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Monte-Carlo-Neural-Nets

Overview

A very lightweight machine learning package.

Has models that use a unique and simple Monte Carlo approach to training. This method used is very generalizable and can therefore be extended to a variety of models both known and new.

The primary model, the NeuralNetwork class, is on par with other similar models such as the MLPRegressor/MLPClassifier featured in SciKit-Learn, but has more customizability.

The current list of models avaliable and some of their features includes:

  • NeuralNetwork
    • Complete hidden layer size and activations customization
    • Supports externally defined activation functions
    • Allows customizing the input and output activations
    • Easy-access hyperparam ranges for Optuna (via .get_param_ranges_for_optuna)
  • SoupRegressor
    • A unique combination of many various functions with trainable coefficients
    • Typically on-par with the NeuralNetwork; slightly more interpretable
    • Many hyperparams to adjust, with more to come

Some QoL functions and features included are:

  • TTSplit: Included train-test splitter
  • cross_val: Simple cross validation system
  • Built-in scorer functions with support for external functions
  • Ability to save and load models at any point (.save & load_model)
  • Ability to copy a model via .copy

GitHub and QuickStart

More explanations, examples, and technicals can be found on the GitHub page: https://github.com/SciCapt/Monte-Carlo-Neural-Nets

Release files for mcnets 2.0.7

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for mcnets 2.0.7
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Built distribution (wheel)

Table of built distributions (wheels) for mcnets 2.0.7
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mcnets-2.0.7-py3-none-any.whl Python 3 none any Details

Total release size: 131.7 kB

Release files / mcnets-2.0.7.tar.gz

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