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.
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.