class_docstr: Results class for Gaussian process regression models.
code: def __init__(self, model, mlefit):
      super().__init__(model, mlefit)
  
      pa = model.unpack(mlefit.params)
  
      self.mean_params = pa[0]
      self.scale_params = pa[1]
      self.smooth_params = pa[2]
      self.no_params = pa[3]
  
      self.df_resid = model.endog.shape[0] - len(mlefit.params)
  
      self.k_exog = self.model.exog.shape[1]
      self.k_scale = self.model.exog_scale.shape[1]
      self.k_smooth = self.model.exog_smooth.shape[1]
  
      self._has_noise = model._has_noise
      if model._has_noise:
          self.k_noise = self.model.exog_noise.shape[1]
  
imports: import numpy
  import pandas
  import patsy
  import statsmodels.base.model
  from statsmodels.regression.linear_model import OLS
  import collections
  from scipy.optimize import minimize
  from statsmodels.iolib import summary2
  from statsmodels.tools.numdiff import approx_fprime
  import warnings
label_desc: Use image node_id 1 to create a new ProcessMLEResults object from inherited base classes: base with example: obj = ProcessMLEResults(model, mlefit)