Open Source Python Algorithms

The Modular toolkit for Data Processing (MDP) is a Python data processing framework. From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. From the scientific developer's perspective, MDP is a modular framework, which can easily be expanded. The implementation of new algorithms is easy and intuitive. The new implemented units are then automatically integrated with the rest of the library. The base of available algorithms is steadily increasing and includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.

Multi-categorization Recommendation Adjusting (MRA) is to optimize the results of recommendation based on traditional(basic) recommendation models, through introducing objective category information and taking use of the feature that users always get the habits of preferring certain categories. Besides this, there are two advantages of this improved model: 1) it can be easily applied to any kind of existing recommendation models. And 2) a controller is set in this improved model to provide controllable adjustment range, which thereby makes it possible to provide optional modes of recommendation aiming different kinds of users.

LASS : Library of Assembled Shared Source. Library of C++ code for scientific purposes.

C++, Matlab and Python library for Hidden-state Conditional Random Fields. Implements 3 algorithms: LDCRF, HCRF and CRF. For Windows and Linux, 32- and 64-bits. Optimized for multi-threading. Works with sparse or dense input features.

A univariate and multivariate analysis UI. This project is no longer under development. Please use as you wish.

DEBay: Deconvolution of Ensemble through Bayes-approach DEBay estimates cell type-specific gene expression by deconvolution of quantitative PCR data of a mixed population. It will be useful in experiments where the segregation of different cell types in a sample is arduous, but the proportion of different cell types in the sample can be measured. DEBay uses the population distribution data and the qPCR data to calculate the relative expression of the target gene in different cell types in the sample. The user manual of DEBay: https://sourceforge.net/projects/debay/files/UserManual.pdf Sample data: https://sourceforge.net/projects/debay/files/Test_data/ Citation Information: Vimalathithan Devaraj, Biplab Bose. DEBay: A computational tool for deconvolution of quantitative PCR data for estimation of cell type-specific gene expression in a mixed population. Heliyon, 2020, 6(7), e04489. https://doi.org/10.1016/j.heliyon.2020.e04489

This collection of Python3 modules provides a large range of implemented decision aiding algorithms useful in the field of outranking digraphs based Multiple Criteria Decision Aid (MCDA), especially best choice, linear ranking and absolute or relative rating algorithms with multiple incommensurable criteria. Technical documentation and tutorials are available under the following link: https://digraph3.readthedocs.io/en/latest/ The tutorials introduce the main objects like digraphs, outranking digraphs and performance tableaux. There is also a tutorial provided on undirected graphs. Some tutorials are problem oriented and show how to compute the winner of an election, how to build a best choice recommendation, or how to linearly rank or rate with multiple incommensurable performance criteria. Other tutorials concern more specifically operational aspects of computing maximal independent sets (MISs) and kernels in graphs and digraphs.

A cross-platform library that computes fast and accurate SIFT image features. libsiftfast provides Octave/Matlab scripts, a command line interface, and a python interface (siftfastpy). Optimized with SIMD instructions and OpenMP .

The Automatic Model Optimization Reference Implementation, AMORI, is a framework that integrates the modelling and the optimization processes by providing a plug-in interface for both. A genetic algorithm and Markov simulations are currently implemented.

A GUI sorting visualizer desktop application that helps to visualize various sorting algorithms interactively. Visualizer the sorting algorithms like Bubble sort, Insertion sort, Selection sort, Gnome sort, Shaker sort and Odd even sort. Change the bar color and background by customizing. Increase or decrease speed of animation to visualize the sorting process. Download now!

This project has moved to github at http://lmwtree.devries.ninja.

TAROT is a easy-to-use framework for Monte Carlo simulations in python. Calculations between different kinds of randomly distributed numbers are made as easy as basic arithmetics. Tarot provides an interactive graphical interface for interpretation.

"Blue Planet" is a research project simulating the behaviour and darwinian evolution of unicellular lifeforms, each controlled by its own genetic program. Moreover, "Blue Planet Inhabitants" are suited for swarm intelligence and swarm research.

Narcissistic number library for Python

Open Metaheuristic (oMetah) is a library aimed at the conception and the rigourous testing of metaheuristics (i.e. genetic algorithms, simulated annealing, ...). The code design is separated in components : algorithms, problems and a test report generator

PyLife is an implementation of the game of life algorithm featuring parallel programming. It uses MPI and python to achieve a consistent software architecture and reliably performance.

PyVision is a object-oriented Computer Vision Toolkit for researchers that contains vision and machine learning algorithms and algorithm analysis and easily interfaces with scipy/numpy, PIL, opencv and other computer and machine learning libraries.

This Python module provides basic facilities for mathematics on fixed-point numbers. The number of fractional binary digits is tunable, allowing near-arbitrary precision arithmetic. Arithmetic & functions such as sqrt, exp, log, sin & cos are available. For the latest releases please visit https://github.com/rwpenney/spfpm

Design and develop Recommendation and Adaptive Prediction Engines to address eCommerce opportunities. Build a portfolio of engines by creating and porting algorithms from multiple disciplines to a usable form. Try to solve NetFlix and other challenges.

Python module to track the overall median of a stream of values "on-line" in reasonably efficient fashion.

Python library for a fast and flexible graph data structure.

IMPORTANT: The project moved over to github! You can find it at: https://github.com/exhuma/python-cluster

ngram is a module to compute the similarity between two strings. It is different to python's "difflib.SequenceMatcher" in that it cares more about the size of both strings. ngram is an port and extension of the perl module called "String::Trigram

Charting tools, backtesting tools, and data visualization tools for the most popular cryptocurrencies.

This is a Python script for Blender which uses short (quaternion-based, floretion-based) algorithms to draw curves in space. The user can create new shapes and curves by setting a variety of parameters.