Compute cosine similarity between samples in X and Y.
Cosine similarity, or the cosine kernel, computes similarity as the normalized dot product of X and Y:
K(X, Y) = <X, Y> / (||X||*||Y||)
On L2-normalized data, this function is equivalent to linear_kernel.
Read more in the User Guide .
Input data.
Input data. If None , the output will be the pairwise similarities between all samples in X .
None
X
Whether to return dense output even when the input is sparse. If False , the output is sparse if both input arrays are sparse.
False
Added in version 0.17: parameter dense_output for dense output.
dense_output
Examples
>>> from sklearn.metrics.pairwise import cosine_similarity >>> X = [[0, 0, 0], [1, 1, 1]] >>> Y = [[1, 0, 0], [1, 1, 0]] >>> cosine_similarity(X, Y) array([[0. , 0. ], [0.57..., 0.81...]])
