INFO:root:Namespace(input_file='data/bimodal.npy', output_dir='outputs/936', architecture='k-jumps', number_of_states=28, 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: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
  success: True
   status: 0
      fun: 3.8801901997030437
        x: [ 9.947e+00 -1.326e+01 ...  8.658e-01  7.349e-01]
      nit: 71
      jac: [-1.776e-07  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 6320
     njev: 80
 hess_inv: <78x78 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: 3.8809502865625882
        x: [ 1.500e+01 -1.500e+01 ...  4.290e-01  2.180e-01]
      nit: 62
      jac: [-4.441e-08  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 5372
     njev: 68
 hess_inv: <78x78 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: 3.8804379922792362
        x: [ 1.005e+01 -1.121e+01 ...  9.767e-01  3.123e-01]
      nit: 67
      jac: [-8.882e-08  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 6004
     njev: 76
 hess_inv: <78x78 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: 3.880948791523809
        x: [ 1.500e+01 -1.500e+01 ...  5.203e-01  8.395e-01]
      nit: 68
      jac: [ 0.000e+00  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 5846
     njev: 74
 hess_inv: <78x78 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: 3.8802467740060447
        x: [ 1.496e+01 -1.500e+01 ...  1.083e-01  8.743e-01]
      nit: 80
      jac: [ 4.441e-08  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 7189
     njev: 91
 hess_inv: <78x78 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: 3.880987269201606
        x: [ 1.500e+01 -1.500e+01 ...  9.684e-02  2.467e-01]
      nit: 77
      jac: [ 0.000e+00  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 7663
     njev: 97
 hess_inv: <78x78 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: 3.880631361501935
        x: [ 1.341e+01 -1.500e+01 ...  2.876e-01  4.719e-01]
      nit: 62
      jac: [ 0.000e+00  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 5925
     njev: 75
 hess_inv: <78x78 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: 3.879808023554141
        x: [ 1.253e+01 -1.500e+01 ...  5.922e-02  2.211e-01]
      nit: 102
      jac: [ 0.000e+00  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 9164
     njev: 116
 hess_inv: <78x78 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: 3.8802104732544653
        x: [ 1.500e+01 -1.500e+01 ...  3.264e-01  4.135e-01]
      nit: 96
      jac: [ 4.441e-08  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 8611
     njev: 109
 hess_inv: <78x78 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: 3.8807742850900113
        x: [ 1.395e+01 -1.461e+01 ...  1.042e-02  3.228e-01]
      nit: 68
      jac: [ 4.441e-08  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 6320
     njev: 80
 hess_inv: <78x78 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.
