rbf_kernel

sklearn.metrics.pairwise.rbf_kernel(X, Y=None, gamma=None)[source]

Compute the rbf (gaussian) kernel between X and Y.

K(x, y) = exp(-gamma ||x-y||^2)

for each pair of rows x in X and y in 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, uses Y=X.

gammafloat, default=None

If None, defaults to 1.0 / n_features.

Returns:

kernelndarray of shape (n_samples_X, n_samples_Y)

The RBF kernel.

Examples

>>> from sklearn.metrics.pairwise import rbf_kernel
>>> X = [[0, 0, 0], [1, 1, 1]]
>>> Y = [[1, 0, 0], [1, 1, 0]]
>>> rbf_kernel(X, Y)
array([[0.71, 0.51],
       [0.51, 0.71]])

Gallery examples

Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset