next() and
* setSeed(long) method. In that case the above
* paragraph doesn't apply to you.
*
* This class shouldn't be used for security sensitive purposes (like
* generating passwords or encryption keys. See SecureRandom
* in package java.security for this purpose.
*
* For simple random doubles between 0.0 and 1.0, you may consider using
* Math.random instead.
*
* @see java.security.SecureRandom
* @see Math#random()
* @author Jochen Hoenicke
* @author Eric Blake (ebb9@email.byu.edu)
* @status updated to 1.4
*/
public class Random //implements Serializable
{
/**
* True if the next nextGaussian is available. This is used by
* nextGaussian, which generates two gaussian numbers by one call,
* and returns the second on the second call.
*
* @serial whether nextNextGaussian is available
* @see #nextGaussian()
* @see #nextNextGaussian
*/
private boolean haveNextNextGaussian;
/**
* The next nextGaussian, when available. This is used by nextGaussian,
* which generates two gaussian numbers by one call, and returns the
* second on the second call.
*
* @serial the second gaussian of a pair
* @see #nextGaussian()
* @see #haveNextNextGaussian
*/
private double nextNextGaussian;
/**
* The seed. This is the number set by setSeed and which is used
* in next.
*
* @serial the internal state of this generator
* @see #next()
*/
private long seed;
/**
* Compatible with JDK 1.0+.
*/
private static final long serialVersionUID = 3905348978240129619L;
/**
* Creates a new pseudorandom number generator. The seed is initialized
* to the current time, as if by
* setSeed(System.currentTimeMillis());.
*
* @see System#currentTimeMillis()
*/
public Random()
{
this(System.currentTimeMillis());
}
/**
* Creates a new pseudorandom number generator, starting with the
* specified seed, using setSeed(seed);.
*
* @param seed the initial seed
*/
public Random(long seed)
{
setSeed(seed);
}
/**
* Sets the seed for this pseudorandom number generator. As described
* above, two instances of the same random class, starting with the
* same seed, should produce the same results, if the same methods
* are called. The implementation for java.util.Random is:
*
public synchronized void setSeed(long seed)
{
this.seed = (seed ^ 0x5DEECE66DL) & ((1L << 48) - 1);
haveNextNextGaussian = false;
}
*
* @param seed the new seed
*/
public synchronized void setSeed(long seed)
{
this.seed = (seed ^ 0x5DEECE66DL) & ((1L << 48) - 1);
haveNextNextGaussian = false;
}
/**
* Generates the next pseudorandom number. This returns
* an int value whose bits low order bits are
* independent chosen random bits (0 and 1 are equally likely).
* The implementation for java.util.Random is:
*
protected synchronized int next(int bits)
{
seed = (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1);
return (int) (seed >>> (48 - bits));
}
*
* @param bits the number of random bits to generate, in the range 1..32
* @return the next pseudorandom value
* @since 1.1
*/
protected synchronized int next(int bits)
{
seed = (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1);
return (int) (seed >>> (48 - bits));
}
/**
* Fills an array of bytes with random numbers. All possible values
* are (approximately) equally likely.
* The JDK documentation gives no implementation, but it seems to be:
*
public void nextBytes(byte[] bytes)
{
for (int i = 0; i < bytes.length; i += 4)
{
int random = next(32);
for (int j = 0; i + j < bytes.length && j < 4; j++)
{
bytes[i+j] = (byte) (random & 0xff)
random >>= 8;
}
}
}
*
* @param bytes the byte array that should be filled
* @throws NullPointerException if bytes is null
* @since 1.1
*/
public void nextBytes(byte[] bytes)
{
int random;
// Do a little bit unrolling of the above algorithm.
int max = bytes.length & ~0x3;
for (int i = 0; i < max; i += 4)
{
random = next(32);
bytes[i] = (byte) random;
bytes[i + 1] = (byte) (random >> 8);
bytes[i + 2] = (byte) (random >> 16);
bytes[i + 3] = (byte) (random >> 24);
}
if (max < bytes.length)
{
random = next(32);
for (int j = max; j < bytes.length; j++)
{
bytes[j] = (byte) random;
random >>= 8;
}
}
}
/**
* Generates the next pseudorandom number. This returns
* an int value whose 32 bits are independent chosen random bits
* (0 and 1 are equally likely). The implementation for
* java.util.Random is:
*
public int nextInt()
{
return next(32);
}
*
* @return the next pseudorandom value
*/
public int nextInt()
{
return next(32);
}
/**
* Generates the next pseudorandom number. This returns
* a value between 0(inclusive) and n(exclusive), and
* each value has the same likelihodd (1/n).
* (0 and 1 are equally likely). The implementation for
* java.util.Random is:
*
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;
}
*
* This algorithm would return every value with exactly the same * probability, if the next()-method would be a perfect random number * generator. * * The loop at the bottom only accepts a value, if the random * number was between 0 and the highest number less then 1<<31, * which is divisible by n. The probability for this is high for small * n, and the worst case is 1/2 (for n=(1<<30)+1). * * The special treatment for n = power of 2, selects the high bits of * the random number (the loop at the bottom would select the low order * bits). This is done, because the low order bits of linear congruential * number generators (like the one used in this class) are known to be * ``less random'' than the high order bits. * * @param n the upper bound * @throws IllegalArgumentException if the given upper bound is negative * @return the next pseudorandom value * @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; } /** * Generates the next pseudorandom long number. All bits of this * long are independently chosen and 0 and 1 have equal likelihood. * The implementation for java.util.Random is: *
public long nextLong()
{
return ((long) next(32) << 32) + next(32);
}
*
* @return the next pseudorandom value
*/
public long nextLong()
{
return ((long) next(32) << 32) + next(32);
}
/**
* Generates the next pseudorandom boolean. True and false have
* the same probability. The implementation is:
*
public boolean nextBoolean()
{
return next(1) != 0;
}
*
* @return the next pseudorandom boolean
* @since 1.2
*/
public boolean nextBoolean()
{
return next(1) != 0;
}
/**
* Generates the next pseudorandom float uniformly distributed
* between 0.0f (inclusive) and 1.0f (exclusive). The
* implementation is as follows.
*
public float nextFloat()
{
return next(24) / ((float)(1 << 24));
}
*
* @return the next pseudorandom float
*/
public float nextFloat()
{
return next(24) / (float) (1 << 24);
}
/**
* Generates the next pseudorandom double uniformly distributed
* between 0.0 (inclusive) and 1.0 (exclusive). The
* implementation is as follows.
*
public double nextDouble()
{
return (((long) next(26) << 27) + next(27)) / (double)(1L << 53);
}
*
* @return the next pseudorandom double
*/
public double nextDouble()
{
return (((long) next(26) << 27) + next(27)) / (double) (1L << 53);
}
/**
* Generates the next pseudorandom, Gaussian (normally) distributed
* double value, with mean 0.0 and standard deviation 1.0.
* The algorithm is as follows.
*
public synchronized 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);
double norm = fnattbase.Math.sqrt(-2 * fnattbase.Math.log(s) / s);
nextNextGaussian = v2 * norm;
haveNextNextGaussian = true;
return v1 * norm;
}
}
*
* This is described in section 3.4.1 of The Art of Computer * Programming, Volume 2 by Donald Knuth. * * @return the next pseudorandom Gaussian distributed double */ public synchronized double nextGaussian() { if (haveNextNextGaussian) { haveNextNextGaussian = false; return nextNextGaussian; } 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); double norm = Math.sqrt(-2 * Math.log(s) / s); nextNextGaussian = v2 * norm; haveNextNextGaussian = true; return v1 * norm; } }