本文整理汇总了Python中sklearn.neighbors.LSHForest.kneighbors_graph方法的典型用法代码示例。如果您正苦于以下问题：Python LSHForest.kneighbors_graph方法的具体用法？Python LSHForest.kneighbors_graph怎么用？Python LSHForest.kneighbors_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.LSHForest的用法示例。
在下文中一共展示了LSHForest.kneighbors_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: __init__
# 需要导入模块: from sklearn.neighbors import LSHForest [as 别名]
# 或者: from sklearn.neighbors.LSHForest import kneighbors_graph [as 别名]
class EmbeddingNetworkBuilder:
""" Basically a wrapper around sklearns LSH forest """
def __init__(self, lsh_init=None):
if lsh_init == None:
self._lsh_forest = LSHForest(n_estimators=25, n_candidates=1000)
else:
self._lsh_forest = lsh_init
self.iw = None
self.m = None
def fit_lsh_forest(self, embedding):
self._lsh_forest.fit(embedding.m)
self._embedding = embedding
def extract_nn_network(self, nn=20):
dir_graph_mat = self._lsh_forest.kneighbors_graph(X=self._embedding.m, n_neighbors=nn+1)
return dir_graph_mat
def make_undirected(self, dir_graph_mat):
nodes = set(range(dir_graph_mat.shape[0]))
edges = set([])
for node_i in dir_graph_mat.shape[0]:
for node_j in dir_graph_mat[node_i].nonzero()[1]:
edges.add((node_i, node_j))
return nodes, edges
def get_forest(self):
return self._lsh_forest
def get_node_to_word(self):
return self.iw示例2: test_graphs
# 需要导入模块: from sklearn.neighbors import LSHForest [as 别名]
# 或者: from sklearn.neighbors.LSHForest import kneighbors_graph [as 别名]
def test_graphs():
# Smoke tests for graph methods.
n_samples_sizes = [5, 10, 20]
n_features = 3
rng = np.random.RandomState(42)
for n_samples in n_samples_sizes:
X = rng.rand(n_samples, n_features)
lshf = LSHForest(min_hash_match=0)
ignore_warnings(lshf.fit)(X)
kneighbors_graph = lshf.kneighbors_graph(X)
radius_neighbors_graph = lshf.radius_neighbors_graph(X)
assert_equal(kneighbors_graph.shape[0], n_samples)
assert_equal(kneighbors_graph.shape[1], n_samples)
assert_equal(radius_neighbors_graph.shape[0], n_samples)
assert_equal(radius_neighbors_graph.shape[1], n_samples)