def test_cauchy():
x0 = Symbol("x0")
gamma = Symbol("gamma", positive=True)
X = Cauchy('x', x0, gamma)
assert density(X)(x) == 1 / (pi * gamma * (1 + (x - x0)**2 / gamma**2))
gamma = Symbol("gamma", positive=False)
raises(ValueError, lambda: Cauchy('x', x0, gamma))
def test_cauchy():
x0 = Symbol("x0")
gamma = Symbol("gamma", positive=True)
X = Cauchy('x', x0, gamma)
assert density(X)(x) == 1 / (pi * gamma * (1 + (x - x0)**2 / gamma**2))
assert cdf(X)(x) == atan((x - x0) / gamma) / pi + S.Half
assert diff(cdf(X)(x), x) == density(X)(x)
gamma = Symbol("gamma", positive=False)
raises(ValueError, lambda: Cauchy('x', x0, gamma))
def test_cauchy():
x0 = Symbol("x0")
gamma = Symbol("gamma", positive=True)
p = Symbol("p", positive=True)
X = Cauchy('x', x0, gamma)
assert density(X)(x) == 1 / (pi * gamma * (1 + (x - x0)**2 / gamma**2))
assert diff(cdf(X)(x), x) == density(X)(x)
assert quantile(X)(p) == gamma * tan(pi * (p - S.Half)) + x0
gamma = Symbol("gamma", nonpositive=True)
raises(ValueError, lambda: Cauchy('x', x0, gamma))
def test_cauchy():
x0 = Symbol("x0")
gamma = Symbol("gamma", positive=True)
p = Symbol("p", positive=True)
X = Cauchy('x', x0, gamma)
# Tests the characteristic function
assert characteristic_function(X)(x) == exp(-gamma * Abs(x) + I * x * x0)
assert density(X)(x) == 1 / (pi * gamma * (1 + (x - x0)**2 / gamma**2))
assert diff(cdf(X)(x), x) == density(X)(x)
assert quantile(X)(p) == gamma * tan(pi * (p - S.Half)) + x0
gamma = Symbol("gamma", nonpositive=True)
raises(ValueError, lambda: Cauchy('x', x0, gamma))
def test_cauchy():
x0 = Symbol("x0")
gamma = Symbol("gamma", positive=True)
X = Cauchy('x', x0, gamma)
assert density(X) == Lambda(
_x, 1 / (pi * gamma * (1 + (_x - x0)**2 / gamma**2)))
def test_sample_scipy():
distribs_scipy = [
Beta("B", 1, 1),
BetaPrime("BP", 1, 1),
Cauchy("C", 1, 1),
Chi("C", 1),
Normal("N", 0, 1),
Gamma("G", 2, 7),
GammaInverse("GI", 1, 1),
GaussianInverse("GUI", 1, 1),
Exponential("E", 2),
LogNormal("LN", 0, 1),
Pareto("P", 1, 1),
StudentT("S", 2),
ChiSquared("CS", 2),
Uniform("U", 0, 1)
]
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_sample_pymc3():
distribs_pymc3 = [
Beta("B", 1, 1),
Cauchy("C", 1, 1),
Normal("N", 0, 1),
Gamma("G", 2, 7),
GaussianInverse("GI", 1, 1),
Exponential("E", 2),
LogNormal("LN", 0, 1),
Pareto("P", 1, 1),
ChiSquared("CS", 2),
Uniform("U", 0, 1)
]
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(Chi("C", 1), library='pymc3')))
def test_sample_scipy():
distribs_scipy = [
Beta("B", 1, 1),
BetaPrime("BP", 1, 1),
Cauchy("C", 1, 1),
Chi("C", 1),
Normal("N", 0, 1),
Gamma("G", 2, 7),
GammaInverse("GI", 1, 1),
GaussianInverse("GUI", 1, 1),
Exponential("E", 2),
LogNormal("LN", 0, 1),
Pareto("P", 1, 1),
StudentT("S", 2),
ChiSquared("CS", 2),
Uniform("U", 0, 1)
]
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
g_sample = list(
sample(Gamma("G", 2, 7), size=size, numsamples=numsamples))
assert len(g_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
