Invent random_normal() to provide normally-distributed random numbers.

There is already a version of this in contrib/tablefunc, but it
seems sufficiently widely useful to justify having it in core.

Paul Ramsey

Discussion: https://postgr.es/m/CACowWR0DqHAvOKUCNxTrASFkWsDLqKMd6WiXvVvaWg4pV1BMnQ@mail.gmail.com

1 files changed, 3 insertions, 21 deletions

diff --git a/src/bin/pgbench/pgbench.c b/src/bin/pgbench/pgbench.c
index 18d9c94ebd3..9c12ffaea93 100644
--- a/src/bin/pgbench/pgbench.c
+++ b/src/bin/pgbench/pgbench.c
@@ -1136,8 +1136,8 @@ getGaussianRand(pg_prng_state *state, int64 min, int64 max,
 	Assert(parameter >= MIN_GAUSSIAN_PARAM);
 
 	/*
-	 * Get user specified random number from this loop, with -parameter <
-	 * stdev <= parameter
+	 * Get normally-distributed random number in the range -parameter <= stdev
+	 * < parameter.
 	 *
 	 * This loop is executed until the number is in the expected range.
 	 *
@@ -1149,25 +1149,7 @@ getGaussianRand(pg_prng_state *state, int64 min, int64 max,
 	 */
 	do
 	{
-		/*
-		 * pg_prng_double generates [0, 1), but for the basic version of the
-		 * Box-Muller transform the two uniformly distributed random numbers
-		 * are expected to be in (0, 1] (see
-		 * https://en.wikipedia.org/wiki/Box-Muller_transform)
-		 */
-		double		rand1 = 1.0 - pg_prng_double(state);
-		double		rand2 = 1.0 - pg_prng_double(state);
-
-		/* Box-Muller basic form transform */
-		double		var_sqrt = sqrt(-2.0 * log(rand1));
-
-		stdev = var_sqrt * sin(2.0 * M_PI * rand2);
-
-		/*
-		 * we may try with cos, but there may be a bias induced if the
-		 * previous value fails the test. To be on the safe side, let us try
-		 * over.
-		 */
+		stdev = pg_prng_double_normal(state);
 	}
 	while (stdev < -parameter || stdev >= parameter);