random.java

java源代码 请看看啊 提点宝贵的意见

            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 the next pseudorandom, uniformly distributed <code>long</code>
     * value from this random number generator's sequence. The general 
     * contract of <tt>nextLong</tt> is that one long value is pseudorandomly 
     * generated and returned. All 2<font size="-1"><sup>64</sup></font> 
     * possible <tt>long</tt> values are produced with (approximately) equal 
     * probability. The method <tt>nextLong</tt> is implemented by class 
     * <tt>Random</tt> as follows:
     * <blockquote><pre>
     * public long nextLong() {
     *       return ((long)next(32) << 32) + next(32);
     * }</pre></blockquote>
     *
     * @return  the next pseudorandom, uniformly distributed <code>long</code>
     *          value from this random number generator's sequence.
     */
    public long nextLong() {
        // it's okay that the bottom word remains signed.
        return ((long)(next(32)) << 32) + next(32);
    }

    /**
     * Returns the next pseudorandom, uniformly distributed
     * <code>boolean</code> value from this random number generator's
     * sequence. The general contract of <tt>nextBoolean</tt> is that one
     * <tt>boolean</tt> value is pseudorandomly generated and returned.  The
     * values <code>true</code> and <code>false</code> are produced with
     * (approximately) equal probability. The method <tt>nextBoolean</tt> is
     * implemented by class <tt>Random</tt> as follows:
     * <blockquote><pre>
     * public boolean nextBoolean() {return next(1) != 0;}
     * </pre></blockquote>
     * @return  the next pseudorandom, uniformly distributed
     *          <code>boolean</code> value from this random number generator's
     *		sequence.
     * @since 1.2
     */
    public boolean nextBoolean() {return next(1) != 0;}

    /**
     * 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 general contract of <tt>nextFloat</tt> is that one <tt>float</tt> 
     * value, chosen (approximately) uniformly from the range <tt>0.0f</tt> 
     * (inclusive) to <tt>1.0f</tt> (exclusive), is pseudorandomly
     * generated and returned. All 2<font size="-1"><sup>24</sup></font> 
     * possible <tt>float</tt> values of the form 
     * <i>m&nbsp;x&nbsp</i>2<font size="-1"><sup>-24</sup></font>, where 
     * <i>m</i> is a positive integer less than 2<font size="-1"><sup>24</sup>
     * </font>, are produced with (approximately) equal probability. The 
     * method <tt>nextFloat</tt> is implemented by class <tt>Random</tt> as 
     * follows:
     * <blockquote><pre>
     * public float nextFloat() {
     *      return next(24) / ((float)(1 << 24));
     * }</pre></blockquote>
     * The hedge "approximately" is used in the foregoing description only 
     * because the next method is only approximately an unbiased source of 
     * independently chosen bits. If it were a perfect source or randomly 
     * chosen bits, then the algorithm shown would choose <tt>float</tt> 
     * values from the stated range with perfect uniformity.<p>
     * [In early versions of Java, the result was incorrectly calculated as:
     * <blockquote><pre>
     * return next(30) / ((float)(1 << 30));</pre></blockquote>
     * This might seem to be equivalent, if not better, but in fact it 
     * introduced a slight nonuniformity because of the bias in the rounding 
     * of floating-point numbers: it was slightly more likely that the 
     * low-order bit of the significand would be 0 than that it would be 1.] 
     *
     * @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() {
        int i = next(24);
        return i / ((float)(1 << 24));
    }

    /**
     * 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>
     * The general contract of <tt>nextDouble</tt> is that one 
     * <tt>double</tt> value, chosen (approximately) uniformly from the 
     * range <tt>0.0d</tt> (inclusive) to <tt>1.0d</tt> (exclusive), is 
     * pseudorandomly generated and returned. All 
     * 2<font size="-1"><sup>53</sup></font> possible <tt>float</tt> 
     * values of the form <i>m&nbsp;x&nbsp;</i>2<font size="-1"><sup>-53</sup>
     * </font>, where <i>m</i> is a positive integer less than 
     * 2<font size="-1"><sup>53</sup></font>, are produced with 
     * (approximately) equal probability. The method <tt>nextDouble</tt> is 
     * implemented by class <tt>Random</tt> as follows:
     * <blockquote><pre>
     * public double nextDouble() {
     *       return (((long)next(26) << 27) + next(27))
     *           / (double)(1L << 53);
     * }</pre></blockquote><p>
     * The hedge "approximately" is used in the foregoing description only 
     * because the <tt>next</tt> method is only approximately an unbiased 
     * source of independently chosen bits. If it were a perfect source or 
     * randomly chosen bits, then the algorithm shown would choose 
     * <tt>double</tt> values from the stated range with perfect uniformity. 
     * <p>[In early versions of Java, the result was incorrectly calculated as:
     * <blockquote><pre>
     *  return (((long)next(27) << 27) + next(27))
     *      / (double)(1L << 54);</pre></blockquote>
     * This might seem to be equivalent, if not better, but in fact it 
     * introduced a large nonuniformity because of the bias in the rounding 
     * of floating-point numbers: it was three times as likely that the 
     * low-order bit of the significand would be 0 than that it would be
     * 1! This nonuniformity probably doesn't matter much in practice, but 
     * we strive for perfection.] 
     *
     * @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 double nextDouble() {
        long l = ((long)(next(26)) << 27) + next(27);
        return l / (double)(1L << 53);
    }

    private double nextNextGaussian;
    private boolean haveNextNextGaussian = false;

    /**
     * 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 general contract of <tt>nextGaussian</tt> is that one 
     * <tt>double</tt> value, chosen from (approximately) the usual 
     * normal distribution with mean <tt>0.0</tt> and standard deviation 
     * <tt>1.0</tt>, is pseudorandomly generated and returned. The method 
     * <tt>nextGaussian</tt> is implemented by class <tt>Random</tt> as follows:
     * <blockquote><pre>
     * synchronized public double nextGaussian() {
     *    if (haveNextNextGaussian) {
     *            haveNextNextGaussian = false;
     *            return nextNextGaussian;
     *    } else {
     *            double v1, v2, s;
     *            do { 
     *                    v1 = 2 * nextDouble() - 1;   // between -1.0 and 1.0
     *                    v2 = 2 * nextDouble() - 1;   // between -1.0 and 1.0
     *                    s = v1 * v1 + v2 * v2;
     *            } while (s >= 1 || s == 0);
     *            double multiplier = Math.sqrt(-2 * Math.log(s)/s);
     *            nextNextGaussian = v2 * multiplier;
     *            haveNextNextGaussian = true;
     *            return v1 * multiplier;
     *    }
     * }</pre></blockquote>
     * This uses the <i>polar method</i> of G. E. P. Box, M. E. Muller, and 
     * G. Marsaglia, as described by Donald E. Knuth in <i>The Art of 
     * Computer Programming</i>, Volume 2: <i>Seminumerical Algorithms</i>, 
     * section 3.4.1, subsection C, algorithm P. Note that it generates two
     * independent values at the cost of only one call to <tt>Math.log</tt> 
     * and one call to <tt>Math.sqrt</tt>. 
     *
     * @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.
     */
    synchronized public double nextGaussian() {
        // See Knuth, ACP, Section 3.4.1 Algorithm C.
        if (haveNextNextGaussian) {
    	    haveNextNextGaussian = false;
    	    return nextNextGaussian;
    	} else {
            double v1, v2, s;
    	    do { 
                v1 = 2 * nextDouble() - 1; // between -1 and 1
            	v2 = 2 * nextDouble() - 1; // between -1 and 1 
                s = v1 * v1 + v2 * v2;
    	    } while (s >= 1 || s == 0);
    	    double multiplier = Math.sqrt(-2 * Math.log(s)/s);
    	    nextNextGaussian = v2 * multiplier;
    	    haveNextNextGaussian = true;
    	    return v1 * multiplier;
        }
    }

    /**
     * Serializable fields for Random.
     *
     * @serialField    seed long;
     *              seed for random computations
     * @serialField    nextNextGaussian double;
     *              next Gaussian to be returned
     * @serialField      haveNextNextGaussian boolean
     *              nextNextGaussian is valid
     */
    private static final ObjectStreamField[] serialPersistentFields = {
        new ObjectStreamField("seed", Long.TYPE),
        new ObjectStreamField("nextNextGaussian", Double.TYPE),
        new ObjectStreamField("haveNextNextGaussian", Boolean.TYPE)
        };

    /**
     * Reconstitute the <tt>Random</tt> instance from a stream (that is,
     * deserialize it). The seed is read in as long for
     * historical reasons, but it is converted to an AtomicLong.
     */
    private void readObject(java.io.ObjectInputStream s)
        throws java.io.IOException, ClassNotFoundException {

        ObjectInputStream.GetField fields = s.readFields();
        long seedVal;

        seedVal = (long) fields.get("seed", -1L);
        if (seedVal < 0)
          throw new java.io.StreamCorruptedException(
                              "Random: invalid seed");
        seed = AtomicLong.newAtomicLong(seedVal);
        nextNextGaussian = fields.get("nextNextGaussian", 0.0);
        haveNextNextGaussian = fields.get("haveNextNextGaussian", false);
    }


    /**
     * Save the <tt>Random</tt> instance to a stream.
     * The seed of a Random is serialized as a long for
     * historical reasons.
     *
     */
    synchronized private void writeObject(ObjectOutputStream s) throws IOException {
        // set the values of the Serializable fields
        ObjectOutputStream.PutField fields = s.putFields();
        fields.put("seed", seed.get());
        fields.put("nextNextGaussian", nextNextGaussian);
        fields.put("haveNextNextGaussian", haveNextNextGaussian);

        // save them
        s.writeFields();

    }

}