def test_zeroinputs(self):
# Verify that distributions can handle a series of zero inputs'
g = random.Random()
x = [g.random() for i in xrange(50)] + [0.0]*5
g.random = x[:].pop; g.uniform(1,10)
g.random = x[:].pop; g.paretovariate(1.0)
g.random = x[:].pop; g.expovariate(1.0)
g.random = x[:].pop; g.weibullvariate(1.0, 1.0)
g.random = x[:].pop; g.vonmisesvariate(1.0, 1.0)
g.random = x[:].pop; g.normalvariate(0.0, 1.0)
g.random = x[:].pop; g.gauss(0.0, 1.0)
g.random = x[:].pop; g.lognormvariate(0.0, 1.0)
g.random = x[:].pop; g.vonmisesvariate(0.0, 1.0)
g.random = x[:].pop; g.gammavariate(0.01, 1.0)
g.random = x[:].pop; g.gammavariate(1.0, 1.0)
g.random = x[:].pop; g.gammavariate(200.0, 1.0)
g.random = x[:].pop; g.betavariate(3.0, 3.0)
g.random = x[:].pop; g.triangular(0.0, 1.0, 1.0/3.0)
python类vonmisesvariate()的实例源码
def test_constant(self):
g = random.Random()
N = 100
for variate, args, expected in [
(g.uniform, (10.0, 10.0), 10.0),
(g.triangular, (10.0, 10.0), 10.0),
(g.triangular, (10.0, 10.0, 10.0), 10.0),
(g.expovariate, (float('inf'),), 0.0),
(g.vonmisesvariate, (3.0, float('inf')), 3.0),
(g.gauss, (10.0, 0.0), 10.0),
(g.lognormvariate, (0.0, 0.0), 1.0),
(g.lognormvariate, (-float('inf'), 0.0), 0.0),
(g.normalvariate, (10.0, 0.0), 10.0),
(g.paretovariate, (float('inf'),), 1.0),
(g.weibullvariate, (10.0, float('inf')), 10.0),
(g.weibullvariate, (0.0, 10.0), 0.0),
]:
for i in range(N):
self.assertEqual(variate(*args), expected)
def test_zeroinputs(self):
# Verify that distributions can handle a series of zero inputs'
g = random.Random()
x = [g.random() for i in xrange(50)] + [0.0]*5
g.random = x[:].pop; g.uniform(1,10)
g.random = x[:].pop; g.paretovariate(1.0)
g.random = x[:].pop; g.expovariate(1.0)
g.random = x[:].pop; g.weibullvariate(1.0, 1.0)
g.random = x[:].pop; g.vonmisesvariate(1.0, 1.0)
g.random = x[:].pop; g.normalvariate(0.0, 1.0)
g.random = x[:].pop; g.gauss(0.0, 1.0)
g.random = x[:].pop; g.lognormvariate(0.0, 1.0)
g.random = x[:].pop; g.vonmisesvariate(0.0, 1.0)
g.random = x[:].pop; g.gammavariate(0.01, 1.0)
g.random = x[:].pop; g.gammavariate(1.0, 1.0)
g.random = x[:].pop; g.gammavariate(200.0, 1.0)
g.random = x[:].pop; g.betavariate(3.0, 3.0)
g.random = x[:].pop; g.triangular(0.0, 1.0, 1.0/3.0)
def test_constant(self):
g = random.Random()
N = 100
for variate, args, expected in [
(g.uniform, (10.0, 10.0), 10.0),
(g.triangular, (10.0, 10.0), 10.0),
(g.triangular, (10.0, 10.0, 10.0), 10.0),
(g.expovariate, (float('inf'),), 0.0),
(g.vonmisesvariate, (3.0, float('inf')), 3.0),
(g.gauss, (10.0, 0.0), 10.0),
(g.lognormvariate, (0.0, 0.0), 1.0),
(g.lognormvariate, (-float('inf'), 0.0), 0.0),
(g.normalvariate, (10.0, 0.0), 10.0),
(g.paretovariate, (float('inf'),), 1.0),
(g.weibullvariate, (10.0, float('inf')), 10.0),
(g.weibullvariate, (0.0, 10.0), 0.0),
]:
for i in range(N):
self.assertEqual(variate(*args), expected)
def test_zeroinputs(self):
# Verify that distributions can handle a series of zero inputs'
g = random.Random()
x = [g.random() for i in range(50)] + [0.0]*5
g.random = x[:].pop; g.uniform(1,10)
g.random = x[:].pop; g.paretovariate(1.0)
g.random = x[:].pop; g.expovariate(1.0)
g.random = x[:].pop; g.weibullvariate(1.0, 1.0)
g.random = x[:].pop; g.vonmisesvariate(1.0, 1.0)
g.random = x[:].pop; g.normalvariate(0.0, 1.0)
g.random = x[:].pop; g.gauss(0.0, 1.0)
g.random = x[:].pop; g.lognormvariate(0.0, 1.0)
g.random = x[:].pop; g.vonmisesvariate(0.0, 1.0)
g.random = x[:].pop; g.gammavariate(0.01, 1.0)
g.random = x[:].pop; g.gammavariate(1.0, 1.0)
g.random = x[:].pop; g.gammavariate(200.0, 1.0)
g.random = x[:].pop; g.betavariate(3.0, 3.0)
g.random = x[:].pop; g.triangular(0.0, 1.0, 1.0/3.0)
def test_constant(self):
g = random.Random()
N = 100
for variate, args, expected in [
(g.uniform, (10.0, 10.0), 10.0),
(g.triangular, (10.0, 10.0), 10.0),
#(g.triangular, (10.0, 10.0, 10.0), 10.0),
(g.expovariate, (float('inf'),), 0.0),
(g.vonmisesvariate, (3.0, float('inf')), 3.0),
(g.gauss, (10.0, 0.0), 10.0),
(g.lognormvariate, (0.0, 0.0), 1.0),
(g.lognormvariate, (-float('inf'), 0.0), 0.0),
(g.normalvariate, (10.0, 0.0), 10.0),
(g.paretovariate, (float('inf'),), 1.0),
(g.weibullvariate, (10.0, float('inf')), 10.0),
(g.weibullvariate, (0.0, 10.0), 0.0),
]:
for i in range(N):
self.assertEqual(variate(*args), expected)
def test_zeroinputs(self):
# Verify that distributions can handle a series of zero inputs'
g = random.Random()
x = [g.random() for i in xrange(50)] + [0.0]*5
g.random = x[:].pop; g.uniform(1,10)
g.random = x[:].pop; g.paretovariate(1.0)
g.random = x[:].pop; g.expovariate(1.0)
g.random = x[:].pop; g.weibullvariate(1.0, 1.0)
g.random = x[:].pop; g.vonmisesvariate(1.0, 1.0)
g.random = x[:].pop; g.normalvariate(0.0, 1.0)
g.random = x[:].pop; g.gauss(0.0, 1.0)
g.random = x[:].pop; g.lognormvariate(0.0, 1.0)
g.random = x[:].pop; g.vonmisesvariate(0.0, 1.0)
g.random = x[:].pop; g.gammavariate(0.01, 1.0)
g.random = x[:].pop; g.gammavariate(1.0, 1.0)
g.random = x[:].pop; g.gammavariate(200.0, 1.0)
g.random = x[:].pop; g.betavariate(3.0, 3.0)
g.random = x[:].pop; g.triangular(0.0, 1.0, 1.0/3.0)
def test_constant(self):
g = random.Random()
N = 100
for variate, args, expected in [
(g.uniform, (10.0, 10.0), 10.0),
(g.triangular, (10.0, 10.0), 10.0),
(g.triangular, (10.0, 10.0, 10.0), 10.0),
(g.expovariate, (float('inf'),), 0.0),
(g.vonmisesvariate, (3.0, float('inf')), 3.0),
(g.gauss, (10.0, 0.0), 10.0),
(g.lognormvariate, (0.0, 0.0), 1.0),
(g.lognormvariate, (-float('inf'), 0.0), 0.0),
(g.normalvariate, (10.0, 0.0), 10.0),
(g.paretovariate, (float('inf'),), 1.0),
(g.weibullvariate, (10.0, float('inf')), 10.0),
(g.weibullvariate, (0.0, 10.0), 0.0),
]:
for i in range(N):
self.assertEqual(variate(*args), expected)
def test_zeroinputs(self):
# Verify that distributions can handle a series of zero inputs'
g = random.Random()
x = [g.random() for i in range(50)] + [0.0]*5
g.random = x[:].pop; g.uniform(1,10)
g.random = x[:].pop; g.paretovariate(1.0)
g.random = x[:].pop; g.expovariate(1.0)
g.random = x[:].pop; g.weibullvariate(1.0, 1.0)
g.random = x[:].pop; g.vonmisesvariate(1.0, 1.0)
g.random = x[:].pop; g.normalvariate(0.0, 1.0)
g.random = x[:].pop; g.gauss(0.0, 1.0)
g.random = x[:].pop; g.lognormvariate(0.0, 1.0)
g.random = x[:].pop; g.vonmisesvariate(0.0, 1.0)
g.random = x[:].pop; g.gammavariate(0.01, 1.0)
g.random = x[:].pop; g.gammavariate(1.0, 1.0)
g.random = x[:].pop; g.gammavariate(200.0, 1.0)
g.random = x[:].pop; g.betavariate(3.0, 3.0)
g.random = x[:].pop; g.triangular(0.0, 1.0, 1.0/3.0)
def test_constant(self):
g = random.Random()
N = 100
for variate, args, expected in [
(g.uniform, (10.0, 10.0), 10.0),
(g.triangular, (10.0, 10.0), 10.0),
(g.triangular, (10.0, 10.0, 10.0), 10.0),
(g.expovariate, (float('inf'),), 0.0),
(g.vonmisesvariate, (3.0, float('inf')), 3.0),
(g.gauss, (10.0, 0.0), 10.0),
(g.lognormvariate, (0.0, 0.0), 1.0),
(g.lognormvariate, (-float('inf'), 0.0), 0.0),
(g.normalvariate, (10.0, 0.0), 10.0),
(g.paretovariate, (float('inf'),), 1.0),
(g.weibullvariate, (10.0, float('inf')), 10.0),
(g.weibullvariate, (0.0, 10.0), 0.0),
]:
for i in range(N):
self.assertEqual(variate(*args), expected)
def test_zeroinputs(self):
# Verify that distributions can handle a series of zero inputs'
g = random.Random()
x = [g.random() for i in xrange(50)] + [0.0]*5
g.random = x[:].pop; g.uniform(1,10)
g.random = x[:].pop; g.paretovariate(1.0)
g.random = x[:].pop; g.expovariate(1.0)
g.random = x[:].pop; g.weibullvariate(1.0, 1.0)
g.random = x[:].pop; g.vonmisesvariate(1.0, 1.0)
g.random = x[:].pop; g.normalvariate(0.0, 1.0)
g.random = x[:].pop; g.gauss(0.0, 1.0)
g.random = x[:].pop; g.lognormvariate(0.0, 1.0)
g.random = x[:].pop; g.vonmisesvariate(0.0, 1.0)
g.random = x[:].pop; g.gammavariate(0.01, 1.0)
g.random = x[:].pop; g.gammavariate(1.0, 1.0)
g.random = x[:].pop; g.gammavariate(200.0, 1.0)
g.random = x[:].pop; g.betavariate(3.0, 3.0)
g.random = x[:].pop; g.triangular(0.0, 1.0, 1.0/3.0)
def test_constant(self):
g = random.Random()
N = 100
for variate, args, expected in [
(g.uniform, (10.0, 10.0), 10.0),
(g.triangular, (10.0, 10.0), 10.0),
#(g.triangular, (10.0, 10.0, 10.0), 10.0),
(g.expovariate, (float('inf'),), 0.0),
(g.vonmisesvariate, (3.0, float('inf')), 3.0),
(g.gauss, (10.0, 0.0), 10.0),
(g.lognormvariate, (0.0, 0.0), 1.0),
(g.lognormvariate, (-float('inf'), 0.0), 0.0),
(g.normalvariate, (10.0, 0.0), 10.0),
(g.paretovariate, (float('inf'),), 1.0),
(g.weibullvariate, (10.0, float('inf')), 10.0),
(g.weibullvariate, (0.0, 10.0), 0.0),
]:
for i in range(N):
self.assertEqual(variate(*args), expected)
def test_zeroinputs(self):
# Verify that distributions can handle a series of zero inputs'
g = random.Random()
x = [g.random() for i in range(50)] + [0.0]*5
g.random = x[:].pop; g.uniform(1,10)
g.random = x[:].pop; g.paretovariate(1.0)
g.random = x[:].pop; g.expovariate(1.0)
g.random = x[:].pop; g.weibullvariate(1.0, 1.0)
g.random = x[:].pop; g.vonmisesvariate(1.0, 1.0)
g.random = x[:].pop; g.normalvariate(0.0, 1.0)
g.random = x[:].pop; g.gauss(0.0, 1.0)
g.random = x[:].pop; g.lognormvariate(0.0, 1.0)
g.random = x[:].pop; g.vonmisesvariate(0.0, 1.0)
g.random = x[:].pop; g.gammavariate(0.01, 1.0)
g.random = x[:].pop; g.gammavariate(1.0, 1.0)
g.random = x[:].pop; g.gammavariate(200.0, 1.0)
g.random = x[:].pop; g.betavariate(3.0, 3.0)
g.random = x[:].pop; g.triangular(0.0, 1.0, 1.0/3.0)
def test_constant(self):
g = random.Random()
N = 100
for variate, args, expected in [
(g.uniform, (10.0, 10.0), 10.0),
(g.triangular, (10.0, 10.0), 10.0),
(g.triangular, (10.0, 10.0, 10.0), 10.0),
(g.expovariate, (float('inf'),), 0.0),
(g.vonmisesvariate, (3.0, float('inf')), 3.0),
(g.gauss, (10.0, 0.0), 10.0),
(g.lognormvariate, (0.0, 0.0), 1.0),
(g.lognormvariate, (-float('inf'), 0.0), 0.0),
(g.normalvariate, (10.0, 0.0), 10.0),
(g.paretovariate, (float('inf'),), 1.0),
(g.weibullvariate, (10.0, float('inf')), 10.0),
(g.weibullvariate, (0.0, 10.0), 0.0),
]:
for i in range(N):
self.assertEqual(variate(*args), expected)
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
x = [i/float(N) for i in xrange(1,N)]
for variate, args, mu, sigmasqrd in [
(g.uniform, (1.0,10.0), (10.0+1.0)/2, (10.0-1.0)**2/12),
(g.triangular, (0.0, 1.0, 1.0/3.0), 4.0/9.0, 7.0/9.0/18.0),
(g.expovariate, (1.5,), 1/1.5, 1/1.5**2),
(g.vonmisesvariate, (1.23, 0), pi, pi**2/3),
(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) ]:
g.random = 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, places=2,
msg='%s%r' % (variate.__name__, args))
self.assertAlmostEqual(s2/(N-1), sigmasqrd, places=2,
msg='%s%r' % (variate.__name__, args))
def test_von_mises_range(self):
# Issue 17149: von mises variates were not consistently in the
# range [0, 2*PI].
g = random.Random()
N = 100
for mu in 0.0, 0.1, 3.1, 6.2:
for kappa in 0.0, 2.3, 500.0:
for _ in range(N):
sample = g.vonmisesvariate(mu, kappa)
self.assertTrue(
0 <= sample <= random.TWOPI,
msg=("vonmisesvariate({}, {}) produced a result {} out"
" of range [0, 2*pi]").format(mu, kappa, sample))
def test_von_mises_large_kappa(self):
# Issue #17141: vonmisesvariate() was hang for large kappas
random.vonmisesvariate(0, 1e15)
random.vonmisesvariate(0, 1e100)
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
x = [i/float(N) for i in xrange(1,N)]
for variate, args, mu, sigmasqrd in [
(g.uniform, (1.0,10.0), (10.0+1.0)/2, (10.0-1.0)**2/12),
(g.triangular, (0.0, 1.0, 1.0/3.0), 4.0/9.0, 7.0/9.0/18.0),
(g.expovariate, (1.5,), 1/1.5, 1/1.5**2),
(g.vonmisesvariate, (1.23, 0), pi, pi**2/3),
(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) ]:
g.random = 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, places=2,
msg='%s%r' % (variate.__name__, args))
self.assertAlmostEqual(s2/(N-1), sigmasqrd, places=2,
msg='%s%r' % (variate.__name__, args))
def test_von_mises_range(self):
# Issue 17149: von mises variates were not consistently in the
# range [0, 2*PI].
g = random.Random()
N = 100
for mu in 0.0, 0.1, 3.1, 6.2:
for kappa in 0.0, 2.3, 500.0:
for _ in range(N):
sample = g.vonmisesvariate(mu, kappa)
self.assertTrue(
0 <= sample <= random.TWOPI,
msg=("vonmisesvariate({}, {}) produced a result {} out"
" of range [0, 2*pi]").format(mu, kappa, sample))
def test_von_mises_large_kappa(self):
# Issue #17141: vonmisesvariate() was hang for large kappas
random.vonmisesvariate(0, 1e15)
random.vonmisesvariate(0, 1e100)
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
x = [i/float(N) for i in range(1,N)]
for variate, args, mu, sigmasqrd in [
(g.uniform, (1.0,10.0), (10.0+1.0)/2, (10.0-1.0)**2/12),
(g.triangular, (0.0, 1.0, 1.0/3.0), 4.0/9.0, 7.0/9.0/18.0),
(g.expovariate, (1.5,), 1/1.5, 1/1.5**2),
(g.vonmisesvariate, (1.23, 0), pi, pi**2/3),
(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) ]:
g.random = x[:].pop
y = []
for i in range(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, places=2,
msg='%s%r' % (variate.__name__, args))
self.assertAlmostEqual(s2/(N-1), sigmasqrd, places=2,
msg='%s%r' % (variate.__name__, args))
def test_von_mises_range(self):
# Issue 17149: von mises variates were not consistently in the
# range [0, 2*PI].
g = random.Random()
N = 100
for mu in 0.0, 0.1, 3.1, 6.2:
for kappa in 0.0, 2.3, 500.0:
for _ in range(N):
sample = g.vonmisesvariate(mu, kappa)
self.assertTrue(
0 <= sample <= random.TWOPI,
msg=("vonmisesvariate({}, {}) produced a result {} out"
" of range [0, 2*pi]").format(mu, kappa, sample))
def test_von_mises_large_kappa(self):
# Issue #17141: vonmisesvariate() was hang for large kappas
random.vonmisesvariate(0, 1e15)
random.vonmisesvariate(0, 1e100)
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
x = [i/float(N) for i in xrange(1,N)]
for variate, args, mu, sigmasqrd in [
(g.uniform, (1.0,10.0), (10.0+1.0)/2, (10.0-1.0)**2/12),
(g.triangular, (0.0, 1.0, 1.0/3.0), 4.0/9.0, 7.0/9.0/18.0),
(g.expovariate, (1.5,), 1/1.5, 1/1.5**2),
(g.vonmisesvariate, (1.23, 0), pi, pi**2/3),
(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) ]:
g.random = 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, places=2,
msg='%s%r' % (variate.__name__, args))
self.assertAlmostEqual(s2/(N-1), sigmasqrd, places=2,
msg='%s%r' % (variate.__name__, args))
def test_von_mises_range(self):
# Issue 17149: von mises variates were not consistently in the
# range [0, 2*PI].
g = random.Random()
N = 100
for mu in 0.0, 0.1, 3.1, 6.2:
for kappa in 0.0, 2.3, 500.0:
for _ in range(N):
sample = g.vonmisesvariate(mu, kappa)
self.assertTrue(
0 <= sample <= random.TWOPI,
msg=("vonmisesvariate({}, {}) produced a result {} out"
" of range [0, 2*pi]").format(mu, kappa, sample))
def test_von_mises_large_kappa(self):
# Issue #17141: vonmisesvariate() was hang for large kappas
random.vonmisesvariate(0, 1e15)
random.vonmisesvariate(0, 1e100)
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
x = [i/float(N) for i in range(1,N)]
for variate, args, mu, sigmasqrd in [
(g.uniform, (1.0,10.0), (10.0+1.0)/2, (10.0-1.0)**2/12),
(g.triangular, (0.0, 1.0, 1.0/3.0), 4.0/9.0, 7.0/9.0/18.0),
(g.expovariate, (1.5,), 1/1.5, 1/1.5**2),
(g.vonmisesvariate, (1.23, 0), pi, pi**2/3),
(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) ]:
g.random = x[:].pop
y = []
for i in range(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, places=2,
msg='%s%r' % (variate.__name__, args))
self.assertAlmostEqual(s2/(N-1), sigmasqrd, places=2,
msg='%s%r' % (variate.__name__, args))
def test_von_mises_range(self):
# Issue 17149: von mises variates were not consistently in the
# range [0, 2*PI].
g = random.Random()
N = 100
for mu in 0.0, 0.1, 3.1, 6.2:
for kappa in 0.0, 2.3, 500.0:
for _ in range(N):
sample = g.vonmisesvariate(mu, kappa)
self.assertTrue(
0 <= sample <= random.TWOPI,
msg=("vonmisesvariate({}, {}) produced a result {} out"
" of range [0, 2*pi]").format(mu, kappa, sample))
def test_von_mises_large_kappa(self):
# Issue #17141: vonmisesvariate() was hang for large kappas
random.vonmisesvariate(0, 1e15)
random.vonmisesvariate(0, 1e100)
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
x = [i/float(N) for i in xrange(1,N)]
for variate, args, mu, sigmasqrd in [
(g.uniform, (1.0,10.0), (10.0+1.0)/2, (10.0-1.0)**2/12),
(g.triangular, (0.0, 1.0, 1.0/3.0), 4.0/9.0, 7.0/9.0/18.0),
(g.expovariate, (1.5,), 1/1.5, 1/1.5**2),
(g.vonmisesvariate, (1.23, 0), pi, pi**2/3),
(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) ]:
g.random = 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, places=2,
msg='%s%r' % (variate.__name__, args))
self.assertAlmostEqual(s2/(N-1), sigmasqrd, places=2,
msg='%s%r' % (variate.__name__, args))
