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EmPy.Report is a C# .NET assembly to generate Report based on Xml ReportStructure and DataSet driven. At the moment it's only a working alpha version, help me to build a final version. Recuitment is open!
A visual Domain-Specific Language for Data Transformations, which generates programs for non-programmer domain-experts. The generated program will extract data from one source and converts it into a meaningful format defined by the user.
The aim of the project is to provide short description of many programming languages, dialects, IDEs, etc. with sample programs (realizations of common algorithms like 'hello, world', 'dot product', 'quine program').
ClipsGen is a Lightweight Eclipse plugin for easy code generation.
ClipsGen uses simple XML files as model and the velocity template engine for code generation. The model can be accessed by the dot-notation which is a feature of velocity.
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Banana.NET (banana-dot-net) is a compilation of architectural guidelines for designing software solutions built on the Microsoft.NET framework. It is also a set of class libraries that provide basic services and framework packages often used in projects
NoUnit Graphs your code to give you a picture of how good your JUnit tests are. NoUnit.Net is a .NET version of the program. (More Information at http://nounit.sourceforge.net).
Projects written in C# for Microsoft's .NET platform devoted to XML technologies. *** This project is now defunct. Please see the NDoc (http://ndoc.sf.net/) project for the latest version of DOC.NET. ***
This module provides a Perl interface to layout and image generation of
directed and undirected graphs in a variety of formats (PostScript,
PNG, etc.) using the "dot" and "neato" programs from the GraphViz
project (http://www.graphviz.org/).
Antidoto is a java technology mashup providing building blocks for modular architectures. Antídoto is the portuguese word for antidote. An antidote is a substance which can counteract a form of poisoning.
A standalone, STL interface to the Torch library's Support Vector Machine (SVM). It supports single or multiclass (one vs. all) classification using dot product, polynomial, Gaussian and sigmoid kernels.