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
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
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
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