class_docstr: The problem of fitting the sum of exponentials with linear degrees
  to data, [1]_.
  
  Number of variables --- 5, number of residuals --- 33, no bounds.
  
  .. [1] Brett M. Averick et al. "The MINPACK-2 Test Problem Collection",
         p. 26
class_docstr_tok: ["The","problem","of","fitting","the","sum","of","exponentials","with","linear","degrees","to","data",",","[","1","]","_",".","Number","of","variables","--","-","5",",","number","of","residuals","--","-","33",",","no","bounds",".","..","[","1","]","Brett","M.","Averick","et","al",".","``","The","MINPACK-2","Test","Problem","Collection","''",",","p.","26"]
code: def __init__(self, x0):
      super().__init__(5, 33, 5.464895e-5, x0)
      self.t = np.arange(self.m, dtype=float) * 10
      self.y = 1e-1 * np.array(
          [
              8.44,
              9.08,
              9.32,
              9.36,
              9.25,
              9.08,
              8.81,
              8.5,
              8.18,
              7.84,
              7.51,
              7.18,
              6.85,
              6.58,
              6.28,
              6.03,
              5.8,
              5.58,
              5.38,
              5.22,
              5.06,
              4.9,
              4.78,
              4.67,
              4.57,
              4.48,
              4.38,
              4.31,
              4.24,
              4.2,
              4.14,
              4.11,
              4.06,
          ]
      )
  
imports: import inspect
  import sys
  import numpy
  from numpy.polynomial.chebyshev import Chebyshev
  from scipy.integrate import odeint
label_desc: Use image node_id 1 to create a new ExponentialFitting object from inherited base classes: LSQBenchmarkProblem with example: obj = ExponentialFitting(x0)