Peax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders

FMI-compliant Model Estimation in Python

Scalable Insets for HiGlass: a new technique for interactively exploring and navigating large numbers of annotated patterns in multiscale visual spaces such as gigapixel images, matrices, or maps.

Auntie, my dear ultra-fast module for untying/splitting/counting a stream of data by a chosen separator sequence.

Machine Learning Notes 2024

python script to search pattern in large files

Implementation and performance comparison of Boyer Moore, Horspool and Brute Force in c programming language.

Regular expression and pattern search.

C++ implementation of the Hooke-Jeeves method

Go Pattern Search

Repository containing the code for "Searching for patterns in a large collection of children's drawings" - Ravinithesh's Master Thesis Project.

keywords: nonlinear optimization, pattern search, augmented lagrangian, karush-kuhn-tucker, constrained optimization, conjugate gradient methods, quasi newton methods, line search descent methods, onedimensional and multidimensional optimazation

Single header C++ implementation of derivative-free optimization algorithms

Метод Хука-Дживса на C

Pattern Search Optimization Teaching Toolbox

Implementation of various of algorithms and data structures as well as comparison of their efficiency for "Algorithms and Data Structures" Course

This is a demo of pattern finding search in a given text, built with Vue.

This is the latest version of Peax - a tool for interactive visual pattern search and exploration in epigenomic data