(self, domain)
Enables and configures the :class:`.DataScaler` objects wrapping the GP models.
        
        Sets the _needs_setup attribute to True so the contained models are optimized and :meth:`setup` is run again
        right before evaluating the :class:`Acquisition` function. Note that the models are modified directly and
        references to them outside of the object will also point to scaled instances.

        :param domain: :class:`.Domain` object, the input transform of the data scalers is configured as a transform
            from domain to the unit cube with the same dimensionality.
Enables and configures the :class:`.DataScaler` objects wrapping the GP models.
        
        Sets the _needs_setup attribute to True so the contained models are optimized and :meth:`setup` is run again
        right before evaluating the :class:`Acquisition` function. Note that the models are modified directly and
        references to them outside of the object will also point to scaled instances.
Enables and configures the : class : . DataScaler objects wrapping the GP models . Sets the _needs_setup attribute to True so the contained models are optimized and : meth : setup is run again right before evaluating the : class : Acquisition function . Note that the models are modified directly and references to them outside of the object will also point to scaled instances .
def enable_scaling(self, domain):
        """
        Enables and configures the :class:`.DataScaler` objects wrapping the GP models.
        
        Sets the _needs_setup attribute to True so the contained models are optimized and :meth:`setup` is run again
        right before evaluating the :class:`Acquisition` function. Note that the models are modified directly and
        references to them outside of the object will also point to scaled instances.

        :param domain: :class:`.Domain` object, the input transform of the data scalers is configured as a transform
            from domain to the unit cube with the same dimensionality.
        """
        n_inputs = self.data[0].shape[1]
        assert (domain.size == n_inputs)
        for m in self.models:
            m.input_transform = domain >> UnitCube(n_inputs)
            m.normalize_output = True
        self.highest_parent._needs_setup = True
def enable_scaling ( self , domain ) : n_inputs = self . data [ 0 ] . shape [ 1 ] assert ( domain . size == n_inputs ) for m in self . models : m . input_transform = domain >> UnitCube ( n_inputs ) m . normalize_output = True self . highest_parent . _needs_setup = True