@@ -0,0 +1,1389 @@
+from numpy.testing import assert_%0Afrom statsmodels.base.model import (_fit_mle_newton, _fit_mle_nm,%0A                                    _fit_mle_bfgs, _fit_mle_cg,%0A                                    _fit_mle_ncg, _fit_mle_powell)%0A%0Afit_funcs = %7B%0A    'newton': _fit_mle_newton,%0A    'nm': _fit_mle_nm,  # Nelder-Mead%0A    'bfgs': _fit_mle_bfgs,%0A    'cg': _fit_mle_cg,%0A    'ncg': _fit_mle_ncg,%0A    'powell': _fit_mle_powell%0A            %7D%0A%0A%0Adef dummy_func(x):%0A    return x**2%0A%0Adef dummy_score(x):%0A    return 2*x%0A%0Adef dummy_hess(x):%0A    return %5B%5B2%5D%5D%0A%0Adef test_full_output_false():%0A    # just a smoke test%0A%0A    # newton needs f, score, start, fargs, kwargs%0A    # bfgs needs f, score start, fargs, kwargs%0A    # nm needs %22%22%0A    # cg %22%22%0A    # ncg %22%22%0A    # powell %22%22%0A    for method in fit_funcs:%0A        func = fit_funcs%5Bmethod%5D%0A        if method == %22newton%22:%0A            xopts, retvals = func(dummy_func, dummy_score, %5B1%5D, (), %7B%7D,%0A                    hess=dummy_hess, full_output=False, disp=0)%0A%0A        else:%0A            xopts, retvals = func(dummy_func, dummy_score, %5B1%5D, (), %7B%7D,%0A                full_output=False, disp=0)%0A        assert_(xopts == None)%0A        if method == %22powell%22:%0A            #NOTE: I think I reported this? Might be version/optimize API%0A            # dependent%0A            assert_(retvals.shape == () and retvals.size == 1)%0A        else:%0A            assert_(len(retvals)==1)%0A

Test fit funcs for full_output = False