https://github.com/nikeshbajaj/regularization_for_machine_learning
Regularization for Machine Learning-RegML GUI
https://github.com/nikeshbajaj/regularization_for_machine_learning
gaussian gui kernel kernel-methods machine-learning polynomial regularization svd
Last synced: 18 days ago
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Regularization for Machine Learning-RegML GUI
- Host: GitHub
- URL: https://github.com/nikeshbajaj/regularization_for_machine_learning
- Owner: Nikeshbajaj
- License: mit
- Created: 2017-10-22T07:07:10.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2022-12-03T04:17:48.000Z (over 3 years ago)
- Last Synced: 2025-12-17T01:12:07.481Z (4 months ago)
- Topics: gaussian, gui, kernel, kernel-methods, machine-learning, polynomial, regularization, svd
- Language: Jupyter Notebook
- Homepage: https://nikeshbajaj.github.io/Regularization_for_Machine_Learning/
- Size: 772 KB
- Stars: 6
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Regularization methods 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.
[](https://zenodo.org/badge/latestdoi/107844831)
[](https://opensource.org/licenses/MIT)
[](https://pypi.org/project/regml/)
[](https://pypi.python.org/pypi/regml/)
[](https://github.com/Nikeshbajaj/Regularization_for_Machine_Learning/releases/)
[](https://pypi.python.org/pypi/regml/)
[](https://pypi.python.org/pypi/regml/)
[](http://hits.dwyl.io/nikeshbajaj/regml)
[](http://isitmaintained.com/project/nikeshbajaj/Regularization_for_Machine_Learning "Percentage of issues still open")
[](https://pypi.org/project/regml/)
[](https://pypi.org/project/regml/)
[](https://pypi.org/project/regml/)
[](mailto:n.bajaj@qmul.ac.uk)
Total: [](https://pepy.tech/project/regml)
#### 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)
## [Github Page](https://nikeshbajaj.github.io/Regularization_for_Machine_Learning/)
## [PyPi -project](https://pypi.org/project/regml/)
## Installation
```
pip install regml
```
## Opening GUI:
```
import regml
regml.GUI()
```
### Regularization 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**: $K(X,Y) = X'Y$
* 
* **Polynomial**: $K(X,Y) = (X'Y +1)^p$
* 
* **Gaussian (RBF)**: $K(X,Y) = exp(-||X-Y||^2/2\sigma^2)$
* 
### **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)
______________________
# 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](http://nikeshbajaj.in)