def pass_msg_down(self, sender, tree, N, receiver):
"""
downward message is the sum over all states of the product of
the downward message from sender's parent,
the product of upward messages to the sender from his other children,
the sender-receiver edge potential,
the sender node potential
If the sender is the root, the calculation is somewhat simplified.
:param sender: parent
:param receiver: child
"""
down_msg_temp = np.zeros(N, 'f') + logzero()
curr_edge = tree.get_edge_by_nodes(sender, receiver)
# can be zero if there's not children
product_child = sum([
tree.get_edge_by_nodes(sender, c).up_msg
for c in sender.get_children() if c != receiver
])
if sender.is_root():
down_msg_temp = curr_edge.potentials + product_child
else:
prev_edge = tree.get_edge_by_nodes(sender.get_parent(), sender)
product = project_kbest(
prev_edge.down_msg + product_child
) if self.approximate else prev_edge.down_msg + product_child
product += sender.potentials
for curr_state in range(N): # TODO optimize
down_msg_temp[curr_state] = sselogsum(
product + curr_edge.potentials[:, curr_state])
curr_edge.down_msg = down_msg_temp
def pass_msg_down(self, sender, tree, N, receiver):
"""
downward message is the sum over all states of the product of
the downward message from sender's parent,
the product of upward messages to the sender from his other children,
the sender-receiver edge potential,
the sender node potential
If the sender is the root, the calculation is somewhat simplified.
:param sender: parent
:param receiver: child
"""
down_msg_temp = np.zeros(N, 'f') + logzero()
curr_edge = tree.get_edge_by_nodes(sender, receiver)
# can be zero if there's not children
product_child = sum([tree.get_edge_by_nodes(sender, c).up_msg
for c in sender.get_children()
if c != receiver])
if sender.is_root():
down_msg_temp = curr_edge.potentials + product_child
else:
prev_edge = tree.get_edge_by_nodes(sender.get_parent(), sender)
product = project_kbest(
prev_edge.down_msg + product_child) if self.approximate else prev_edge.down_msg + product_child
product += sender.potentials
for curr_state in range(N): # TODO optimize
down_msg_temp[curr_state] = sselogsum(product + curr_edge.potentials[:, curr_state])
curr_edge.down_msg = down_msg_temp
def compute_belief(self, node, tree):
"""
belief is the product of
node's potential (emission prob) and
the product of messages sent to the node
"""
if not node.has_children(): # is leaf
product = node.initial_potentials
else:
product = sum([tree.get_edge_by_nodes(node, child).up_msg for child in node.get_children()])
if node.is_root(): # no potential for root
return product
else:
return project_kbest(product + node.potentials) if self.approximate else product + node.potentials
def compute_belief(self, node, tree):
"""
belief is the product of
node's potential (emission prob) and
the product of messages sent to the node
"""
if not node.has_children(): # is leaf
product = node.initial_potentials
else:
product = sum([
tree.get_edge_by_nodes(node, child).up_msg
for child in node.get_children()
])
if node.is_root(): # no potential for root
return product
else:
return project_kbest(
product + node.potentials
) if self.approximate else product + node.potentials
