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Regularization methods for machine learning

These contents were taugh in summer school RegML 2016 by Lorenzo Rosasco and this GUI in python was submitted as part of final exam.

DOI

License: MIT PyPI version fury.io PyPI pyversions GitHub release PyPI format PyPI implementation HitCount GitHub commit activity Percentage of issues still open PyPI download month PyPI download week

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PyPI - Downloads

All the coded and tested functions are in RegML.py and GUIs code structure is in RegML_GUIv2.1.py

Github Page

PyPi -project

Installation

pip install regml

Opening GUI:

import regml
regml.GUI()

Regularization Methods

Kernal Learning

(Linear, Polynomial, Gaussian)

  • Linear: $K(X,Y) = X'Y$
    • equation1
  • Polynomial: $K(X,Y) = (X'Y +1)^p$
    • equation2
  • Gaussian (RBF): $K(X,Y) = exp(-||X-Y||^2/2\sigma^2)$
    • equation3

K-Fold Cross Validation

GUI

Regularization for Machine Learning


Files

  1. RegML.py
  2. RegML_GUIv2.1.py
  3. Getting_Started_Demo.ipynb

Requirments

Following libraries are required to use all the functions in RegML library

  1. Python(=2.7)
  2. Numpy(>=1.10.4) Numpy
  3. Matplotlib(>=0.98) Matplotlib
  4. Scipy(>=0.12) Optional -(If you need to import .mat data files) Scipy

Tested with following version

GUI is tested on followwing version of libraries

  • Python 2.7 / 3
  • Numpy 1.10.4
  • Matplotlib 1.15.1
  • Scipy 0.17.0

Getting starting with GUI

Windows------------------------

After lauching python, go to directory containing RegML.py and RegML_GUIv2.1.py files and run following command on python shell

>> run RegML_GUIv2.1.py

If you are using Spyder or ipython qt, browes to directory, open RegML_GUIv2.1.py file and run it

Ubuntu/Linux-------------------

Open terminal, cd to directory contaning all the files and execute following command

$ python RegML_GUIv2.1.py

if you have both python 2 and python 3

$ python2 RegML_GUIv2.1.py

If you are using Spyder or ipython qt, browes to directory, open RegML_GUIv2.1.py file and run it

Getting Started with DEMO

Getting_Started_Demo is a IPython -Notebook, which can be open in Ipython-Notebook or Jupyter

Notebook

RegML Library


Cite As

@software{nikesh_bajaj_2019_2646550,
  author       = {Nikesh Bajaj},
  title        = {{Nikeshbajaj/Regularization\_for\_Machine\_Learning 
                   0.0.2}},
  month        = apr,
  year         = 2019,
  publisher    = {Zenodo},
  version      = {0.0.2},
  doi          = {10.5281/zenodo.2646550},
  url          = {https://doi.org/10.5281/zenodo.2646550}
}

Nikesh Bajaj

n.bajaj@qmul.ac.uk

nikesh.bajaj@elios.unige.it

http://nikeshbajaj.in