class_docstr: Holds the param-space coordinates (X) and target values (Y)
  
  Parameters
  ----------
  pbounds : dict
      Dictionary with parameters names and legal values.
  
  random_state : int, RandomState, or None
      optionally specify a seed for a random number generator, by default None.
class_docstr_tok: ["Holds","the","param-space","coordinates","(","X",")","and","target","values","(","Y",")","Parameters","--","--","--","--","--","pbounds",":","dict","Dictionary","with","parameters","names","and","legal","values",".","random_state",":","int",",","RandomState",",","or","None","optionally","specify","a","seed","for","a","random","number","generator",",","by","default","None","."]
docstr: Append a point and its target value to the known data.
  
  Parameters
  ----------
  params : dict
      parameters
  
  target : float
      target function value
docstr_tok: ["Append","a","point","and","its","target","value","to","the","known","data",".","Parameters","--","--","--","--","--","params",":","dict","parameters","target",":","float","target","function","value"]
code: def register(self, params, target):
      """
      Append a point and its target value to the known data.
  
      Parameters
      ----------
      params : dict
          parameters
  
      target : float
          target function value
      """
  
      x = self.params_to_array(params)
      if x in self:
          print("Data point {} is not unique".format(x))
  
      # Insert data into unique dictionary
      self._cache[_hashable(x.ravel())] = target
  
      self._params = np.concatenate([self._params, x.reshape(1, -1)])
      self._target = np.concatenate([self._target, [target]])
  
file_path: nni/nni/algorithms/hpo/gp_tuner/target_space.py
imports: import numpy
  import nni.parameter_expressions
label_desc: Use image node_id 11 for calling the TargetSpace obj's underlying member method code with example usage: obj.register(params, target) without return types