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)