Alex Herbert created RNG-190:
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Summary: ContinuousUniformSample to specialize generation of the
open interval (0, 1)
Key: RNG-190
URL: https://issues.apache.org/jira/browse/RNG-190
Project: Commons RNG
Issue Type: Improvement
Components: sampling
Affects Versions: 1.6
Reporter: Alex Herbert
The ContinuousUniformSampler can generate double values in the open interval
(lo, hi) using a constructor flag:
{code:java}
UniformRandomProvider rng = ...
double lo = ...
double hi = ...
boolean excludeBounds = true;
double x = ContinuousUniformSampler.of(rng, lo, hi, excludeBounds)
.sample();
{code}
The bounds are excluded using a rejection algorithm. This is used as rounding
during floating-point operations to map the underlying double value in [0, 1)
to (lo, hi) can generate values at the bounds.
In the case where the interval is (0, 1) then it is possible to generate the
random value with a branchless operation that is more performant. Examples are
setting the lowest 1-bit using various methods:
{code:java}
// Using bits to double
Double.longBitsToDouble(
(source.nextLong() >>> 12) | 0x3ff0000000000001L) - 1.0;
// Using float addition to force a lowest 1-bit
((source.nextLong() >>> 12) + 0.5) * 0x1.0p-52d
// Adding a bit before conversion to float
((source.nextLong() >>> 11) | 1) * 0x1.0p-53d
{code}
Note that the output would be limited to 2^52 evenly spaced rationals in (0,
1). Rejection algorithms would output 2^53 - 1 rationals. In practice this
difference would not be noticeable.
Provision of a guarantee of a fast non-zero double would be a benefit to
certain applications such as an inverse probability distribution defined on the
open interval.
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