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Basic implementations of machine learning algorithms

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Name		Name	Last commit message	Last commit date
Latest commit History 26 Commits
de		de
.gitignore		.gitignore
LICENSE		LICENSE
README		README
__init__.py		__init__.py
classify.py		classify.py
demo.py		demo.py
demo_a1a.sh		demo_a1a.sh
demo_example_data_classification.sh		demo_example_data_classification.sh
testAllPyUnits.sh		testAllPyUnits.sh

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## Overview
Some practical implementations of popular machine learning algorithms.
Data is partially taken from the LIBSVM data sets[1].

### Usage
Running the demo file is easy:

    $ python demo.py
    
This should do it. Make sure NumPy is installed and added to your PYTHONPATH. To check this, type

    $ python
    
and in the Python shell

    >> import numpy
    
if no error occurs, NumPy is installed correctly.

### Currently implemented
* Ridge Regression

### TODO
* fine tuning Ridge Regression
* output validation R.R.
* logistic regression
* SVMs (SMO +variants)
* make test suite

### Links
[1] http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/