def check_message_to_parent(self, plates_child, plates_message,
plates_mask, plates_parent, dims=(2,)):
# Dummy message
msg = np.random.randn(*(plates_message+dims))
# Mask with every other True and every other False
mask = np.mod(np.arange(np.prod(plates_mask)).reshape(plates_mask),
2) == 0
# Set up the dummy model
class Dummy(Node):
def _get_message_and_mask_to_parent(self, index):
return ([msg], mask)
parent = Dummy(dims=[dims], plates=plates_parent)
child = Dummy(parent, dims=[dims], plates=plates_child)
m = child._message_to_parent(0)[0] * np.ones(plates_parent+dims)
# Brute-force computation of the message without too much checking
m_true = msg * utils.squeeze(mask[...,np.newaxis]) * np.ones(plates_child+dims)
for ind in range(len(plates_child)):
axis = -ind - 2
if ind >= len(plates_parent):
m_true = np.sum(m_true, axis=axis, keepdims=False)
elif plates_parent[-ind-1] == 1:
m_true = np.sum(m_true, axis=axis, keepdims=True)
testing.assert_allclose(m, m_true,
err_msg="Incorrect message.")
def check_message_to_parent(self,
plates_child,
plates_message,
plates_mask,
plates_parent,
dims=(2, )):
# Dummy message
msg = np.random.randn(*(plates_message + dims))
# Mask with every other True and every other False
mask = np.mod(np.arange(np.prod(plates_mask)).reshape(plates_mask),
2) == 0
# Set up the dummy model
class Dummy(Node):
_moments = Moments()
def __init__(self, *args, **kwargs):
self._parent_moments = len(args) * (Moments(), )
super().__init__(*args, **kwargs)
def _get_message_and_mask_to_parent(self, index):
return ([msg], mask)
parent = Dummy(dims=[dims], plates=plates_parent)
child = Dummy(parent, dims=[dims], plates=plates_child)
m = child._message_to_parent(0)[0] * np.ones(plates_parent + dims)
# Brute-force computation of the message without too much checking
m_true = msg * utils.squeeze(
mask[..., np.newaxis]) * np.ones(plates_child + dims)
for ind in range(len(plates_child)):
axis = -ind - 2
if ind >= len(plates_parent):
m_true = np.sum(m_true, axis=axis, keepdims=False)
elif plates_parent[-ind - 1] == 1:
m_true = np.sum(m_true, axis=axis, keepdims=True)
testing.assert_allclose(m, m_true, err_msg="Incorrect message.")
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