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Welcome to deforce's documentation!
===================================

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deforce (Metaheuristic-optimized Multi-Layer Perceptron) is a Python library that implements variants and the
traditional version of Multi-Layer Perceptron models. These include Metaheuristic-optimized MLP models (GA, PSO, WOA, TLO, DE, ...)
and Gradient Descent-optimized MLP models (SGD, Adam, Adelta, Adagrad, ...). It provides a comprehensive list of
optimizers for training MLP models and is also compatible with the Scikit-Learn library. With deforce,
you can perform searches and hyperparameter tuning using the features provided by the Scikit-Learn library.

* **Free software:** GNU General Public License (GPL) V3 license
* **Provided Estimator**: CfnRegressor, CfnClassifier, DfoCfnRegressor, DfoCfnClassifier
* **Total Metaheuristic-based MLP Regressor**: > 200 Models
* **Total Metaheuristic-based MLP Classifier**: > 200 Models
* **Total Gradient Descent-based MLP Regressor**: 12 Models
* **Total Gradient Descent-based MLP Classifier**: 12 Models
* **Supported performance metrics**: >= 67 (47 regressions and 20 classifications)
* **Supported objective functions (as fitness functions or loss functions)**: >= 67 (47 regressions and 20 classifications)
* **Documentation:** https://deforce.readthedocs.io
* **Python versions:** >= 3.8.x
* **Dependencies:** numpy, scipy, scikit-learn, pandas, mealpy, permetrics, torch, skorch

.. toctree::
   :maxdepth: 4
   :caption: Quick Start:

   pages/quick_start.rst

.. toctree::
   :maxdepth: 4
   :caption: Models API:

   pages/deforce.rst

.. toctree::
   :maxdepth: 4
   :caption: Support:

   pages/support.rst



Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`