INFO:root:Namespace(input_file='data/bimodal.npy', output_dir='outputs/805', architecture='cyclic', 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: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 8.610537202405319e-06
        x: [-1.500e+01]
      nit: 5
      jac: [ 8.125e-06]
     nfev: 24
     njev: 12
 hess_inv: <1x1 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 1 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 8.610537202405319e-06
        x: [-1.500e+01]
      nit: 1
      jac: [ 8.125e-06]
     nfev: 4
     njev: 2
 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: 8.610537202405319e-06
        x: [-1.500e+01]
      nit: 11
      jac: [ 8.125e-06]
     nfev: 30
     njev: 15
 hess_inv: <1x1 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 3 :   message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
  success: True
   status: 0
      fun: 1.1721043776305558e-05
        x: [-1.469e+01]
      nit: 2
      jac: [ 1.125e-05]
     nfev: 18
     njev: 9
 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: 8.610537202405319e-06
        x: [-1.500e+01]
      nit: 2
      jac: [ 8.125e-06]
     nfev: 18
     njev: 9
 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: 8.610537202405319e-06
        x: [-1.500e+01]
      nit: 3
      jac: [ 8.125e-06]
     nfev: 20
     njev: 10
 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: 8.610537202405319e-06
        x: [-1.500e+01]
      nit: 5
      jac: [ 8.125e-06]
     nfev: 24
     njev: 12
 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: 8.610537202405319e-06
        x: [-1.500e+01]
      nit: 13
      jac: [ 8.125e-06]
     nfev: 32
     njev: 16
 hess_inv: <1x1 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 8 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 8.610537202405319e-06
        x: [-1.500e+01]
      nit: 1
      jac: [ 8.125e-06]
     nfev: 4
     njev: 2
 hess_inv: <1x1 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 9 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 8.610537202405319e-06
        x: [-1.500e+01]
      nit: 1
      jac: [ 8.125e-06]
     nfev: 4
     njev: 2
 hess_inv: <1x1 LbfgsInvHessProduct with dtype=float64>
