cosine_similarity (original) (raw)
sklearn.metrics.pairwise.cosine_similarity(X, Y=None, dense_output=True)[source]#
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.
Parameters:
X{array-like, sparse matrix} of shape (n_samples_X, n_features)
Input data.
Y{array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None
Input data. If None, the output will be the pairwise similarities between all samples in X.
dense_outputbool, default=True
Whether to return dense output even when the input is sparse. IfFalse, the output is sparse if both input arrays are sparse.
Added in version 0.17: parameter dense_output for dense output.
Returns:
similaritiesndarray or sparse matrix of shape (n_samples_X, n_samples_Y)
Returns the cosine similarity between samples in X and Y.
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. ], [0.577, 0.816]])