def test_sample_pymc3():
distribs_pymc3 = [
MultivariateNormal("M", [5, 2], [[1, 0], [0, 1]]),
MultivariateBeta("B", [0.4, 5, 15]),
Multinomial("N", 4, [0.3, 0.2, 0.1, 0.4])
]
size = 3
pymc3 = import_module('pymc3')
if not pymc3:
skip('PyMC3 is not installed. Abort tests for _sample_pymc3.')
else:
for X in distribs_pymc3:
samps = sample(X, size=size, library='pymc3')
for sam in samps:
assert tuple(sam.flatten()) in X.pspace.distribution.set
N_c = NegativeMultinomial('N', 3, 0.1, 0.1, 0.1)
raises(NotImplementedError, lambda: sample(N_c, library='pymc3'))
def test_sample_numpy():
distribs_numpy = [
MultivariateNormal("M", [3, 4], [[2, 1], [1, 2]]),
MultivariateBeta("B", [0.4, 5, 15, 50, 203]),
Multinomial("N", 50, [0.3, 0.2, 0.1, 0.25, 0.15])
]
size = 3
numpy = import_module('numpy')
if not numpy:
skip('Numpy is not installed. Abort tests for _sample_numpy.')
else:
for X in distribs_numpy:
samps = sample(X, size=size, library='numpy')
for sam in samps:
assert tuple(sam) in X.pspace.distribution.set
N_c = NegativeMultinomial('N', 3, 0.1, 0.1, 0.1)
raises(NotImplementedError, lambda: sample(N_c, library='numpy'))
def test_MultivariateBeta():
a1, a2 = symbols('a1, a2', positive=True)
a1_f, a2_f = symbols('a1, a2', positive=False, real=True)
mb = MultivariateBeta('B', [a1, a2])
mb_c = MultivariateBeta('C', a1, a2)
assert density(mb)(1, 2) == S(2)**(a2 - 1)*gamma(a1 + a2)/\
(gamma(a1)*gamma(a2))
assert marginal_distribution(mb_c, 0)(3) == S(3)**(a1 - 1)*gamma(a1 + a2)/\
(a2*gamma(a1)*gamma(a2))
raises(ValueError, lambda: MultivariateBeta('b1', [a1_f, a2]))
raises(ValueError, lambda: MultivariateBeta('b2', [a1, a2_f]))
raises(ValueError, lambda: MultivariateBeta('b3', [0, 0]))
raises(ValueError, lambda: MultivariateBeta('b4', [a1_f, a2_f]))
assert mb.pspace.distribution.set == ProductSet(Interval(0, 1), Interval(0, 1))
def test_MultivariateBeta():
from sympy.stats.joint_rv_types import MultivariateBeta
from sympy import gamma
a1, a2 = symbols('a1, a2', positive=True)
a1_f, a2_f = symbols('a1, a2', positive=False)
mb = MultivariateBeta('B', [a1, a2])
mb_c = MultivariateBeta('C', a1, a2)
assert density(mb)(1, 2) == S(2)**(a2 - 1)*gamma(a1 + a2)/\
(gamma(a1)*gamma(a2))
assert marginal_distribution(mb_c, 0)(3) == S(3)**(a1 - 1)*gamma(a1 + a2)/\
(a2*gamma(a1)*gamma(a2))
raises(ValueError, lambda: MultivariateBeta('b1', [a1_f, a2]))
raises(ValueError, lambda: MultivariateBeta('b2', [a1, a2_f]))
raises(ValueError, lambda: MultivariateBeta('b3', [0, 0]))
raises(ValueError, lambda: MultivariateBeta('b4', [a1_f, a2_f]))
def test_sample_scipy():
distribs_scipy = [
MultivariateNormal("M", [0, 0], [[0.1, 0.025], [0.025, 0.1]]),
MultivariateBeta("B", [0.4, 5, 15]),
Multinomial("N", 8, [0.3, 0.2, 0.1, 0.4])
]
size = 3
scipy = import_module('scipy')
if not scipy:
skip('Scipy not installed. Abort tests for _sample_scipy.')
else:
for X in distribs_scipy:
samps = sample(X, size=size)
samps2 = sample(X, size=(2, 2))
for sam in samps:
assert tuple(sam) in X.pspace.distribution.set
for i in range(2):
for j in range(2):
assert tuple(samps2[i][j]) in X.pspace.distribution.set
N_c = NegativeMultinomial('N', 3, 0.1, 0.1, 0.1)
raises(NotImplementedError, lambda: sample(N_c))
def test_MultivariateBeta():
from sympy.stats.joint_rv_types import MultivariateBeta
from sympy import gamma
a1, a2 = symbols("a1, a2", positive=True)
a1_f, a2_f = symbols("a1, a2", positive=False, real=True)
mb = MultivariateBeta("B", [a1, a2])
mb_c = MultivariateBeta("C", a1, a2)
assert density(mb)(
1, 2) == S(2)**(a2 - 1) * gamma(a1 + a2) / (gamma(a1) * gamma(a2))
assert marginal_distribution(
mb_c,
0)(3) == S(3)**(a1 - 1) * gamma(a1 + a2) / (a2 * gamma(a1) * gamma(a2))
raises(ValueError, lambda: MultivariateBeta("b1", [a1_f, a2]))
raises(ValueError, lambda: MultivariateBeta("b2", [a1, a2_f]))
raises(ValueError, lambda: MultivariateBeta("b3", [0, 0]))
raises(ValueError, lambda: MultivariateBeta("b4", [a1_f, a2_f]))