[jira] [Created] (RNG-95) DiscreteUniformSampler

["Alex D Herbert (JIRA)" <ji...@apache.org>]

Alex D Herbert created RNG-95:
---------------------------------

             Summary: DiscreteUniformSampler
                 Key: RNG-95
                 URL: https://issues.apache.org/jira/browse/RNG-95
             Project: Commons RNG
          Issue Type: Improvement
          Components: sampling
    Affects Versions: 1.3
            Reporter: Alex D Herbert
            Assignee: Alex D Herbert


The {{DiscreteUniformSampler}} delegates the creation of an integer in the range {{[0, n)}} to the {{UniformRandomProvider}}.

This sampler will be repeatedly used to sample the same range. The default method in {{BaseProvider}} uses a dynamic algorithm that handles {{n}} differently when a power of 2.

When the range is a power of 2 the method can use a series of bits from a random integer to generate a uniform integer in the range. This is fast.

When the range is not a power of 2 the algorithm must reject samples when the sample would result in an over-representation of a particular value in the uniform range. This is necessary as {{n}} does not exactly fit into the number of possible values {{[0, 2^31)}} that can be produced by the generator (when using 31-bit signed integers). The rejection method uses integer arithmetic to determine the number of samples that fit into the range: {{samples = 2^31 / n}}. Extra samples that lead to over-representation are rejected: {{extra = 2^31 % n}}.

Since {{n}} will not change a pre-computation step is possible to select the best algorithm. 

n is a power of 2:

{code:java}
// Favouring the least significant bits

// Pre-compute
int mask = n - 1;

return nextInt() & mask;

// Or favouring the most significant bits

// Pre-compute
int shift = Integer.numberOfLeadingZeros(n) + 1;

return nextInt() >>> shift;
{code}

n is not a power of 2:

{code:java}
// Sample using modulus

// Pre-compute
final int fence = (int)(0x80000000L - 0x80000000L % n - 1);

int bits;
do {
    bits = rng.nextInt() >>> 1;
} while (bits > fence);
return bits % n;

// Or using 32-bit unsigned arithmetic avoiding modulus

// Pre-compute
final long fence = (1L << 32) % n;

long result;
do {
    // Compute 64-bit unsigned product of n * [0, 2^32 - 1)
    result = n * (rng.nextInt() & 0xffffffffL);
    // Test the sample uniformity.
} while ((result & 0xffffffffL) < fence);
// Divide by 2^32 to get the sample
return (int)(result >>> 32);
{code}

The second method uses a range of 2^32 instead of 2^31 so reducing the rejection probability and avoids the modulus operator; these both increase speed.

Note algorithm 1 returns sample values in a repeat cycle from all values in the range {{[0, 2^31)}} due to the use of modulus, e.g.

{noformat}
0, 1, 2, ..., 0, 1, 2, ...
{noformat}

Algorithm 2 returns sample values in a linear order, e.g.

{noformat}
0, 0, 1, 1, 2, 2, ...
{noformat}

The suggested change is to implement smart pre-computation in the {{DiscreteUniformSampler}} based on the range and use the algorithms that favour the most significant bits from the generator.




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