python - 如何在不使用循环的情况下使用自己的度量创建距离矩阵？

Question

我有一个这样的 np.arrray:

[[ 1.3 , 2.7 , 0.5 , NaN , NaN],
[ 2.0 , 8.9 , 2.5 , 5.6 , 3.5],
[ 0.6 , 3.4 , 9.5 , 7.4 , NaN]]

还有一个函数来计算两行之间的距离:

def nan_manhattan(X, Y):
    nan_diff = np.absolute(X - Y)
    length = nan_diff.size
    return np.nansum(nan_diff) * length / (length - np.isnan(nan_diff).sum())

我需要所有成对距离，而且我不想使用循环。我怎么做？

Answer 1

使用 pdist :

import numpy as np
from scipy.spatial.distance import pdist, squareform


def nan_manhattan(X, Y):
    nan_diff = np.absolute(X - Y)
    length = nan_diff.size
    return np.nansum(nan_diff) * length / (length - np.isnan(nan_diff).sum())


arr = np.array([[1.3, 2.7, 0.5, np.nan, np.nan],
                [2.0, 8.9, 2.5, 5.6, 3.5],
                [0.6, 3.4, 9.5, 7.4, np.nan]])

result = squareform(pdist(arr, nan_manhattan))

print(result)

输出

[[ 0.         14.83333333 17.33333333]
 [14.83333333  0.         19.625     ]
 [17.33333333 19.625       0.        ]]