Github user erikerlandson commented on a diff in the pull request:
https://github.com/apache/spark/pull/2455#discussion_r18189003
--- Diff:
core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala ---
@@ -43,9 +46,34 @@ trait RandomSampler[T, U] extends Pseudorandom with
Cloneable with Serializable
throw new NotImplementedError("clone() is not implemented.")
}
+private [spark]
+object RandomSampler {
+ // Default random number generator used by random samplers
+ def rngDefault: Random = new XORShiftRandom
+
+ // Default gap sampling maximum
+ // For sampling fractions <= this value, the gap sampling optimization
will be applied.
+ // Above this value, it is assumed that "tradtional" bernoulli sampling
is faster. The
+ // optimal value for this will depend on the RNG. More expensive RNGs
will tend to make
+ // the optimal value higher. The most reliable way to determine this
value for a given RNG
+ // is to experiment. I would expect a value of 0.5 to be close in most
cases.
+ def gsmDefault: Double = 0.4
+
+ // Default gap sampling epsilon
+ // When sampling random floating point values the gap sampling logic
requires value > 0. An
+ // optimal value for this parameter is at or near the minimum positive
floating point value
+ // returned by nextDouble() for the RNG being used.
+ def epsDefault: Double = 5e-11
--- End diff --
I don't see how the code can be any more clear than when the type is
written out. That's as clear as it gets.
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