INFO:root:Namespace(input_file='data/bimodal.npy', output_dir='outputs/1123', architecture='k-jumps', number_of_states=30, 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=7)
INFO:root:Best Optimization Result for iteration 0 :   message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
  success: True
   status: 0
      fun: 3.9194128351175195
        x: [ 9.259e+00 -1.200e+01 ...  9.768e-01  5.621e-01]
      nit: 74
      jac: [-1.776e-07  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 6723
     njev: 83
 hess_inv: <80x80 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 1 :   message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
  success: True
   status: 0
      fun: 3.919694414802473
        x: [ 1.205e+01 -1.171e+01 ...  3.892e-01  2.275e-01]
      nit: 79
      jac: [ 0.000e+00  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 7047
     njev: 87
 hess_inv: <80x80 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 2 :   message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
  success: True
   status: 0
      fun: 3.9193094431666866
        x: [ 1.106e+01 -1.174e+01 ...  2.193e-01  8.014e-02]
      nit: 95
      jac: [-4.441e-08  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 9072
     njev: 112
 hess_inv: <80x80 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 3 :   message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
  success: True
   status: 0
      fun: 3.919242746481819
        x: [ 1.247e+01 -1.220e+01 ...  9.882e-01  1.498e-01]
      nit: 90
      jac: [ 0.000e+00  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 8100
     njev: 100
 hess_inv: <80x80 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 4 :   message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
  success: True
   status: 0
      fun: 3.9192565442744534
        x: [ 1.043e+01 -1.072e+01 ...  3.300e-01  2.533e-01]
      nit: 93
      jac: [-8.882e-08  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 8505
     njev: 105
 hess_inv: <80x80 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 5 :   message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
  success: True
   status: 0
      fun: 3.920239761885635
        x: [ 8.437e+00 -1.064e+01 ...  6.880e-01  9.574e-01]
      nit: 52
      jac: [-4.885e-07  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 4698
     njev: 58
 hess_inv: <80x80 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 6 :   message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
  success: True
   status: 0
      fun: 3.9196186974761122
        x: [ 9.954e+00 -1.100e+01 ...  5.622e-01  7.845e-01]
      nit: 87
      jac: [-8.882e-08  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 7614
     njev: 94
 hess_inv: <80x80 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 7 :   message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
  success: True
   status: 0
      fun: 3.919705771723924
        x: [ 8.913e+00 -1.060e+01 ...  7.850e-01  3.432e-01]
      nit: 88
      jac: [-2.665e-07  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 8181
     njev: 101
 hess_inv: <80x80 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 8 :   message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
  success: True
   status: 0
      fun: 3.919757528686655
        x: [ 1.468e+01 -1.408e+01 ...  4.950e-01  2.358e-01]
      nit: 75
      jac: [ 0.000e+00  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 6723
     njev: 83
 hess_inv: <80x80 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 9 :   message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
  success: True
   status: 0
      fun: 3.920418532329974
        x: [ 9.959e+00 -1.178e+01 ...  7.154e-01  9.594e-01]
      nit: 80
      jac: [-1.332e-07  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 6966
     njev: 86
 hess_inv: <80x80 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.
