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deb_control

Simple ruby gem to parse debian control files

deb_finder

A python script to find all .deb files for all apt-get installed packages and dependencies on a system.

deb_subscription

debops-tools

Your Debian-based data center in a box -

debpak

Helper for creating Flatpak Apps from Debian sources

debpkg

Rust libary for parsing debian packages

debr

debtools

debrebuild

Given a buildinfo file from a Debian package, generate instructions for attempting to reproduce the binary packages built from the associated source and build information.

debseeker

A debian package and dependencies finder

debsources

https://anonscm.debian.org/cgit/qa/debsources.git

debuerreotype

reproducible, snapshot-based Debian rootfs builder

debug-menu

Chrome-like debugging context menu for electron.

debugtocpp

Tool for generating C++ classes and json from PDB, DWARF and ELF symbols (Work in progress)

debuild.me

automagic debian package linter

debut

A python library to parse Debian deb822-style control and copyright files

debutils

Debian package utility library and scripts

debversioncomp

check all the available debian version comparison algorithms for correctness

debwatch

A tool getting the latest upstream version of tarbarll for given package name with using debian watch file

dec

Discrete Exterior Calculus

decaf-1

DECAF(short for Dynamic Executable Code Analysis Framework) is a binary analysis platform based on QEMU. This is also the home of the DroidScope dynamic Android malware analysis platform. DroidScope is now an extension to DECAF.

decaf2many

Java to Python transpiler

decima

decision-tree

C4.5 Decision Tree python implementation with validation, pruning, and attribute multi-splitting

decision-tree-generator

Program for generating decision tree LaTeX code of array-based algorithms

decision-tree-id3

decision-tree-in-python-for-continuous-attributes

This code constructs a Decision Tree for a dataset with continuous Attributes. Each training instance has 16 numeric attributes (features) and a classification label, all separated by commas. In deciding which attribute to test at any point, the information gain metric is used. The node test threshold for each potential attribute is set using this same metric i.e. at each point, all the values that exist for a particular attribute in the remaining instances are ordered, and threshold values that are (half way) between successive attribute values are used to find the Information Gain. The threshold value that gives the highest information gain is used. The same attribute can be tested again later in the tree (with a different threshold).

decisiontree

Python Implementation of ID3

decisiontrees

decisiontrees-1

Decision Trees Inference

decisiontrees-2

Seminar work "Decision Trees - An Introduction" with presentation, seminar paper, and Python implementation