scikit-learn：4.7. Pairwise metrics, Affinities and Kernels

参考：http://scikit-learn.org/stable/modules/metrics.html

The sklearn.metrics.pairwise submodule implements utilities to evaluate pairwise distances（样本对的距离） or affinity of sets of samples（样本集的相似度）。

Distance metrics are functions d(a, b) such that d(a, b) < d(a, c) if objects a and b are considered “more similar” than objects a and c. 

Kernels are measures of similarity, i.e. s(a, b) > s(a, c) if objects a and b are considered “more similar” than objects a and c. 

1、Cosine similarity

向量点积的L2-norm：

if  and  are row vectors, their cosine similarity