Python Web Graph Generator is a threaded Web graph (Power law random graph) generator. It can generate a synthetic Web graph of about one million nodes in a few minutes on a desktop machine. It supports both directed and undirected graphs. It implements a threaded variant of the RMAT algorithm. A little tweak can produce graphs representing social networks or community networks. It can also output connected components in a graph.
Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). This approach is one of the most efficient and simple to combine continuous and nominal values. This implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable amount of time/memory.
PED is a dialogue management system that uses a probabilistic nested belief model to choose dialogue strategies. The dialogue system designer need only supply a set of plan rules to PED as a dialogue grammar with preconditions. Using this grammar, PED constructs game trees (like the one below) to represent the outcomes of the dialogue, so that a dialogue strategy can be chosen for the current turn in the dialogue. PED automatically maintains a belief model by a belief revision process that uses the observed acts in the dialogue. The game tree is evaluated in the context of this belief model. PED is efficient because it uses probabilistic estimates of belief rather than a plain logical belief model.
Connect-k is a framework for experimenting with and testing general AIs for games within the connect-k family. This includes Tic-Tac-Toe, Go-moku (without removal rules), and Connect-6. The program features an attractive GTK+ interface, a convenient API, and several challenging AIs.
Presage (formerly known as Soothsayer) is an intelligent predictive text entry platform. It exploits redundant information embedded in natural languages to generate predictions. Its modular and pluggable architecture allows its language model to be extended and customized to utilize statistical, syntactic, and semantic information sources.
PySWIP is a Python/SWI-Prolog bridge that enables you to query in prolog using SWI-Prolog in your Python programs. It includes both a SWI-Prolog foreign language interface and a utility class that makes it easy to query with SWI-Python. Since it uses SWI-Prolog as a shared library and ctypes to access it, it doesn't require compilation to be installed.
Neural Network Framework is a C++ framework to develop, simulate, and analyze arbitrary complex neural networks. The programmer can use the classes provided to create neural networks with arbitrary topology and mixed type of neurons. It's very easy to add customized neurons and layers.
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.
Reasonable Python is a Python module which adds logic programming constructs borrowed from F-Logic. It's build upon the Flora-2 and XSB engines and uses ZODB for permanent knowledgebase storage. Usage possibilities include knowledge bases, ontology management and semantic web applications.