surge synthesizer -- wavetable oscillator
Read, write and validate optical spectral data files (UV-Vis, visible-range spectra)
Functional analysis on multivector spaces - Hilbert spaces, linear operators, and spectral theory
Rust Spectral Colorimetry library with JavaScript/WASM interfaces
Spectral first integral I(x) = γ(x) + H(x) conservation tracker for coupled nonlinear dynamics
A lightweight, zero-dependency library utilizing Spectral Graph Theory to isolate topological network anomalies.
Spectral analysis of tension graphs for anomaly detection, fingerprinting, and structural health
Spectral clustering via normalized Laplacian, LOBPCG, and k-means
High-performance Rust engine for SIA² (Self-Improving AI with Spectral Architecture)
Spectral graph theory analysis for Meilisearch HNSW vector indexes
Exact proof that the spectral gap of the 96-vertex Resonance Classes graph Laplacian is λ₁ = 1, via block tridiagonal decomposition into Q₄ hypercube blocks. Zero floating-point, zero unsafe code.
Pure-Rust DSP (resampling, gain, spectral) for OxiAudio
Rumale::Clustering provides cluster analysis algorithms, such as K-Means, Gaussian Mixture Model, DBSCAN, and Spectral Clustering, with Rumale interface.
Sonus extracts time-domain, spectral, and perceptual audio features in pure Ruby with optional FFTW acceleration.
Muze is a Ruby audio analysis and feature extraction library that provides audio loading, spectral analysis, feature extraction, rhythm analysis, and audio effects.
Rumale is a machine learning library in Ruby. Rumale provides machine learning algorithms with interfaces similar to Scikit-Learn in Python. Rumale supports Support Vector Machine, Logistic Regression, Ridge, Lasso, Multi-layer Perceptron, Naive Bayes, Decision Tree, Gradient Tree Boosting, Random Forest, K-Means, Gaussian Mixture Model, DBSCAN, Spectral Clustering, Mutidimensional Scaling, t-SNE, Fisher Discriminant Analysis, Neighbourhood Component Analysis, Principal Component Analysis, Non-negative Matrix Factorization, and many other algorithms.
Maps ideas as stars with magnitude and spectral class, groups them into constellations with pattern types, and enables celestial navigation between related concepts across cognitive domains.
Spectral decomposition of complex ideas into band-specific components — white light in, rainbow out. Decompose ideas across abstraction wavelengths, attenuate and amplify components, then recompose synthesized understanding.