Milk is a machine learning toolkit in Python. Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, and decision trees. It also performs feature selection. These classifiers can be combined in many ways to form different classification systems. For unsupervised learning, milk supports k-means clustering and affinity propagation.
ARS (Autonomous Robot Simulator) is a physically-accurate simulation suite for research and development of mobile manipulators and, in general, any multi-body system. It is modular, easy to learn and use, and can be a valuable tool in the process of robot design, in the development of control and reasoning algorithms, and in teaching and educational activities. It will encompass a wide range of tools spanning from kinematics and dynamics simulation to robot interfacing and control.
itch41 parses a file of level 3 NASDAQ stock exchange data, such as ftp://emi.nasdaq.com/ITCH/S030711-v41.txt.gz. This is mainly for demonstration purposes, lacking column domains, column constraints, data validation, timestamp calculations, stock symbol lookup for execution/cancel/delete messages, derived columns (e.g. for partitioning), and the general kinds of pre-processing one would expect from a production tick data loader.
rpm-depend can easily find packages fitting the capabilities required by a given package. It works only on directories with RPMs like mounted media for now. RPMs in a given directory are recorded in caches to enable quick lookup on the next query. It includes the ability to omit packages installed in the current system (i.e. list only missing packages) and resolve package dependencies recursively (i.e. if A depends on B, resolve B's dependencies as well).