Calculates the eigenvalues and normalized eigenvectors for a matrix, using Lapack
A library to parse expressions, solve and simplify systems of linear equations, find eigenvalues and eigenvectors
Get the eigenvalues and eigenvectors of a matrix
A professional, comprehensive and high-performance library for you to manipulate matrices.
Principal Components Analysis in javascript
MCP server providing symbolic mathematics via SageMath computer algebra system
BLAS/LAPACK for JavaScript
A library for spectral graph theory.
A TeX math parser that can evaluate TeX math and convert it into a MathJS expression tree.
Advanced calculus, finance, linear algebra, probability, and engineering math tools for AI agents via MCP (Model Context Protocol).
Maths utilties
ML semantic detection for cascadeflow TypeScript - Feature parity with Python
Principal Components Analysis in javascript
Detects ReDoS vulnerabilities in regexes using Thompson NFA construction and spectral radius analysis.
kabsch algorithm
Set of machine learning and linear algebra tools.
🎼 Listen to your code breathe - Every function has a chord, every module a melody
BLAS/LAPACK for JavaScript
Genkit MCP Server Implementation
Tools for high-dimensional small sample size data
MCP server for generating 3Blue1Brown-style math animation videos with AI narration in Claude
 
A library that make Maths calculations, easy. It can perform calculations ranging from basic Arithmetic to complex Algebra!
generate random covariance matrices, and MVN samples using them.
algorithms to compute eigenvalue/eigenvectors of symmetric matrices
Utility-first eigenvalue and eigensystem primitives for RustUse
The missing center — Universal Dirac operator and closure object proving all 14 executable theorems share one spectrum
Classical and modern control theory for agents — stability, controllability, observability, optimal control
Principal Coordinates Analysis (PCoA) of a symmetric distance matrix — scikit-bio skbio.stats.ordination.pcoa equivalent (Gower double-centering + symmetric eigendecomposition)
Functional analysis on multivector spaces - Hilbert spaces, linear operators, and spectral theory
command line
The algebra of dynamical systems — operator algebras from evolution
Resolvent Leverage Theorem — ‖R(λ)‖=1/dist, zero work at the still point, infinite sensitivity, mutual constitution of center and periphery
LAPACK operations for OxiBLAS - pure Rust implementation
Krylov subspace and preconditioned iterative solvers for dense and sparse linear systems, with shared and distributed memory parallelism.
Lightweight solvers for generalized eigenvalue problems
The project consists of some enhancements to the Ruby "Matrix" module and includes: LU and QR (Householder, Givens, Gram Schmidt, Hessenberg) decompositions, bidiagonalization, eigenvalue and eigenvector calculations. Includes some aditional code to obtains marginal for rows and columns.
A linear algebra library cooperating with NArray. This library calls blas and lapack routines for fast computations. This library has following functionalities. * xGEMM (multiply two matrices and add other matrix) * Solve LLS(Least Square Sum) problems * Computing determinant (using QR decomposition) * Solve eigenproblems (compute eigenvalues and eigenvectors) * (Pivoted) LU decompotision * SVD(Singular value decomposition) * QR decomposition * Cholesky decomposition
GRYDRA v2.0 is a complete, modular Ruby library for building, training, and deploying neural networks. NEW in v2.0: - Complete modular architecture with 29 organized files - Keyword arguments API for better readability - Full implementations (no more "simplified" versions) - 8 loss functions (MSE, MAE, Huber, Cross-Entropy, Hinge, Log-Cosh, Quantile) - 5 optimizers (Adam, SGD, RMSprop, AdaGrad, AdamW) - 6 training callbacks (EarlyStopping, LearningRateScheduler, ReduceLROnPlateau, ModelCheckpoint, CSVLogger, ProgressBar) - Complete LSTM implementation with backpropagation - Complete 2D Convolutional layer with padding and stride - Real PCA with eigenvalue decomposition using Power Iteration - Multiple activation functions (Tanh, ReLU, Leaky ReLU, Sigmoid, Swish, GELU, Softmax) - Regularization (Dropout, L1, L2) - Weight initialization (Xavier, He) - Data normalization (Z-Score, Min-Max) - Comprehensive metrics (MSE, MAE, Accuracy, Precision, Recall, F1, Confusion Matrix, AUC-ROC) - Advanced training (mini-batch, early stopping, learning rate decay, validation split) - Cross-validation and hyperparameter search - Text processing (vocabulary, binary vectorization, TF-IDF) - Model persistence (save/load with Marshal) - Network visualization and gradient analysis - Simplified EasyNetwork interface - 100% backward compatibility with v1.x Perfect for machine learning projects, research, and education in Ruby.
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