'Predict using the kernel ridge model
Parameters
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Samples.
Returns
C : array, shape = [n_samples] or [n_samples, n_targets]
Returns predicted values.'

def predict(self, X):

    check_is_fitted(self, ['X_fit_', 'dual_coef_'])
    K = self._get_kernel(X, self.X_fit_)
    return np.dot(K, self.dual_coef_)
