class_docstr: Holds the param-space coordinates (X) and target values (Y)
  Allows for constant-time appends while ensuring no duplicates are added
  
  Example
  -------
  >>> def target_func(p1, p2):
  >>>     return p1 + p2
  >>> pbounds = {'p1': (0, 1), 'p2': (1, 100)}
  >>> space = TargetSpace(target_func, pbounds, random_state=0)
  >>> x = np.array([4 , 5])
  >>> y = target_func(x)
  >>> space.register(x, y)
  >>> assert self.max()['target'] == 9
  >>> assert self.max()['params'] == {'p1': 1.0, 'p2': 2.0}
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docstr: Get all target values and constraint fulfillment for all parameters.
  
  Returns
  ----------
  res: list
      A list of dictionaries with the keys 'target', 'params', and
      'constraint'. The value of 'target' is the target value, the value
      of 'params' is a dictionary with the parameter names as keys and the
      parameter values as values, and the value of 'constraint' is the
      constraint fulfillment.
  
  Notes
  -----
  Does not report if points are within the bounds of the parameter space.
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code: def res(self):
      """Get all target values and constraint fulfillment for all parameters.
  
      Returns
      ----------
      res: list
          A list of dictionaries with the keys 'target', 'params', and
          'constraint'. The value of 'target' is the target value, the value
          of 'params' is a dictionary with the parameter names as keys and the
          parameter values as values, and the value of 'constraint' is the
          constraint fulfillment.
  
      Notes
      -----
      Does not report if points are within the bounds of the parameter space.
  
      """
  
      if self._constraint is None:
          params = [dict(zip(self.keys, p)) for p in self.params]
  
          return [
              {"target": target, "params": param}
              for target, param in zip(self.target, params)
          ]
      else:
          params = [dict(zip(self.keys, p)) for p in self.params]
  
          return [
              {
                  "target": target,
                  "constraint": constraint_value,
                  "params": param,
                  "allowed": allowed,
              }
              for target, constraint_value, param, allowed in zip(
                  self.target,
                  self._constraint_values,
                  params,
                  self._constraint.allowed(self._constraint_values),
              )
          ]
  
imports: import numpy
  from warnings import warn
  from .util import ensure_rng, NotUniqueError
  from .util import Colours
label_desc: Use image node_id 21 for calling the TargetSpace obj's underlying member method code with example usage: obj.res() and returns: unknown, unknown