Metadata-Version: 2.1
Name: regml
Version: 0.0.1
Summary: Regularization Methods for Machine Learning
Home-page: https://nikeshbajaj.github.io/Regularization_for_Machine_Learning/
Author: Nikesh Bajaj
Author-email: nikkeshbajaj@gmail.com
License: MIT
Download-URL: https://github.com/Nikeshbajaj/Regularization_for_Machine_Learning/tarball/0.0.1
Description: # Regularization for Machine Learning
        ### These contents were taugh in summer school [**RegML 2016**](http://lcsl.mit.edu/courses/regml/regml2016/) by [Lorenzo Rosasco](http://web.mit.edu/lrosasco/www/) and this GUI in python was submitted as part of final exam.
        
        #### All the coded and tested functions are in [RegML.py](https://github.com/Nikeshbajaj/Regularization_for_Machine_Learning/blob/master/RegML.py) and GUIs code structure is in [RegML_GUIv2.1.py](https://github.com/Nikeshbajaj/Regularization_for_Machine_Learning/blob/master/RegML_GUIv2.1.py)
        
        #### [Page](https://nikeshbajaj.github.io/Regularization_for_Machine_Learning/)
        
        
        
        #### Methods
        * Regularized Least Squares -RLS [Referance](https://en.wikipedia.org/wiki/Regularized_least_squares)
        * Nu-Method [Referance]()
        * Iterative Landweber Method [Referance](https://en.wikipedia.org/wiki/Landweber_iteration)
        * Singular Value Decomposition [Reference](https://en.wikipedia.org/wiki/Singular-value_decomposition)
        * Trunctated SVD [Referance 1](http://arxiv.org/pdf/0909.4061) [Referance 2](http://langvillea.people.cofc.edu/DISSECTION-LAB/Emmie%27sLSI-SVDModule/p5module.html)
        * Spectral cut-off
        
        #### Kernal Learning 
        (Linear, Polynomial, Gaussian)
        * Linear ![equation1](http://latex.codecogs.com/gif.latex?%5Clarge%20K%28X%2CY%29%20%3D%20X%5ETY)
        * Polynomial ![equation2](http://latex.codecogs.com/gif.latex?%5Clarge%20K%28X%2CY%29%20%3D%20%28X%5ET%20Y%20+%201%29%5Ep)
        * Gaussian (RBF) ![equation3](http://latex.codecogs.com/gif.latex?%5Clarge%20K%28X%2CY%29%20%3D%20exp%5E%7B-%5Cleft%20%5C%7C%20X-Y%20%5Cright%20%5C%7C%5E2%20/%202%5Csigma%20%5E2%7D)
        
        **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](https://pypi.python.org/pypi/numpy) 
        3. Matplotlib(>=0.98)  [Matplotlib](https://github.com/matplotlib/matplotlib) 
        4. Scipy(>=0.12)       Optional -(If you need to import .mat data files)  [Scipy](https://www.scipy.org/install.html) 
        
        ## 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**](https://github.com/Nikeshbajaj/Regularization_for_Machine_Learning/blob/master/Getting_Started_Demo.ipynb)
        
        
        # [**RegML Library**](https://github.com/Nikeshbajaj/Regularization_for_Machine_Learning/blob/master/RegML.py)
        
        ______________________
        
        ### Nikesh Bajaj
        
        n.bajaj@qmul.ac.uk
        
        nikesh.bajaj@elios.unige.it
        
        [http://nikeshbajaj.in](http://nikeshbajaj.in)
        
Keywords: Regularization methods Machine Learning Regularized Least Squares Nu-Method Iterative Landweber Method Singular Value Decomposition,Kernal Lerning K-Fold
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 2.7
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
