ridge.py 文件源码

def fit(self, X, y):
        """Fit OVK ridge regression model.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape = [n_samples, n_features]
            Training data.

        y : {array-like}, shape = [n_samples] or [n_samples, n_targets]
            Target values. numpy.NaN for missing targets (semi-supervised
            learning).

        Returns
        -------
        self : returns an instance of self.
        """
        X = check_array(X, force_all_finite=True, accept_sparse=False,
                        ensure_2d=True)
        y = check_array(y, force_all_finite=False, accept_sparse=False,
                        ensure_2d=False)
        if y.ndim == 1:
            y = check_array(y, force_all_finite=True, accept_sparse=False,
                            ensure_2d=False)
        self._validate_params()

        solver_params = self.solver_params or {}

        self.linop_ = self._get_kernel_map(X, y)
        Gram = self.linop_(X)
        risk = OVKRidgeRisk(self.lbda)

        if not issubdtype(y.dtype, number):
            raise ValueError("Unknown label type: %r" % y.dtype)
        if y.ndim > 1:
            is_sup = ~all(isnan(y), axis=1)
        else:
            is_sup = ~isnan(y)

        if sum(~is_sup) > 0:
            self.L_ = _graph_Laplacian(rbf_kernel(X[~is_sup, :],
                                                  gamma=self.gamma_m))
        else:
            self.L_ = empty((0, 0))

        p = y.shape[1] if y.ndim > 1 else 1
        weight, zeronan = _SemisupLinop(self.lbda_m, is_sup, self.L_, p).gen()

        self.solver_res_ = minimize(risk.functional_grad_val,
                                    zeros(Gram.shape[1]),
                                    args=(y.ravel(), Gram, weight, zeronan),
                                    method=self.solver,
                                    jac=True,
                                    options=solver_params)
        self.dual_coefs_ = self.solver_res_.x
        return self