INFO:root:Namespace(input_file='data/bimodal.npy', output_dir='outputs/1031', 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=5)
INFO:root:Best Optimization Result for iteration 0 :   message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
  success: True
   status: 0
      fun: 3.8810415601538293
        x: [ 8.249e+00 -1.393e+01 ...  5.067e-01  5.774e-01]
      nit: 127
      jac: [-7.105e-07  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 12948
     njev: 156
 hess_inv: <82x82 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: 3.8830648628048796
        x: [ 1.192e+01 -1.298e+01 ...  8.299e-01  3.455e-01]
      nit: 96
      jac: [-4.441e-08  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 9462
     njev: 114
 hess_inv: <82x82 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: 3.890564523715486
        x: [ 9.288e+00 -1.097e+01 ...  9.644e-01  5.193e-01]
      nit: 98
      jac: [ 0.000e+00  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 9545
     njev: 115
 hess_inv: <82x82 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: 3.8811255248523615
        x: [ 1.487e+01 -1.489e+01 ...  4.169e-02  9.650e-01]
      nit: 115
      jac: [-4.441e-08  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 11952
     njev: 144
 hess_inv: <82x82 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: 3.889092606404498
        x: [ 1.163e+01 -1.097e+01 ...  9.625e-01  8.416e-01]
      nit: 57
      jac: [-4.441e-08  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 5312
     njev: 64
 hess_inv: <82x82 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: 3.89034242632023
        x: [ 1.054e+01 -1.057e+01 ...  5.310e-01  4.874e-01]
      nit: 79
      jac: [-4.441e-08  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 7636
     njev: 92
 hess_inv: <82x82 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: 3.8907517791764565
        x: [ 9.760e+00 -1.071e+01 ...  1.122e-01  1.240e-01]
      nit: 71
      jac: [-1.776e-07  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 7138
     njev: 86
 hess_inv: <82x82 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: 3.890268169532412
        x: [ 1.220e+01 -1.253e+01 ...  6.566e-01  5.669e-01]
      nit: 65
      jac: [ 0.000e+00  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 6059
     njev: 73
 hess_inv: <82x82 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: 3.8893953326730406
        x: [ 1.042e+01 -1.500e+01 ...  1.247e-01  8.044e-01]
      nit: 80
      jac: [-8.882e-08  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 7885
     njev: 95
 hess_inv: <82x82 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: 3.888957790456867
        x: [ 1.384e+01 -1.500e+01 ...  9.455e-01  3.881e-02]
      nit: 89
      jac: [ 4.441e-08  0.000e+00 ...  0.000e+00  0.000e+00]
     nfev: 8217
     njev: 99
 hess_inv: <82x82 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.
