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################################################################################
# Copyright (C) 2015 Jaakko Luttinen
#
# This file is licensed under the MIT License.
################################################################################
import numpy as np
from .expfamily import ExponentialFamily, useconstructor
from .stochastic import Distribution
from .node import Moments
class LogPDFDistribution(Distribution):
pass
class LogPDF(ExponentialFamily):
"""
General node with arbitrary probability density function
"""
def __init__(self, logpdf, *parents, **kwargs):
self._logpdf = logpdf
super().__init__(logpdf,
*parents,
initialize=False,
**kwargs)
@classmethod
def _constructor(cls, logpdf, *parents, approximation=None, shape=None, samples=10, **kwargs):
r"""
Constructs distribution and moments objects.
"""
if approximation is not None:
raise NotImplementedError() #self._distribution = approximation._constructor
dims = ( shape, )
_distribution = LogPDFDistribution()
_moments = np.nan
_parent_moments = [Moments()] * len(parents)
parent_plates = [_distribution.plates_from_parent(i, parent.plates)
for (i, parent) in enumerate(parents)]
return (parents,
kwargs,
dims,
cls._total_plates(kwargs.get('plates'),
*parent_plates),
_distribution,
_moments,
_parent_moments)
def _get_message_and_mask_to_parent(self, index):
def logpdf_sampler(x):
inputs = [self.parents[j].random() if j != index
else x
for j in range(len(self.parents))]
return self._logpdf(self.random(), *inputs)
mask = self._distribution.compute_weights_to_parent(index, self.mask) != 0
return (logpdf_sampler, mask)
def observe(self, x, *args, mask=True):
"""
Fix moments, compute f and propagate mask.
"""
# Compute fixed moments
if not np.isnan(self._moments):
u = self._moments.compute_fixed_moments(x, *args, mask=mask)
else:
u = (x,) + args
# Check the dimensionality of the observations
for (i,v) in enumerate(u):
# This is what the dimensionality "should" be
s = self.plates + self.dims[i]
t = np.shape(v)
if s != t:
msg = "Dimensionality of the observations incorrect."
msg += "\nShape of input: " + str(t)
msg += "\nExpected shape: " + str(s)
msg += "\nCheck plates."
raise Exception(msg)
# Set the moments
self._set_moments(u, mask=mask)
# Observed nodes should not be ignored
self.observed = mask
self._update_mask()
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