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A mini python machine library

Project description


Toothpick is a mini "library" which contains some implementation of machine learning algorithms in Python3.6. These algorithm
are built on NumPy. **This "library" is just a toy**, the reason why I create it is to share my ideas and codes.
All algorithms have `fit` and `predict` interface like `scikit-learn`. When I implemented these algorithms, I referenced
some books as follows: _Machine Learning_ writen by ZhiHua Zhou, _Statistical Learning Method_ writen by Hang Li and
_Machine Learning in Action_ writen by Peter Harrington.

By the way, I am a novice in python and machine learning field, so you can put forward issues if you find some bugs or questions.

## Why named toothpick?

A toothpick is a small stick of wood, plastic, bamboo, or other substance used to remove detritus from the teeth, usually
after a meal(from wikipedia). This library is similarly small and you can "take" it after meal :P.

## How to guarantee the correctness?

I compared my implementation with scikit-learn's when I was implementing these algorithms, which you can find in every single python file
and these performance were approximate on some very simple data set.

## Algorithms implemented

- Logistic Regression
- Naive Bayes
- K Nearest Neighbours
- KMeans
- Learning Vector Quantization
- Ensemble Learning Algorithms
- AdaBoost(only support binary classification)
- Bagging
- Stacking

## Todo List

- Decision Tree
- SVM
- Regression
- Linear Regressiong
- Ridge Regression
- Lasso Regression
- Neural Network
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Requires-Python: ~=3.6

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