Hardware-accelerated distance metrics and similarity measures for high-dimensional data

Similarity and distance measures for clustering and record linkage applications in R

KNN using brute force and ball trees implemented in Python/Cython

The Python 3 library for Multi-Criteria Decision Analysis.

PyTorch implementations of the beta divergence loss.

Lab Experiments under Lab component of CSE3018 - Content-based Image and Video Retrieval course at Vellore Institute of Technology, Chennai

Distance metrics are one of the most important parts of some machine learning algorithms, supervised and unsupervised learning, it will help us to calculate and measure similarities between numerical values expressed as data points

Python 3 library for Multi-Criteria Decision Analysis based on distance metrics, providing twenty different distance metrics.

DTW(Dynamic Time Warping) & Subsequence-DTW Python Module

Repository on Approximate Bayesian Computation and the different distance metrics which can be implemented.

🪓 Predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset

🌼 Classify the different species of the Iris flower.

Classification model to categorize clothing items into distinct classes

Classification of IRIS Dataset using various distance metrics.

Distance Metrics Detective Story – An interactive Jupyter notebook that explores when to use Cosine, Euclidean, Manhattan, Dot Product, and Hamming distances in vector search. Featuring hands‑on financial contracts dataset, visual comparisons, and a practical decision framework to help engineers select the right similarity measure

This Jupyter Notebook demonstrates the implementation of a K-Nearest Neighbors (KNN) algorithm using the concept of nearest neighbors without using direct classifiers. It also includes exploratory data analysis (EDA) and comparison of three classifiers.

Practice Material