sklearn.metrics.pairwise
.laplacian_kernel#
- sklearn.metrics.pairwise.laplacian_kernel(X, Y=None, gamma=None)[source]#
Compute the laplacian kernel between X and Y.
The laplacian kernel is defined as:
K(x, y) = exp(-gamma ||x-y||_1)
for each pair of rows x in X and y in Y. Read more in the User Guide.
New in version 0.17.
- 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
.- gammafloat, default=None
If None, defaults to 1.0 / n_features. Otherwise it should be strictly positive.
- Returns:
- kernelndarray of shape (n_samples_X, n_samples_Y)
The kernel matrix.
Examples
>>> from sklearn.metrics.pairwise import laplacian_kernel >>> X = [[0, 0, 0], [1, 1, 1]] >>> Y = [[1, 0, 0], [1, 1, 0]] >>> laplacian_kernel(X, Y) array([[0.71..., 0.51...], [0.51..., 0.71...]])