mars.learn.metrics.pairwise.cosine_similarity#

mars.learn.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 (Tensor or sparse tensor, shape: (n_samples_X, n_features)) – Input data.

  • Y (Tensor or sparse tensor, shape: (n_samples_Y, n_features)) – Input data. If None, the output will be the pairwise similarities between all samples in X.

  • dense_output (boolean (optional), default True) – Whether to return dense output even when the input is sparse. If False, the output is sparse if both input tensors are sparse.

Returns

kernel matrix – A tensor with shape (n_samples_X, n_samples_Y).

Return type

Tensor