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 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><DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>the next pseudorandom, uniformly distributed <code>long</code>
          value from this random number generator's sequence.</DL>
</DD>
</DL>
<HR>

<A NAME="nextBoolean()"><!-- --></A><H3>
nextBoolean</H3>
<PRE>
public boolean <B>nextBoolean</B>()</PRE>
<DL>
<DD>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><DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>the next pseudorandom, uniformly distributed
          <code>boolean</code> value from this random number generator's
		sequence.<DT><B>Since: </B><DD>1.2</DD>
</DL>
</DD>
</DL>
<HR>

<A NAME="nextFloat()"><!-- --></A><H3>
nextFloat</H3>
<PRE>
public float <B>nextFloat</B>()</PRE>
<DL>
<DD>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.]<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>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.</DL>
</DD>
</DL>
<HR>

<A NAME="nextDouble()"><!-- --></A><H3>
nextDouble</H3>
<PRE>
public double <B>nextDouble</B>()</PRE>
<DL>
<DD>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.]<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>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.</DL>
</DD>
</DL>
<HR>

<A NAME="nextGaussian()"><!-- --></A><H3>
nextGaussian</H3>
<PRE>
public double <B>nextGaussian</B>()</PRE>
<DL>
<DD>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>.<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>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.</DL>
</DD>
</DL>
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