各种各样的距离

前言

各种各样的距离

from sklearn.metrics.pairwise import pairwise_distances
user_similarity = pairwise_distances(train_data_matrix, metric='cosine')

metric可以从下面范围取值：

• From scikit-learn: [‘cityblock’, ‘cosine’, ‘euclidean’, ‘l1’, ‘l2’, ‘manhattan’]. These metrics support sparse matrix inputs.

• From scipy.spatial.distance: [‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘correlation’, ‘dice’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ‘matching’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘yule’] See the documentation for scipy.spatial.distance for details on these metrics. These metrics do not support sparse matrix inputs.

欧式距离

$$dist(A, B) = \sqrt((x_A - x_B)^2, (y_A - y_B)^2)$$

余弦距离

$$dist(A, B) = \frac{x_A y_A + x_B y_B}{\sqrt{x_A^2 + x_B^2} \sqrt{y_A^2 + y_B^2}}$$

曼哈顿距离

$$dist(A, B) = |x_A - x_B|, |y_A - y_B|$$

切比雪夫距离

$$dist(A, B) = max(|x_A - x_B|, |y_A - y_B|)$$