余弦距离（Cosine距离）与编辑距离分别是什么？各有什么优势和使用场景？

Posted 2021-05-18 Data+Science+Insight

余弦距离（Cosine距离）与编辑距离分别是什么？各有什么优势和使用场景？

 

import numpy as np
from sklearn.metrics import pairwise_distances
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.metrics.pairwise import cosine_distances
from sklearn.metrics.pairwise import cosine_similarity

X = np.array([[2, 3], [3, 5]])
Y = np.array([[1, 0], [2, 1]])
pairwise_distances(X, Y, metric=\'manhattan\')

pairwise_distances(X, metric=\'manhattan\')

pairwise_kernels(X, Y, metric=\'linear\')


print(cosine_distances(X,Y))
print(\'--------------------------------------\')
print(cosine_similarity(X,Y))

# Cosine distance is defined as 1.0 minus the cosine similarity.

[[0.4452998  0.13175686]
 [0.48550424 0.15633851]]
--------------------------------------
[[0.5547002  0.86824314]
 [0.51449576 0.84366149]]