本文整理汇总了Python中sklearn.neighbors.kd_tree.KDTree.query_radius方法的典型用法代码示例。如果您正苦于以下问题：Python KDTree.query_radius方法的具体用法？Python KDTree.query_radius怎么用？Python KDTree.query_radius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_radius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: test_kd_tree_query_radius
# 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_radius [as 别名]
def test_kd_tree_query_radius(n_samples=100, n_features=10):
np.random.seed(0)
X = 2 * np.random.random(size=(n_samples, n_features)) - 1
query_pt = np.zeros(n_features, dtype=float)
eps = 1E-15 # roundoff error can cause test to fail
kdt = KDTree(X, leaf_size=5)
rad = np.sqrt(((X - query_pt) ** 2).sum(1))
for r in np.linspace(rad[0], rad[-1], 100):
ind = kdt.query_radius(query_pt, r + eps)[0]
i = np.where(rad <= r + eps)[0]
ind.sort()
i.sort()
assert_allclose(i, ind)示例2: test_kd_tree_query_radius
# 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_radius [as 别名]
def test_kd_tree_query_radius(n_samples=100, n_features=10):
rng = check_random_state(0)
X = 2 * rng.random_sample(size=(n_samples, n_features)) - 1
query_pt = np.zeros(n_features, dtype=float)
eps = 1E-15 # roundoff error can cause test to fail
kdt = KDTree(X, leaf_size=5)
rad = np.sqrt(((X - query_pt) ** 2).sum(1))
for r in np.linspace(rad[0], rad[-1], 100):
ind = kdt.query_radius([query_pt], r + eps)[0]
i = np.where(rad <= r + eps)[0]
ind.sort()
i.sort()
assert_array_almost_equal(i, ind)示例3: test_kd_tree_query_radius_distance
# 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_radius [as 别名]
def test_kd_tree_query_radius_distance(n_samples=100, n_features=10):
rng = check_random_state(0)
X = 2 * rng.random_sample(size=(n_samples, n_features)) - 1
query_pt = np.zeros(n_features, dtype=float)
eps = 1E-15 # roundoff error can cause test to fail
kdt = KDTree(X, leaf_size=5)
rad = np.sqrt(((X - query_pt) ** 2).sum(1))
for r in np.linspace(rad[0], rad[-1], 100):
ind, dist = kdt.query_radius([query_pt], r + eps, return_distance=True)
ind = ind[0]
dist = dist[0]
d = np.sqrt(((query_pt - X[ind]) ** 2).sum(1))
assert_array_almost_equal(d, dist)示例4: test_kd_tree_query_radius_distance
# 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_radius [as 别名]
def test_kd_tree_query_radius_distance(n_samples=100, n_features=10):
np.random.seed(0)
X = 2 * np.random.random(size=(n_samples, n_features)) - 1
query_pt = np.zeros(n_features, dtype=float)
eps = 1e-15 # roundoff error can cause test to fail
kdt = KDTree(X, leaf_size=5)
rad = np.sqrt(((X - query_pt) ** 2).sum(1))
for r in np.linspace(rad[0], rad[-1], 100):
ind, dist = kdt.query_radius(query_pt, r + eps, return_distance=True)
ind = ind[0]
dist = dist[0]
d = np.sqrt(((query_pt - X[ind]) ** 2).sum(1))
assert_allclose(d, dist)