Open Source Python Algorithms

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

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.

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.

Narcissistic number library for Python

Image processing tool that encodes pixel data as indices within the first 16.7 million digits of PI (π). Features high-performance Numba-accelerated search and a signature 'film-grain' aesthetic upon reconstruction. ZIP also include 16 MB file with 16,7 mil numbers of PI Benchmark(Single-Thread): Hardware & Environment Apple Silicon: Apple M2 (Mac mini/MacBook) x86_64 Platform: Intel Core Ultra 5 225F (Arrow Lake, 10 Cores) OS 1: Fedora 43 (GNOME) OS 2: Windows 11 Pro (23H2/24H2) Software: Python 3.14.3 + Numba JIT (latest) Results (Lower is better) Platform / OS CPU Time (Seconds) macOS (Native) Apple M2 52.151311 s (in default setup) Fedora Linux Intel Core Ultra 5 225F 58.536457 s (in default Power Management: Balanced) Windows 11 Intel Core Ultra 5 225F 59.681427 s (important! Power Management set as High Performance .. in Balanced is slow)

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.

An experimental language designed to be very simple, but expressive enough to represent mathematical constructions and have strong introspective capabilities.

This program generates customizable hyper-surfaces (multi-dimensional input and output) and samples data from them to be used further as benchmark for response surface modeling tasks or optimization algorithms.

MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack

Math tools in Python to tackle down problems in Operational Research fields. Comes with a Django based web interface to allow remote access to complex simulation means.

The NUMIPAD library implements several methods/algorithms to solve inverse problems and adaptive decomposition (i.e. Tikhonov regularization, Total Variation, Basis Pursuit, etc.)

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.

Abandoned version of qbc. DOES NOT WORK properly

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

libGo is a C++ class library containing all kinds of things that proved useful to me. Included are: - Linear algebra, using LAPACK and CBLAS - V4L(1) image grabber - Multithreading - Image containers (up to 3D) - Some simple optimisation code - Python embedding helper - Matlab interface - .. and other things, have a look at the HTML documentation! golib grew over many years, things I had use for have been added now and then. Some parts are better taken care of than others. If you find anything spectacularly wrong or badly documented, and need assistance, please drop me a line.

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.

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.

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