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: Constructor. 
docstr_tok: ["Constructor","."]
code: def __init__(self, X, Y, kernel, mean_func, l2_pen_coeff):
      """Constructor."""
      super(SALSA, self).__init__()
      _check_feature_label_lengths_and_format(X, Y)
      self.set_data(X, Y)
      self.kernel = kernel
      self.mean_func = mean_func
      self.l2_pen_coeff = l2_pen_coeff
      # Initialise other attributes we will need.
      self.L = None
      self.alpha = None
      # Compute parameters needed for test time.
      self._compute_estimation_parameters()
  
imports: from __future__ import print_function
  import numpy
  from scipy.linalg import solve_triangular
label_desc: Use image node_id 1 to create a new SALSA object from inherited base classes: object with example: obj = SALSA(X, Y, kernel, mean_func, l2_pen_coeff)