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A threaded Web graph (Power law random graph) generator written in Python. It can generate a synthetic Web graph of about one million nodes in a few minutes on a desktop machine. It implements a threaded variant of the RMAT algorithm.
Hipo is a hypothetical computer to facilitate the learning of machine language. The student can use hipo to develop simple programs and understand the internal logic of a computer. There is a plan to implement Donald Knuth's MMIX machine language, also.
Conrad is both a high performance Conditional Random Field engine which can be applied to a variety of machinelearning problems and a specific set of models for gene prediction using semi-Markov CRFs.
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KNN-WEKA provides a implementation of the K-nearest neighbour algorithm for Weka. Weka is a collection of machinelearning algorithms for data mining tasks. For more information on Weka, see http://www.cs.waikato.ac.nz/ml/weka/.
MultiBoost is a C++ implementation of the multi-class AdaBoost algorithm. AdaBoost is a powerful meta-learning algorithm commonly used in machinelearning. The code is well documented and easy to extend, especially for adding new weak learners.
Weka++ is a collection of machinelearning and data mining algorithm implementations ported from Weka (http://www.cs.waikato.ac.nz/ml/weka/) from Java to C++, with enhancements for usability as embedded components.
A human-readable ISC-Licensed implementation of the LZO1X algorithm.
...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.