Skip to content

jasonleaster/Machine_Learning

Repository files navigation

Machine Learning

My Practice about Machine Learning. The aim that I create this project is to understand algorithm in machine learning better. But not only play with mathmatic equations.

Tell me and I forget, teach me and I may remember, involve me and I learn. -- Benjamin Franklin

Algorithms which I have implemented:

  • Percetron
  • K Nearest Neighbor
  • Decision Tree
  • K Means
  • Naive Bayesian
  • AdaBoost (Adaptive Boosting, Real Number Version.)
  • Boosting Tree
  • SVM (Supported Vecter Machine. Base on SMO algorithm)
python ./tester.py

You can test these implementation with the corresponding test file which I name it with tester.py in each directory.

You could calculate the accuracy of this algorithm like what I have done. The picture below there is the accuracy of AdaBoost with test file tester6.py

images

If you find any thing wrong with my program, you are welcome to touch me by e-mail: jasonleaster@163.com

Thank you :)

Yours, EOF


Style Of Implementation:

All training samples are intialized as self._Mat which is organized like a matrix. self._Mat[i][j] means that the i-th feature value of the j-th sample in the training set. In the same way. If this model is supervised, there will be a data member self._Label or self._Tag in that class. self.[i] is the label of training sample self._Mat[:, i]

Object oriented programming is used to this implementation. It's convenient to help people to test these implementation and easy to understand.

If my style is not well, tell me your suggestion. I will be glad to accept it and refactor my implementation.

About

Implementation of classical algorithms in Machine Learning

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages