INFO:root:Namespace(input_file='data/bimodal.npy', output_dir='outputs/612', architecture='full', number_of_states=2, log_f='trainig_log', no_mean=1, threads=1, fit='d', training_opt=[10, 500, 100], opt_options=[1e-06, 1e-06, 15000.0, 10.0])
INFO:root:Best Optimization Result for iteration 0 :   message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
  success: True
   status: 0
      fun: 4.276632407029676
        x: [-3.258e+00]
      nit: 8
      jac: [-2.576e-06]
     nfev: 34
     njev: 17
 hess_inv: <1x1 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 1 :   message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
  success: True
   status: 0
      fun: 4.276632407039795
        x: [-3.258e+00]
      nit: 7
      jac: [ 4.530e-06]
     nfev: 32
     njev: 16
 hess_inv: <1x1 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 2 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 4.276632407027693
        x: [-3.258e+00]
      nit: 8
      jac: [-8.882e-07]
     nfev: 34
     njev: 17
 hess_inv: <1x1 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 3 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 4.276632407027477
        x: [-3.258e+00]
      nit: 9
      jac: [-3.553e-07]
     nfev: 32
     njev: 16
 hess_inv: <1x1 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 4 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 4.276632407027664
        x: [-3.258e+00]
      nit: 8
      jac: [-8.882e-07]
     nfev: 34
     njev: 17
 hess_inv: <1x1 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 5 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 4.276632407027537
        x: [-3.258e+00]
      nit: 9
      jac: [ 3.553e-07]
     nfev: 32
     njev: 16
 hess_inv: <1x1 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 6 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 4.2766324070277975
        x: [-3.258e+00]
      nit: 9
      jac: [-7.994e-07]
     nfev: 32
     njev: 16
 hess_inv: <1x1 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 7 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 4.276632407027723
        x: [-3.258e+00]
      nit: 8
      jac: [ 8.882e-07]
     nfev: 26
     njev: 13
 hess_inv: <1x1 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 8 :   message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
  success: True
   status: 0
      fun: 4.276632407030387
        x: [-3.258e+00]
      nit: 8
      jac: [-2.753e-06]
     nfev: 34
     njev: 17
 hess_inv: <1x1 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 9 :   message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
  success: True
   status: 0
      fun: 4.276632407028521
        x: [-3.258e+00]
      nit: 8
      jac: [-1.599e-06]
     nfev: 34
     njev: 17
 hess_inv: <1x1 LbfgsInvHessProduct with dtype=float64>
