def test_sample_continuous():
z = Symbol('z')
Z = ContinuousRV(z, exp(-z), set=Interval(0, oo))
assert sample(Z) in Z.pspace.domain.set
sym, val = list(Z.pspace.sample().items())[0]
assert sym == Z and val in Interval(0, oo)
assert density(Z)(-1) == 0
def test_sample_continuous():
z = Symbol('z')
Z = ContinuousRV(z, exp(-z), set=Interval(0, oo))
assert density(Z)(-1) == 0
scipy = import_module('scipy')
if not scipy:
skip('Scipy is not installed. Abort tests')
assert sample(Z) in Z.pspace.domain.set
sym, val = list(Z.pspace.sample().items())[0]
assert sym == Z and val in Interval(0, oo)
libraries = ['scipy', 'numpy', 'pymc3']
for lib in libraries:
try:
imported_lib = import_module(lib)
if imported_lib:
s0, s1, s2 = [], [], []
s0 = sample(Z, size=10, library=lib, seed=0)
s1 = sample(Z, size=10, library=lib, seed=0)
s2 = sample(Z, size=10, library=lib, seed=1)
assert all(s0 == s1)
assert all(s1 != s2)
except NotImplementedError:
continue
def test_sample_continuous():
z = Symbol('z')
Z = ContinuousRV(z, exp(-z), set=Interval(0, oo))
assert density(Z)(-1) == 0
scipy = import_module('scipy')
if not scipy:
skip('Scipy is not installed. Abort tests')
with ignore_warnings(
UserWarning): ### TODO: Restore tests once warnings are removed
assert next(sample(Z)) in Z.pspace.domain.set
sym, val = list(Z.pspace.sample().items())[0]
assert sym == Z and val in Interval(0, oo)
libraries = ['scipy', 'numpy', 'pymc3']
for lib in libraries:
try:
imported_lib = import_module(lib)
if imported_lib:
s0, s1, s2 = [], [], []
s0 = list(sample(Z, numsamples=10, library=lib, seed=0))
s1 = list(sample(Z, numsamples=10, library=lib, seed=0))
s2 = list(sample(Z, numsamples=10, library=lib, seed=1))
assert s0 == s1
assert s1 != s2
except NotImplementedError:
continue
def test_ContinuousRV():
pdf = sqrt(2) * exp(-x**2 / 2) / (2 * sqrt(pi)) # Normal distribution
# X and Y should be equivalent
X = ContinuousRV(x, pdf)
Y = Normal('y', 0, 1)
assert variance(X) == variance(Y)
assert P(X > 0) == P(Y > 0)
def test_ContinuousRV():
x = Symbol('x')
pdf = sqrt(2)*exp(-x**2/2)/(2*sqrt(pi)) # Normal distribution
# X and Y should be equivalent
X = ContinuousRV(x, pdf)
Y = Normal(0, 1)
assert Var(X) == Var(Y)
assert P(X>0) == P(Y>0)
def test_sample():
z = Symbol('z')
Z = ContinuousRV(z, exp(-z), set=Interval(0, oo))
assert sample(Z) in Z.pspace.domain.set
sym, val = Z.pspace.sample().items()[0]
assert sym == Z and val in Interval(0, oo)