text: # -*- coding: utf-8 -*-
  """
  Created on Tue Apr 30 18:02:25 2019
  
  @author: Zoltan
  """
  
  from collections import OrderedDict
  
  from sklearn.linear_model import Lars
  
  from ..Algorithm import Algorithm
  
  
  const_param = {
      'copy_X': True
  }
  
  dicrete_hyper_parameter_list_of_intercept = [True, False]
  dicrete_hyper_parameter_list_of_normalize = [True, False]
  
  continuous_hyper_parameter_mapping_index_key_mapping = ["eps"]
  discrete_hyper_parameter_mapping = ["normalize", "fit_intercept"]
  
  discrete_parameter_dict = OrderedDict()
  discrete_parameter_dict["fit_intercept"] = dicrete_hyper_parameter_list_of_intercept
  discrete_parameter_dict["normalize"] = dicrete_hyper_parameter_list_of_normalize
  parameter_constraint_dict = OrderedDict()
  
  # dictionary of parameters
  param_dict = OrderedDict()
  param_dict['fit_intercept'] = True
  param_dict['normalize'] = True
  param_dict['eps'] = 0.001
  
  bounds=[(0.0001,100.99),(0.001,1.99),(0.001,1.99)]
  
  
  def get_algorithm():
      return Algorithm(algorithm_type=Lars,
                       algorithm_name="LARS REGRESSOR",
                       hyper_parameter_dict=param_dict,
                       discrete_hyper_parameter_dict=discrete_parameter_dict,
                       discrete_hyper_parameter_mapping=discrete_hyper_parameter_mapping,
                       continuous_hyper_parameter_mapping=continuous_hyper_parameter_mapping_index_key_mapping,
                       parameter_constraint_dict=parameter_constraint_dict,
                       constant_hyper_parameter_dict=const_param,
                       bounds=bounds)