Based on the introduction of Genetic Algorithms in the excellent book "Collective Intelligence" I have put together some python classes to extend the original concepts.
The Automatic Model Optimization Reference Implementation, AMORI, is a framework that integrates the modelling and the optimization processes by providing a plug-in interface for both. A genetic algorithm and Markov simulations are currently implemented.
a customizable number-guessing system
Belkerda is a simple Python AI program that takes a user's input, builds a log of random numbers, picks a random entry, and displays it. If it is correct, then it reenters that number back into the log several times, overwriting the original, random numbers. If it is not, however, it overwrites a lower amount of entries.
This is a Python script for Blender which uses short (quaternion-based, floretion-based) algorithms to draw curves in space. The user can create new shapes and curves by setting a variety of parameters.
Modules for developing, configuring and running a computation based on function blocks entirely in Python. Function block based computation is a data, event and state driven approach to data processing.
"Blue Planet" is a research project simulating the behaviour and darwinian evolution of unicellular lifeforms, each controlled by its own genetic program. Moreover, "Blue Planet Inhabitants" are suited for swarm intelligence and swarm research.
Project aims to be a repository of oft needed CS assignments (university grade). The focus will NOT be on feeding readily available code but on detailed explanation of algorithms, tips focusing on networking related projects.
Cascade and Sharing Survival Trees, an ensemble for survival analysis
Cascading and Sharing Survival Trees (CSST) is a tree-based enseble that allows to efficiently analize survival data. It is a strightforward extension of the CS4 method for lifetime collections of data. The CSST software comes along with its companion the CSST Prediction tool, to use the ensemble prediction in everyday life. Please, refer to the user's manual for further information.
Polygon and line clipping and offsetting library (C++, C#, Delphi)
The Clipper library performs clipping and offsetting for both lines and polygons. All four boolean clipping operations are supported - intersection, union, difference and exclusive-or. Polygons can be of any shape including self-intersecting polygons. 17 March 2016: Since it's been some time since the last update, some may be wondering if I no longer plan further updates. I do have plans for more updates but I also have a chronic health condition (not life threatening) that's set me back, and I don't really have a good idea when I'll restart development.
Classic & Modern Cryptography tools
Cryptography Tools is a project to develop demonstration tools on classic (currently Caesar and Playfair) & modern crypto-systems, including private & public key encryptions, digital signatures, cryptographic hashes and authenticated encryption.
Beamforming and Speech Recognition Toolkit
BTK contains C++ and Python libraries that implement speech processing and microphone array techniques such as speech feature extraction, speech enhancement, speaker tracking, beamforming, dereverberation and echo cancellation algorithms. The Millennium ASR provides C++ and python libraries for automatic speech recognition. The Millennium ASR implements a weighted finite state transducer (WFST) decoder, training and adaptation methods. These toolkits are meant for facilitating research and development of automatic distant speech recognition.
Open-Source Framework for Distributed Constraint Optimization (DCOP)
FRODO is a Java platform to solve Distributed Constraint Satisfaction Problems (DisCSPs) and Optimization Problems (DCOPs). It provides implementations for a variety of algorithms, including DPOP (and its variants), ADOPT, SynchBB, DSA...
A cross-platform library that computes fast and accurate SIFT image features. libsiftfast provides Octave/Matlab scripts, a command line interface, and a python interface (siftfastpy). Optimized with SIMD instructions and OpenMP .
This is a python implementation that handles floating points correctly,there are still some bugs but I'm working on it . The point was to work around some stuff that made no sense for floats,like 0.1+0.2 == 0.3 is false.
C++, Matlab and Python library for Hidden-state Conditional Random Fields. Implements 3 algorithms: LDCRF, HCRF and CRF. For Windows and Linux, 32- and 64-bits. Optimized for multi-threading. Works with sparse or dense input features.
HDRFlow is a framework to process high-dynamic range (HDR) and RAW images. It's written in C++, and is both cross-platform and hardware accelerated on modern GPUs.
This program generates customizable hyper-surfaces (multi-dimensional input and output) and samples data from them to be used further as benchmark for response surface modeling tasks or optimization algorithms.
Institute of Technology, Blanchardstown Computer Science code by the class of 2007-2011 on course BN104. In this project we are open sourcing all of our project work to the public in the hopes it can be reused, built-upon, and used in education.
Iterative implementation for finding shortest distance using kd trees
Iterative implementation of an algorithm for finding the shortest distance between two points using kd trees for further implementation in hardware.
The solution to all your problems
The Jsolution package is a collection of programs, scripts,libraries and documents that i have written and use in my daily life.
Free Java Flowchart simulator / interpreter
LASS : Library of Assembled Shared Source. Library of C++ code for scientific purposes.
The LisBON Framework is an adaptable framework for developing new parallel Memetic Algorithms (hybrid search algorithms for efficiently solving optimisation problems).
A general recommender system with basic models and MRA
Multi-categorization Recommendation Adjusting (MRA) is to optimize the results of recommendation based on traditional(basic) recommendation models, through introducing objective category information and taking use of the feature that users always get the habits of preferring certain categories. Besides this, there are two advantages of this improved model: 1) it can be easily applied to any kind of existing recommendation models. And 2) a controller is set in this improved model to provide controllable adjustment range, which thereby makes it possible to provide optional modes of recommendation aiming different kinds of users.
Math tools in Python to tackle down problems in Operational Research fields. Comes with a Django based web interface to allow remote access to complex simulation means.