sklearn.metrics.pairwise
.linear_kernel#
- sklearn.metrics.pairwise.linear_kernel(X, Y=None, dense_output=True)[source]#
Compute the linear kernel between X and Y.
Read more in the User Guide.
- Parameters:
- X{array-like, sparse matrix} of shape (n_samples_X, n_features)
A feature array.
- Y{array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None
An optional second feature array. If
None
, usesY=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 0.20.
- Returns:
- kernelndarray of shape (n_samples_X, n_samples_Y)
The Gram matrix of the linear kernel, i.e.
X @ Y.T
.
Examples
>>> from sklearn.metrics.pairwise import linear_kernel >>> X = [[0, 0, 0], [1, 1, 1]] >>> Y = [[1, 0, 0], [1, 1, 0]] >>> linear_kernel(X, Y) array([[0., 0.], [1., 2.]])