本文整理汇总了Python中sklearn.neighbors.NearestNeighbors.radius_neighbors_graph方法的典型用法代码示例。如果您正苦于以下问题：Python NearestNeighbors.radius_neighbors_graph方法的具体用法？Python NearestNeighbors.radius_neighbors_graph怎么用？Python NearestNeighbors.radius_neighbors_graph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.NearestNeighbors的用法示例。
在下文中一共展示了NearestNeighbors.radius_neighbors_graph方法的2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_dbscan_sparse_precomputed
# 需要导入模块: from sklearn.neighbors import NearestNeighbors [as 别名]
# 或者: from sklearn.neighbors.NearestNeighbors import radius_neighbors_graph [as 别名]
def test_dbscan_sparse_precomputed():
D = pairwise_distances(X)
nn = NearestNeighbors(radius=0.9).fit(X)
D_sparse = nn.radius_neighbors_graph(mode="distance")
# Ensure it is sparse not merely on diagonals:
assert D_sparse.nnz < D.shape[0] * (D.shape[0] - 1)
core_sparse, labels_sparse = dbscan(D_sparse, eps=0.8, min_samples=10, metric="precomputed")
core_dense, labels_dense = dbscan(D, eps=0.8, min_samples=10, metric="precomputed")
assert_array_equal(core_dense, core_sparse)
assert_array_equal(labels_dense, labels_sparse)示例2: len
# 需要导入模块: from sklearn.neighbors import NearestNeighbors [as 别名]
# 或者: from sklearn.neighbors.NearestNeighbors import radius_neighbors_graph [as 别名]
print "dimension : ", len(dimen)
X = np.array(X_tmp)
from sklearn.neighbors import NearestNeighbors
from sklearn.neighbors import LSHForest
for n in range(2, 10):
print "[[[[[" + str(n) + "]]]]]"
start = time.time()
# nbrs = NearestNeighbors(n_neighbors=n, algorithm='ball_tree').fit(X)
# print nbrs.kneighbors_graph(X).toarray()
neigh = NearestNeighbors(n_neighbors=n)
neigh.fit(X)
a = neigh.radius_neighbors_graph(X).toarray()
print a
# a = neigh.kneighbors_graph(X).toarray()
pc.dump(a, open("knn" + str(n) + ".txt", "w"))
end = time.time()
print "NearestNeighbors", end - start
start = time.time()
lshf = LSHForest(n_neighbors=n, random_state=10000)
lshf.fit(X)
# distances, indices = lshf.kneighbors(X, n_neighbors=n)
# print lshf.kneighbors_graph(X).toarray()
a = lshf.radius_neighbors_graph(X).toarray()
print a
pc.dump(a, open("lsh" + str(n) + ".txt", "w"))
end = time.time()