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 inX
.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