def test_rademacher():
X = Rademacher('X')
assert E(X) == 0
assert variance(X) == 1
assert density(X)[-1] == S.Half
assert density(X)[1] == S.Half
def test_sample_scipy():
distribs_scipy = [
FiniteRV('F', {
1: S.Half,
2: Rational(1, 4),
3: Rational(1, 4)
}),
DiscreteUniform("Y", list(range(5))),
Die("D"),
Bernoulli("Be", 0.3),
Binomial("Bi", 5, 0.4),
BetaBinomial("Bb", 2, 1, 1),
Hypergeometric("H", 1, 1, 1),
Rademacher("R")
]
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 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_scipy():
distribs_scipy = [
FiniteRV('F', {1: S.Half, 2: Rational(1, 4), 3: Rational(1, 4)}),
DiscreteUniform("Y", list(range(5))),
Die("D"),
Bernoulli("Be", 0.3),
Binomial("Bi", 5, 0.4),
BetaBinomial("Bb", 2, 1, 1),
Hypergeometric("H", 1, 1, 1),
Rademacher("R")
]
size = 3
numsamples = 5
scipy = import_module('scipy')
if not scipy:
skip('Scipy not installed. Abort tests for _sample_scipy.')
else:
with ignore_warnings(UserWarning): ### TODO: Restore tests once warnings are removed
h_sample = list(sample(Hypergeometric("H", 1, 1, 1), size=size, numsamples=numsamples))
assert len(h_sample) == numsamples
for X in distribs_scipy:
samps = next(sample(X, size=size))
samps2 = next(sample(X, size=(2, 2)))
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_rademacher():
X = Rademacher('X')
t = Symbol('t')
assert E(X) == 0
assert variance(X) == 1
assert density(X)[-1] == S.Half
assert density(X)[1] == S.Half
assert characteristic_function(X)(t) == exp(I * t) / 2 + exp(-I * t) / 2
def test_rademacher():
X = Rademacher("X")
t = Symbol("t")
assert E(X) == 0
assert variance(X) == 1
assert density(X)[-1] == S.Half
assert density(X)[1] == S.Half
assert characteristic_function(X)(t) == exp(I * t) / 2 + exp(-I * t) / 2
assert moment_generating_function(X)(t) == exp(t) / 2 + exp(-t) / 2