of the ZipfDistribution
* @param exponent the exponent of the ZipfDistribution
* @return random value sampled from the Zipf(numberOfElements, exponent) distribution
* @since 2.2
*/
public int nextZipf(int numberOfElements, double exponent) {
return nextInversionDeviate(new ZipfDistribution(numberOfElements, exponent));
}
/**
* Returns the RandomGenerator used to generate non-secure random data.
*
* Creates and initializes a default generator if null. Uses a {@link Well19937c}
* generator with {@code System.currentTimeMillis() + System.identityHashCode(this))} as the default seed.
*
*
* @return the Random used to generate random data
* @since 1.1
*/
private RandomGenerator getRan() {
if (rand == null) {
initRan();
}
return rand;
}
/**
* Sets the default generator to a {@link Well19937c} generator seeded with
* {@code System.currentTimeMillis() + System.identityHashCode(this))}.
*/
private void initRan() {
rand = new Well19937c(System.currentTimeMillis() + System.identityHashCode(this));
}
/**
* Returns the SecureRandom used to generate secure random data.
*
* Creates and initializes if null. Uses
* {@code System.currentTimeMillis() + System.identityHashCode(this)} as the default seed.
*
*
* @return the SecureRandom used to generate secure random data
*/
private SecureRandom getSecRan() {
if (secRand == null) {
secRand = new SecureRandom();
secRand.setSeed(System.currentTimeMillis() + System.identityHashCode(this));
}
return secRand;
}
/**
* Reseeds the random number generator with the supplied seed.
*
* Will create and initialize if null.
*
*
* @param seed
* the seed value to use
*/
public void reSeed(long seed) {
if (rand == null) {
initRan();
}
rand.setSeed(seed);
}
/**
* Reseeds the secure random number generator with the current time in
* milliseconds.
*
* Will create and initialize if null.
*
*/
public void reSeedSecure() {
if (secRand == null) {
secRand = new SecureRandom();
}
secRand.setSeed(System.currentTimeMillis());
}
/**
* Reseeds the secure random number generator with the supplied seed.
*
* Will create and initialize if null.
*
*
* @param seed
* the seed value to use
*/
public void reSeedSecure(long seed) {
if (secRand == null) {
secRand = new SecureRandom();
}
secRand.setSeed(seed);
}
/**
* Reseeds the random number generator with
* {@code System.currentTimeMillis() + System.identityHashCode(this))}.
*/
public void reSeed() {
if (rand == null) {
initRan();
}
rand.setSeed(System.currentTimeMillis() + System.identityHashCode(this));
}
/**
* Sets the PRNG algorithm for the underlying SecureRandom instance using
* the Security Provider API. The Security Provider API is defined in
* Java Cryptography Architecture API Specification & Reference.
*
* USAGE NOTE: This method carries significant
* overhead and may take several seconds to execute.
*
*
* @param algorithm
* the name of the PRNG algorithm
* @param provider
* the name of the provider
* @throws NoSuchAlgorithmException
* if the specified algorithm is not available
* @throws NoSuchProviderException
* if the specified provider is not installed
*/
public void setSecureAlgorithm(String algorithm, String provider)
throws NoSuchAlgorithmException, NoSuchProviderException {
secRand = SecureRandom.getInstance(algorithm, provider);
}
/**
* {@inheritDoc}
*
*
* Uses a 2-cycle permutation shuffle. The shuffling process is described
* here.
*
*/
public int[] nextPermutation(int n, int k) {
if (k > n) {
throw new NumberIsTooLargeException(LocalizedFormats.PERMUTATION_EXCEEDS_N,
k, n, true);
}
if (k <= 0) {
throw new NotStrictlyPositiveException(LocalizedFormats.PERMUTATION_SIZE,
k);
}
int[] index = getNatural(n);
shuffle(index, n - k);
int[] result = new int[k];
for (int i = 0; i < k; i++) {
result[i] = index[n - i - 1];
}
return result;
}
/**
* {@inheritDoc}
*
*
* Algorithm Description: Uses a 2-cycle permutation
* shuffle to generate a random permutation of c.size() and
* then returns the elements whose indexes correspond to the elements of the
* generated permutation. This technique is described, and proven to
* generate random samples
* here
*
*/
public Object[] nextSample(Collection c, int k) {
int len = c.size();
if (k > len) {
throw new NumberIsTooLargeException(LocalizedFormats.SAMPLE_SIZE_EXCEEDS_COLLECTION_SIZE,
k, len, true);
}
if (k <= 0) {
throw new NotStrictlyPositiveException(LocalizedFormats.NUMBER_OF_SAMPLES, k);
}
Object[] objects = c.toArray();
int[] index = nextPermutation(len, k);
Object[] result = new Object[k];
for (int i = 0; i < k; i++) {
result[i] = objects[index[i]];
}
return result;
}
/**
* Generate a random deviate from the given distribution using the
* inversion method.
*
* @param distribution Continuous distribution to generate a random value from
* @return a random value sampled from the given distribution
* @since 2.2
*/
public double nextInversionDeviate(RealDistribution distribution) {
return distribution.inverseCumulativeProbability(nextUniform(0, 1));
}
/**
* Generate a random deviate from the given distribution using the
* inversion method.
*
* @param distribution Integer distribution to generate a random value from
* @return a random value sampled from the given distribution
* @since 2.2
*/
public int nextInversionDeviate(IntegerDistribution distribution) {
return distribution.inverseCumulativeProbability(nextUniform(0, 1));
}
// ------------------------Private methods----------------------------------
/**
* Uses a 2-cycle permutation shuffle to randomly re-order the last elements
* of list.
*
* @param list
* list to be shuffled
* @param end
* element past which shuffling begins
*/
private void shuffle(int[] list, int end) {
int target = 0;
for (int i = list.length - 1; i >= end; i--) {
if (i == 0) {
target = 0;
} else {
target = nextInt(0, i);
}
int temp = list[target];
list[target] = list[i];
list[i] = temp;
}
}
/**
* Returns an array representing n.
*
* @param n
* the natural number to represent
* @return array with entries = elements of n
*/
private int[] getNatural(int n) {
int[] natural = new int[n];
for (int i = 0; i < n; i++) {
natural[i] = i;
}
return natural;
}
}
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