def inference(self, test_set, real_test, golden_set):
correct = 0
total = 0
result_dict = {}
for sentence in test_set:
graph_with_all_weights = self.feature_maker.create_weighted_graph_for_sentence(
sentence, self.weights, test_set)
maximum_spanning_tree = mst.mst(0, graph_with_all_weights)
if not real_test:
golden_standard = golden_set[sentence]
correct += self.number_of_correct(maximum_spanning_tree,
golden_standard)
total += (len(test_set[sentence]) - 1)
accuracy = float(float(correct) / total)
else:
result_dict[sentence] = maximum_spanning_tree
if not real_test:
print("The final accuracy is ", accuracy, " achieved with ",
self.global_iterations, " iterations")
return accuracy
else:
return result_dict
def run(self):
self.init_weights()
begin = time.time()
problem = 0
for iteration in range(0, self.global_iterations):
for sentence in self.feature_maker.train_data:
graph_with_all_weights = self.feature_maker.create_weighted_graph_for_sentence(
sentence, self.weights, self.feature_maker.train_data)
maximum_spanning_tree = mst.mst(0, graph_with_all_weights)
"""if 0 not in maximum_spanning_tree:
maximum_spanning_tree = self.connect_tree(maximum_spanning_tree)"""
golden_standard = self.feature_maker.golden_standard[sentence]
if not self.compare_trees(maximum_spanning_tree,
golden_standard):
self.add(self.feature_maker.
sentence_feature_dictionary[sentence])
self.subtract(
self.feature_maker.create_feature_vector_from_tree(
sentence, maximum_spanning_tree))
print("perceptron run took :",
time.time() - begin, " with ", self.global_iterations,
" iterations")
print("problem with ", problem)
return self.weights
