-
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 : ndarray or sparse array, shape: (n_samples_X, n_features)
Input data.
Y : ndarray or sparse array, 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 arrays are sparse.New in version 0.17: parameter
dense_output
for dense output.Returns: kernel matrix : array
An array with shape (n_samples_X, n_samples_Y).
sklearn.metrics.pairwise.cosine_similarity()
2017-01-15 04:26:31
Please login to continue.