INFO:root:Namespace(input_file='data/far_gaussian.npy', output_dir='outputs/948', architecture='k-jumps', number_of_states=4, 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], k=1, l=3)
INFO:root:Best Optimization Result for iteration 0 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 46.05170185988091
        x: [ 6.044e-01  8.678e-01  8.892e-01  4.255e-01  8.722e-01
             7.719e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00]
     nfev: 7
     njev: 1
 hess_inv: <6x6 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: [ 4.157e-01  1.571e-01  1.158e-01  2.103e-01  9.843e-01
             8.120e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00]
     nfev: 7
     njev: 1
 hess_inv: <6x6 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: [ 8.219e-01  4.393e-02  4.894e-01  2.586e-01  5.808e-01
             9.259e-02]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00]
     nfev: 7
     njev: 1
 hess_inv: <6x6 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: [ 5.366e-01  1.871e-01  6.783e-01  5.388e-01  9.631e-01
             3.609e-02]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00]
     nfev: 7
     njev: 1
 hess_inv: <6x6 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: [ 9.974e-01  6.021e-01  1.238e-01  5.961e-01  5.759e-01
             2.433e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00]
     nfev: 7
     njev: 1
 hess_inv: <6x6 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: [ 7.052e-01  2.193e-01  5.183e-01  5.222e-01  9.149e-01
             7.445e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00]
     nfev: 7
     njev: 1
 hess_inv: <6x6 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: [ 7.089e-01  3.638e-01  9.918e-01  1.542e-01  4.410e-01
             3.524e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00]
     nfev: 7
     njev: 1
 hess_inv: <6x6 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: [ 8.867e-01  6.491e-01  6.642e-01  8.164e-01  4.585e-01
             3.527e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00]
     nfev: 7
     njev: 1
 hess_inv: <6x6 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: [ 6.919e-01  1.344e-01  4.292e-01  9.078e-01  8.365e-01
             6.421e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00]
     nfev: 7
     njev: 1
 hess_inv: <6x6 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: [ 7.142e-01  2.553e-01  6.885e-01  7.921e-01  6.294e-01
             8.037e-01]
      nit: 0
      jac: [ 0.000e+00  0.000e+00  0.000e+00  0.000e+00  0.000e+00
             0.000e+00]
     nfev: 7
     njev: 1
 hess_inv: <6x6 LbfgsInvHessProduct with dtype=float64>
WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
