How to calculate the cosine similarity of the rows of a NumPy array in Python

Calculating the cosine similarity of the rows of a NumPy array results in a new array containing the cosine of the angle between each pair of rows in the original array, where each row is thought of as a vector. For example, the cosine similarity of the rows [3,0] and [3,4] is 0.6.

Solution for How to calculate the cosine similarity of the rows of a NumPy array in Python : You can use sklearn.metrics.pairwise.cosine_similarity() to return the cosine similarities of the rows of an array Call sklearn.metrics.pairwise.cosine_similarity(array) to return an array containing the cosine similarities of the rows of array. The value in the i-th row and j-th column of the result is the cosine similarity between the i-th and j-th row of array.


how-to-calculate-the-cosine-similarity-of-the-rows-of-a-numpy-array-in-python