INFO:root:Namespace(input_file='data/bimodal.npy', output_dir='outputs/232', architecture='escape_chain', number_of_states=4, log_f='trainig_log', no_mean=0, 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: [-1.500e+01 -1.112e+01  7.662e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00]
     nfev: 4
     njev: 1
 hess_inv: <3x3 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: [-1.500e+01 -1.086e+01  5.349e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00]
     nfev: 4
     njev: 1
 hess_inv: <3x3 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: [-1.500e+01 -1.079e+01  2.394e-03]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00]
     nfev: 4
     njev: 1
 hess_inv: <3x3 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: [-1.500e+01 -1.125e+01  9.510e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00]
     nfev: 4
     njev: 1
 hess_inv: <3x3 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: [-1.500e+01 -9.169e+00  9.942e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00]
     nfev: 4
     njev: 1
 hess_inv: <3x3 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: [-1.500e+01 -1.137e+01  4.766e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00]
     nfev: 4
     njev: 1
 hess_inv: <3x3 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: [-1.500e+01 -1.111e+01  9.566e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00]
     nfev: 4
     njev: 1
 hess_inv: <3x3 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: [-1.500e+01 -1.099e+01  1.351e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00]
     nfev: 4
     njev: 1
 hess_inv: <3x3 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: [-1.500e+01 -9.231e+00  2.216e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00]
     nfev: 4
     njev: 1
 hess_inv: <3x3 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: [-1.500e+01 -9.050e+00  4.467e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00]
     nfev: 4
     njev: 1
 hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>
