计算两个numpy数组之间的距离 - python

我对计算两个numpy数组(x和y)之间的各种空间距离感兴趣。

http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.spatial.distance.cdist.html

import numpy as np
from scipy.spatial.distance import cdist

x = np.array([[[1,2,3,4,5],
               [5,6,7,8,5],
               [5,6,7,8,5]],
              [[11,22,23,24,5],
               [25,26,27,28,5],
               [5,6,7,8,5]]])
i,j,k = x.shape

xx = x.reshape(i,j*k).T

y = np.array([[[31,32,33,34,5],
               [35,36,37,38,5],
               [5,6,7,8,5]],
              [[41,42,43,44,5],
               [45,46,47,48,5],
               [5,6,7,8,5]]])

yy = y.reshape(i,j*k).T

results =  cdist(xx,yy,'euclidean')
print results

但是，以上结果会产生太多不必要的结果。我如何仅将其限制为所需的结果。

我想计算[1,11]和[31,41]之间的距离； [2,22]和[32,42]等。

python大神给出的解决方案

如果只需要每对点之间的距离，则无需计算完整的距离矩阵。

而是直接计算:

import numpy as np

x = np.array([[[1,2,3,4,5],
               [5,6,7,8,5],
               [5,6,7,8,5]],
              [[11,22,23,24,5],
               [25,26,27,28,5],
               [5,6,7,8,5]]])

y = np.array([[[31,32,33,34,5],
               [35,36,37,38,5],
               [5,6,7,8,5]],
              [[41,42,43,44,5],
               [45,46,47,48,5],
               [5,6,7,8,5]]])

xx = x.reshape(2, -1)
yy = y.reshape(2, -1)
dist = np.hypot(*(xx - yy))

print dist

为了进一步说明正在发生的事情，首先，我们对数组进行整形，使其具有2xN的形状(-1是一个占位符，告诉numpy沿该轴自动计算正确的大小):

In [2]: x.reshape(2, -1)
Out[2]: 
array([[ 1,  2,  3,  4,  5,  5,  6,  7,  8,  5,  5,  6,  7,  8,  5],
       [11, 22, 23, 24,  5, 25, 26, 27, 28,  5,  5,  6,  7,  8,  5]])

因此，当我们减去xx和yy时，将得到一个2xN数组:

In [3]: xx - yy
Out[3]: 
array([[-30, -30, -30, -30,   0, -30, -30, -30, -30,   0,   0,   0,   0,
          0,   0],
       [-30, -20, -20, -20,   0, -20, -20, -20, -20,   0,   0,   0,   0,
          0,   0]])

然后，我们可以将其解压缩为dx和dy组件:

In [4]: dx, dy = xx - yy

In [5]: dx
Out[5]: 
array([-30, -30, -30, -30,   0, -30, -30, -30, -30,   0,   0,   0,   0,
         0,   0])

In [6]: dy
Out[6]: 
array([-30, -20, -20, -20,   0, -20, -20, -20, -20,   0,   0,   0,   0,
         0,   0])

并计算距离(np.hypot等同于np.sqrt(dx**2 + dy**2)):

In [7]: np.hypot(dx, dy)
Out[7]: 
array([ 42.42640687,  36.05551275,  36.05551275,  36.05551275,
         0.        ,  36.05551275,  36.05551275,  36.05551275,
        36.05551275,   0.        ,   0.        ,   0.        ,
         0.        ,   0.        ,   0.        ])

或者，我们可以自动完成拆包并一步一步完成:

In [8]: np.hypot(*(xx - yy))
Out[8]: 
array([ 42.42640687,  36.05551275,  36.05551275,  36.05551275,
         0.        ,  36.05551275,  36.05551275,  36.05551275,
        36.05551275,   0.        ,   0.        ,   0.        ,
         0.        ,   0.        ,   0.        ])

如果要计算其他类型的距离，只需将np.hypot更改为要使用的函数。例如，对于曼哈顿/城市街区距离:

In [9]: dist = np.sum(np.abs(xx - yy), axis=0)

In [10]: dist
Out[10]: array([60, 50, 50, 50,  0, 50, 50, 50, 50,  0,  0,  0,  0,  0,  0])