abstractrandomgenerator.java

Apache的common math数学软件包

/*
 * 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;

/**
 * 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>nextDouble().</code>
 * Concrete implementations <strong>must</strong> override
 * this method and <strong>should</strong> provide better / more
 * performant implementations of the other methods if the underlying PRNG
 * supplies them.</p>
 *
 * @since 1.1
 * @version $Revision: 615734 $ $Date: 2008-01-27 23:10:03 -0700 (Sun, 27 Jan 2008) $
 */
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</code> 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}. Implemementations that do not override the
     * default implementation of <code>nextGaussian</code> should call this
     * method in the implementation of {@link #setSeed(long)}
     */
    public void clear() {
        cachedNormalDeviate = Double.NaN;
    }
    
    /**
     * Sets the seed of the underyling random number generator using a 
     * <code>long</code> 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</code> 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 = randInt >> 8;
              }
              bytes[bytesOut++] = (byte) randInt;
              if (bytesOut == bytes.length) {
                  return;
              }
          }
        }
    }

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

    /**
     * Returns a pseudorandom, uniformly distributed <tt>int</tt> 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</code>
     * </pre></p>
     *
     * @param n the bound on the random number to be returned.  Must be
     * positive.
     * @return  a pseudorandom, uniformly distributed <tt>int</tt>
     * value between 0 (inclusive) and n (exclusive).
     * @throws IllegalArgumentException if n is not positive.
     */
    public int nextInt(int n) {
        if (n <= 0 ) {
            throw new IllegalArgumentException("upper bound must be positive");
        }
        int result = (int) (nextDouble() * n);
        return result < n ? result : n - 1;
    }

     /**
     * Returns the next pseudorandom, uniformly distributed <code>long</code>
     * value from this random number generator's sequence.  All 
     * 2<font size="-1"><sup>64</sup></font> possible <tt>long</tt> values 
     * should be produced with (approximately) equal probability. 
     * <p>  
     * The default implementation returns 
     * <pre>
     * <code>(long) (nextDouble() * Long.MAX_VALUE)</code>
     * </pre></p>
     *
     * @return  the next pseudorandom, uniformly distributed <code>long</code>
     *value from this random number generator's sequence
     */
    public long nextLong() {
        return (long) (nextDouble() * Long.MAX_VALUE);
    }

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

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

    /**
     * Returns the next pseudorandom, uniformly distributed 
     * <code>double</code> value between <code>0.0</code> and
     * <code>1.0</code> 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</code> value between <code>0.0</code> and
     *  <code>1.0</code> from this random number generator's sequence
     */  
    public abstract double nextDouble();  

    /**
     * Returns the next pseudorandom, Gaussian ("normally") distributed
     * <code>double</code> value with mean <code>0.0</code> and standard
     * deviation <code>1.0</code> from this random number generator's sequence.
     * <p>
     * The default implementation uses the <em>Polar Method</em>
     * due to G.E.P. Box, M.E. Muller and G. Marsaglia, as described in 
     * D. Knuth, <u>The Art of Computer Programming</u>, 3.4.1C.</p>
     * <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</code> value with mean <code>0.0</code> and
     * standard deviation <code>1.0</code> 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 = Math.sqrt(-2 * Math.log(s) / s);   
        }
        cachedNormalDeviate = v2 * s;
        return v1 * s;      
    }
}