Source code for funsor.montecarlo

from collections import OrderedDict
from contextlib import contextmanager

from funsor.integrate import Integrate
from funsor.interpreter import dispatched_interpretation, interpretation
from funsor.terms import Funsor, eager

[docs]@dispatched_interpretation def monte_carlo(cls, *args): """ A Monte Carlo interpretation of :class:`~funsor.integrate.Integrate` expressions. This falls back to :class:`~funsor.terms.eager` in other cases. """ # TODO Memoize sample statements in a context manager. result = monte_carlo.dispatch(cls, *args)(*args) if result is None: result = eager(cls, *args) return result
# This is a globally configurable parameter to draw multiple samples. monte_carlo.sample_inputs = OrderedDict()
[docs]@contextmanager def monte_carlo_interpretation(**sample_inputs): """ Context manager to set ``monte_carlo.sample_inputs`` and install the :func:`monte_carlo` interpretation. """ old = monte_carlo.sample_inputs monte_carlo.sample_inputs = OrderedDict(sample_inputs) try: with interpretation(monte_carlo): yield finally: monte_carlo.sample_inputs = old
@monte_carlo.register(Integrate, Funsor, Funsor, frozenset) def monte_carlo_integrate(log_measure, integrand, reduced_vars): sample = log_measure.sample(reduced_vars, monte_carlo.sample_inputs) if sample is log_measure: return None # cannot progress reduced_vars |= frozenset(monte_carlo.sample_inputs).intersection(sample.inputs) return Integrate(sample, integrand, reduced_vars) __all__ = [ 'monte_carlo', 'monte_carlo_interpretation' ]