计算数据X中点的k-邻居图
1 基本使用方法
sklearn.neighbors.kneighbors_graph(
X,
n_neighbors,
*,
mode='connectivity',
metric='minkowski',
p=2,
metric_params=None,
include_self=False,
n_jobs=None)2 参数介绍
| X | 输入数据 |
| n_neighbors | 每个样本点的邻居数量 |
| mode | 返回矩阵的类型
|
| p | minkowski距离中的p |
| include_self | 自己是否是自己的最近邻居 |
3 举例
X = [[0], [3], [1]]
from sklearn.neighbors import kneighbors_graph
A = kneighbors_graph(X, 2, mode='connectivity', include_self=True)
A.toarray()
'''
array([[1., 0., 1.],
[0., 1., 1.],
[1., 0., 1.]])
'''
A = kneighbors_graph(X, 2, mode='distance', include_self=True)
A.toarray()
'''
array([[0., 0., 1.],
[0., 0., 2.],
[1., 0., 0.]])
'''