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：

cdist 与pdist 区别

例：求欧几里得距离

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])