The Noble Ape Simulation is a collection of a number of autonomous simulation components including a landscape simulation, biological simulation, weather simulation, sentient creature (Noble Ape) simulation, and a simple intelligent-agent scripting language (ApeScript). Noble Ape also contains a social simulation where the Noble Apes can be tracked in terms of social groups and also over many generations to explain social phenomenon to users looking to study this kind of interaction. It has been in development for more than a fifteen years.
imaverage uses the viewing frequency and viewing time from a spawned image viewer to build a dynamic database entry for images to gauge their relative preference for a given user. Once the entries have been created, imaverage will continue to show images randomly, with dynamic preference weights. On average, your favorite images should show up most frequently.
PEXESO Evolutionary Methods Library is the library of Evolutionary Optimization Methods for Real Domains. It is based on the original Object Oriented Algorithmic model that consists of the multi- operators technology (currently it supports 13 operator types) and "open policy" on the selection strategy (currently 4 selection strategy types). Using this method you have a possibility to compose your own optimization method using some combination of operators and selection strategies, or you can use one of 3 precomposed algorithms. It is provided with several examples and comprehensive HTML documentation.
DREAM (The Distributed Resource Evolutionary Algorithm Machine) seeks to provide the technology and software infrastructure necessary to support the next generation of evolving infohabitants in a way that makes that infrastructure universal, open, and scalable. It will use existing hardware infrastructure in a much more efficient manner, by utilising otherwise-unused CPU time. It will allow infohabitants to co-operate, communicate, negotiate and trade; and emergent behaviour is expected to result. It is expected that there will be an emergent economy that results from the provision and use of CPU cycles by infohabitants and their owners. The DREAM infrastructure will be evaluated with new work on distributed data mining, distributed scheduling, and the modelling of economic and social behaviour.
SHOGUN is a machine learning toolbox whose focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It provides a generic SVM object interfacing to several different SVM implementations, all making use of the same underlying, efficient kernel implementations. Apart from SVMs and regression, SHOGUN also features a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons, and algorithms to train hidden Markov models. SHOGUN can be used from within C++, Matlab, R, Octave, and Python.
PennAspect is a software implementation of the two-way aspect model, which is a latent class statistical mixture model for performing soft clustering of co-occurrence data observations. It acts on data such as document/word pairs (words occurring in documents) or movie/people pairs (people see certain movies) to produce their joint distribution estimate. The distribution is packaged as Java source and class files.