`sklearn.metrics.pairwise`.cosine_similarity¶

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. If False, the output is sparse if both input arrays are sparse.

New in version 0.17: parameter dense_output for dense output.

Returns:

kernel matrixndarray of shape (n_samples_X, n_samples_Y): Returns the cosine similarity between samples in X and Y.

Examples using `sklearn.metrics.pairwise.cosine_similarity`¶