|  | ## Set Parameters

#### Synopsis

Applies a set of parameters. Operator names may be remapped.

#### Description

Sets a set of parameters. These parameters can either be generated by a ParameterOptimizationOperator or read by a ParameterSetLoader. This operator is useful, e.g. in the following scenario. If one wants to find the best parameters for a certain learning scheme, one usually is also interested in the model generated with this parameters. While the first is easily possible using a ParameterOptimizationOperator, the latter is not possible because the ParameterOptimizationOperator does not return the IOObjects produced within, but only a parameter set. This is, because the parameter optimization operator knows nothing about models, but only about the performance vectors produced within. Producing performance vectors does not necessarily require a model. To solve this problem, one can use a `ParameterSetter`. Usually, a process with a `ParameterSetter` contains at least two operators of the same type, typically a learner. One learner may be an inner operator of the ParameterOptimizationOperator and may be named "Learner", whereas a second learner of the same type named "OptimalLearner" follows the parameter optimization and should use the optimal parameter set found by the optimization. In order to make the `ParameterSetter` set the optimal parameters of the right operator, one must specify its name. Therefore, the parameter list name_map was introduced. Each parameter in this list maps the name of an operator that was used during optimization (in our case this is "Learner") to an operator that should now use these parameters (in our case this is "OptimalLearner").

#### Input

- parameter set: expects: ParameterSet

#### Output

#### Parameters

- name map: A list mapping operator names from the parameter set to operator names in the process setup.

#### ExampleProcess