Introduction
This package is a Matlab implementation of the algorithms described in the classical machine learning textbook: Pattern Recognition and Machine Learning by C. Bishop (PRML).
Note: this package requires Matlab R2016b or latter, since it utilizes a new syntax of Matlab called Implicit expansion (a.k.a. broadcasting in Python).
Description
While developing this package, I stick to following principles
- Succinct: The code is extremely terse. Minimizing the number of lines is one of the primal goals. As a result, the core of the algorithms can be easily spot.
- Efficient: Many tricks for making Matlab scripts fast were applied (eg. vectorization and matrix factorization). Many functions are even comparable with C implementations. Usually, functions in this package are orders faster than Matlab builtin ones which provide the same functionality (eg. kmeans). If anyone have found any Matlab implementation that is faster than mine, I am happy to further optimize.
- Robust: Many tricks for numerical stability are applied, such as probability computation in log scale and square root matrix update to enforce matrix symmetry, etc.
- Readable: The code is heavily commented. Reference formulas in PRML book are indicated for corresponding code lines. Symbols are in sync with the book.
- Practical: The package is designed not only to be easily read, but also to be easily used to facilitate ML research. Many functions in this package are already widely used (see Matlab file exchange).
Installation
-
Download the package to your local path (e.g. PRMLT/) by running:
git clone https://github.com/PRML/PRMLT.git. -
Run Matlab and navigate to PRMLT/, then run the init.m script.
-
Try demos in PRMLT/demo directory to verify installation correctness. Enjoy!
FeedBack
If you found any bug or have any suggestion, please do file issues. I am graceful for any feedback and will do my best to improve this package.
License
Currently Released Under GPLv3
Contact
sth4nth at gmail dot com