def doit(self, **hints):
if self.limits:
function = self.function
for limit in self.limits:
x, distribution = limit
_x = x.copy(distribution=distribution)
expr = function._subs(x, _x)
if expr.is_Conditioned:
expr = expr.doit()
if not expr.is_random: # expr isn't random?
return expr
ps = pspace(expr)
if ps == PSpace():
return self.func(expr)
# Otherwise case is simple, pass work off to the ProbabilitySpace
function = ps.compute_expectation(expr, evaluate=True)
if hasattr(function, 'doit'):
function = function.doit(**hints)
return function
else:
deep = hints.get('deep', True)
expr = self.args[0]
numsamples = hints.get('numsamples', False)
for_rewrite = not hints.get('for_rewrite', False)
if deep:
expr = expr.doit(**hints)
if not expr.is_random or isinstance(
expr, Expectation): # expr isn't random?
return expr
if numsamples: # Computing by monte carlo sampling?
evalf = hints.get('evalf', True)
return sampling_E(expr, numsamples=numsamples, evalf=evalf)
if expr.has(RandomIndexedSymbol):
return pspace(expr).compute_expectation(expr)
# A few known statements for efficiency
if expr.is_Add: # We know that E is Linear
return Add(*[
self.func(arg).doit(**hints)
if not isinstance(arg, Expectation) else self.func(arg)
for arg in expr.args
])
if expr.is_Mul:
if expr.atoms(Expectation):
return expr
ps = pspace(expr)
if ps == PSpace():
return self.func(expr)
# Otherwise case is simple, pass work off to the ProbabilitySpace
result = ps.compute_expectation(expr, evaluate=for_rewrite)
if hasattr(result, 'doit') and for_rewrite:
return result.doit(**hints)
else:
return result
def doit(self, **hints):
deep = hints.get('deep', True)
condition = self._condition
expr = self.args[0]
numsamples = hints.get('numsamples', False)
for_rewrite = not hints.get('for_rewrite', False)
if deep:
expr = expr.doit(**hints)
if not is_random(expr) or isinstance(
expr, Expectation): # expr isn't random?
return expr
if numsamples: # Computing by monte carlo sampling?
evalf = hints.get('evalf', True)
return sampling_E(expr,
condition,
numsamples=numsamples,
evalf=evalf)
if expr.has(RandomIndexedSymbol):
return pspace(expr).compute_expectation(expr, condition)
# Create new expr and recompute E
if condition is not None: # If there is a condition
return self.func(given(expr, condition)).doit(**hints)
# A few known statements for efficiency
if expr.is_Add: # We know that E is Linear
return Add(*[
self.func(arg, condition).doit(
**hints) if not isinstance(arg, Expectation) else self.
func(arg, condition) for arg in expr.args
])
if expr.is_Mul:
if expr.atoms(Expectation):
return expr
if pspace(expr) == PSpace():
return self.func(expr)
# Otherwise case is simple, pass work off to the ProbabilitySpace
result = pspace(expr).compute_expectation(expr, evaluate=for_rewrite)
if hasattr(result, 'doit') and for_rewrite:
return result.doit(**hints)
else:
return result
