Python中求距离
sklearn中的pairwise_distances_argmin()方法
API:sklearn.metrics.pairwise_distances_argmin(X,Y,axis=1,metric='euclidean',metric_kwargs=None)
作用:使用欧几里得距离,返回X中距离Y最近点的索引,shape与X一致
过程:逐个查找X列表中的点,返回距离Y列表每个点最近的X点索引。
from sklearn.metrics.pairwise import pairwise_distances_argmin #加载包
d = pairwise_distances_argmin([(1,1),(0,0),(4,4)],[(2,2),(3,3),(0,0)], axis=1,metric='euclidean')
# array([0,2,1],dtype=int64)
scipy中distance.cdist()方法
API:scipy.spatial.distance.cdist(XA, XB, metric='euclidean', *args, **kwargs)
作用:求具体距离,距离类型有:
‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’(曼哈顿距离), ‘correlation’, ‘cosine’, ‘dice’, ‘euclidean’(欧几里得距离), ‘hamming’, ‘jaccard’, ‘jensenshannon’, ‘kulsinski’, ‘mahalanobis’, ‘matching’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘wminkowski’, ‘yule’
例:欧几里得距离
from scipy.spatial import distance #导包
import numpy as np
#在二维数据点中求欧几里得距离
py.spatial.distance.cdist
coords = [(1, 1),
(2, 2),
(3, 3),
(4, 4)]
print(distance.cdist(coords, coords, 'euclidean'))
# array([[0. , 1.41421356, 2.82842712, 4.24264069],
# [1.41421356, 0. , 1.41421356, 2.82842712],
# [2.82842712, 1.41421356, 0. , 1.41421356],
# [4.24264069, 2.82842712, 1.41421356, 0. ]])
print(distance.cdist(coords,[(0,0)], 'euclidean'))
# array([[1.41421356],
# [2.82842712],
# [4.24264069],
# [5.65685425]])
#从3D数据点中找到曼哈顿距离,目标是X=a,Y=b X是对象,Y是目标
a = np.array([[0, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 1],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0],
[1, 1, 1]])
b = np.array([[ 0.1, 0.2, 0.4]])
print(distance.cdist(a, b, 'cityblock'))
# array([[0.7],
# [0.9],
# [1.3],
# [1.5],
# [1.5],
# [1.7],
# [2.1],
# [2.3]])
scipy中的distance.pdist()方法
API:scipy.spatial.distance.pdist(X, metric='euclidean', *args, **kwargs)
与distance.cdist()
类似,区别如下:
例:求欧几里得距离
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from scipy.spatial import distance
coords = [(1, 1),
(2, 2),
(3, 3),
(4, 4)]
print(distance.pdist(coords, 'euclidean'))
# array([1.41421356, 2.82842712, 4.24264069, 1.41421356, 2.82842712, 1.41421356])