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Statistical sampling library for use in virtdata libraries, based
on apache commons math 4
/*
* 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.math4.random;
import java.util.Random;
import java.security.MessageDigest;
import java.security.NoSuchAlgorithmException;
import org.apache.commons.math4.exception.MathInternalError;
import org.apache.commons.math4.exception.NotANumberException;
import org.apache.commons.math4.exception.NotFiniteNumberException;
import org.apache.commons.math4.exception.NotStrictlyPositiveException;
import org.apache.commons.math4.exception.NumberIsTooLargeException;
import org.apache.commons.math4.exception.util.LocalizedFormats;
import org.apache.commons.rng.UniformRandomProvider;
/**
* Factory for creating generators of miscellaneous data.
*
* @since 4.0
*/
public class RandomUtils {
/**
* Class contains only static methods.
*/
private RandomUtils() {}
/**
* @param rng Underlying generator. Reference is copied so the RNG
* is shared with the caller.
* @return a {@link DataGenerator data generator}.
*/
public static DataGenerator createDataGenerator(final UniformRandomProvider rng) {
return new DataGenerator(rng);
}
/**
* Wraps an instance of the JDK's {@link Random} class.
* The actual generation of random numbers will be delegated to that
* instance.
*
* If cryptographically secure data is required, one can use this
* factory method, with an instance of the {@link java.security.SecureRandom}
* class as the argument.
* Note that data generation will be much slower in this case.
*
*
* @param rng Underlying generator. Reference is copied so the RNG
* is shared with the caller.
* @return a {@link DataGenerator data generator}.
*/
public static DataGenerator createDataGenerator(final Random rng) {
return createDataGenerator(asUniformRandomProvider(rng));
}
/**
* Wraps a {@link Random} instance.
*
* @param rng JDK {@link Random} instance to which the random number
* generation is delegated. Reference is copied so the RNG is shared
* with the caller.
* @return a {@link UniformRandomProvider} instance.
*/
public static UniformRandomProvider asUniformRandomProvider(final Random rng) {
return new UniformRandomProvider() {
/** {@inheritDoc} */
@Override
public void nextBytes(byte[] bytes) {
rng.nextBytes(bytes);
}
/** {@inheritDoc} */
@Override
public void nextBytes(byte[] bytes,
int start,
int len) {
final byte[] reduced = new byte[len];
rng.nextBytes(reduced);
System.arraycopy(reduced, 0, bytes, start, len);
}
/** {@inheritDoc} */
@Override
public int nextInt() {
return rng.nextInt();
}
/** {@inheritDoc} */
@Override
public int nextInt(int n) {
if (n <= 0) {
throw new NotStrictlyPositiveException(n);
}
return rng.nextInt(n);
}
/** {@inheritDoc} */
@Override
public long nextLong() {
return rng.nextLong();
}
/** {@inheritDoc} */
@Override
public long nextLong(long n) {
// Code copied from "o.a.c.m.rng.internal.BaseProvider".
if (n > 0) {
long bits;
long val;
do {
bits = rng.nextLong() >>> 1;
val = bits % n;
} while (bits - val + (n - 1) < 0);
return val;
}
throw new NotStrictlyPositiveException(n);
}
/** {@inheritDoc} */
@Override
public boolean nextBoolean() {
return rng.nextBoolean();
}
/** {@inheritDoc} */
@Override
public float nextFloat() {
return rng.nextFloat();
}
/** {@inheritDoc} */
@Override
public double nextDouble() {
return rng.nextDouble();
}
};
}
/**
* Various random data generation routines.
*/
public static class DataGenerator {
/** Underlying RNG. */
private final UniformRandomProvider rng;
/**
* @param rng Underlying generator.
*/
DataGenerator(UniformRandomProvider rng) {
this.rng = rng;
}
/**
* Generates a random string of hex characters of length {@code len}.
*
* Algorithm Description: how hexadecimal strings are
* generated depends on the value of the {@code useSha1} argument.
*
*
* - If {@code useSha1 == false}, a 2-step process is used:
*
* -
* {@code len / 2 + 1} binary bytes are generated using the underlying
* generator.
*
* -
* Each binary byte is translated into 2 hex digits.
*
*
*
* -
* If {@code useSha1 == true}, hex strings are generated in 40-byte
* segments using a 3-step process:
*
* -
* 20 random bytes are generated using the underlying generator.
*
* -
* 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 Length of the generated string.
* @param useSha1 Whether to use a digest.
* If {@code true} (resp. {@code false}), the 3-step (resp. 2-step)
* process will be used.
* @return the random string.
* @throws NotStrictlyPositiveException if {@code len <= 0}.
*/
public String nextHexString(int len,
boolean useSha1) {
if (len <= 0) {
throw new NotStrictlyPositiveException(LocalizedFormats.LENGTH, len);
}
// Initialize output buffer.
final StringBuilder outBuffer = new StringBuilder();
if (!useSha1) {
// Generate int(len/2)+1 random bytes.
final byte[] randomBytes = new byte[(len / 2) + 1];
rng.nextBytes(randomBytes);
// Convert each byte to 2 hex digits.
for (int i = 0; i < randomBytes.length; i++) {
final Integer c = Integer.valueOf(randomBytes[i]);
// Add 128 to byte value to make interval 0-255 before
// conversion to hex.
// 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);
}
} else {
MessageDigest alg = null;
try {
alg = MessageDigest.getInstance("SHA-1");
} catch (NoSuchAlgorithmException ex) {
// Should never happen.
throw new MathInternalError(ex);
}
alg.reset();
// Compute number of iterations required (40 bytes each).
final int numIter = (len / 40) + 1;
for (int iter = 1; iter < numIter + 1; iter++) {
final byte[] randomBytes = new byte[40];
rng.nextBytes(randomBytes);
alg.update(randomBytes);
// Create 20-byte binary hash.
final byte[] hash = alg.digest();
// Loop over the hash, converting each byte to 2 hex digits
for (int i = 0; i < hash.length; i++) {
final 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);
}
/**
* Generates a uniformly distributed random long integer between {@code lower}
* and {@code upper} (endpoints included).
*
* @param lower Lower bound for generated long integer.
* @param upper Upper bound for generated long integer.
* @return a random long integer greater than or equal to {@code lower}
* and less than or equal to {@code upper}
* @throws NumberIsTooLargeException if {@code lower >= upper}
*/
public long nextLong(final long lower,
final long upper) {
if (lower >= upper) {
throw new NumberIsTooLargeException(LocalizedFormats.LOWER_BOUND_NOT_BELOW_UPPER_BOUND,
lower, upper, false);
}
final long max = (upper - lower) + 1;
if (max <= 0) {
// Range is too wide to fit in a positive long (larger than 2^63);
// as it covers more than half the long range, we use directly a
// simple rejection method.
while (true) {
final long r = rng.nextLong();
if (r >= lower && r <= upper) {
return r;
}
}
} else if (max < Integer.MAX_VALUE){
// We can shift the range and generate directly a positive int.
return lower + rng.nextInt((int) max);
} else {
// We can shift the range and generate directly a positive long.
return lower + rng.nextLong(max);
}
}
/**
* Generates a uniformly distributed random value from the open interval
* {@code (lower, upper)} (i.e., endpoints excluded).
*
* Definition:
*
* Uniform Distribution {@code lower} and {@code upper - lower} are the
*
* location and scale parameters, respectively.
*
* 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 Lower bound of the support (excluded).
* @param upper Upper bound of the support (excluded).
* @return a uniformly distributed random value between lower and upper
* (both excluded).
* @throws NumberIsTooLargeException if {@code lower >= upper}.
* @throws NotFiniteNumberException if one of the bounds is infinite.
* @throws NotANumberException if one of the bounds is NaN.
*/
public double nextUniform(double lower, double upper) {
return nextUniform(lower, upper, false);
}
/**
* Generates a uniformly distributed random value from the interval
* {@code (lower, upper)} or the interval {@code [lower, upper)}. The lower
* bound is thus optionally included, while the upper bound is always
* excluded.
*
* Definition:
*
* Uniform Distribution {@code lower} and {@code upper - lower} are the
*
* location and scale parameters, respectively.
*
* Algorithm Description: if the lower bound is excluded,
* scales the output of "nextDouble()", but rejects 0 values (i.e. it
* will generate another random double if "nextDouble()" returns 0).
* This is necessary to provide a symmetric output interval (both
* endpoints excluded).
*
*
* @param lower Lower bound of the support.
* @param upper Exclusive upper bound of the support.
* @param lowerInclusive {@code true} if the lower bound is inclusive.
* @return a uniformly distributed random value in the {@code (lower, upper)}
* interval, if {@code lowerInclusive} is {@code false}, or in the
* {@code [lower, upper)} interval, if {@code lowerInclusive} is
* {@code true}.
* @throws NumberIsTooLargeException if {@code lower >= upper}.
* @throws NotFiniteNumberException if one of the bounds is infinite.
* @throws NotANumberException if one of the bounds is NaN.
*/
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)) {
throw new NotFiniteNumberException(LocalizedFormats.INFINITE_BOUND, lower);
}
if (Double.isInfinite(upper)) {
throw new NotFiniteNumberException(LocalizedFormats.INFINITE_BOUND, upper);
}
if (Double.isNaN(lower) || Double.isNaN(upper)) {
throw new NotANumberException();
}
// Ensure nextDouble() isn't 0.0
double u = rng.nextDouble();
while (!lowerInclusive && u <= 0.0) {
u = rng.nextDouble();
}
return u * upper + (1.0 - u) * lower;
}
}
}