.. _`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)



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

*Output ports*:
    **results** : table
        results
    **parameters** : table
        parameters
    **out-model** : model
        out-model



**Cross validation splits** (cv)
    Number of fold in the default K-Fold cross validation. Ignored when cross-validation port is given
**iterations** (n_iter)
    Number of randomized searches done
**Cooling method** (cooling)
    Method for lowering temperature
**Cooling argument** (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