def rv(symbol, cls, *args):
args = list(map(sympify, args))
dist = cls(*args)
dist.check(*args)
pspace = SingleDiscretePSpace(symbol, dist)
if any(is_random(arg) for arg in args):
from sympy.stats.compound_rv import CompoundPSpace, CompoundDistribution
pspace = CompoundPSpace(symbol, CompoundDistribution(dist))
return pspace.value
def rv(name, cls, *args, **kwargs):
args = list(map(sympify, args))
dist = cls(*args)
if kwargs.pop('check', True):
dist.check(*args)
pspace = SingleFinitePSpace(name, dist)
if any(is_random(arg) for arg in args):
from sympy.stats.compound_rv import CompoundPSpace, CompoundDistribution
pspace = CompoundPSpace(name, CompoundDistribution(dist))
return pspace.value
def test_compound_pspace():
X = Normal('X', 2, 4)
Y = Normal('Y', 3, 6)
assert not isinstance(Y.pspace, CompoundPSpace)
N = NormalDistribution(1, 2)
D = PoissonDistribution(3)
B = BernoulliDistribution(0.2, 1, 0)
pspace1 = CompoundPSpace('N', N)
pspace2 = CompoundPSpace('D', D)
pspace3 = CompoundPSpace('B', B)
assert not isinstance(pspace1, CompoundPSpace)
assert not isinstance(pspace2, CompoundPSpace)
assert not isinstance(pspace3, CompoundPSpace)
M = MultivariateNormalDistribution([1, 2], [[2, 1], [1, 2]])
raises(ValueError, lambda: CompoundPSpace('M', M))
Y = Normal('Y', X, 6)
assert isinstance(Y.pspace, CompoundPSpace)
assert Y.pspace.distribution == CompoundDistribution(NormalDistribution(X, 6))
assert Y.pspace.domain.set == Interval(-oo, oo)
def test_Compound_Distribution():
X = Normal('X', 2, 4)
N = NormalDistribution(X, 4)
C = CompoundDistribution(N)
assert C.is_Continuous
assert C.set == Interval(-oo, oo)
assert C.pdf(x, evaluate=True).simplify() == exp(-x**2/64 + x/16 - S(1)/16)/(8*sqrt(pi))
assert not isinstance(CompoundDistribution(NormalDistribution(2, 3)),
CompoundDistribution)
M = MultivariateNormalDistribution([1, 2], [[2, 1], [1, 2]])
raises(NotImplementedError, lambda: CompoundDistribution(M))
X = Beta('X', 2, 4)
B = BernoulliDistribution(X, 1, 0)
C = CompoundDistribution(B)
assert C.is_Finite
assert C.set == {0, 1}
y = symbols('y', negative=False, integer=True)
assert C.pdf(y, evaluate=True) == Piecewise((S(1)/(30*beta(2, 4)), Eq(y, 0)),
(S(1)/(60*beta(2, 4)), Eq(y, 1)), (0, True))
k, t, z = symbols('k t z', positive=True, real=True)
G = Gamma('G', k, t)
X = PoissonDistribution(G)
C = CompoundDistribution(X)
assert C.is_Discrete
assert C.set == S.Naturals0
assert C.pdf(z, evaluate=True).simplify() == t**z*(t + 1)**(-k - z)*gamma(k \
+ z)/(gamma(k)*gamma(z + 1))