Fixed a bug in the gauss() function. The bug was reported by Mike

Miller, who complained that its kurtosis was bad, and then fixed by
Lambert Meertens (author of the original algorithm) who discovered
that the mathematical analysis leading to his solution was wrong, and
provided a corrected version.  Mike then tested the fix and reported
that the kurtosis was now good.

Lib/random.py

@@ -182,12 +182,13 @@ def gauss(mu, sigma):
 	# When x and y are two variables from [0, 1), uniformly
 	# distributed, then
 	#
-	#    cos(2*pi*x)*log(1-y)
-	#    sin(2*pi*x)*log(1-y)
+	#    cos(2*pi*x)*sqrt(-2*log(1-y))
+	#    sin(2*pi*x)*sqrt(-2*log(1-y))
 	#
 	# are two *independent* variables with normal distribution
 	# (mu = 0, sigma = 1).
 	# (Lambert Meertens)
+	# (corrected version; bug discovered by Mike Miller, fixed by LM)
 
 	global gauss_next
 
@@ -196,9 +197,9 @@ def gauss(mu, sigma):
 		gauss_next = None
 	else:
 		x2pi = random() * TWOPI
-		log1_y = log(1.0 - random())
-		z = cos(x2pi) * log1_y
-		gauss_next = sin(x2pi) * log1_y
+		g2rad = sqrt(-2.0 * log(1.0 - random()))
+		z = cos(x2pi) * g2rad
+		gauss_next = sin(x2pi) * g2rad
 
 	return mu + z*sigma