Random variable generators

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diff --git a/Lib/random.py b/Lib/random.py
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+#	R A N D O M   V A R I A B L E   G E N E R A T O R S
+#
+#	distributions on the real line:
+#	------------------------------
+#	       normal (Gaussian)
+#	       lognormal
+#	       negative exponential
+#	       gamma
+#
+#	distributions on the circle (angles 0 to 2pi)
+#	---------------------------------------------
+#	       circular uniform
+#	       von Mises
+
+# 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
+
+# Housekeeping function to verify that magic constants have been
+# computed correctly
+
+def verify(name, expected):
+	computed = eval(name)
+	if abs(computed - expected) > 1e-7:
+		raise ValueError, \
+  'computed value for %s deviates too much (computed %g, expected %g)' % \
+  (name, computed, expected)
+
+# -------------------- normal distribution --------------------
+
+NV_MAGICCONST = 4*exp(-0.5)/sqrt(2)
+verify('NV_MAGICCONST', 1.71552776992141)
+def normalvariate(mu, sigma):
+	# mu = mean, sigma = standard deviation
+
+	# Uses Kinderman and Monahan method. Reference: Kinderman,
+	# A.J. and Monahan, J.F., "Computer generation of random
+	# variables using the ratio of uniform deviates", ACM Trans
+	# Math Software, 3, (1977), pp257-260.
+
+	while 1:
+		u1 = random()
+		u2 = random()
+		z = NV_MAGICCONST*(u1-0.5)/u2
+		zz = z*z/4
+		if zz <= -log(u2):
+			break
+	return mu+z*sigma
+
+# -------------------- lognormal distribution --------------------
+
+def lognormvariate(mu, sigma):
+	return exp(normalvariate(mu, sigma))
+
+# -------------------- circular uniform --------------------
+
+def cunifvariate(mean, arc):
+	# mean: mean angle (in radians between 0 and pi)
+	# arc:  range of distribution (in radians between 0 and pi)
+
+	return (mean + arc * (random() - 0.5)) % pi
+
+# -------------------- exponential distribution --------------------
+
+def expovariate(lambd):
+	# lambd: rate lambd = 1/mean
+	# ('lambda' is a Python reserved word)
+
+	u = random()
+	while u <= 1e-7:
+		u = random()
+	return -log(u)/lambd
+
+# -------------------- von Mises distribution --------------------
+
+TWOPI = 2*pi
+verify('TWOPI', 6.28318530718)
+
+def vonmisesvariate(mu, kappa):
+	# mu:    mean angle (in radians between 0 and 180 degrees)
+	# kappa: concentration parameter kappa (>= 0)
+	
+	# if kappa = 0 generate uniform random angle
+	if kappa <= 1e-6:
+		return TWOPI * random()
+
+	a = 1.0 + sqrt(1 + 4 * kappa * kappa)
+	b = (a - sqrt(2 * a))/(2 * kappa)
+	r = (1 + b * b)/(2 * b)
+
+	while 1:
+		u1 = random()
+
+		z = cos(pi * u1)
+		f = (1 + r * z)/(r + z)
+		c = kappa * (r - f)
+
+		u2 = random()
+
+		if not (u2 >= c * (2.0 - c) and u2 > c * exp(1.0 - c)):
+			break
+
+	u3 = random()
+	if u3 > 0.5:
+		theta = mu + 0.5*acos(f)
+	else:
+		theta = mu - 0.5*acos(f)
+
+	return theta % pi
+
+# -------------------- gamma distribution --------------------
+
+LOG4 = log(4)
+verify('LOG4', 1.38629436111989)
+
+def gammavariate(alpha, beta):
+        # beta times standard gamma
+	ainv = sqrt(2 * alpha - 1)
+	return beta * stdgamma(alpha, ainv, alpha - LOG4, alpha + ainv)
+
+SG_MAGICCONST = 1+log(4.5)
+verify('SG_MAGICCONST', 2.50407739677627)
+
+def stdgamma(alpha, ainv, bbb, ccc):
+	# ainv = sqrt(2 * alpha - 1)
+	# bbb = alpha - log(4)
+	# ccc = alpha + ainv
+
+	if alpha <= 0.0:
+		raise ValueError, 'stdgamma: alpha must be > 0.0'
+
+	if alpha > 1.0:
+
+		# Uses R.C.H. Cheng, "The generation of Gamma
+		# variables with non-integral shape parameters",
+		# Applied Statistics, (1977), 26, No. 1, p71-74
+
+		while 1:
+			u1 = random()
+			u2 = random()
+			v = log(u1/(1-u1))/ainv
+			x = alpha*exp(v)
+			z = u1*u1*u2
+			r = bbb+ccc*v-x
+			if r + SG_MAGICCONST - 4.5*z >= 0 or r >= log(z):
+				return x
+
+	elif alpha == 1.0:
+		# expovariate(1)
+		u = random()
+		while u <= 1e-7:
+			u = random()
+		return -log(u)
+
+	else:	# alpha is between 0 and 1 (exclusive)
+
+		# Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle
+
+		while 1:
+			u = random()
+			b = (e + alpha)/e
+			p = b*u
+			if p <= 1.0:
+				x = pow(p, 1.0/alpha)
+			else:
+				# p > 1
+				x = -log((b-p)/alpha)
+			u1 = random()
+			if not (((p <= 1.0) and (u1 > exp(-x))) or
+				  ((p > 1)  and  (u1 > pow(x, alpha - 1.0)))):
+				break
+		return x
+
+# -------------------- test program --------------------
+
+def test():
+	print 'TWOPI         =', TWOPI
+	print 'LOG4          =', LOG4
+	print 'NV_MAGICCONST =', NV_MAGICCONST
+	print 'SG_MAGICCONST =', SG_MAGICCONST
+	N = 100
+	test_generator(N, 'random()')
+	test_generator(N, 'normalvariate(0.0, 1.0)')
+	test_generator(N, 'lognormvariate(0.0, 1.0)')
+	test_generator(N, 'cunifvariate(0.0, 1.0)')
+	test_generator(N, 'expovariate(1.0)')
+	test_generator(N, 'vonmisesvariate(0.0, 1.0)')
+	test_generator(N, 'gammavariate(0.5, 1.0)')
+	test_generator(N, 'gammavariate(0.9, 1.0)')
+	test_generator(N, 'gammavariate(1.0, 1.0)')
+	test_generator(N, 'gammavariate(2.0, 1.0)')
+	test_generator(N, 'gammavariate(20.0, 1.0)')
+	test_generator(N, 'gammavariate(200.0, 1.0)')
+
+def test_generator(n, funccall):
+	import sys
+	print '%d calls to %s:' % (n, funccall),
+	sys.stdout.flush()
+	code = compile(funccall, funccall, 'eval')
+	sum = 0.0
+	sqsum = 0.0
+	for i in range(n):
+		x = eval(code)
+		sum = sum + x
+		sqsum = sqsum + x*x
+	avg = sum/n
+	stddev = sqrt(sqsum/n - avg*avg)
+	print 'avg %g, stddev %g' % (avg, stddev)
+
+if __name__ == '__main__':
+	test()