INFO:root:Namespace(input_file='data/far_gaussian.npy', output_dir='outputs/261', architecture='combined', number_of_states=2, log_f='trainig_log', no_mean=1, threads=20, 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: 46.05170185988091
        x: [ 6.632e-01  4.980e-02]
      nit: 0
      jac: [ 0.000e+00  0.000e+00]
     nfev: 3
     njev: 1
 hess_inv: <2x2 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: [ 5.717e-01  3.900e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00]
     nfev: 3
     njev: 1
 hess_inv: <2x2 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: [ 3.221e-01  2.855e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00]
     nfev: 3
     njev: 1
 hess_inv: <2x2 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: [ 1.103e-01  7.884e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00]
     nfev: 3
     njev: 1
 hess_inv: <2x2 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: [ 2.717e-01  2.571e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00]
     nfev: 3
     njev: 1
 hess_inv: <2x2 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: [ 7.206e-01  9.234e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00]
     nfev: 3
     njev: 1
 hess_inv: <2x2 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: [ 1.081e-01  8.192e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00]
     nfev: 3
     njev: 1
 hess_inv: <2x2 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: [ 8.632e-01  6.392e-02]
      nit: 0
      jac: [ 0.000e+00  0.000e+00]
     nfev: 3
     njev: 1
 hess_inv: <2x2 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: [ 3.364e-01  3.025e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00]
     nfev: 3
     njev: 1
 hess_inv: <2x2 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: [ 5.945e-01  9.053e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00]
     nfev: 3
     njev: 1
 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>
