def test_lognormal():
mean = Symbol('mu', real=True)
std = Symbol('sigma', positive=True, real=True)
X = LogNormal('x', mean, std)
# The diofant integrator can't do this too well
# assert E(X) == exp(mean+std**2/2)
# assert variance(X) == (exp(std**2)-1) * exp(2*mean + std**2)
# Right now, only density function and sampling works
# Test sampling: Only e^mean in sample std of 0
for i in range(3):
X = LogNormal('x', i, 0)
assert sample(X) == N(exp(i))
# The diofant integrator can't do this too well
# assert E(X) ==
mu = Symbol("mu", extended_real=True)
sigma = Symbol("sigma", positive=True)
X = LogNormal('x', mu, sigma)
assert density(X)(x) == (sqrt(2)*exp(-(-mu + log(x))**2
/ (2*sigma**2))/(2*x*sqrt(pi)*sigma))
X = LogNormal('x', 0, 1) # Mean 0, standard deviation 1
assert density(X)(x) == sqrt(2)*exp(-log(x)**2/2)/(2*x*sqrt(pi))
def test_lognormal():
mean = Symbol('mu', real=True)
std = Symbol('sigma', positive=True, real=True)
X = LogNormal('x', mean, std)
# The diofant integrator can't do this too well
# assert E(X) == exp(mean+std**2/2)
# assert variance(X) == (exp(std**2)-1) * exp(2*mean + std**2)
# Right now, only density function and sampling works
# Test sampling: Only e^mean in sample std of 0
for i in range(3):
X = LogNormal('x', i, 0)
assert sample(X) == N(exp(i))
# The diofant integrator can't do this too well
# assert E(X) ==
mu = Symbol('mu', extended_real=True)
sigma = Symbol('sigma', positive=True)
X = LogNormal('x', mu, sigma)
assert density(X)(x) == (sqrt(2)*exp(-(-mu + log(x))**2
/ (2*sigma**2))/(2*x*sqrt(pi)*sigma))
X = LogNormal('x', 0, 1) # Mean 0, standard deviation 1
assert density(X)(x) == sqrt(2)*exp(-log(x)**2/2)/(2*x*sqrt(pi))
def test_Sample():
X = Die('X', 6)
Y = Normal('Y', 0, 1)
z = Symbol('z')
assert sample(X) in [1, 2, 3, 4, 5, 6]
assert sample(X + Y).is_Float
P(X + Y > 0, Y < 0, numsamples=10).is_number
assert E(X + Y, numsamples=10).is_number
assert variance(X + Y, numsamples=10).is_number
pytest.raises(ValueError, lambda: P(Y > z, numsamples=5))
assert P(sin(Y) <= 1, numsamples=10) == 1
assert P(sin(Y) <= 1, cos(Y) < 1, numsamples=10) == 1
# Make sure this doesn't raise an error
E(Sum(1 / z**Y, (z, 1, oo)), Y > 2, numsamples=3)
assert all(i in range(1, 7) for i in density(X, numsamples=10))
assert all(i in range(4, 7) for i in density(X, X > 3, numsamples=10))
def test_Sample():
X = Die('X', 6)
Y = Normal('Y', 0, 1)
assert sample(X) in [1, 2, 3, 4, 5, 6]
assert sample(X + Y).is_Float
assert P(X + Y > 0, Y < 0, numsamples=10).is_number
assert P(X > 10, numsamples=10).is_number
assert E(X + Y, numsamples=10).is_number
assert variance(X + Y, numsamples=10).is_number
pytest.raises(TypeError, lambda: P(Y > z, numsamples=5))
assert P(sin(Y) <= 1, numsamples=10, modules=["math"]) == 1
assert P(sin(Y) <= 1, cos(Y) < 1, numsamples=10, modules=["math"]) == 1
assert all(i in range(1, 7) for i in density(X, numsamples=10))
assert all(i in range(4, 7) for i in density(X, X > 3, numsamples=10))
# Make sure this doesn't raise an error
Y = Normal('Y', 0, 1)
E(Sum(1/z**Y, (z, 1, oo)), Y > 2, numsamples=3, modules="mpmath")
def test_prefab_sampling():
N = Normal('X', 0, 1)
L = LogNormal('L', 0, 1)
E = Exponential('Ex', 1)
P = Pareto('P', 1, 3)
W = Weibull('W', 1, 1)
U = Uniform('U', 0, 1)
B = Beta('B', 2, 5)
G = Gamma('G', 1, 3)
variables = [N, L, E, P, W, U, B, G]
niter = 10
for var in variables:
for i in range(niter):
assert sample(var) in var.pspace.domain.set
def test_prefab_sampling():
N = Normal('X', 0, 1)
L = LogNormal('L', 0, 1)
E = Exponential('Ex', 1)
P = Pareto('P', 1, 3)
W = Weibull('W', 1, 1)
U = Uniform('U', 0, 1)
B = Beta('B', 2, 5)
G = Gamma('G', 1, 3)
variables = [N, L, E, P, W, U, B, G]
niter = 10
for var in variables:
for i in range(niter):
assert sample(var) in var.pspace.domain.set
def test_given():
X = Die('X', 6)
assert density(X, X > 5) == {6: 1}
assert where(X > 2, X > 5).as_boolean() == Eq(X.symbol, 6)
assert sample(X, X > 5) == 6
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 = list(Z.pspace.sample().items())[0]
assert sym == Z and val in Interval(0, oo)
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 = list(Z.pspace.sample().items())[0]
assert sym == Z and val in Interval(0, oo)
def test_given():
X = Die('X', 6)
assert density(X, X > 5) == {6: 1}
assert where(X > 2, X > 5).as_boolean() == Eq(X.symbol, 6)
assert sample(X, X > 5) == 6
