INFO:root:Namespace(input_file='data/bimodal.npy', output_dir='outputs/408', architecture='escape_chain', number_of_states=2, log_f='trainig_log', no_mean=1, threads=1, fit='d', training_opt=[10, 1000, 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: 46.051701859880914
        x: [ 8.328e-01]
      nit: 0
      jac: [ 0.000e+00]
     nfev: 2
     njev: 1
 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: 46.051701859880914
        x: [ 9.462e-01]
      nit: 0
      jac: [ 0.000e+00]
     nfev: 2
     njev: 1
 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: 46.051701859880914
        x: [ 6.707e-01]
      nit: 0
      jac: [ 0.000e+00]
     nfev: 2
     njev: 1
 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: 46.051701859880914
        x: [ 5.748e-01]
      nit: 0
      jac: [ 0.000e+00]
     nfev: 2
     njev: 1
 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: 46.051701859880914
        x: [ 4.992e-01]
      nit: 0
      jac: [ 0.000e+00]
     nfev: 2
     njev: 1
 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: 46.051701859880914
        x: [ 7.054e-01]
      nit: 0
      jac: [ 0.000e+00]
     nfev: 2
     njev: 1
 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: 46.051701859880914
        x: [ 8.940e-01]
      nit: 0
      jac: [ 0.000e+00]
     nfev: 2
     njev: 1
 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: 46.051701859880914
        x: [ 4.975e-01]
      nit: 0
      jac: [ 0.000e+00]
     nfev: 2
     njev: 1
 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: 46.051701859880914
        x: [ 8.286e-01]
      nit: 0
      jac: [ 0.000e+00]
     nfev: 2
     njev: 1
 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: 46.051701859880914
        x: [ 1.867e-01]
      nit: 0
      jac: [ 0.000e+00]
     nfev: 2
     njev: 1
 hess_inv: <1x1 LbfgsInvHessProduct with dtype=float64>
