def probability(self, condition, **kwargs):
cond_inv = False
if isinstance(condition, Ne):
condition = Eq(condition.args[0], condition.args[1])
cond_inv = True
elif isinstance(condition, And): # they are independent
return Mul(*[self.probability(arg) for arg in condition.args])
elif isinstance(condition, Or): # they are independent
return Add(*[self.probability(arg) for arg in condition.args])
expr = condition.lhs - condition.rhs
rvs = random_symbols(expr)
dens = self.compute_density(expr)
if any([pspace(rv).is_Continuous for rv in rvs]):
from sympy.stats.crv import SingleContinuousPSpace
from sympy.stats.crv_types import ContinuousDistributionHandmade
if expr in self.values:
# Marginalize all other random symbols out of the density
randomsymbols = tuple(set(self.values) - frozenset([expr]))
symbols = tuple(rs.symbol for rs in randomsymbols)
pdf = self.domain.integrate(self.pdf, symbols, **kwargs)
return Lambda(expr.symbol, pdf)
dens = ContinuousDistributionHandmade(dens)
z = Dummy('z', real=True)
space = SingleContinuousPSpace(z, dens)
result = space.probability(condition.__class__(space.value, 0))
else:
from sympy.stats.drv import SingleDiscretePSpace
from sympy.stats.drv_types import DiscreteDistributionHandmade
dens = DiscreteDistributionHandmade(dens)
z = Dummy('z', integer=True)
space = SingleDiscretePSpace(z, dens)
result = space.probability(condition.__class__(space.value, 0))
return result if not cond_inv else S.One - result
def _transform_pspace(self, sym, dist, pdf):
"""
This function returns the new pspace of the distribution using handmade
Distributions and their corresponding pspace.
"""
pdf = Lambda(sym, pdf(sym))
_set = dist.set
if isinstance(dist, ContinuousDistribution):
return SingleContinuousPSpace(sym, ContinuousDistributionHandmade(pdf, _set))
elif isinstance(dist, DiscreteDistribution):
return SingleDiscretePSpace(sym, DiscreteDistributionHandmade(pdf, _set))
elif isinstance(dist, SingleFiniteDistribution):
dens = dict((k, pdf(k)) for k in _set)
return SingleFinitePSpace(sym, FiniteDistributionHandmade(dens))
def probability(self, condition):
complement = isinstance(condition, Ne)
if complement:
condition = Eq(condition.args[0], condition.args[1])
try:
_domain = self.where(condition).set
if condition == False or _domain is S.EmptySet:
return S.Zero
if condition == True or _domain == self.domain.set:
return S.One
prob = self.eval_prob(_domain)
except NotImplementedError:
from sympy.stats.rv import density
expr = condition.lhs - condition.rhs
dens = density(expr)
if not isinstance(dens, DiscreteDistribution):
from sympy.stats.drv_types import DiscreteDistributionHandmade
dens = DiscreteDistributionHandmade(dens)
z = Dummy('z', real=True)
space = SingleDiscretePSpace(z, dens)
prob = space.probability(condition.__class__(space.value, 0))
if prob is None:
prob = Probability(condition)
return prob if not complement else S.One - prob