https://github.com/aligusnet/mltool

Machine Learning Toolbox
https://github.com/aligusnet/mltool

haskell machine-learning neural-network

Last synced: 2 months ago
JSON representation

Machine Learning Toolbox

• Host: GitHub
• URL: https://github.com/aligusnet/mltool
• Owner: aligusnet
• License: bsd-3-clause
• Created: 2016-12-27T00:13:34.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2018-06-10T16:23:17.000Z (over 6 years ago)
• Last Synced: 2024-04-24T09:17:41.676Z (8 months ago)
• Topics: haskell, machine-learning, neural-network
• Language: Haskell
• Homepage:
• Size: 335 KB
• Stars: 35
• Watchers: 6
• Forks: 4
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
## Machine Learning Toolbox

[![Build Status](https://travis-ci.org/aligusnet/mltool.svg?branch=master)](https://travis-ci.org/aligusnet/mltool)

[![Coverage Status](https://coveralls.io/repos/github/aligusnet/mltool/badge.svg)](https://coveralls.io/github/aligusnet/mltool)

[![Documentation](https://img.shields.io/badge/mltool-documentation-blue.svg)](https://aligusnet.github.io/mltool-docs/doc/index.html)

[![Hackage](https://img.shields.io/hackage/v/mltool.svg)](https://hackage.haskell.org/package/mltool)

### 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.

### Usage

#### OS X/macOS prerequisites setup

* Using [Homebrew](https://brew.sh/):

```

brew install pkg-config gsl

```

or 

* Using [MacPorts](https://www.macports.org/):

```

sudo port install pkgconfig gsl

```

#### Build the project

    stack build

#### Run examples app

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

```bash

cd examples

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

### Examples

* Linear Regression: [source code](https://github.com/aligusnet/mltool/blob/master/examples/linear_regression/Main.hs);

* Logistic Regression: [source code](https://github.com/aligusnet/mltool/blob/master/examples/logistic_regression/Main.hs);

* Multiclass Logistic Regression: [source code](https://github.com/aligusnet/mltool/blob/master/examples/digits_classification/Main.hs);

* Multiclass Logistic Regression with PCA: [source code](https://github.com/aligusnet/mltool/blob/master/examples/digits_classification_pca/Main.hs);

* Multiclass Support Vector Machine: [source code](https://github.com/aligusnet/mltool/blob/master/examples/digits_classification_svm/Main.hs);

* Neural Networks: [source code](https://github.com/aligusnet/mltool/blob/master/examples/neural_networks/Main.hs);

* K-Means: [source code](https://github.com/aligusnet/mltool/blob/master/examples/kmeans/Main.hs).