本文整理汇总了Python中sklearn.neighbors.KDTree.build方法的典型用法代码示例。如果您正苦于以下问题：Python KDTree.build方法的具体用法？Python KDTree.build怎么用？Python KDTree.build使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.neighbors.KDTree的用法示例。
在下文中一共展示了KDTree.build方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: make_test
# 需要导入模块: from sklearn.neighbors import KDTree [as 别名]
# 或者: from sklearn.neighbors.KDTree import build [as 别名]
def make_test(test_start=1000, test_end=1050):
f1 = open('states.pkl', 'r')
f2 = open('states_for_test.pkl', 'r')
data_states = cPickle.load(f1)
test_states = cPickle.load(f2)
f1.close()
f2.close()
time_brute = []
time_sk_kd = []
time_sk_ball = []
time_kdtree = []
time_annoy = []
time_flann = []
time_brute_tot = time_sk_kd_tot = time_sk_ball_tot = time_kdtree_tot = time_annoy_tot = time_flann_tot = 0
kdtree_tree = None
for items in xrange(test_start, test_end):
print "item:", items
ground_truth = np.zeros((test_num_for_each, K), dtype=np.int32)
time_brute_start = time.time()
for no_test in xrange(test_num_for_each):
distance_list = []
current_state = test_states[items, no_test]
for target in xrange(items):
target_state = data_states[target]
distance_list.append(DistanceNode(np.sum(np.absolute(current_state - target_state)**2), target))
smallest = heapq.nsmallest(K, distance_list, key=lambda x: x.distance)
ground_truth[no_test] = [x.index for x in smallest]
time_brute_end = time.time()
time_brute.append(time_brute_end - time_brute_start)
time_brute_tot += time_brute[-1]
# print ground_truth
time_sk_kd_start = time.time()
tree = KDTree(data_states[:items, :])
dist, indices = tree.query(test_states[items], K)
time_sk_kd_end = time.time()
time_sk_kd.append(time_sk_kd_end - time_sk_kd_start)
time_sk_kd_tot += time_sk_kd[-1]
# print indices
time_sk_ball_start = time.time()
tree = BallTree(data_states[:items, :], 10000)
dist, indices = tree.query(test_states[items], K)
time_sk_ball_end = time.time()
time_sk_ball.append(time_sk_ball_end - time_sk_ball_start)
time_sk_ball_tot += time_sk_ball[-1]
# print indices
"""
annoy is absolutely disappointing for its low speed and poor accuracy.
"""
time_annoy_start = time.time()
annoy_result = np.zeros((test_num_for_each, K), dtype=np.int32)
tree = AnnoyIndex(dimension_result)
for i in xrange(items):
tree.add_item(i, data_states[i, :])
tree.build(10)
for no_test in xrange(test_num_for_each):
current_state = test_states[items, no_test]
annoy_result[no_test] = tree.get_nns_by_vector(current_state, K)
time_annoy_end = time.time()
time_annoy.append(time_annoy_end - time_annoy_start)
time_annoy_tot += time_annoy[-1]
# print annoy_result
# print annoy_result - indices
"""
flann is still not very ideal
"""
time_flann_start = time.time()
flann = FLANN()
result, dist = flann.nn(data_states[:items, :], test_states[items], K, algorithm='kdtree', trees=10, checks=16)
time_flann_end = time.time()
time_flann.append(time_flann_end - time_flann_start)
time_flann_tot += time_flann[-1]
# print result-indices
"""
This kdtree module is so disappointing!!!! It is 100 times slower than Sklearn and even slower than brute force,
more over it even makes mistakes.
This kdtree module supports online insertion and deletion. I thought it would be much faster than Sklearn
KdTree which rebuilds the tree every time. But the truth is the opposite.
"""
# time_kdtree_start = time.time()
# if kdtree_tree is None:
# point_list = [MyTuple(data_states[i, :], i) for i in xrange(items)]
# kdtree_tree = kdtree.create(point_list)
# else:
# point = MyTuple(data_states[items, :], items)
# kdtree_tree.add(point)
# kdtree_result = np.zeros((test_num_for_each, K), dtype=np.int32)
# for no_test in xrange(test_num_for_each):
# current_state = test_states[items, no_test]
# smallest = kdtree_tree.search_knn(MyTuple(current_state, -1), K)
#.........这里部分代码省略.........