<commit_before># python-online-machine-learning-library

## Description

This is a python online machine learning library (POMLL) for some famous classifiers.  
The following classifiers are implemented.

* Passive Agrressive Algorithm  
    * L1-hinge loss  
    * L2-hinge loss  
* Confidence Weighted  
    * variance version  
* Multi-Class Multi-Label Confidence Weighted   
    * single contraint and diagonal matrix version  
* Adaptive Regularization of Weights  
    * dense matrix  
    * diagonal matrix  
* Softconfidence Weighted  
    * L1-hinge loss  
    * L2-hinge loss  
* Softconfidence Weighted  
    * L1-hinge loss, single contraint, and diagonal matrix  
    * L2-hinge loss, single contraint, and diagonal matrix  
* Logistic Regression (should not be used)  
    * solved with stochastic gradient  

## Dependency
* numpy
* scipy
* scikit-learn

## How to use
Refer to the main function

## Data Interface
* Learn inteface takes two arguments of X and y whcih are a 2d-numpy array or a scipy sparse martix and an array-like object respectively like scikit-learn fit interface.<commit_msg>correct typo<commit_after># python-online-machine-learning-library

## Description

This is a python online machine learning library (POMLL) for some famous classifiers.  
The following classifiers are implemented.

* Passive Agrressive Algorithm  
    * L1-hinge loss  
    * L2-hinge loss  
* Confidence Weighted  
    * variance version  
* Multi-Class Multi-Label Confidence Weighted   
    * single contraint and diagonal matrix version  
* Adaptive Regularization of Weights  
    * dense matrix  
    * diagonal matrix  
* Softconfidence Weighted  
    * L1-hinge loss  
    * L2-hinge loss  
* Softconfidence Weighted  
    * L1-hinge loss, single contraint, and diagonal matrix  
    * L2-hinge loss, single contraint, and diagonal matrix  
* Logistic Regression (should not be used)  
    * solved with stochastic gradient  

## Dependency
* numpy
* scipy
* scikit-learn

## How to use
Refer to the main function

## Data Interface
* Learn inteface takes two arguments of X and y which are a 2d-numpy array or a scipy sparse martix and an array-like object respectively like scikit-learn fit interface.
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