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.

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, uses Y=X.

gammafloat, default=None

If None, defaults to 1.0 / n_features. Otherwise it should be strictly positive.

kernel_matrixndarray of shape (n_samples_X, n_samples_Y)

The kernel matrix.