INFO:root:Namespace(input_file='data/far_gaussian.npy', output_dir='outputs/328', architecture='escape_chain', number_of_states=12, log_f='trainig_log', no_mean=0, 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: [-1.500e+01 -1.500e+01 -1.235e+01 -1.500e+01 -1.500e+01
            -1.482e+01 -1.196e+01 -1.052e+01 -9.465e+00 -9.713e+00
             4.579e-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: [-1.500e+01 -1.500e+01 -1.500e+01 -1.500e+01 -1.500e+01
            -1.288e+01 -1.109e+01 -1.025e+01 -9.915e+00 -1.182e+01
             7.880e-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: [-1.500e+01 -1.500e+01 -1.258e+01 -1.500e+01 -1.500e+01
            -1.476e+01 -1.065e+01 -1.045e+01 -1.035e+01 -9.706e+00
             2.673e-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: [-1.500e+01 -1.500e+01 -1.363e+01 -1.500e+01 -1.500e+01
            -1.167e+01 -1.132e+01 -1.085e+01 -1.044e+01 -1.288e+01
             2.411e-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: [-1.500e+01 -1.500e+01 -1.314e+01 -1.500e+01 -1.500e+01
            -1.214e+01 -1.131e+01 -1.069e+01 -1.006e+01 -1.125e+01
             1.460e-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: [-1.500e+01 -1.500e+01 -1.290e+01 -1.500e+01 -1.500e+01
            -1.500e+01 -1.418e+01 -1.066e+01 -9.575e+00 -1.072e+01
             7.187e-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: [-1.500e+01 -1.500e+01 -1.292e+01 -1.500e+01 -1.500e+01
            -1.075e+01 -1.055e+01 -1.064e+01 -1.070e+01 -1.243e+01
             4.223e-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: [-1.500e+01 -1.500e+01 -1.500e+01 -1.500e+01 -1.500e+01
            -1.120e+01 -1.075e+01 -1.129e+01 -1.061e+01 -1.100e+01
             7.232e-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: [-1.500e+01 -1.500e+01 -1.291e+01 -1.500e+01 -1.500e+01
            -1.500e+01 -1.078e+01 -1.058e+01 -1.063e+01 -1.028e+01
             7.703e-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: [-1.500e+01 -1.500e+01 -1.391e+01 -1.500e+01 -1.500e+01
            -1.500e+01 -1.150e+01 -1.056e+01 -9.513e+00 -1.353e+01
             8.138e-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>
