def __call__(self, history):
status = history.status()
if status.x_diff is None:
pydecode.pairwise_dot(self.potentials, status.x, self.dual_weights)
else:
pydecode.pairwise_dot(self.potentials, status.x_diff, self.dual_weights)
path = self.dual_weights.kind.viterbi(self.current_graph,
self.dual_weights,
self.chart).path
# path = pydecode.best_path(self.current_graph,
# self.dual_weights)
score = self.dual_weights.dot(path)
vec = self.potentials.dot(path)
subgrad = np.zeros(len(status.x))
prune = None
# Call prune function
#if self.use_prune and history.gap() < history.last_prune - 5:
if self.pruning_function is not None:
#print "pruning "
pruning = \
self.pruning_function(self.current_graph,
self.dual_weights,
self.potentials,
history)
# pruning = pydecode.prune_hypergraph(self.current_graph,
# self.dual_weights,
# history.best_primal)
if pruning is not None:
graph, dual_weights, potentials = pruning
self.dual_weights = dual_weights
self.potentials = potentials
print "OLD SIZE", len(self.current_grapydecode.nodes)
self.current_graph = graph
self.chart = pydecode.LogViterbiChart(self.current_graph)
print "NEW SIZE", len(self.current_graph.nodes)
prune = history.gap()
for i, j in vec:
subgrad[i] = j
return SubgradientRoundResult(
dual=score, subgrad=subgrad,
extra=path, prune=prune,
constraints=vec)
def __call__(self, x, x_diff):
"""
Parameters
----------
Returns
--------
path, subgradient, dual_score
"""
if x_diff is None:
pydecode.pairwise_dot(self.constraint_potentials, x, self.dual_weights)
else:
pydecode.pairwise_dot(self.constraint_potentials, x_diff,
self.dual_weights)
path = pydecode.best_path(self.current_graph, self.dual_weights)
dual_score = self.dual_weights.dot(path)
constraint_vector = self.constraint_potentials.dot(path)
subgradient = np.zeros(len(x))
for i, j in constraint_vector:
subgradient[i] = j
return path, subgradient, dual_score
