java.util.Random Maven / Gradle / Ivy
/*
* 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 java.util;
import java.io.Serializable;
/**
* This class provides methods that return pseudo-random values.
*
* It is dangerous to seed {@code Random} with the current time because
* that value is more predictable to an attacker than the default seed.
*
*
This class is thread-safe.
*
* @see java.security.SecureRandom
*/
public class Random implements Serializable {
private static final long serialVersionUID = 3905348978240129619L;
private static final long multiplier = 0x5deece66dL;
/**
* The boolean value indicating if the second Gaussian number is available.
*
* @serial
*/
private boolean haveNextNextGaussian;
/**
* @serial It is associated with the internal state of this generator.
*/
private long seed;
/**
* The second Gaussian generated number.
*
* @serial
*/
private double nextNextGaussian;
/**
* Constructs a random generator with an initial state that is
* unlikely to be duplicated by a subsequent instantiation.
*
*
The initial state (that is, the seed) is partially based
* on the current time of day in milliseconds.
*/
public Random() {
// Note: Using identityHashCode() to be hermetic wrt subclasses.
setSeed(System.currentTimeMillis() + System.identityHashCode(this));
}
/**
* Construct a random generator with the given {@code seed} as the
* initial state. Equivalent to {@code Random r = new Random(); r.setSeed(seed);}.
*
*
This constructor is mainly useful for predictability in tests.
* The default constructor is likely to provide better randomness.
*/
public Random(long seed) {
setSeed(seed);
}
/**
* Returns a pseudo-random uniformly distributed {@code int} value of
* the number of bits specified by the argument {@code bits} as
* described by Donald E. Knuth in The Art of Computer Programming,
* Volume 2: Seminumerical Algorithms, section 3.2.1.
*
*
Most applications will want to use one of this class' convenience methods instead.
*/
protected synchronized int next(int bits) {
seed = (seed * multiplier + 0xbL) & ((1L << 48) - 1);
return (int) (seed >>> (48 - bits));
}
/**
* Returns a pseudo-random uniformly distributed {@code boolean}.
*/
public boolean nextBoolean() {
return next(1) != 0;
}
/**
* Fills {@code buf} with random bytes.
*/
public void nextBytes(byte[] buf) {
int rand = 0, count = 0, loop = 0;
while (count < buf.length) {
if (loop == 0) {
rand = nextInt();
loop = 3;
} else {
loop--;
}
buf[count++] = (byte) rand;
rand >>= 8;
}
}
/**
* Returns a pseudo-random uniformly distributed {@code double}
* in the half-open range [0.0, 1.0).
*/
public double nextDouble() {
return ((((long) next(26) << 27) + next(27)) / (double) (1L << 53));
}
/**
* Returns a pseudo-random uniformly distributed {@code float}
* in the half-open range [0.0, 1.0).
*/
public float nextFloat() {
return (next(24) / 16777216f);
}
/**
* Returns a pseudo-random (approximately) normally distributed
* {@code double} with mean 0.0 and standard deviation 1.0.
* This method uses the polar method of G. E. P. Box, M.
* E. Muller, and G. Marsaglia, as described by Donald E. Knuth in The
* Art of Computer Programming, Volume 2: Seminumerical Algorithms,
* section 3.4.1, subsection C, algorithm P.
*/
public synchronized double nextGaussian() {
if (haveNextNextGaussian) {
haveNextNextGaussian = false;
return nextNextGaussian;
}
double v1, v2, s;
do {
v1 = 2 * nextDouble() - 1;
v2 = 2 * nextDouble() - 1;
s = v1 * v1 + v2 * v2;
} while (s >= 1 || s == 0);
// The specification says this uses StrictMath.
double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s) / s);
nextNextGaussian = v2 * multiplier;
haveNextNextGaussian = true;
return v1 * multiplier;
}
/**
* Returns a pseudo-random uniformly distributed {@code int}.
*/
public int nextInt() {
return next(32);
}
/**
* Returns a pseudo-random uniformly distributed {@code int}
* in the half-open range [0, n).
*/
public int nextInt(int n) {
if (n <= 0) {
throw new IllegalArgumentException("n <= 0: " + n);
}
if ((n & -n) == n) {
return (int) ((n * (long) next(31)) >> 31);
}
int bits, val;
do {
bits = next(31);
val = bits % n;
} while (bits - val + (n - 1) < 0);
return val;
}
/**
* Returns a pseudo-random uniformly distributed {@code long}.
*/
public long nextLong() {
return ((long) next(32) << 32) + next(32);
}
/**
* Modifies the seed using a linear congruential formula presented in The
* Art of Computer Programming, Volume 2, Section 3.2.1.
*/
public synchronized void setSeed(long seed) {
this.seed = (seed ^ multiplier) & ((1L << 48) - 1);
haveNextNextGaussian = false;
}
}