--- /dev/null
+/*
+ * @(#)Random.java 1.39 03/01/23
+ *
+ * Copyright 2003 Sun Microsystems, Inc. All rights reserved.
+ * SUN PROPRIETARY/CONFIDENTIAL. Use is subject to license terms.
+ */
+
+package instrumented.java.util;
+import sun.misc.AtomicLong;
+
+//import java.util.con
+
+import java.io.IOException;
+import java.io.ObjectInputStream;
+import java.io.ObjectOutputStream;
+import java.io.ObjectStreamField;
+//import java.util.concurrent.atomic.AtomicLong;
+
+/**
+ * An instance of this class is used to generate a stream of
+ * pseudorandom numbers. The class uses a 48-bit seed, which is
+ * modified using a linear congruential formula. (See Donald Knuth,
+ * <i>The Art of Computer Programming, Volume 2</i>, Section 3.2.1.)
+ * <p>
+ * If two instances of <code>Random</code> are created with the same
+ * seed, and the same sequence of method calls is made for each, they
+ * will generate and return identical sequences of numbers. In order to
+ * guarantee this property, particular algorithms are specified for the
+ * class <tt>Random</tt>. Java implementations must use all the algorithms
+ * shown here for the class <tt>Random</tt>, for the sake of absolute
+ * portability of Java code. However, subclasses of class <tt>Random</tt>
+ * are permitted to use other algorithms, so long as they adhere to the
+ * general contracts for all the methods.
+ * <p>
+ * The algorithms implemented by class <tt>Random</tt> use a
+ * <tt>protected</tt> utility method that on each invocation can supply
+ * up to 32 pseudorandomly generated bits.
+ * <p>
+ * Many applications will find the <code>random</code> method in
+ * class <code>Math</code> simpler to use.
+ *
+ * @author Frank Yellin
+ * @version 1.39, 01/23/03
+ * @see java.lang.Math#random()
+ * @since JDK1.0
+ */
+public
+class Random implements java.io.Serializable {
+ /** use serialVersionUID from JDK 1.1 for interoperability */
+ static final long serialVersionUID = 3905348978240129619L;
+
+ /**
+ * The internal state associated with this pseudorandom number generator.
+ * (The specs for the methods in this class describe the ongoing
+ * computation of this value.)
+ *
+ * @serial
+ */
+ private AtomicLong seed;
+
+ private final static long multiplier = 0x5DEECE66DL;
+ private final static long addend = 0xBL;
+ private final static long mask = (1L << 48) - 1;
+
+ /**
+ * Creates a new random number generator. Its seed is initialized to
+ * a value based on the current time:
+ * <blockquote><pre>
+ * public Random() { this(System.currentTimeMillis()); }</pre></blockquote>
+ * Two Random objects created within the same millisecond will have
+ * the same sequence of random numbers.
+ *
+ * @see java.lang.System#currentTimeMillis()
+ */
+ public Random() { this(System.currentTimeMillis()); }
+
+ /**
+ * Creates a new random number generator using a single
+ * <code>long</code> seed:
+ * <blockquote><pre>
+ * public Random(long seed) { setSeed(seed); }</pre></blockquote>
+ * Used by method <tt>next</tt> to hold
+ * the state of the pseudorandom number generator.
+ *
+ * @param seed the initial seed.
+ * @see benchmarks.instrumented.java.util.Random#setSeed(long)
+ */
+ public Random(long seed) {
+ this.seed = AtomicLong.newAtomicLong(0L);
+ setSeed(seed);
+ }
+
+ /**
+ * Sets the seed of this random number generator using a single
+ * <code>long</code> seed. The general contract of <tt>setSeed</tt>
+ * is that it alters the state of this random number generator
+ * object so as to be in exactly the same state as if it had just
+ * been created with the argument <tt>seed</tt> as a seed. The method
+ * <tt>setSeed</tt> is implemented by class Random as follows:
+ * <blockquote><pre>
+ * synchronized public void setSeed(long seed) {
+ * this.seed = (seed ^ 0x5DEECE66DL) & ((1L << 48) - 1);
+ * haveNextNextGaussian = false;
+ * }</pre></blockquote>
+ * The implementation of <tt>setSeed</tt> by class <tt>Random</tt>
+ * happens to use only 48 bits of the given seed. In general, however,
+ * an overriding method may use all 64 bits of the long argument
+ * as a seed value.
+ *
+ * Note: Although the seed value is an AtomicLong, this method
+ * must still be synchronized to ensure correct semantics
+ * of haveNextNextGaussian.
+ *
+ * @param seed the initial seed.
+ */
+ synchronized public void setSeed(long seed) {
+ seed = (seed ^ multiplier) & mask;
+ while(!this.seed.attemptSet(seed));
+ haveNextNextGaussian = false;
+ }
+
+ /**
+ * Generates the next pseudorandom number. Subclass should
+ * override this, as this is used by all other methods.<p>
+ * The general contract of <tt>next</tt> is that it returns an
+ * <tt>int</tt> value and if the argument bits is between <tt>1</tt>
+ * and <tt>32</tt> (inclusive), then that many low-order bits of the
+ * returned value will be (approximately) independently chosen bit
+ * values, each of which is (approximately) equally likely to be
+ * <tt>0</tt> or <tt>1</tt>. The method <tt>next</tt> is implemented
+ * by class <tt>Random</tt> as follows:
+ * <blockquote><pre>
+ * synchronized protected int next(int bits) {
+ * seed = (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1);
+ * return (int)(seed >>> (48 - bits));
+ * }</pre></blockquote>
+ * This is a linear congruential pseudorandom number generator, as
+ * defined by D. H. Lehmer and described by Donald E. Knuth in <i>The
+ * Art of Computer Programming,</i> Volume 2: <i>Seminumerical
+ * Algorithms</i>, section 3.2.1.
+ *
+ * @param bits random bits
+ * @return the next pseudorandom value from this random number generator's sequence.
+ * @since JDK1.1
+ */
+ protected int next(int bits) {
+ long oldseed, nextseed;
+ do {
+ oldseed = seed.get();
+ nextseed = (oldseed * multiplier + addend) & mask;
+ } while (!seed.attemptUpdate(oldseed, nextseed));
+ return (int)(nextseed >>> (48 - bits));
+ }
+
+ private static final int BITS_PER_BYTE = 8;
+ private static final int BYTES_PER_INT = 4;
+
+ /**
+ * 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.
+ *
+ * @param bytes the non-null byte array in which to put the
+ * random bytes.
+ * @since JDK1.1
+ */
+ public void nextBytes(byte[] bytes) {
+ int numRequested = bytes.length;
+
+ int numGot = 0, rnd = 0;
+
+ while (true) {
+ for (int i = 0; i < BYTES_PER_INT; i++) {
+ if (numGot == numRequested)
+ return;
+
+ rnd = (i==0 ? next(BITS_PER_BYTE * BYTES_PER_INT)
+ : rnd >> BITS_PER_BYTE);
+ bytes[numGot++] = (byte)rnd;
+ }
+ }
+ }
+
+ /**
+ * Returns the next pseudorandom, uniformly distributed <code>int</code>
+ * value from this random number generator's sequence. The general
+ * contract of <tt>nextInt</tt> is that one <tt>int</tt> value is
+ * pseudorandomly generated and returned. All 2<font size="-1"><sup>32
+ * </sup></font> possible <tt>int</tt> values are produced with
+ * (approximately) equal probability. The method <tt>nextInt</tt> is
+ * implemented by class <tt>Random</tt> as follows:
+ * <blockquote><pre>
+ * public int nextInt() { return next(32); }</pre></blockquote>
+ *
+ * @return the next pseudorandom, uniformly distributed <code>int</code>
+ * value from this random number generator's sequence.
+ */
+ public int nextInt() { return next(32); }
+
+ /**
+ * 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. The general contract of
+ * <tt>nextInt</tt> is that one <tt>int</tt> value in the specified range
+ * is pseudorandomly generated and returned. All <tt>n</tt> possible
+ * <tt>int</tt> values are produced with (approximately) equal
+ * probability. The method <tt>nextInt(int n)</tt> is implemented by
+ * class <tt>Random</tt> as follows:
+ * <blockquote><pre>
+ * public int nextInt(int n) {
+ * if (n<=0)
+ * throw new IllegalArgumentException("n must be positive");
+ *
+ * if ((n & -n) == n) // i.e., n is a power of 2
+ * 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;
+ * }
+ * </pre></blockquote>
+ * <p>
+ * 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 of randomly
+ * chosen bits, then the algorithm shown would choose <tt>int</tt>
+ * values from the stated range with perfect uniformity.
+ * <p>
+ * The algorithm is slightly tricky. It rejects values that would result
+ * in an uneven distribution (due to the fact that 2^31 is not divisible
+ * by n). The probability of a value being rejected depends on n. The
+ * worst case is n=2^30+1, for which the probability of a reject is 1/2,
+ * and the expected number of iterations before the loop terminates is 2.
+ * <p>
+ * The algorithm treats the case where n is a power of two specially: it
+ * returns the correct number of high-order bits from the underlying
+ * pseudo-random number generator. In the absence of special treatment,
+ * the correct number of <i>low-order</i> bits would be returned. Linear
+ * congruential pseudo-random number generators such as the one
+ * implemented by this class are known to have short periods in the
+ * sequence of values of their low-order bits. Thus, this special case
+ * greatly increases the length of the sequence of values returned by
+ * successive calls to this method if n is a small power of two.
+ *
+ * @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).
+ * @exception IllegalArgumentException n is not positive.
+ * @since 1.2
+ */
+
+ public int nextInt(int n) {
+ if (n<=0)
+ throw new IllegalArgumentException("n must be positive");
+
+ if ((n & -n) == n) // i.e., n is a power of 2
+ 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 x </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 x </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();
+
+ }
+
+}
projects / Benchmarks_CSolver.git / blobdiff