NumPy:向量化到一组点的距离之和 [英] NumPy: vectorize sum of distances to a set of points

问题描述

我正在尝试实施 k -medoids Python/NumPy中的聚类算法.作为该算法的一部分，我必须计算从对象到它们的"medoids"(集群代表)的距离之和.

I'm trying to implementing a k-medoids clustering algorithm in Python/NumPy. As part of this algo, I have to compute the sum of distances from objects to their "medoids" (cluster representatives).

我有:五点距离矩阵

n_samples = 5
D = np.array([[ 0.        ,  3.04959014,  4.74341649,  3.72424489,  6.70298441],
              [ 3.04959014,  0.        ,  5.38516481,  4.52216762,  6.16846821],
              [ 4.74341649,  5.38516481,  0.        ,  1.02469508,  8.23711114],
              [ 3.72424489,  4.52216762,  1.02469508,  0.        ,  7.69025357],
              [ 6.70298441,  6.16846821,  8.23711114,  7.69025357,  0.        ]])

一组初始类固醇

medoids = np.array([0, 3])

和集群成员身份

cl = np.array([0, 0, 1, 1, 0])

我可以使用来计算所需的总和

I can compute the required sum using

>>> np.sum(D[i, medoids[cl[i]]] for i in xrange(n_samples))
10.777269622938899

但是使用Python循环.我是否缺少某种矢量化的惯用法来计算该总和?

but that uses a Python loop. Am I missing some kind of vectorized idiom for computing this sum?

推荐答案

怎么样:

In [17]: D[np.arange(n_samples),medoids[cl]].sum()
Out[17]: 10.777269629999999

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