libacme-bleach-perl

Perl module for really clean programs

The first time you run a program under use Acme::Bleach, the module removes all the unsightly printable characters from your source file.

jel-java

Library for evaluating algebraic expressions in Java

The JEL library enables users to enter algebraic expressions into their program. Since JEL converts expressions directly into Java bytecode, it significantly speeds up their evaluation time. If the user's Java virtual machine has a JIT compiler, expressions are transparently compiled into native machine code.

yambar

Lightweight and configurable status panel

yambar is a lightweight and configurable status panel (bar, for short) for X11 and Wayland, that goes to great lengths to be both CPU and battery efficient - polling is only done when absolutely necessary.

libmongoc-1.0-0

MongoDB C client library - runtime files

libmongoc is the officially supported MongoDB client library for C applications.
lightweight user-configuration application

mugshot

lightweight user-configuration application

Mugshot is a lightweight user configuration utility that allows you to easily update personal user details. This includes: - Linux profile image: ~/.face - User details stored in /etc/passwd (used by finger) - Pidgin buddy icon - LibreOffice user details

python-shogun

Large Scale Machine Learning Toolbox

SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing.