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Java example source code file (AbstractRandomGenerator.java)

This example Java source code file (AbstractRandomGenerator.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

abstractrandomgenerator, notstrictlypositiveexception, randomgenerator

The AbstractRandomGenerator.java Java example source code

/*
 * 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 org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.util.FastMath;

/**
 * Abstract class implementing the {@link  RandomGenerator} interface.
 * Default implementations for all methods other than {@link #nextDouble()} and
 * {@link #setSeed(long)} are provided.
 * <p>
 * All data generation methods are based on {@code code nextDouble()}.
 * Concrete implementations <strong>must override
 * this method and <strong>should provide better / more
 * performant implementations of the other methods if the underlying PRNG
 * supplies them.</p>
 *
 * @since 1.1
 */
public abstract class AbstractRandomGenerator implements RandomGenerator {

    /**
     * Cached random normal value.  The default implementation for
     * {@link #nextGaussian} generates pairs of values and this field caches the
     * second value so that the full algorithm is not executed for every
     * activation.  The value {@code Double.NaN} signals that there is
     * no cached value.  Use {@link #clear} to clear the cached value.
     */
    private double cachedNormalDeviate = Double.NaN;

    /**
     * Construct a RandomGenerator.
     */
    public AbstractRandomGenerator() {
        super();

    }

    /**
     * Clears the cache used by the default implementation of
     * {@link #nextGaussian}. Implementations that do not override the
     * default implementation of {@code nextGaussian} should call this
     * method in the implementation of {@link #setSeed(long)}
     */
    public void clear() {
        cachedNormalDeviate = Double.NaN;
    }

    /** {@inheritDoc} */
    public void setSeed(int seed) {
        setSeed((long) seed);
    }

    /** {@inheritDoc} */
    public void setSeed(int[] seed) {
        // the following number is the largest prime that fits in 32 bits (it is 2^32 - 5)
        final long prime = 4294967291l;

        long combined = 0l;
        for (int s : seed) {
            combined = combined * prime + s;
        }
        setSeed(combined);
    }

    /**
     * Sets the seed of the underlying random number generator using a
     * {@code long} seed.  Sequences of values generated starting with the
     * same seeds should be identical.
     * <p>
     * Implementations that do not override the default implementation of
     * {@code nextGaussian} should include a call to {@link #clear} in the
     * implementation of this method.</p>
     *
     * @param seed the seed value
     */
    public abstract void setSeed(long seed);

    /**
     * Generates random bytes and places them into a user-supplied
     * byte array.  The number of random bytes produced is equal to
     * the length of the byte array.
     * <p>
     * The default implementation fills the array with bytes extracted from
     * random integers generated using {@link #nextInt}.</p>
     *
     * @param bytes the non-null byte array in which to put the
     * random bytes
     */
    public void nextBytes(byte[] bytes) {
        int bytesOut = 0;
        while (bytesOut < bytes.length) {
          int randInt = nextInt();
          for (int i = 0; i < 3; i++) {
              if ( i > 0) {
                  randInt >>= 8;
              }
              bytes[bytesOut++] = (byte) randInt;
              if (bytesOut == bytes.length) {
                  return;
              }
          }
        }
    }

     /**
     * Returns the next pseudorandom, uniformly distributed {@code int}
     * value from this random number generator's sequence.
     * All 2<font size="-1">32 possible {@code int} values
     * should be produced with  (approximately) equal probability.
     * <p>
     * The default implementation provided here returns
     * <pre>
     * <code>(int) (nextDouble() * Integer.MAX_VALUE)
     * </pre>

* * @return the next pseudorandom, uniformly distributed {@code int} * value from this random number generator's sequence */ public int nextInt() { return (int) ((2d * nextDouble() - 1d) * Integer.MAX_VALUE); } /** * Returns a pseudorandom, uniformly distributed {@code int} value * between 0 (inclusive) and the specified value (exclusive), drawn from * this random number generator's sequence. * <p> * The default implementation returns * <pre> * <code>(int) (nextDouble() * n * </pre>

* * @param n the bound on the random number to be returned. Must be * positive. * @return a pseudorandom, uniformly distributed {@code int} * value between 0 (inclusive) and n (exclusive). * @throws NotStrictlyPositiveException if {@code n <= 0}. */ public int nextInt(int n) { if (n <= 0 ) { throw new NotStrictlyPositiveException(n); } int result = (int) (nextDouble() * n); return result < n ? result : n - 1; } /** * Returns the next pseudorandom, uniformly distributed {@code long} * value from this random number generator's sequence. All * 2<font size="-1">64 possible {@code long} values * should be produced with (approximately) equal probability. * <p> * The default implementation returns * <pre> * <code>(long) (nextDouble() * Long.MAX_VALUE) * </pre>

* * @return the next pseudorandom, uniformly distributed {@code long} *value from this random number generator's sequence */ public long nextLong() { return (long) ((2d * nextDouble() - 1d) * Long.MAX_VALUE); } /** * Returns the next pseudorandom, uniformly distributed * {@code boolean} value from this random number generator's * sequence. * <p> * The default implementation returns * <pre> * <code>nextDouble() <= 0.5 * </pre>

* * @return the next pseudorandom, uniformly distributed * {@code boolean} value from this random number generator's * sequence */ public boolean nextBoolean() { return nextDouble() <= 0.5; } /** * Returns the next pseudorandom, uniformly distributed {@code float} * value between {@code 0.0} and {@code 1.0} from this random * number generator's sequence. * <p> * The default implementation returns * <pre> * <code>(float) nextDouble() * </pre>

* * @return the next pseudorandom, uniformly distributed {@code float} * value between {@code 0.0} and {@code 1.0} from this * random number generator's sequence */ public float nextFloat() { return (float) nextDouble(); } /** * Returns the next pseudorandom, uniformly distributed * {@code double} value between {@code 0.0} and * {@code 1.0} from this random number generator's sequence. * <p> * This method provides the underlying source of random data used by the * other methods.</p> * * @return the next pseudorandom, uniformly distributed * {@code double} value between {@code 0.0} and * {@code 1.0} from this random number generator's sequence */ public abstract double nextDouble(); /** * Returns the next pseudorandom, Gaussian ("normally") distributed * {@code double} value with mean {@code 0.0} and standard * deviation {@code 1.0} from this random number generator's sequence. * <p> * The default implementation uses the <em>Polar Method * due to G.E.P. Box, M.E. Muller and G. Marsaglia, as described in * D. Knuth, <u>The Art of Computer Programming, 3.4.1C.

* <p> * The algorithm generates a pair of independent random values. One of * these is cached for reuse, so the full algorithm is not executed on each * activation. Implementations that do not override this method should * make sure to call {@link #clear} to clear the cached value in the * implementation of {@link #setSeed(long)}.</p> * * @return the next pseudorandom, Gaussian ("normally") distributed * {@code double} value with mean {@code 0.0} and * standard deviation {@code 1.0} from this random number * generator's sequence */ public double nextGaussian() { if (!Double.isNaN(cachedNormalDeviate)) { double dev = cachedNormalDeviate; cachedNormalDeviate = Double.NaN; return dev; } double v1 = 0; double v2 = 0; double s = 1; while (s >=1 ) { v1 = 2 * nextDouble() - 1; v2 = 2 * nextDouble() - 1; s = v1 * v1 + v2 * v2; } if (s != 0) { s = FastMath.sqrt(-2 * FastMath.log(s) / s); } cachedNormalDeviate = v2 * s; return v1 * s; } }

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