Source code for sympy.stats.frv

Finite Discrete Random Variables Module

See Also
from __future__ import print_function, division

from itertools import product

from sympy import (Basic, Symbol, cacheit, sympify, Mul,
        And, Or, Tuple, Piecewise, Eq, Lambda)
from sympy.sets.sets import FiniteSet
from sympy.stats.rv import (RandomDomain, ProductDomain, ConditionalDomain,
        PSpace, ProductPSpace, SinglePSpace, random_symbols, sumsets, rv_subs,
from sympy.core.containers import Dict
import random

class FiniteDensity(dict):
    def __call__(self, item):
        item = sympify(item)
        if item in self:
            return self[item]
            return 0

    def dict(self):
        return dict(self)

[docs]class FiniteDomain(RandomDomain): """ A domain with discrete finite support Represented using a FiniteSet. """ is_Finite = True @property def symbols(self): return FiniteSet(sym for sym, val in self.elements) @property def elements(self): return self.args[0] @property def dict(self): return FiniteSet(*[Dict(dict(el)) for el in self.elements]) def __contains__(self, other): return other in self.elements def __iter__(self): return self.elements.__iter__() def as_boolean(self): return Or(*[And(*[Eq(sym, val) for sym, val in item]) for item in self])
class SingleFiniteDomain(FiniteDomain): """ A FiniteDomain over a single symbol/set Example: The possibilities of a *single* die roll. """ def __new__(cls, symbol, set): if not isinstance(set, FiniteSet): set = FiniteSet(*set) return Basic.__new__(cls, symbol, set) @property def symbol(self): return self.args[0] return tuple(self.symbols)[0] @property def symbols(self): return FiniteSet(self.symbol) @property def set(self): return self.args[1] @property def elements(self): return FiniteSet(*[frozenset(((self.symbol, elem), )) for elem in self.set]) def __iter__(self): return (frozenset(((self.symbol, elem),)) for elem in self.set) def __contains__(self, other): sym, val = tuple(other)[0] return sym == self.symbol and val in self.set class ProductFiniteDomain(ProductDomain, FiniteDomain): """ A Finite domain consisting of several other FiniteDomains Example: The possibilities of the rolls of three independent dice """ def __iter__(self): proditer = product(* return (sumsets(items) for items in proditer) @property def elements(self): return FiniteSet(*self) class ConditionalFiniteDomain(ConditionalDomain, ProductFiniteDomain): """ A FiniteDomain that has been restricted by a condition Example: The possibilities of a die roll under the condition that the roll is even. """ def __new__(cls, domain, condition): if condition is True: return domain cond = rv_subs(condition) # Check that we aren't passed a condition like die1 == z # where 'z' is a symbol that we don't know about # We will never be able to test this equality through iteration if not cond.free_symbols.issubset(domain.free_symbols): raise ValueError('Condition "%s" contains foreign symbols \n%s.\n' % ( condition, tuple(cond.free_symbols - domain.free_symbols)) + "Will be unable to iterate using this condition") return Basic.__new__(cls, domain, cond) def _test(self, elem): val = self.condition.xreplace(dict(elem)) if val in [True, False]: return val elif val.is_Equality: return val.lhs == val.rhs raise ValueError("Undeciable if %s" % str(val)) def __contains__(self, other): return other in self.fulldomain and self._test(other) def __iter__(self): return (elem for elem in self.fulldomain if self._test(elem)) @property def set(self): if self.fulldomain.__class__ is SingleFiniteDomain: return FiniteSet(*[elem for elem in self.fulldomain.set if frozenset(((self.fulldomain.symbol, elem),)) in self]) else: raise NotImplementedError( "Not implemented on multi-dimensional conditional domain") def as_boolean(self): return FiniteDomain.as_boolean(self) class SingleFiniteDistribution(Basic, NamedArgsMixin): def __new__(cls, *args): args = list(map(sympify, args)) return Basic.__new__(cls, *args) @property @cacheit def dict(self): return dict((k, self.pdf(k)) for k in self.set) @property def pdf(self): x = Symbol('x') return Lambda(x, Piecewise(*( [(v, Eq(k, x)) for k, v in self.dict.items()] + [(0, True)]))) @property def set(self): return list(self.dict.keys()) values = property(lambda self: self.dict.values) items = property(lambda self: self.dict.items) __iter__ = property(lambda self: self.dict.__iter__) __getitem__ = property(lambda self: self.dict.__getitem__) __call__ = pdf def __contains__(self, other): return other in self.set #============================================= #========= Probability Space =============== #=============================================
[docs]class FinitePSpace(PSpace): """ A Finite Probability Space Represents the probabilities of a finite number of events. """ is_Finite = True @property def domain(self): return self.args[0] @property def density(self): return self.args[0] def __new__(cls, domain, density): density = dict((sympify(key), sympify(val)) for key, val in density.items()) public_density = Dict(density) obj = PSpace.__new__(cls, domain, public_density) obj._density = density return obj def prob_of(self, elem): return self._density.get(elem, 0) def where(self, condition): assert all(r.symbol in self.symbols for r in random_symbols(condition)) return ConditionalFiniteDomain(self.domain, condition) def compute_density(self, expr): expr = expr.xreplace(dict(((rs, rs.symbol) for rs in self.values))) d = FiniteDensity() for elem in self.domain: val = expr.xreplace(dict(elem)) prob = self.prob_of(elem) d[val] = d.get(val, 0) + prob return d @cacheit def compute_cdf(self, expr): d = self.compute_density(expr) cum_prob = 0 cdf = [] for key in sorted(d): prob = d[key] cum_prob += prob cdf.append((key, cum_prob)) return dict(cdf) @cacheit def sorted_cdf(self, expr, python_float=False): cdf = self.compute_cdf(expr) items = list(cdf.items()) sorted_items = sorted(items, key=lambda val_cumprob: val_cumprob[1]) if python_float: sorted_items = [(v, float(cum_prob)) for v, cum_prob in sorted_items] return sorted_items def integrate(self, expr, rvs=None): rvs = rvs or self.values expr = expr.xreplace(dict((rs, rs.symbol) for rs in rvs)) return sum([expr.xreplace(dict(elem)) * self.prob_of(elem) for elem in self.domain]) def probability(self, condition): cond_symbols = frozenset(rs.symbol for rs in random_symbols(condition)) assert cond_symbols.issubset(self.symbols) return sum(self.prob_of(elem) for elem in self.where(condition)) def conditional_space(self, condition): domain = self.where(condition) prob = self.probability(condition) density = dict((key, val / prob) for key, val in self._density.items() if key in domain) return FinitePSpace(domain, density) def sample(self): """ Internal sample method Returns dictionary mapping RandomSymbol to realization value. """ expr = Tuple(*self.values) cdf = self.sorted_cdf(expr, python_float=True) x = random.uniform(0, 1) # Find first occurence with cumulative probability less than x # This should be replaced with binary search for value, cum_prob in cdf: if x < cum_prob: # return dictionary mapping RandomSymbols to values return dict(list(zip(expr, value))) assert False, "We should never have gotten to this point"
class SingleFinitePSpace(SinglePSpace, FinitePSpace): """ A single finite probability space Represents the probabilities of a set of random events that can be attributed to a single variable/symbol. This class is implemented by many of the standard FiniteRV types such as Die, Bernoulli, Coin, etc.... """ @property def domain(self): return SingleFiniteDomain(self.symbol, self.distribution.set) @property @cacheit def _density(self): return dict((frozenset(((self.symbol, val),)), prob) for val, prob in self.distribution.dict.items()) class ProductFinitePSpace(ProductPSpace, FinitePSpace): """ A collection of several independent finite probability spaces """ @property def domain(self): return ProductFiniteDomain(*[space.domain for space in self.spaces]) @property @cacheit def _density(self): proditer = product(*[iter(space._density.items()) for space in self.spaces]) d = {} for items in proditer: elems, probs = list(zip(*items)) elem = sumsets(elems) prob = Mul(*probs) d[elem] = d.get(elem, 0) + prob return Dict(d) @property @cacheit def density(self): return Dict(self._density)