Exemple #1
0
def _message_sum_multiply(plates_parent, dims_parent, *arrays):
    """
    Compute message to parent and sum over plates.
    """
    # The shape of the full message
    shapes = [np.shape(array) for array in arrays]
    shape_full = utils.broadcasted_shape(*shapes)
    # Find axes that should be summed
    shape_parent = plates_parent + dims_parent
    sum_axes = utils.axes_to_collapse(shape_full, shape_parent)
    # Compute the multiplier for cancelling the
    # plate-multiplier.  Because we are summing over the
    # dimensions already in this function (for efficiency), we
    # need to cancel the effect of the plate-multiplier
    # applied in the message_to_parent function.
    r = 1
    for j in sum_axes:
        if j >= 0 and j < len(plates_parent):
            r *= shape_full[j]
        elif j < 0 and j < -len(dims_parent):
            r *= shape_full[j]
    # Compute the sum-product
    m = utils.sum_multiply(*arrays, axis=sum_axes, sumaxis=True,
                           keepdims=True) / r
    # Remove extra axes
    m = utils.squeeze_to_dim(m, len(shape_parent))
    return m
Exemple #2
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def message_sum_multiply(plates_parent, dims_parent, *arrays):
    """
    Compute message to parent and sum over plates.

    Divide by the plate multiplier.
    """
    # The shape of the full message
    shapes = [np.shape(array) for array in arrays]
    shape_full = utils.broadcasted_shape(*shapes)
    # Find axes that should be summed
    shape_parent = plates_parent + dims_parent
    sum_axes = utils.axes_to_collapse(shape_full, shape_parent)
    # Compute the multiplier for cancelling the
    # plate-multiplier.  Because we are summing over the
    # dimensions already in this function (for efficiency), we
    # need to cancel the effect of the plate-multiplier
    # applied in the message_to_parent function.
    r = 1
    for j in sum_axes:
        if j >= 0 and j < len(plates_parent):
            r *= shape_full[j]
        elif j < 0 and j < -len(dims_parent):
            r *= shape_full[j]
    # Compute the sum-product
    m = utils.sum_multiply(*arrays,
                           axis=sum_axes,
                           sumaxis=True,
                           keepdims=True) / r
    # Remove extra axes
    m = utils.squeeze_to_dim(m, len(shape_parent))
    return m
Exemple #3
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    def _message_to_parent(self, index):

        # Compute the message, check plates, apply mask and sum over some plates
        if index >= len(self.parents):
            raise ValueError("Parent index larger than the number of parents")

        # Compute the message and mask
        (m, mask) = self._get_message_and_mask_to_parent(index)
        mask = utils.squeeze(mask)

        # Plates in the mask
        plates_mask = np.shape(mask)

        # The parent we're sending the message to
        parent = self.parents[index]

        # Compact the message to a proper shape
        for i in range(len(m)):

            # Empty messages are given as None. We can ignore those.
            if m[i] is not None:

                # Plates in the message
                shape_m = np.shape(m[i])
                dim_parent = len(parent.dims[i])
                if dim_parent > 0:
                    plates_m = shape_m[:-dim_parent]
                else:
                    plates_m = shape_m

                # Compute the multiplier (multiply by the number of plates for
                # which the message, the mask and the parent have single
                # plates).  Such a plate is meant to be broadcasted but because
                # the parent has singular plate axis, it won't broadcast (and
                # sum over it), so we need to multiply it.
                plates_self = self._plates_to_parent(index)
                try:
                    r = self._plate_multiplier(plates_self, 
                                               plates_m,
                                               plates_mask,
                                               parent.plates)
                except ValueError:
                    raise ValueError("The plates of the message, the mask and "
                                     "parent[%d] node (%s) are not a "
                                     "broadcastable subset of the plates of "
                                     "this node (%s).  The message has shape "
                                     "%s, meaning plates %s. The mask has "
                                     "plates %s. This node has plates %s with "
                                     "respect to the parent[%d], which has "
                                     "plates %s."
                                     % (index,
                                        parent.name,
                                        self.name,
                                        np.shape(m[i]), 
                                        plates_m, 
                                        plates_mask,
                                        plates_self,
                                        index, 
                                        parent.plates))

                # Add variable axes to the mask
                shape_mask = np.shape(mask) + (1,) * len(parent.dims[i])
                mask_i = np.reshape(mask, shape_mask)

                # Sum over plates that are not in the message nor in the parent
                shape_parent = parent.get_shape(i)
                shape_msg = utils.broadcasted_shape(shape_m, shape_parent)
                axes_mask = utils.axes_to_collapse(shape_mask, shape_msg)
                mask_i = np.sum(mask_i, axis=axes_mask, keepdims=True)

                # Compute the masked message and sum over the plates that the
                # parent does not have.
                axes_msg = utils.axes_to_collapse(shape_msg, shape_parent)
                m[i] = utils.sum_multiply(mask_i, m[i], r, 
                                          axis=axes_msg, 
                                          keepdims=True)

                # Remove leading singular plates if the parent does not have
                # those plate axes.
                m[i] = utils.squeeze_to_dim(m[i], len(shape_parent))

        return m
Exemple #4
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    def _message_to_parent(self, index):

        # Compute the message, check plates, apply mask and sum over some plates
        if index >= len(self.parents):
            raise ValueError("Parent index larger than the number of parents")

        # Compute the message and mask
        (m, mask) = self._get_message_and_mask_to_parent(index)
        mask = utils.squeeze(mask)

        # Plates in the mask
        plates_mask = np.shape(mask)

        # The parent we're sending the message to
        parent = self.parents[index]

        # Compact the message to a proper shape
        for i in range(len(m)):

            # Empty messages are given as None. We can ignore those.
            if m[i] is not None:

                # Plates in the message
                shape_m = np.shape(m[i])
                dim_parent = len(parent.dims[i])
                if dim_parent > 0:
                    plates_m = shape_m[:-dim_parent]
                else:
                    plates_m = shape_m

                # Compute the multiplier (multiply by the number of plates for
                # which the message, the mask and the parent have single
                # plates).  Such a plate is meant to be broadcasted but because
                # the parent has singular plate axis, it won't broadcast (and
                # sum over it), so we need to multiply it.
                plates_self = self._plates_to_parent(index)
                try:
                    r = self._plate_multiplier(plates_self, 
                                               plates_m,
                                               plates_mask,
                                               parent.plates)
                except ValueError:
                    raise ValueError("The plates of the message, the mask and "
                                     "parent[%d] node (%s) are not a "
                                     "broadcastable subset of the plates of "
                                     "this node (%s).  The message has shape "
                                     "%s, meaning plates %s. The mask has "
                                     "plates %s. This node has plates %s with "
                                     "respect to the parent[%d], which has "
                                     "plates %s."
                                     % (index,
                                        parent.name,
                                        self.name,
                                        np.shape(m[i]), 
                                        plates_m, 
                                        plates_mask,
                                        plates_self,
                                        index, 
                                        parent.plates))

                # Add variable axes to the mask
                shape_mask = np.shape(mask) + (1,) * len(parent.dims[i])
                mask_i = np.reshape(mask, shape_mask)

                # Sum over plates that are not in the message nor in the parent
                shape_parent = parent.get_shape(i)
                shape_msg = utils.broadcasted_shape(shape_m, shape_parent)
                axes_mask = utils.axes_to_collapse(shape_mask, shape_msg)
                mask_i = np.sum(mask_i, axis=axes_mask, keepdims=True)

                # Compute the masked message and sum over the plates that the
                # parent does not have.
                axes_msg = utils.axes_to_collapse(shape_msg, shape_parent)
                m[i] = utils.sum_multiply(mask_i, m[i], r, 
                                          axis=axes_msg, 
                                          keepdims=True)

                # Remove leading singular plates if the parent does not have
                # those plate axes.
                m[i] = utils.squeeze_to_dim(m[i], len(shape_parent))

        return m