INFO:root:Namespace(input_file='data/far_gaussian.npy', output_dir='outputs/388', architecture='escape_chain', number_of_states=12, 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: 46.05170185988091
        x: [ 7.536e-02  4.053e-01  8.338e-01  4.466e-01  8.299e-01
             7.515e-01  9.054e-01  8.068e-01  7.760e-01  8.820e-01
             1.538e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00]
     nfev: 12
     njev: 1
 hess_inv: <11x11 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 1 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 46.05170185988091
        x: [ 7.658e-01  4.410e-01  6.504e-01  6.262e-01  5.031e-01
             1.905e-01  7.196e-01  3.811e-01  5.943e-01  8.585e-01
             1.248e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00]
     nfev: 12
     njev: 1
 hess_inv: <11x11 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 2 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 46.05170185988091
        x: [ 8.641e-01  2.352e-01  8.525e-01  1.795e-01  1.830e-01
             6.910e-03  4.772e-01  1.736e-01  7.930e-01  3.017e-01
             8.492e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00]
     nfev: 12
     njev: 1
 hess_inv: <11x11 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 3 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 46.05170185988091
        x: [ 2.040e-01  6.675e-01  4.493e-01  1.223e-01  7.503e-01
             2.144e-01  5.080e-01  2.300e-01  4.806e-01  2.186e-01
             8.157e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00]
     nfev: 12
     njev: 1
 hess_inv: <11x11 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 4 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 46.05170185988091
        x: [ 8.419e-01  7.790e-01  7.911e-01  6.053e-01  5.694e-01
             4.202e-01  4.239e-01  8.232e-01  6.332e-01  7.554e-02
             1.876e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00]
     nfev: 12
     njev: 1
 hess_inv: <11x11 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 5 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 46.05170185988091
        x: [ 7.260e-01  8.983e-01  3.178e-01  3.481e-02  1.111e-01
             9.209e-01  8.999e-01  8.327e-01  5.272e-01  3.552e-01
             4.068e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00]
     nfev: 12
     njev: 1
 hess_inv: <11x11 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 6 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 46.05170185988091
        x: [ 9.638e-01  3.493e-01  8.532e-01  9.863e-01  3.458e-01
             9.736e-01  5.499e-01  1.349e-01  5.933e-01  7.197e-01
             6.963e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00]
     nfev: 12
     njev: 1
 hess_inv: <11x11 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 7 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 46.05170185988091
        x: [ 3.121e-01  6.200e-01  9.299e-03  9.506e-01  7.348e-01
             1.197e-01  1.514e-01  9.459e-01  5.641e-01  7.084e-01
             6.077e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00]
     nfev: 12
     njev: 1
 hess_inv: <11x11 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 8 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 46.05170185988091
        x: [ 1.783e-01  7.665e-01  3.736e-02  7.957e-01  2.989e-01
             6.759e-01  1.536e-01  8.041e-01  4.443e-01  5.052e-01
             5.105e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00]
     nfev: 12
     njev: 1
 hess_inv: <11x11 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 9 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 46.05170185988091
        x: [ 1.114e-01  3.017e-01  4.107e-01  7.530e-01  3.141e-01
             1.153e-01  2.778e-01  4.721e-01  2.770e-01  6.944e-01
             6.897e-02]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00]
     nfev: 12
     njev: 1
 hess_inv: <11x11 LbfgsInvHessProduct with dtype=float64>
