hjmshi / noise-tolerant-bfgs Goto Github PK

UnboundLocalError                         Traceback (most recent call last)
~/Projects/multinomial_probit/safe/asan2.py in <module>
      9 idx = rng.choice(X.shape[0], size=10_000)
     10 model = MultinomialProbitRegression(lambda_=1e-4, presolve_logistic=False)
---> 11 model.fit(X[:10_000], y[:10_000])
     12 np.mean(model.predict(X[:10_000]) == y[:10_000])

~/Projects/multinomial_probit/multinomial_probit/__init__.py in fit(self, X, y, sample_weights)
    196         maxferr = X.shape[0] * 0.1
    197         import ntqn
--> 198         x_opt, f_opt, iters, f_evals, g_evals, flag, results = ntqn.bfgs_e(
    199             _mnp_fun_glob, _mnp_grad_glob, optvars, eps_f=maxferr,
    200             eps_g=maxferr

~/Projects/noise-tolerant-bfgs-master/ntqn.py in bfgs_e(func, grad, x0, eps_f, eps_g, callback, options)
    331         f_old = f_k
    332         alpha, beta, mu_hat, f_k, g_new, eps_fk, eps_gk, ls_fevals, ls_gevals, armijo_flag, wolfe_flag, split_flag = \
--> 333             _line_search_nt_wolfe(func, grad, x_k, p_k, eps_f=eps_f, eps_g=eps_g, f_k=f_k, g_k=g_k,
    334                                   alpha_init=options['alpha_init'], beta_init=options['beta_init'], mu=mu,
    335                                   c1=options['c1'], c2=options['c2'], c3=options['c3'],

~/Projects/noise-tolerant-bfgs-master/ntqn.py in _line_search_nt_wolfe(func, grad, x_k, p_k, eps_f, eps_g, f_k, g_k, alpha_init, beta_init, mu, c1, c2, c3, split_iter, max_ls_iter, verbose)
    860         if not wolfe_flag:
    861             beta, g_new, gtp_new, eps_gp, fevals_length, gevals_length, wolfe_flag = \
...
--> 862                 _lengthening(grad, x_k, p_k, beta=beta, eps_g=eps_g, g_k=g_k, eps_gk=eps_gk, eps_gp=eps_gp, gtp=gtp,
    863                              g_new=g_new, gtp_new=gtp_new, norm_pk=norm_pk, c3=c3, max_ls_iter=max_ls_iter,
    864                              verbose=verbose)

UnboundLocalError: local variable 'eps_gp' referenced before assignment