Source code for sympy.stats.crv

Continuous Random Variables Module

See Also

from __future__ import print_function, division

from sympy.stats.rv import (RandomDomain, SingleDomain, ConditionalDomain,
        ProductDomain, PSpace, SinglePSpace, random_symbols, ProductPSpace,
from sympy.functions.special.delta_functions import DiracDelta
from sympy import (Interval, Intersection, symbols, sympify, Dummy, Mul,
        Integral, And, Or, Piecewise, cacheit, integrate, oo, Lambda,
        Basic, S)
from sympy.solvers.solveset import solveset
from sympy.solvers.inequalities import reduce_rational_inequalities
from sympy.polys.polyerrors import PolynomialError
import random

[docs]class ContinuousDomain(RandomDomain): """ A domain with continuous support Represented using symbols and Intervals. """ is_Continuous = True def as_boolean(self): raise NotImplementedError("Not Implemented for generic Domains")
class SingleContinuousDomain(ContinuousDomain, SingleDomain): """ A univariate domain with continuous support Represented using a single symbol and interval. """ def integrate(self, expr, variables=None, **kwargs): if variables is None: variables = self.symbols if not variables: return expr if frozenset(variables) != frozenset(self.symbols): raise ValueError("Values should be equal") # assumes only intervals return Integral(expr, (self.symbol, self.set), **kwargs) def as_boolean(self): return self.set.as_relational(self.symbol) class ProductContinuousDomain(ProductDomain, ContinuousDomain): """ A collection of independent domains with continuous support """ def integrate(self, expr, variables=None, **kwargs): if variables is None: variables = self.symbols for domain in domain_vars = frozenset(variables) & frozenset(domain.symbols) if domain_vars: expr = domain.integrate(expr, domain_vars, **kwargs) return expr def as_boolean(self): return And(*[domain.as_boolean() for domain in]) class ConditionalContinuousDomain(ContinuousDomain, ConditionalDomain): """ A domain with continuous support that has been further restricted by a condition such as x > 3 """ def integrate(self, expr, variables=None, **kwargs): if variables is None: variables = self.symbols if not variables: return expr # Extract the full integral fullintgrl = self.fulldomain.integrate(expr, variables) # separate into integrand and limits integrand, limits = fullintgrl.function, list(fullintgrl.limits) conditions = [self.condition] while conditions: cond = conditions.pop() if cond.is_Boolean: if isinstance(cond, And): conditions.extend(cond.args) elif isinstance(cond, Or): raise NotImplementedError("Or not implemented here") elif cond.is_Relational: if cond.is_Equality: # Add the appropriate Delta to the integrand integrand *= DiracDelta(cond.lhs - cond.rhs) else: symbols = cond.free_symbols & set(self.symbols) if len(symbols) != 1: # Can't handle x > y raise NotImplementedError( "Multivariate Inequalities not yet implemented") # Can handle x > 0 symbol = symbols.pop() # Find the limit with x, such as (x, -oo, oo) for i, limit in enumerate(limits): if limit[0] == symbol: # Make condition into an Interval like [0, oo] cintvl = reduce_rational_inequalities_wrap( cond, symbol) # Make limit into an Interval like [-oo, oo] lintvl = Interval(limit[1], limit[2]) # Intersect them to get [0, oo] intvl = cintvl.intersect(lintvl) # Put back into limits list limits[i] = (symbol, intvl.left, intvl.right) else: raise TypeError( "Condition %s is not a relational or Boolean" % cond) return Integral(integrand, *limits, **kwargs) def as_boolean(self): return And(self.fulldomain.as_boolean(), self.condition) @property def set(self): if len(self.symbols) == 1: return (self.fulldomain.set & reduce_rational_inequalities_wrap( self.condition, tuple(self.symbols)[0])) else: raise NotImplementedError( "Set of Conditional Domain not Implemented") class ContinuousDistribution(Basic): def __call__(self, *args): return self.pdf(*args) class SingleContinuousDistribution(ContinuousDistribution, NamedArgsMixin): """ Continuous distribution of a single variable Serves as superclass for Normal/Exponential/UniformDistribution etc.... Represented by parameters for each of the specific classes. E.g NormalDistribution is represented by a mean and standard deviation. Provides methods for pdf, cdf, and sampling See Also: sympy.stats.crv_types.* """ set = Interval(-oo, oo) def __new__(cls, *args): args = list(map(sympify, args)) return Basic.__new__(cls, *args) @staticmethod def check(*args): pass def sample(self): """ A random realization from the distribution """ icdf = self._inverse_cdf_expression() return icdf(random.uniform(0, 1)) @cacheit def _inverse_cdf_expression(self): """ Inverse of the CDF Used by sample """ x, z = symbols('x, z', real=True, positive=True, cls=Dummy) # Invert CDF try: inverse_cdf = solveset(self.cdf(x) - z, x, S.Reals) if isinstance(inverse_cdf, Intersection) and S.Reals in inverse_cdf.args: inverse_cdf = list(inverse_cdf.args[1]) except NotImplementedError: inverse_cdf = None if not inverse_cdf or len(inverse_cdf) != 1: raise NotImplementedError("Could not invert CDF") return Lambda(z, inverse_cdf[0]) @cacheit def compute_cdf(self, **kwargs): """ Compute the CDF from the PDF Returns a Lambda """ x, z = symbols('x, z', real=True, finite=True, cls=Dummy) left_bound = self.set.start # CDF is integral of PDF from left bound to z pdf = self.pdf(x) cdf = integrate(pdf, (x, left_bound, z), **kwargs) # CDF Ensure that CDF left of left_bound is zero cdf = Piecewise((cdf, z >= left_bound), (0, True)) return Lambda(z, cdf) def cdf(self, x, **kwargs): """ Cumulative density function """ return self.compute_cdf(**kwargs)(x) def expectation(self, expr, var, evaluate=True, **kwargs): """ Expectation of expression over distribution """ integral = Integral(expr * self.pdf(var), (var, self.set), **kwargs) return integral.doit() if evaluate else integral class ContinuousDistributionHandmade(SingleContinuousDistribution): _argnames = ('pdf',) @property def set(self): return self.args[1] def __new__(cls, pdf, set=Interval(-oo, oo)): return Basic.__new__(cls, pdf, set)
[docs]class ContinuousPSpace(PSpace): """ Continuous Probability Space Represents the likelihood of an event space defined over a continuum. Represented with a ContinuousDomain and a PDF (Lambda-Like) """ is_Continuous = True is_real = True @property def domain(self): return self.args[0] @property def density(self): return self.args[1] @property def pdf(self): return self.density(*self.domain.symbols) def integrate(self, expr, rvs=None, **kwargs): if rvs is None: rvs = self.values else: rvs = frozenset(rvs) expr = expr.xreplace(dict((rv, rv.symbol) for rv in rvs)) domain_symbols = frozenset(rv.symbol for rv in rvs) return self.domain.integrate(self.pdf * expr, domain_symbols, **kwargs) def compute_density(self, expr, **kwargs): # Common case Density(X) where X in self.values 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) z = Dummy('z', real=True, finite=True) return Lambda(z, self.integrate(DiracDelta(expr - z), **kwargs)) @cacheit def compute_cdf(self, expr, **kwargs): if not self.domain.set.is_Interval: raise ValueError( "CDF not well defined on multivariate expressions") d = self.compute_density(expr, **kwargs) x, z = symbols('x, z', real=True, finite=True, cls=Dummy) left_bound = self.domain.set.start # CDF is integral of PDF from left bound to z cdf = integrate(d(x), (x, left_bound, z), **kwargs) # CDF Ensure that CDF left of left_bound is zero cdf = Piecewise((cdf, z >= left_bound), (0, True)) return Lambda(z, cdf) def probability(self, condition, **kwargs): z = Dummy('z', real=True, finite=True) # Univariate case can be handled by where try: domain = self.where(condition) rv = [rv for rv in self.values if rv.symbol == domain.symbol][0] # Integrate out all other random variables pdf = self.compute_density(rv, **kwargs) # return S.Zero if `domain` is empty set if domain.set is S.EmptySet: return S.Zero # Integrate out the last variable over the special domain return Integral(pdf(z), (z, domain.set), **kwargs) # Other cases can be turned into univariate case # by computing a density handled by density computation except NotImplementedError: from sympy.stats.rv import density expr = condition.lhs - condition.rhs dens = density(expr, **kwargs) if not isinstance(dens, ContinuousDistribution): dens = ContinuousDistributionHandmade(dens) # Turn problem into univariate case space = SingleContinuousPSpace(z, dens) return space.probability(condition.__class__(space.value, 0)) def where(self, condition): rvs = frozenset(random_symbols(condition)) if not (len(rvs) == 1 and rvs.issubset(self.values)): raise NotImplementedError( "Multiple continuous random variables not supported") rv = tuple(rvs)[0] interval = reduce_rational_inequalities_wrap(condition, rv) interval = interval.intersect(self.domain.set) return SingleContinuousDomain(rv.symbol, interval) def conditional_space(self, condition, normalize=True, **kwargs): condition = condition.xreplace(dict((rv, rv.symbol) for rv in self.values)) domain = ConditionalContinuousDomain(self.domain, condition) if normalize: pdf = self.pdf / domain.integrate(self.pdf, **kwargs) density = Lambda(domain.symbols, pdf) return ContinuousPSpace(domain, density)
class SingleContinuousPSpace(ContinuousPSpace, SinglePSpace): """ A continuous probability space over a single univariate variable These consist of a Symbol and a SingleContinuousDistribution This class is normally accessed through the various random variable functions, Normal, Exponential, Uniform, etc.... """ @property def set(self): return self.distribution.set @property def domain(self): return SingleContinuousDomain(sympify(self.symbol), self.set) def sample(self): """ Internal sample method Returns dictionary mapping RandomSymbol to realization value. """ return {self.value: self.distribution.sample()} def integrate(self, expr, rvs=None, **kwargs): rvs = rvs or (self.value,) if self.value not in rvs: return expr expr = expr.xreplace(dict((rv, rv.symbol) for rv in rvs)) x = self.value.symbol try: return self.distribution.expectation(expr, x, evaluate=False, **kwargs) except Exception: return Integral(expr * self.pdf, (x, self.set), **kwargs) def compute_cdf(self, expr, **kwargs): if expr == self.value: return self.distribution.compute_cdf(**kwargs) else: return ContinuousPSpace.compute_cdf(self, expr, **kwargs) def compute_density(self, expr, **kwargs): # if expr == self.value: return self.density y = Dummy('y') gs = solveset(expr - y, self.value, S.Reals) if isinstance(gs, Intersection) and S.Reals in gs.args: gs = list(gs.args[1]) if not gs: raise ValueError("Can not solve %s for %s"%(expr, self.value)) fx = self.compute_density(self.value) fy = sum(fx(g) * abs(g.diff(y)) for g in gs) return Lambda(y, fy) class ProductContinuousPSpace(ProductPSpace, ContinuousPSpace): """ A collection of independent continuous probability spaces """ @property def pdf(self): p = Mul(*[space.pdf for space in self.spaces]) return p.subs(dict((rv, rv.symbol) for rv in self.values)) def _reduce_inequalities(conditions, var, **kwargs): try: return reduce_rational_inequalities(conditions, var, **kwargs) except PolynomialError: raise ValueError("Reduction of condition failed %s\n" % conditions[0]) def reduce_rational_inequalities_wrap(condition, var): if condition.is_Relational: return _reduce_inequalities([[condition]], var, relational=False) if condition.__class__ is Or: return _reduce_inequalities([list(condition.args)], var, relational=False) if condition.__class__ is And: intervals = [_reduce_inequalities([[arg]], var, relational=False) for arg in condition.args] I = intervals[0] for i in intervals: I = I.intersect(i) return I