CLA

Compression and Learning Algorithms offers compression and learning algorithms.
The implementation is mainly suitable for ilustrative purposes of the interconection of the two topics.
The source code offers the following:

1. For Entropy type compression:
   • includes Huffman encoding scheme for binary coding
   • includes Shannon-Fano-Elias coding
2. For Dictionary type compression:
   • includes plain LZ77 encoding
   • includes LZW coding based on LZ78
3. For Transforms:
   • includes plain Burrows Wheeler Transform
   • includes bijective Burrows Wheeler Transform based on Lyndon Words
   • includes Move-To-Front encoding
4. For Pattern Recognition:
   • includes Decision Tree based on entropy of information
   • includes Vector Quantization based on K-Means
   • includes Principal Component Analysis based on Eigenvectors and Eigenvalues
   • includes Boosting based on Decision Tree stumps
   • includes Random Forest and Bagged Trees based on Decision Trees

To get started have a look at the test files to see how to use compression and pattern recognition.