lbfgsb.py 文件源码

def fgrad(we, X, y, l1, l2):
        nsamples, nfactors = X.shape

        w0 = we[0]
        w = we[1:(nfactors+1)] - we[(nfactors+1):]
        yz = y * (safe_sparse_dot(X, w) + w0)
        f = - np.sum(log_logistic(yz)) + l1 * np.sum(we[1:]) + 0.5 * l2 * np.dot(w, w)

        e = (expit(yz) - 1) * y
        g = safe_sparse_dot(X.T, e) + l2 * w
        g0 = np.sum(e)

        grad = np.concatenate([g, -g]) + l1
        grad = np.insert(grad, 0, g0)

        return f, grad