INFO:root:Namespace(input_file='data/uniform.npy', output_dir='outputs/462', architecture='combined', number_of_states=2, log_f='trainig_log', no_mean=0, threads=20, fit='d', training_opt=[10, 1000, 100], opt_options=[1e-06, 1e-06, 15000.0, 10.0])
INFO:root:Best Optimization Result for iteration 0 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 3.3021710195264307
        x: [-2.251e+00 -1.500e+01]
      nit: 20
      jac: [-1.776e-07  2.887e-06]
     nfev: 69
     njev: 23
 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 1 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 3.3021710195264133
        x: [-2.251e+00 -1.500e+01]
      nit: 20
      jac: [-1.332e-07  2.931e-06]
     nfev: 69
     njev: 23
 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 2 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 3.3021710195264147
        x: [-2.251e+00 -1.500e+01]
      nit: 19
      jac: [-1.332e-07  3.020e-06]
     nfev: 63
     njev: 21
 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 3 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 3.3021710195265284
        x: [-2.251e+00 -1.500e+01]
      nit: 19
      jac: [ 5.329e-07  3.020e-06]
     nfev: 63
     njev: 21
 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 4 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 3.3021710195264466
        x: [-2.251e+00 -1.500e+01]
      nit: 19
      jac: [ 3.109e-07  2.709e-06]
     nfev: 63
     njev: 21
 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 5 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 3.302171019526417
        x: [-2.251e+00 -1.500e+01]
      nit: 19
      jac: [-1.332e-07  2.975e-06]
     nfev: 63
     njev: 21
 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 6 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 3.302171019526875
        x: [-2.251e+00 -1.500e+01]
      nit: 19
      jac: [-9.770e-07  2.620e-06]
     nfev: 63
     njev: 21
 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 7 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 3.302171019526918
        x: [-2.251e+00 -1.500e+01]
      nit: 19
      jac: [-7.550e-07  2.975e-06]
     nfev: 63
     njev: 21
 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 8 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 3.3021710195265257
        x: [-2.251e+00 -1.500e+01]
      nit: 21
      jac: [-3.553e-07  2.753e-06]
     nfev: 69
     njev: 23
 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>
INFO:root:Best Optimization Result for iteration 9 :   message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
  success: True
   status: 0
      fun: 3.302171019526429
        x: [-2.251e+00 -1.500e+01]
      nit: 19
      jac: [ 1.776e-07  3.020e-06]
     nfev: 63
     njev: 21
 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>
