I do not know whether it this problem is related to the previous one.Runfile('/home/herman/.config/spyder-p圓/temp.py', wdir='/home/herman/.config/spyder-p圓')įile "/home/herman/anaconda3/lib/python3.5/site-packages/spyder/utils/site/sitecustomize.py", line 866, in runfileįile "/home/herman/anaconda3/lib/python3.5/site-packages/spyder/utils/site/sitecustomize.py", line 102, in execfileĮxec(compile(f.read(), filename, 'exec'), namespace)įile "/home/herman/.config/spyder-p圓/temp.py", line 20, in It usually start decrease at the second or the third iteration, even through 'ABNORMAL_TERMINATION_IN_LNSRCH' does not occurs. I also find that the log-likelihood does not monotonically increase: # Convergence !!! # What are the possible reasons that I should investigate? What does it mean by "rounding error dominate computation"? But here I do not provide gradient function because I am using 'approx_grad'. Someone said it occurs often because the objective and gradient functions do not match. I know that it means the minimum can be be reached in this iteration. (Most get 'CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL' or 'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH').
I do not get this warning every time, but sometimes. Subspace minimization time 0.000E+00 seconds. Possible causes: 1 error in function or gradient evaluation Line search cannot locate an adequate point after 20 functionĪnd gradient evaluations. Projg = norm of the final projected gradient Nact = number of active bounds at final generalized Cauchy point Tnint = total number of segments explored during Cauchy searches Tnf = total number of function evaluations Information dictionary: Īt X0 0 variables are exactly at the boundsĪt iterate 0 f= 1.14462D-07 |proj g|= 3.51719D-05 sigma_sp_new, func_val, info_dict = fmin_l_bfgs_b(func_to_minimize, self.sigma_vector,Īrgs=(self.w_vectors, Y, X, E_step_results),Īpprox_grad=True, bounds=, factr=1e02, pgtol=1e-05, epsilon=1e-08)īut sometimes I got a warning 'ABNORMAL_TERMINATION_IN_LNSRCH' in the information dictionary: func_to_minimize value = 1.14462324063e-07 The means of mixture distributions are modeled by regressions whose weights have to be optimized using EM algorithm. I am using _l_bfgs_b to solve a gaussian mixture problem. _l_bfgs_b returns 'ABNORMAL_TERMINATION_IN_LNSRCH'
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