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from UnitTest import UnitTest
import random
from math import log, exp, sqrt, pi
try:
from math import fsum as msum
except:
# fsum is new in 2.6
from math import fabs
def msum(x):
xx = [(fabs(v), i) for i, v in enumerate(x)]
xx.sort()
sum = 0
for i in xx:
sum += x[i[1]]
return sum
_gammacoeff = (0.9999999999995183, 676.5203681218835, -1259.139216722289,
771.3234287757674, -176.6150291498386, 12.50734324009056,
-0.1385710331296526, 0.9934937113930748e-05, 0.1659470187408462e-06)
def gamma(z, cof=_gammacoeff, g=7):
z -= 1.0
# Next line fails when not compiled with --operator-funcs
#s = msum([cof[0]] + [cof[i] / (z+i) for i in range(1,len(cof))])
v1 = [cof[0]]
v2 = [cof[i] / (z+i) for i in range(1,len(cof))]
v1 = v1.__add__(v2)
s = msum(v1)
z += 0.5
return (z+g)**z / exp(z+g) * sqrt(2.0*pi) * s
class RandomModuleTest(UnitTest):
def test_zeroinputs(self):
# Verify that distributions can handle a series of zero inputs'
g = random.Random()
xx = [g.random() for i in xrange(50)]
x = [0.0]
xx = xx.__add__(x.__mul__(5))
x = xx[:]
g.random = getattr(x, 'pop')
g.uniform(1,10)
x = xx[:]
g.random = getattr(x, 'pop')
g.paretovariate(1.0)
x = xx[:]
g.random = getattr(x, 'pop')
g.expovariate(1.0)
x = xx[:]
g.random = getattr(x, 'pop')
g.weibullvariate(1.0, 1.0)
x = xx[:]
g.random = getattr(x, 'pop')
g.normalvariate(0.0, 1.0)
x = xx[:]
g.random = getattr(x, 'pop')
g.gauss(0.0, 1.0)
x = xx[:]
g.random = getattr(x, 'pop')
g.lognormvariate(0.0, 1.0)
x = xx[:]
g.random = getattr(x, 'pop')
g.vonmisesvariate(0.0, 1.0)
x = xx[:]
g.random = getattr(x, 'pop')
g.gammavariate(0.01, 1.0)
x = xx[:]
g.random = getattr(x, 'pop')
g.gammavariate(1.0, 1.0)
x = xx[:]
g.random = getattr(x, 'pop')
g.gammavariate(200.0, 1.0)
x = xx[:]
g.random = getattr(x, 'pop')
g.betavariate(3.0, 3.0)
if hasattr(g, 'triangular'):
x = xx[:]
g.random = getattr(x, 'pop')
g.triangular(0.0, 1.0, 1.0/3.0)
def test_avg_std(self):
# Use integration to test distribution average and standard deviation.
# Only works for distributions which do not consume variates in pairs
g = random.Random()
N = 5000
xx = [i/float(N) for i in xrange(1,N)]
dists = [
(g.uniform, (1.0,10.0), (10.0+1.0)/2, (10.0-1.0)**2/12),
(g.expovariate, (1.5,), 1/1.5, 1/1.5**2),
(g.paretovariate, (5.0,), 5.0/(5.0-1),
5.0/((5.0-1)**2*(5.0-2))),
(g.weibullvariate, (1.0, 3.0), gamma(1+1/3.0),
gamma(1+2/3.0)-gamma(1+1/3.0)**2) ]
if hasattr(g, 'triangular'):
dists.append((g.triangular, (0.0, 1.0, 1.0/3.0), 4.0/9.0, 7.0/9.0/18.0))
for variate, args, mu, sigmasqrd in dists:
x = xx[:]
g.random = getattr(x, 'pop')
y = []
for i in xrange(len(x)):
try:
y.append(variate(*args))
except IndexError:
pass
s1 = s2 = 0
for e in y:
s1 += e
s2 += (e - mu) ** 2
N = len(y)
self.assertAlmostEqual(s1/N, mu, 2)
self.assertAlmostEqual(s2/(N-1), sigmasqrd, 2)
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