`sklearn.metrics.pairwise`.polynomial_kernel¶

sklearn.metrics.pairwise.polynomial_kernel(X, Y=None, degree=3, gamma=None, coef0=1)[source]¶

Compute the polynomial kernel between X and Y.

K(X, Y) = (gamma <X, Y> + coef0) ^ degree

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.
degreefloat, default=3: Kernel degree.
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): The polynomial kernel.

Examples

>>> from sklearn.metrics.pairwise import polynomial_kernel
>>> X = [[0, 0, 0], [1, 1, 1]]
>>> Y = [[1, 0, 0], [1, 1, 0]]
>>> polynomial_kernel(X, Y, degree=2)
array([[1.     , 1.     ],
       [1.77..., 2.77...]])

sklearn.metrics.pairwise.polynomial_kernel¶

`sklearn.metrics.pairwise`.polynomial_kernel¶