https://github.com/pveber/libsvm-ocaml

LIBSVM Bindings for OCaml
https://github.com/pveber/libsvm-ocaml

bindings machine-learning ocaml support-vector-machines svm

Last synced: 28 days ago
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LIBSVM Bindings for OCaml

• Host: GitHub
• URL: https://github.com/pveber/libsvm-ocaml
• Owner: pveber
• License: other
• Created: 2019-08-15T21:33:24.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-09-05T20:38:32.000Z (over 5 years ago)
• Last Synced: 2024-11-09T00:12:23.484Z (3 months ago)
• Topics: bindings, machine-learning, ocaml, support-vector-machines, svm
• Language: C++
• Size: 190 KB
• Stars: 2
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGES.txt
  • License: LICENSE.txt

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README

        
LIBSVM-OCaml - LIBSVM Bindings for OCaml

========================================

---------------------------------------------------------------------------

LIBSVM-OCaml is an [OCaml](http://www.ocaml.org) library with bindings to the

[LIBSVM](http://www.csie.ntu.edu.tw/~cjlin/libsvm/) library, which is a library

for Support Vector Machines. Support Vector Machines are used to create

supervised learning models for classification and regression problems in

machine learning.

Installation

------------

From [OPAM](http://opam.ocaml.org)

    $ opam install libsvm

From Source

    $ make

    $ make install

Usage

-----

### Documentation

The API-documentation of this distribution can be built with `make doc`.

It can also be found [online](http://ogu.bitbucket.io/libsvm-ocaml/api/).

### Examples

This simple program solves the famous XOR-problem:

    :::ocaml

    open Lacaml.D

    open Libsvm

    let () =

      let x = Mat.of_array

        [|

          [| 0.; 0. |];

          [| 0.; 1. |];

          [| 1.; 0. |];

          [| 1.; 1. |];

        |]

      in

      let targets = Vec.of_array [| 0.; 1.; 1.; 0. |] in

      let problem = Svm.Problem.create ~x ~y:targets in

      let model = Svm.train ~kernel:`RBF problem in

      let y = Svm.predict model ~x in

      for i = 1 to 4 do

        Printf.printf "(%1.0f, %1.0f) -> %1.0f\n" x.{i,1} x.{i,2} y.{i}

      done

For more examples please refer to the `examples`- or `test`-directory of this

distribution.

Credits

-------

  * Dominik Brugger wrote the initial release (0.1) of this library.

Contact Information

-------------------

In case of bugs, feature requests and similar, please contact:

  * Oliver Gu