.. _`Simulated Annealing Parameter Search`:

.. _`org.sysess.sympathy.machinelearning.sim_anneal_parsearch`:

Simulated Annealing Parameter Search
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. image:: annealing_hyperparam.svg
   :width: 48


Uses simulated annealing to find the optimal parameters by considering a hyper cube of all possible indices to the given parameter table. Each column of the parameter table corresponds to one axis of this cube with a range corresponding to the non-masked rows of the parameter table. The radius for the annealing process assumes that all axes have unit length regardless of the number of non-masked rows. This node should be considered _experimental_ and may change in the future

:Configuration:


  - *cv*

    Number of fold in the default K-Fold cross validation. Ignored when cross-validation port is given

  - *n_iter*

    Number of randomized searches done

  - *cooling*

    Method for lowering temperature

  - *cooling_arg*

    Argument A to cooling method. Exponential: T=A^t   Linear ignores A   Logarithmic: T=A/log(1+t)



:Inputs:
    **in-model** : model
        in-model
    **parameter space** : table
        param-space
    **X** : table
        X
    **Y** : table
        Y
    **cross-validation** : [(table,table)]
        cross-validation

:Outputs:
    **results** : table
        results
    **parameters** : table
        parameters
    **out-model** : model
        out-model

*Ports*:

    **Inputs**:

        :in-model: model

            in-model
        :parameter space: table

            param-space
        :X: table

            X
        :Y: table

            Y
        :cross-validation: [(table,table)]

            cross-validation

    **Outputs**:

        :results: table

            results
        :parameters: table

            parameters
        :out-model: model

            out-model

*Configuration*:

    **cv**
        Number of fold in the default K-Fold cross validation. Ignored when cross-validation port is given
    **n_iter**
        Number of randomized searches done
    **cooling**
        Method for lowering temperature
    **cooling_arg**
        Argument A to cooling method. Exponential: T=A^t   Linear ignores A   Logarithmic: T=A/log(1+t)

.. automodule:: node_paramsearch

.. class:: ParameterSearch_SimulatedAnnealing