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:

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

Read more in the User Guide.

Parameters:

Parameters:	X : ndarray of shape (n_samples_1, n_features) Y : ndarray of shape (n_samples_2, n_features) degree : int, default 3 gamma : float, default None if None, defaults to 1.0 / n_samples_1 coef0 : int, default 1
Returns:	Gram matrix : array of shape (n_samples_1, n_samples_2)

X : ndarray of shape (n_samples_1, n_features)

Y : ndarray of shape (n_samples_2, n_features)

degree : int, default 3

gamma : float, default None

if None, defaults to 1.0 / n_samples_1

coef0 : int, default 1

Returns:

Gram matrix : array of shape (n_samples_1, n_samples_2)

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