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 theUser 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. IfNone, usesY=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

>>>fromsklearn.metrics.pairwiseimportrbf_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]])