mltool: Machine Learning Toolbox

[ bsd3, library, math ] [ Propose Tags ]

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Dependencies ascii-progress, base (>=4.7 && <5), deepseq, hmatrix, hmatrix-gsl, hmatrix-gsl-stats, MonadRandom, random, vector [details]
License BSD-3-Clause
Copyright Alexander Ignatyev
Author Alexander Ignatyev
Category math
Home page
Source repo head: git clone
Uploaded by aignatyev17 at Mon Jun 26 21:28:52 UTC 2017
Distributions LTSHaskell:, NixOS:, Stackage:
Downloads 860 total (28 in the last 30 days)
Rating 2.0 (votes: 1) [estimated by rule of succession]
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Maintainer's Corner

For package maintainers and hackage trustees

Readme for mltool-

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Machine Learning Toolbox

Build Status Coverage Status Documentation

Supported Methods and Problems

Supervised Learning

Regression Problem
  • Normal Equation;

  • Linear Regression using Least Squares approach.

Classification Problem
  • Softmax Classifier;

  • Multi SVM Classifier;

  • Logistic Regression;

  • Neural Networks, please see the details below.

Unsupervised Learning

  • Principal Component Analysis (Dimensionality reduction problem);

  • K-Means (Clustering).

Neural Networks

  • Activations: ReLu, Tanh, Sigmoid;

  • Loss Functions: Softmax, Multi SVM, Logistic.


Build the project

stack build

Run samples app

Please run sample app from root dir (because paths to training data sets are hardcoded).

cd samples
stack build
stack exec linreg      # Linear Regression Sample App
stack exec logreg      # Logistic Regression (Classification) Sample App
stack exec digits      # Muticlass Classification Sample App
                       # (Recognition of Handwritten Digitts
stack exec digits-pca  # Apply PCA dimensionaly reduction to digits sample app
stack exec digits-svm  # Support Vector Machines
stack exec nn          # Neural Network Sample App
                       # (Recognition of Handwritten Digits)
stack exec kmeans      # Clustering Sample App

Run unit tests

stack test