INFO:root:Namespace(input_file='data/far_gaussian.npy', output_dir='outputs/949', architecture='k-jumps', number_of_states=6, 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: [ 4.638e-01  7.823e-01  2.023e-01  4.850e-01  6.285e-01
             1.366e-01  7.192e-01  6.386e-01  1.956e-01  2.830e-02
             4.894e-01  2.812e-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  0.000e+00]
     nfev: 13
     njev: 1
 hess_inv: <12x12 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: [ 9.247e-01  5.515e-01  6.389e-01  2.607e-02  8.548e-01
             2.104e-01  8.685e-01  1.854e-01  7.885e-01  6.285e-01
             3.533e-01  6.264e-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  0.000e+00]
     nfev: 13
     njev: 1
 hess_inv: <12x12 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: [ 1.879e-01  3.903e-01  9.183e-01  3.781e-01  8.687e-02
             4.078e-01  5.471e-01  6.648e-01  4.781e-01  8.217e-01
             4.776e-01  5.414e-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  0.000e+00]
     nfev: 13
     njev: 1
 hess_inv: <12x12 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: [ 8.561e-01  8.945e-01  5.296e-01  2.964e-01  3.518e-01
             6.209e-01  5.771e-01  6.351e-01  3.517e-01  9.780e-01
             2.951e-01  9.161e-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  0.000e+00]
     nfev: 13
     njev: 1
 hess_inv: <12x12 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: [ 3.194e-01  8.981e-01  2.042e-03  8.370e-01  7.713e-01
             6.354e-01  7.173e-01  8.372e-01  2.201e-01  2.476e-01
             1.946e-01  3.796e-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  0.000e+00]
     nfev: 13
     njev: 1
 hess_inv: <12x12 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: [ 9.973e-02  2.776e-01  5.515e-02  7.077e-01  1.523e-02
             3.952e-02  4.187e-01  4.369e-01  8.077e-01  3.206e-01
             3.576e-01  5.994e-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  0.000e+00]
     nfev: 13
     njev: 1
 hess_inv: <12x12 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: [ 4.117e-01  8.057e-01  9.181e-01  2.496e-02  5.441e-01
             3.279e-01  2.614e-01  1.667e-01  3.744e-01  2.322e-01
             3.991e-02  5.923e-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  0.000e+00]
     nfev: 13
     njev: 1
 hess_inv: <12x12 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: [ 1.386e-01  8.975e-01  9.522e-01  3.565e-01  8.311e-01
             4.819e-01  6.554e-01  7.662e-01  8.071e-01  9.443e-01
             8.788e-01  2.088e-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  0.000e+00]
     nfev: 13
     njev: 1
 hess_inv: <12x12 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: [ 8.651e-01  7.009e-01  7.970e-01  5.440e-01  8.150e-01
             9.299e-01  1.653e-01  4.802e-01  4.151e-01  8.725e-01
             3.409e-01  3.740e-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  0.000e+00]
     nfev: 13
     njev: 1
 hess_inv: <12x12 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: [ 1.983e-01  7.109e-01  6.267e-01  7.845e-01  8.974e-01
             7.074e-01  2.532e-01  6.080e-01  3.179e-02  3.254e-01
             6.019e-01  4.954e-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  0.000e+00]
     nfev: 13
     njev: 1
 hess_inv: <12x12 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.
