sklearn.metrics.pairwise
.sigmoid_kernel#
- sklearn.metrics.pairwise.sigmoid_kernel(X, Y=None, gamma=None, coef0=1)[source]#
Compute the sigmoid kernel between X and Y.
K(X, Y) = tanh(gamma <X, Y> + coef0)
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
.- gammafloat, default=None
Coefficient of the vector inner product. If None, defaults to 1.0 / n_features.
- coef0float, default=1
Constant offset added to scaled inner product.
- Returns:
- kernelndarray of shape (n_samples_X, n_samples_Y)
Sigmoid kernel between two arrays.
Examples
>>> from sklearn.metrics.pairwise import sigmoid_kernel >>> X = [[0, 0, 0], [1, 1, 1]] >>> Y = [[1, 0, 0], [1, 1, 0]] >>> sigmoid_kernel(X, Y) array([[0.76..., 0.76...], [0.87..., 0.93...]])