Search Results for "ns2 code with algorithm"

Simple, fast implementation of LZW (Lempel–Ziv–Welch) data compression algorithm in C. - Console encoder/decoder tools - OS independent - Could be used in embedded projects - Works with raw code-stream LZW features: - Hardcoded dictionary size - Variable code size - Code search is performed by hash table and embedded in dictionary linked lists (encoder) - No dynamic memory allocation

LZHW Command Line Lossless Compression Tool is a Windows command line tool used to compress and decompress files from and to any form, csv, excel etc without any dependencies or installations. Using an optimized algorithm (LZHW) developed from Lempel-Ziv, Huffman and LZ-Welch algorithms. The tool can work in parallel and most of its code is written in Cython, so it is pretty fast. It is based on python lzhw library. Full tool documentation can be found at: https://mnoorfawi.github.io/lzhw/6%20Using%20the%20lzhw%20command%20line%20tool/ While the documentation for the python library is at: https://mnoorfawi.github.io/lzhw/

Implementation of the LZW algorithm in C#, adhering to the built-in System.IO.Compression. The algorithm and code is much improved, compared to projects like SharpLZW. The LZW algorithm is for arbitrary lossless data compression and are noticeable for simplicity and low (compared to others) memory consumption. Making it interesting for eg. embedded use.

...The main problem with LZO is that it is absolutely not human readable. People have done crazy stuff to get LZO to run in their language. Usually it implies inline assembly or trying to execute data which actually contains machine code. This is sick. Whoever is responsible for this sorry situation ought to be ashamed. So I'm going to deobfuscate LZO and provide a ISC implementation of this algorithm in Python and C. In addition, I will provide a textual description of the algorithm so that it can be easily ported to any programming language. I expect a severe performance degradation, but I leave optimizing for speed to other people.