linearSVM.py 文件源码

def _obj_func(self, w, X, Y, out):
        """
        Computes primal value end gradient
        Parameters
        ----------
        w : {array-like} - hyperplane normal vector
        X : {array-like, sparse matrix}, shape = [n_samples, n_features]
            Training vector, where n_samples in the number of samples and
            n_features is the number of features.
        Y : array-like, shape = [n_samples]
            Target vector relative to X
        out: loss function values
        Returns
        -------
        (obj,grad) : tuple, obj - function value, grad - gradient

        """

        l2reg = self.l2reg

        # we remember bias, to recover it after gradient computation
        bias = w[-1]
        # set bias to zero, don't penalize b
        w[-1] = 0

        max_out = np.fmax(0, out)
        obj = np.sum(max_out ** 2) / 2 + l2reg * w.dot(w) / 2

        grad = l2reg * w - np.append([np.dot(max_out * Y, X)], [np.sum(max_out * Y)])

        w[-1] = bias

        return (obj, grad)