Visualiser for basic geometric primitives and fractals in arbitrary-dimensional spaces
-
Updated
Mar 12, 2017 - C++
Visualiser for basic geometric primitives and fractals in arbitrary-dimensional spaces
Nearest neighbor search. Methods: LSH, hypercube, and exhaustive search. C++
LSH and Hypercube algorithms for Approximate Nearest Neighbor. Centroid based clustering using Lloyd's and reverse assignment algorithms.
📈 kNN using LSH and Hypercube projection & Clustering using kMeans++ for n-dim polygonal curves and time series
approximation algorithms for exact nearest neighbors search and clustering on multi-dimensional vectors
Comparison of multiple methods for calculating MNIST hand-written digits similarity.
Search and clustering vectors in C++
First assignment for the University Senior Project course
My flavor of Marlin for my Hypercube Evolution build, running off of an MKS Robin Nano v1.2 with a 300mm^3 build volume.
Clustering methods implementations in C++: Lloyd, K-Means, K-Means++, PAM
Testing the effectiveness of Approximate k-NN search with LSH and Hypercube on MNIST. Also, implemented k-medians++ for the same dataset.
Add a description, image, and links to the hypercube topic page so that developers can more easily learn about it.
To associate your repository with the hypercube topic, visit your repo's landing page and select "manage topics."