Implementation of A New Burrows Wheeler Transform Markov Distance
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Updated
Apr 19, 2020 - Python
Implementation of A New Burrows Wheeler Transform Markov Distance
text similarity search trees based on Normalized Compression Distance
Autonomous Agent Normalized Compression Distance (NCD) & Kolmogorov Complexity Universal Metric
Autonomous Agent Normalized Compression Distance (NCD) & Kolmogorov Complexity Universal Metric
A small Julia library for calculating the normalized compression distance.
Measure HTML structural similarity via Normalized Compression Distance
Normalized Compression Distance in POSIX shell, and compatible with any compressor.
FastNCD is a simple C++ library to calculates the Normalized Compression Distance (NCD) between two given strings
FacialSimilarity: a compression-based face identifier through image similarity.
A new package that uses large language models and pattern matching to perform structured similarity comparisons between textual content based on normalized compression distance. Users provide multiple
Download Beyond The Wire Hack 2026 Toolkit for WWI frontline combat tools: aim, recoil, ESP, wallhack, radar, no recoil, loadouts, and battle analytics.
Predict search relevance given a product name and its text attributes
Structural redundancy for refactoring; independent information for tests. A research note and executable alpha-normalised compression-distance examples.
EEG signal analysis and classification using preprocessing, segmentation and Normalized Compression Distance.
Rust version of the Burrows Wheeler Markov Distance https://github.com/EdwardRaff/pyBWMD
TAI Third Assignment - The objective is to use the Normalized Compression Distance (NCD), with multiple compressors, in order to obtain the similarity between a small music sample and every complete music in the dataset. The sample will then be assigned to the one which has the smaller average distance for all the compressors.
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