class_docstr: Implements the ESP kernel from Kandasamy, Yu 2016, 'Additive Approximations in High
  Dimensional Nonparametric Regression. 
class_docstr_tok: ["Implements","the","ESP","kernel","from","Kandasamy",",","Yu","2016",",","'Additive","Approximations","in","High","Dimensional","Nonparametric","Regression","."]
docstr: Computes parameters that can be used during test time. 
docstr_tok: ["Computes","parameters","that","can","be","used","during","test","time","."]
code: def _compute_estimation_parameters(self):
      """Computes parameters that can be used during test time."""
      K_trtr_wo_noise = self.kernel(self.X, self.X)
      K_trtr_w_noise = K_trtr_wo_noise + self.l2_pen_coeff * np.eye(
          K_trtr_wo_noise.shape[0]
      )
      self.L = stable_cholesky(K_trtr_w_noise)
      Y_centred = self.Y - self.mean_func(self.X)
      self.alpha = solve_upper_triangular(
          self.L.T, solve_lower_triangular(self.L, Y_centred)
      )
  
imports: from __future__ import print_function
  import numpy
  from scipy.linalg import solve_triangular
label_desc: Use image node_id 3 for calling the SALSA obj's underlying member method code with example usage: obj._compute_estimation_parameters() without return types