org.apache.commons.math.random.RandomDataImpl Maven / Gradle / Ivy
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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.Serializable;
import java.security.MessageDigest;
import java.security.SecureRandom;
import java.security.NoSuchAlgorithmException;
import java.security.NoSuchProviderException;
import java.util.Collection;
/**
* 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 generator based on
* {@link java.util.Random}. 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 intialized. If you do not
* explicitly seed the default non-secure generator, it is seeded with the current time
* in milliseconds 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
* andSecureRandom 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 $Revision: 615734 $ $Date: 2008-01-27 23:10:03 -0700 (Sun, 27 Jan 2008) $
*/
public class RandomDataImpl implements RandomData, Serializable {
/** Serializable version identifier */
private static final long serialVersionUID = -626730818244969716L;
/** underlying random number generator */
private RandomGenerator rand = null;
/** underlying secure random number generator */
private SecureRandom secRand = null;
/**
* Construct a RandomDataImpl.
*/
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
* @since 1.1
*/
public RandomDataImpl(RandomGenerator rand) {
super();
this.rand = rand;
}
/**
* {@inheritDoc}
* Algorithm Description: hex strings are generated
* using a 2-step process.
* -
* 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.
*/
public String nextHexString(int len) {
if (len <= 0) {
throw new IllegalArgumentException("length must be positive");
}
//Get a random number generator
RandomGenerator ran = getRan();
//Initialize output buffer
StringBuffer outBuffer = new StringBuffer();
//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 = new Integer(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);
}
/**
* Generate a random int value uniformly distributed between
* lower and upper, inclusive.
*
* @param lower the lower bound.
* @param upper the upper bound.
* @return the random integer.
*/
public int nextInt(int lower, int upper) {
if (lower >= upper) {
throw new IllegalArgumentException
("upper bound must be > lower bound");
}
RandomGenerator rand = getRan();
double r = rand.nextDouble();
return (int)((r * upper) + ((1.0 - r) * lower) + r);
}
/**
* Generate a random long value uniformly distributed between
* lower and upper, inclusive.
*
* @param lower the lower bound.
* @param upper the upper bound.
* @return the random integer.
*/
public long nextLong(long lower, long upper) {
if (lower >= upper) {
throw new IllegalArgumentException
("upper bound must be > lower bound");
}
RandomGenerator rand = getRan();
double r = rand.nextDouble();
return (long)((r * upper) + ((1.0 - r) * lower) + r);
}
/**
* {@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.
*
*
* @param len the length of the generated string
* @return the random string
*/
public String nextSecureHexString(int len) {
if (len <= 0) {
throw new IllegalArgumentException("length must be positive");
}
// Get SecureRandom and setup Digest provider
SecureRandom secRan = getSecRan();
MessageDigest alg = null;
try {
alg = MessageDigest.getInstance("SHA-1");
} catch (NoSuchAlgorithmException ex) {
return null; // gulp FIXME? -- this *should* never fail.
}
alg.reset();
//Compute number of iterations required (40 bytes each)
int numIter = (len / 40) + 1;
StringBuffer outBuffer = new StringBuffer();
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 = new Integer(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);
}
/**
* Generate a random int value uniformly distributed between
* lower and upper, inclusive. This algorithm
* uses a secure random number generator.
*
* @param lower the lower bound.
* @param upper the upper bound.
* @return the random integer.
*/
public int nextSecureInt(int lower, int upper) {
if (lower >= upper) {
throw new IllegalArgumentException
("lower bound must be < upper bound");
}
SecureRandom sec = getSecRan();
return lower + (int) (sec.nextDouble() * (upper - lower + 1));
}
/**
* Generate a random long value uniformly distributed between
* lower and upper, inclusive. This algorithm
* uses a secure random number generator.
*
* @param lower the lower bound.
* @param upper the upper bound.
* @return the random integer.
*/
public long nextSecureLong(long lower, long upper) {
if (lower >= upper) {
throw new IllegalArgumentException
("lower bound must be < upper bound");
}
SecureRandom sec = getSecRan();
return lower + (long) (sec.nextDouble() * (upper - lower + 1));
}
/**
* {@inheritDoc}
*
* Algorithm Description:
* Uses simulation of a Poisson process using Uniform deviates, as
* described
*
* here.
*
* The Poisson process (and hence value returned) is bounded by
* 1000 * mean.
*
* @param mean mean of the Poisson distribution.
* @return the random Poisson value.
*/
public long nextPoisson(double mean) {
if (mean <= 0) {
throw new IllegalArgumentException("Poisson mean must be > 0");
}
double p = Math.exp(-mean);
long n = 0;
double r = 1.0d;
double rnd = 1.0d;
RandomGenerator rand = getRan();
while (n < 1000 * mean) {
rnd = rand.nextDouble();
r = r * rnd;
if (r >= p) {
n++;
} else {
return n;
}
}
return n;
}
/**
* Generate a random value from a Normal (a.k.a. Gaussian) distribution
* with the given mean, mu and the given standard deviation,
* sigma.
*
* @param mu the mean of the distribution
* @param sigma the standard deviation of the distribution
* @return the random Normal value
*/
public double nextGaussian(double mu, double sigma) {
if (sigma <= 0) {
throw new IllegalArgumentException("Gaussian std dev must be > 0");
}
RandomGenerator rand = getRan();
return sigma * rand.nextGaussian() + mu;
}
/**
* Returns a random value from an Exponential distribution with the given
* mean.
*
* Algorithm Description: Uses the
*
* Inversion Method to generate exponentially distributed random values
* from uniform deviates.
*
* @param mean the mean of the distribution
* @return the random Exponential value
*/
public double nextExponential(double mean) {
if (mean < 0.0) {
throw new IllegalArgumentException
("Exponential mean must be >= 0");
}
RandomGenerator rand = getRan();
double unif = rand.nextDouble();
while (unif == 0.0d) {
unif = rand.nextDouble();
}
return -mean * Math.log(unif);
}
/**
* {@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).
*
* @param lower the lower bound.
* @param upper the upper bound.
* @return a uniformly distributed random value from the interval (lower, upper)
*/
public double nextUniform(double lower, double upper) {
if (lower >= upper) {
throw new IllegalArgumentException
("lower bound must be < upper bound");
}
RandomGenerator rand = getRan();
// ensure nextDouble() isn't 0.0
double u = rand.nextDouble();
while(u <= 0.0){
u = rand.nextDouble();
}
return lower + u * (upper - lower);
}
/**
* Returns the RandomGenerator used to generate non-secure
* random data.
*
* Creates and initializes a default generator if null.
*
* @return the Random used to generate random data
* @since 1.1
*/
private RandomGenerator getRan() {
if (rand == null) {
rand = new JDKRandomGenerator();
rand.setSeed(System.currentTimeMillis());
}
return rand;
}
/**
* Returns the SecureRandom used to generate secure random data.
*
* Creates and initializes if null.
*
* @return the SecureRandom used to generate secure random data
*/
private SecureRandom getSecRan() {
if (secRand == null) {
secRand = new SecureRandom();
secRand.setSeed(System.currentTimeMillis());
}
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) {
rand = new JDKRandomGenerator();
}
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 the current time
* in milliseconds.
*/
public void reSeed() {
if (rand == null) {
rand = new JDKRandomGenerator();
}
rand.setSeed(System.currentTimeMillis());
}
/**
* 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);
}
/**
* Uses a 2-cycle permutation shuffle to generate a random permutation.
* The shuffling process is described
*
* here.
* @param n the population size.
* @param k the number to choose.
* @return the random permutation.
*/
public int[] nextPermutation(int n, int k) {
if (k > n) {
throw new IllegalArgumentException
("permutation k exceeds n");
}
if (k == 0) {
throw new IllegalArgumentException
("permutation k must be > 0");
}
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;
}
/**
* Uses a 2-cycle permutation shuffle to generate a random permutation.
* 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
* @param c Collection to sample from.
* @param k sample size.
* @return the random sample.
*/
public Object[] nextSample(Collection c, int k) {
int len = c.size();
if (k > len) {
throw new IllegalArgumentException
("sample size exceeds collection size");
}
if (k == 0) {
throw new IllegalArgumentException
("sample size must be > 0");
}
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;
}
//------------------------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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