class_docstr: Abstract algorithm that generates new set of parameters.
class_docstr_tok: ["Abstract","algorithm","that","generates","new","set","of","parameters","."]
docstr: Returns a suggestion for parameter values.
  
  Args:
      parameters (list[sherpa.Parameter]): the parameters.
      results (pandas.DataFrame): all results so far.
      lower_is_better (bool): whether lower objective values are better.
  
  Returns:
      dict: parameter values.
docstr_tok: ["Returns","a","suggestion","for","parameter","values",".","Args",":","parameters","(","list","[","sherpa.Parameter","]",")",":","the","parameters",".","results","(","pandas.DataFrame",")",":","all","results","so","far",".","lower_is_better","(","bool",")",":","whether","lower","objective","values","are","better",".","Returns",":","dict",":","parameter","values","."]
code: def get_suggestion(self, parameters, results, lower_is_better):
      """
      Returns a suggestion for parameter values.
  
      Args:
          parameters (list[sherpa.Parameter]): the parameters.
          results (pandas.DataFrame): all results so far.
          lower_is_better (bool): whether lower objective values are better.
  
      Returns:
          dict: parameter values.
      """
      raise NotImplementedError("Algorithm class is not usable itself.")
  
imports: import os
  import random
  import numpy
  import logging
  import sherpa
  import pandas
  import sklearn.gaussian_process
  from sherpa.core import Choice, Continuous, Discrete, Ordinal, AlgorithmState
  from sherpa.core import rng
  import sklearn.model_selection
  import collections
label_desc: Use image node_id 1 for calling the Algorithm obj's underlying member method code with example usage: obj.get_suggestion(parameters, results, lower_is_better) without return types