本文整理汇总了Python中sklearn.neighbors.kd_tree.KDTree.query方法的典型用法代码示例。如果您正苦于以下问题：Python KDTree.query方法的具体用法？Python KDTree.query怎么用？Python KDTree.query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.kd_tree.KDTree的用法示例。
在下文中一共展示了KDTree.query方法的4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: check_neighbors
# 需要导入模块: from sklearn.neighbors.kd_tree import KDTree [as 别名]
# 或者: from sklearn.neighbors.kd_tree.KDTree import query [as 别名]
def check_neighbors(dualtree, breadth_first, k, metric, kwargs):
kdt = KDTree(X, leaf_size=1, metric=metric, **kwargs)
dist1, ind1 = kdt.query(Y, k, dualtree=dualtree, breadth_first=breadth_first)
dist2, ind2 = brute_force_neighbors(X, Y, k, metric, **kwargs)
# don't check indices here: if there are any duplicate distances,
# the indices may not match. Distances should not have this problem.
assert_allclose(dist1, dist2)示例2: test_kd_tree_pickle
# 需要导入模块: from sklearn.neighbors.kd_tree import KDTree [as 别名]
# 或者: from sklearn.neighbors.kd_tree.KDTree import query [as 别名]
def test_kd_tree_pickle(protocol):
import pickle
rng = check_random_state(0)
X = rng.random_sample((10, 3))
kdt1 = KDTree(X, leaf_size=1)
ind1, dist1 = kdt1.query(X)
def check_pickle_protocol(protocol):
s = pickle.dumps(kdt1, protocol=protocol)
kdt2 = pickle.loads(s)
ind2, dist2 = kdt2.query(X)
assert_array_almost_equal(ind1, ind2)
assert_array_almost_equal(dist1, dist2)
check_pickle_protocol(protocol)示例3: test_kd_tree_pickle
# 需要导入模块: from sklearn.neighbors.kd_tree import KDTree [as 别名]
# 或者: from sklearn.neighbors.kd_tree.KDTree import query [as 别名]
def test_kd_tree_pickle():
import pickle
np.random.seed(0)
X = np.random.random((10, 3))
kdt1 = KDTree(X, leaf_size=1)
ind1, dist1 = kdt1.query(X)
def check_pickle_protocol(protocol):
s = pickle.dumps(kdt1, protocol=protocol)
kdt2 = pickle.loads(s)
ind2, dist2 = kdt2.query(X)
assert_array_almost_equal(ind1, ind2)
assert_array_almost_equal(dist1, dist2)
for protocol in (0, 1, 2):
yield check_pickle_protocol, protocol示例4: range
# 需要导入模块: from sklearn.neighbors.kd_tree import KDTree [as 别名]
# 或者: from sklearn.neighbors.kd_tree.KDTree import query [as 别名]
#from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import NearestNeighbors
from sklearn.neighbors.kd_tree import KDTree
#from sklearn.neighbors import DistanceMetric
import numpy as np
import get_data2 as gd
headers = gd.get_headers()
dicts = gd.get_data_list_of_dicts()
rows_lol = []
for i in range(len(gd.get_data_slice(headers[0], dicts))):
rows_lol.append([])
for i in range(len(headers)):
if i ==1 or i==4:
column = gd.get_data_slice_numbers(headers[i], dicts)
else:
column = gd.get_data_slice_numbers(headers[i], dicts)
for j in range(len(gd.get_data_slice(headers[0], dicts))):
rows_lol[j].append(column[j])
X = np.array(rows_lol)
#nbrs = NearestNeighbors(n_neighbors=5, algorithm ='kd_tree', metric ='jaccard').fit(X)
kdt = KDTree(X, leaf_size=30, metric='euclidean')
kdt.query(X, k=3, return_distance=False)