Added gauss() (same as normal but twice as fast) and betavariate();
print more statistics in test_generator()
diff --git a/Lib/random.py b/Lib/random.py
index 51ecb32..1fa1377 100644
--- a/Lib/random.py
+++ b/Lib/random.py
@@ -6,6 +6,7 @@
# lognormal
# negative exponential
# gamma
+# beta
#
# distributions on the circle (angles 0 to 2pi)
# ---------------------------------------------
@@ -15,7 +16,7 @@
# Translated from anonymously contributed C/C++ source.
from whrandom import random, uniform, randint, choice # Also for export!
-from math import log, exp, pi, e, sqrt, acos, cos
+from math import log, exp, pi, e, sqrt, acos, cos, sin
# Housekeeping function to verify that magic constants have been
# computed correctly
@@ -172,6 +173,37 @@
break
return x
+
+# -------------------- Gauss (faster alternative) --------------------
+
+# 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)
+#
+# are two *independent* variables with normal distribution (mu = 0, sigma = 1).
+# (Lambert Meertens)
+
+gauss_next = None
+def gauss(mu, sigma):
+ global gauss_next
+ if gauss_next != None:
+ z = gauss_next
+ gauss_next = None
+ else:
+ x2pi = random() * TWOPI
+ log1_y = log(1.0 - random())
+ z = cos(x2pi) * log1_y
+ gauss_next = sin(x2pi) * log1_y
+ return mu + z*sigma
+
+# -------------------- beta --------------------
+
+def betavariate(alpha, beta):
+ y = expovariate(alpha)
+ z = expovariate(1.0/beta)
+ return z/(y+z)
+
# -------------------- test program --------------------
def test():
@@ -179,7 +211,7 @@
print 'LOG4 =', LOG4
print 'NV_MAGICCONST =', NV_MAGICCONST
print 'SG_MAGICCONST =', SG_MAGICCONST
- N = 100
+ N = 200
test_generator(N, 'random()')
test_generator(N, 'normalvariate(0.0, 1.0)')
test_generator(N, 'lognormvariate(0.0, 1.0)')
@@ -192,21 +224,30 @@
test_generator(N, 'gammavariate(2.0, 1.0)')
test_generator(N, 'gammavariate(20.0, 1.0)')
test_generator(N, 'gammavariate(200.0, 1.0)')
+ test_generator(N, 'gauss(0.0, 1.0)')
+ test_generator(N, 'betavariate(3.0, 3.0)')
def test_generator(n, funccall):
- import sys
- print '%d calls to %s:' % (n, funccall),
- sys.stdout.flush()
+ import time
+ print n, 'times', funccall
code = compile(funccall, funccall, 'eval')
sum = 0.0
sqsum = 0.0
+ smallest = 1e10
+ largest = 1e-10
+ t0 = time.time()
for i in range(n):
x = eval(code)
sum = sum + x
sqsum = sqsum + x*x
+ smallest = min(x, smallest)
+ largest = max(x, largest)
+ t1 = time.time()
+ print round(t1-t0, 3), 'sec,',
avg = sum/n
stddev = sqrt(sqsum/n - avg*avg)
- print 'avg %g, stddev %g' % (avg, stddev)
+ print 'avg %g, stddev %g, min %g, max %g' % \
+ (avg, stddev, smallest, largest)
if __name__ == '__main__':
test()