def test_H():
X = Normal('X', 0, 1)
D = Die('D', sides=4)
G = Geometric('G', 0.5)
assert H(X, X > 0) == -log(2) / 2 + S(1) / 2 + log(pi) / 2
assert H(D, D > 2) == log(2)
assert comp(H(G).evalf().round(2), 1.39)
def test_sample_scipy():
p = S(2)/3
x = Symbol('x', integer=True, positive=True)
pdf = p*(1 - p)**(x - 1) # pdf of Geometric Distribution
distribs_scipy = [
DiscreteRV(x, pdf, set=S.Naturals),
Geometric('G', 0.5),
Logarithmic('L', 0.5),
NegativeBinomial('N', 5, 0.4),
Poisson('P', 1),
Skellam('S', 1, 1),
YuleSimon('Y', 1),
Zeta('Z', 2)
]
size = 3
scipy = import_module('scipy')
if not scipy:
skip('Scipy is not installed. Abort tests for _sample_scipy.')
else:
for X in distribs_scipy:
samps = sample(X, size=size, library='scipy')
samps2 = sample(X, size=(2, 2), library='scipy')
for sam in samps:
assert sam in X.pspace.domain.set
for i in range(2):
for j in range(2):
assert samps2[i][j] in X.pspace.domain.set
def test_H():
X = Normal("X", 0, 1)
D = Die("D", sides=4)
G = Geometric("G", 0.5)
assert H(X, X > 0) == -log(2) / 2 + S.Half + log(pi) / 2
assert H(D, D > 2) == log(2)
assert comp(H(G).evalf().round(2), 1.39)
def test_sample_discrete():
X = Geometric('X', S.Half)
scipy = import_module('scipy')
if not scipy:
skip('Scipy not installed. Abort tests')
with ignore_warnings(
UserWarning): ### TODO: Restore tests once warnings are removed
assert next(sample(X)) in X.pspace.domain.set
samps = next(sample(
X, size=2)) # This takes long time if ran without scipy
for samp in samps:
assert samp in X.pspace.domain.set
libraries = ['scipy', 'numpy', 'pymc3']
for lib in libraries:
try:
imported_lib = import_module(lib)
if imported_lib:
s0, s1, s2 = [], [], []
s0 = list(sample(X, numsamples=10, library=lib, seed=0))
s1 = list(sample(X, numsamples=10, library=lib, seed=0))
s2 = list(sample(X, numsamples=10, library=lib, seed=1))
assert s0 == s1
assert s1 != s2
except NotImplementedError:
continue
def test_sample_scipy():
p = S(2) / 3
x = Symbol('x', integer=True, positive=True)
pdf = p * (1 - p)**(x - 1) # pdf of Geometric Distribution
distribs_scipy = [
DiscreteRV(x, pdf, set=S.Naturals),
Geometric('G', 0.5),
Logarithmic('L', 0.5),
NegativeBinomial('N', 5, 0.4),
Poisson('P', 1),
Skellam('S', 1, 1),
YuleSimon('Y', 1),
Zeta('Z', 2)
]
size = 3
numsamples = 5
scipy = import_module('scipy')
if not scipy:
skip('Scipy is not installed. Abort tests for _sample_scipy.')
else:
with ignore_warnings(
UserWarning
): ### TODO: Restore tests once warnings are removed
z_sample = list(
sample(Zeta("G", 7), size=size, numsamples=numsamples))
assert len(z_sample) == numsamples
for X in distribs_scipy:
samps = next(sample(X, size=size, library='scipy'))
samps2 = next(sample(X, size=(2, 2), library='scipy'))
for sam in samps:
assert sam in X.pspace.domain.set
for i in range(2):
for j in range(2):
assert samps2[i][j] in X.pspace.domain.set
def test_sample_pymc3():
distribs_pymc3 = [
Geometric('G', 0.5),
Poisson('P', 1),
NegativeBinomial('N', 5, 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 sam in X.pspace.domain.set
raises(NotImplementedError,
lambda: sample(Skellam('S', 1, 1), library='pymc3'))
def test_sample_numpy():
distribs_numpy = [
Geometric('G', 0.5),
Poisson('P', 1),
Zeta('Z', 2)
]
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 sam in X.pspace.domain.set
raises(NotImplementedError,
lambda: sample(Skellam('S', 1, 1), library='numpy'))
raises(NotImplementedError,
lambda: Skellam('S', 1, 1).pspace.distribution.sample(library='tensorflow'))
def test_sample_numpy():
distribs_numpy = [Geometric('G', 0.5), Poisson('P', 1), Zeta('Z', 2)]
size = 3
numpy = import_module('numpy')
if not numpy:
skip('Numpy is not installed. Abort tests for _sample_numpy.')
else:
with ignore_warnings(
UserWarning
): ### TODO: Restore tests once warnings are removed
for X in distribs_numpy:
samps = next(sample(X, size=size, library='numpy'))
for sam in samps:
assert sam in X.pspace.domain.set
raises(NotImplementedError,
lambda: next(sample(Skellam('S', 1, 1), library='numpy')))
raises(
NotImplementedError, lambda: Skellam('S', 1, 1).pspace.distribution.
sample(library='tensorflow'))
def test_sample_pymc3():
distribs_pymc3 = [
Geometric('G', 0.5),
Poisson('P', 1),
NegativeBinomial('N', 5, 0.4)
]
size = 3
pymc3 = import_module('pymc3')
if not pymc3:
skip('PyMC3 is not installed. Abort tests for _sample_pymc3.')
else:
with ignore_warnings(
UserWarning
): ### TODO: Restore tests once warnings are removed
for X in distribs_pymc3:
samps = next(sample(X, size=size, library='pymc3'))
for sam in samps:
assert sam in X.pspace.domain.set
raises(NotImplementedError,
lambda: next(sample(Skellam('S', 1, 1), library='pymc3')))
def test_sample_discrete():
X = Geometric('X', S.Half)
scipy = import_module('scipy')
if not scipy:
skip('Scipy not installed. Abort tests')
assert sample(X) in X.pspace.domain.set
samps = sample(X, size=2) # This takes long time if ran without scipy
for samp in samps:
assert samp in X.pspace.domain.set
libraries = ['scipy', 'numpy', 'pymc3']
for lib in libraries:
try:
imported_lib = import_module(lib)
if imported_lib:
s0, s1, s2 = [], [], []
s0 = sample(X, size=10, library=lib, seed=0)
s1 = sample(X, size=10, library=lib, seed=0)
s2 = sample(X, size=10, library=lib, seed=1)
assert all(s0 == s1)
assert not all(s1 == s2)
except NotImplementedError:
continue