def _():
"""
Emit the log-likelihood computation.
"""
arrays = StridedArrays.from_numpy({"p": parameters, "s": samples, "o": out})
emitter = self._model.get_emitter()
@arrays.loop_all(len(shape))
def _(l):
emitter.given(l.arrays["p"], l.arrays["s"], l.arrays["o"])
def _():
"""
Emit the log-likelihood computation.
"""
arrays = StridedArrays.from_numpy({"p": priors, "s": samples, "w": weights, "o": out})
emitter = self._model.get_emitter()
@arrays.loop_all(len(shape))
def _(l):
emitter.map(l.arrays["p"], l.arrays["s"], l.arrays["w"], l.arrays["o"], initializations=initializations)
def _():
"""
Emit the log-likelihood computation.
"""
arrays = \
StridedArrays.from_numpy({
"p" : parameters,
"s" : samples,
"o" : out,
})
emitter = self._model.get_emitter()
@arrays.loop_all(len(shape))
def _(l):
emitter.given(l.arrays["p"], l.arrays["s"], l.arrays["o"])
def _():
"""
Emit the log-likelihood computation.
"""
arrays = \
StridedArrays.from_numpy({
"p" : priors,
"s" : samples,
"w" : weights,
"o" : out,
})
emitter = self._model.get_emitter()
@arrays.loop_all(len(shape))
def _(l):
emitter.map(l.arrays["p"],
l.arrays["s"],
l.arrays["w"],
l.arrays["o"],
initializations=initializations)
def _():
arrays = StridedArrays.from_numpy({"in" : in_, "out" : out})
@arrays.loop_all()
def _(l):
l.arrays["in"].data.load().store(l.arrays["out"].data)
