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 __new__(cls, sym, dist): sym = sympify(sym) if isinstance(dist, SingleContinuousDistribution): return SingleContinuousPSpace(sym, dist) if isinstance(dist, SingleDiscreteDistribution): return SingleDiscretePSpace(sym, dist) return Basic.__new__(cls, sym, dist)
def __new__(cls, sym, dist): if isinstance(dist, SingleContinuousDistribution): return SingleContinuousPSpace(sym, dist) if isinstance(dist, SingleDiscreteDistribution): return SingleDiscretePSpace(sym, dist) sym = _symbol_converter(sym) return Basic.__new__(cls, sym, dist)
def rv(symbol, cls, *args): args = list(map(sympify, args)) dist = cls(*args) dist.check(*args) pspace = SingleDiscretePSpace(symbol, dist) if any(isinstance(arg, RandomSymbol) for arg in args): pspace = JointPSpace(symbol, CompoundDistribution(dist)) return pspace.value
def pdf(self, *x): dist = self.args[0] z = Dummy('z') if isinstance(dist, ContinuousDistribution): rv = SingleContinuousPSpace(z, dist).value elif isinstance(dist, DiscreteDistribution): rv = SingleDiscretePSpace(z, dist).value return MarginalDistribution(self, (rv,)).pdf(*x)
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 __new__(cls, sym, dist): if isinstance(dist, SingleContinuousDistribution): return SingleContinuousPSpace(sym, dist) if isinstance(dist, SingleDiscreteDistribution): return SingleDiscretePSpace(sym, dist) if isinstance(sym, str): sym = Symbol(sym) if not isinstance(sym, Symbol): raise TypeError("s should have been string or Symbol") return Basic.__new__(cls, sym, dist)
def __new__(cls, s, distribution): s = _symbol_converter(s) if isinstance(distribution, ContinuousDistribution): return SingleContinuousPSpace(s, distribution) if isinstance(distribution, DiscreteDistribution): return SingleDiscretePSpace(s, distribution) if isinstance(distribution, SingleFiniteDistribution): return SingleFinitePSpace(s, distribution) if not isinstance(distribution, CompoundDistribution): raise ValueError("%s should be an isinstance of " "CompoundDistribution"%(distribution)) return Basic.__new__(cls, s, distribution)
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 rv(symbol, cls, *args): args = list(map(sympify, args)) dist = cls(*args) dist.check(*args) return SingleDiscretePSpace(symbol, dist).value