code: def get_sample_results_and_params():
      """
      Call as:
      ::
  
          parameters, results, lower_is_better = sherpa.algorithms.get_sample_results_and_params()
  
  
      to get a sample set of parameters, results and lower_is_better variable.
      Useful for algorithm development.
  
      Note: losses are obtained from
      ::
  
          loss = param_a / float(iteration + 1) * param_b
  
      """
      here = os.path.abspath(os.path.dirname(__file__))
      results = pandas.read_csv(
          os.path.join(here, "sample_results.csv"), index_col=0
      )
      parameters = [
          Choice(name="param_a", range=[1, 2, 3]),
          Continuous(name="param_b", range=[0, 1]),
      ]
      lower_is_better = True
      return parameters, results, lower_is_better
  
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 a global function with example usage: get_sample_results_and_params() and returns: parameters,  results,  lower_is_better