def predict_smart(self, x_test):
y = []
number_of_test_examples = len(x_test)
for i in xrange(number_of_test_examples):
search_tree = SearchTree(self.number_of_labels)
root = search_tree.add_vertex("root_", 0, 1.0, 1.0, [], x_test[i])
h = []
heapq.heappush(h, (root.get_inversed_cond_prob(), root))
best_leaf = None
best_score = 0.0
while len(h) != 0:
vertex = heapq.heappop(h)[1]
if vertex.get_conditional_prob() < best_score:
break
else:
greedy_leaf = self.look_ahead(search_tree, vertex, h, best_score)
if greedy_leaf is None:
continue
else:
best_leaf = greedy_leaf
best_score = greedy_leaf.get_conditional_prob()
y.append(best_leaf.get_labels())
return np.array(y)
def predict_smart(self, x_test):
y = []
number_of_test_examples = len(x_test)
for i in xrange(number_of_test_examples):
search_tree = SearchTree(self.number_of_labels)
root = search_tree.add_vertex("root_", 0, 1.0, 1.0, [], x_test[i])
h = []
heapq.heappush(h, (root.get_inversed_cond_prob(), root))
best_leaf = None
best_score = 0.0
while len(h) != 0:
vertex = heapq.heappop(h)[1]
if vertex.get_conditional_prob() < best_score:
break
else:
greedy_leaf = self.look_ahead(search_tree, vertex, h,
best_score)
if greedy_leaf is None:
continue
else:
best_leaf = greedy_leaf
best_score = greedy_leaf.get_conditional_prob()
y.append(best_leaf.get_labels())
return np.array(y)
def predict_full(self, x_test):
y = []
number_of_test_examples = len(x_test)
for i in xrange(number_of_test_examples):
search_tree = SearchTree(self.number_of_labels)
root = search_tree.add_vertex("root_", 0, 1.0, 1.0, [], x_test[i])
self.generate_search_tree(search_tree, root)
best_leaf = search_tree.find_best_leaf()
y.append(best_leaf.get_labels())
# print "Done: " + str(i) + "/" + str(number_of_training_examples)
return np.array(y)
def predict_full(self, x_test):
y = []
number_of_test_examples = len(x_test)
for i in xrange(number_of_test_examples):
search_tree = SearchTree(self.number_of_labels)
root = search_tree.add_vertex("root_", 0, 1.0, 1.0, [], x_test[i])
self.generate_search_tree(search_tree, root)
best_leaf = search_tree.find_best_leaf()
y.append(best_leaf.get_labels())
# print "Done: " + str(i) + "/" + str(number_of_training_examples)
return np.array(y)
def predict_ucs(self, x_test, epsilon):
y = []
number_of_test_examples = len(x_test)
for i in xrange(number_of_test_examples):
search_tree = SearchTree(self.number_of_labels)
root = search_tree.add_vertex("root_", 0, 1.0, 1.0, [], x_test[i])
q = []
greedy_q = []
heapq.heappush(q, (root.get_inversed_cond_prob(), root))
best_leaf = None
while len(q) != 0:
vertex = heapq.heappop(q)[1]
if len(vertex.get_labels()
) == self.number_of_labels: # If leaf
best_leaf = vertex
break
else:
children = self.generate_children(search_tree, vertex)
no_children_inserted = True
for child in children:
# print child.get_inversed_cond_prob()
if child.get_probability() > epsilon:
heapq.heappush(
q, (child.get_inversed_cond_prob(), child))
no_children_inserted = False
if no_children_inserted:
heapq.heappush(
greedy_q,
(vertex.get_inversed_cond_prob(), vertex))
if best_leaf is None:
best_score = 0.0
while len(greedy_q) != 0:
vertex = heapq.heappop(greedy_q)[1]
greedy_leaf = self.look_ahead(search_tree, vertex, [],
best_score)
if greedy_leaf is None:
continue
else:
best_leaf = greedy_leaf
best_score = greedy_leaf.get_conditional_prob()
# print best_leaf, best_score
# print("------")
y.append(best_leaf.get_labels())
# print "Done: " + str(i) + "/" + str(number_of_training_examples)
return np.array(y)
def predict_ucs(self, x_test, epsilon):
y = []
number_of_test_examples = len(x_test)
for i in xrange(number_of_test_examples):
search_tree = SearchTree(self.number_of_labels)
root = search_tree.add_vertex("root_", 0, 1.0, 1.0, [], x_test[i])
q = []
greedy_q = []
heapq.heappush(q, (root.get_inversed_cond_prob(), root))
best_leaf = None
while len(q) != 0:
vertex = heapq.heappop(q)[1]
if len(vertex.get_labels()) == self.number_of_labels: # If leaf
best_leaf = vertex
break
else:
children = self.generate_children(search_tree, vertex)
no_children_inserted = True
for child in children:
# print child.get_inversed_cond_prob()
if child.get_probability() > epsilon:
heapq.heappush(q, (child.get_inversed_cond_prob(), child))
no_children_inserted = False
if no_children_inserted:
heapq.heappush(greedy_q, (vertex.get_inversed_cond_prob(), vertex))
if best_leaf is None:
best_score = 0.0
while len(greedy_q) != 0:
vertex = heapq.heappop(greedy_q)[1]
greedy_leaf = self.look_ahead(search_tree, vertex, [], best_score)
if greedy_leaf is None:
continue
else:
best_leaf = greedy_leaf
best_score = greedy_leaf.get_conditional_prob()
# print best_leaf, best_score
# print("------")
y.append(best_leaf.get_labels())
# print "Done: " + str(i) + "/" + str(number_of_training_examples)
return np.array(y)