class_docstr: This class wraps an optimizer to allow a human to provide additional guidance to it.
class_docstr_tok: ["This","class","wraps","an","optimizer","to","allow","a","human","to","provide","additional","guidance","to","it","."]
code: def createFlatParameterValues(
      self, parameters, hyperparameterSpace, root=""
  ):
      if (
          "anyOf" in hyperparameterSpace
          or "oneOf" in hyperparameterSpace
      ):
          return {root: hyperparameterSpace}
      elif "enum" in hyperparameterSpace:
          return {root: hyperparameterSpace}
      elif hyperparameterSpace["type"] == "object":
          flatParams = {}
          for key in hyperparameterSpace["properties"].keys():
              # print("key", key)
              config = hyperparameterSpace["properties"][key]
              subFlatParams = self.createFlatParameterValues(
                  parameters[key], config, root + "." + key
              )
              # print("subFlatParams", subFlatParams)
              for newKey in subFlatParams:
                  flatParams[newKey] = subFlatParams[newKey]
  
          return flatParams
      else:
          return {root: parameters}
  
file_path: hypermax/hypermax/algorithms/human_guided_optimizer_wrapper.py
imports: from .optimization_algorithm_base import OptimizationAlgorithmBase
  import hyperopt
  import functools
  import random
  import numpy
  import math
  import numpy.random
  from pprint import pprint
  from hypermax.hyperparameter import Hyperparameter
label_desc: Use image node_id 5 for calling the HumanGuidedOptimizerWrapper obj's underlying member method code with example usage: obj.createFlatParameterValues(parameters, hyperparameterSpace, root) and returns: dict, dict, flatParams, dict