pyowl.py 文件源码

def _fit_owl_fista(X, y, w, loss, max_iter=500, max_linesearch=20, eta=2.0,
                   tol=1e-3, verbose=0):

    # least squares loss
    def sfunc(coef, grad=False):
        y_scores = safe_sparse_dot(X, coef)
        if grad:
            obj, lp = loss(y, y_scores, return_derivative=True)
            grad = safe_sparse_dot(X.T, lp)
            return obj, grad
        else:
            return loss(y, y_scores)

    def nsfunc(coef, L):
        return prox_owl(coef, w / L)

    coef = np.zeros(X.shape[1])
    return fista(sfunc, nsfunc, coef, max_iter, max_linesearch,
                 eta, tol, verbose)