A functional-programming matrix library
A functional matrix math library for javascript
Javascript Matrix and Vector library for High Performance WebGL apps
Matrix Client-Server SDK for Javascript
WebAssembly bindings of the matrix-sdk-crypto encryption library
Matrix manipulation and computation library
Visualize relationships or network flow with an aesthetically-pleasing circular layout.
Welcome to the [Node.js] binding for the Rust [`matrix-sdk-crypto`] library! This binding is part of the [`matrix-rust-sdk`] project, which is a library implementation of a [Matrix] client-server.
2d transformation matrix functions written in ES6 syntax. Tree shaking ready!
matrix for scena
Canvas for Node.js with skia backend
Use space and slash separated color notation in CSS
Search and Rewrite code at large scale using precise AST pattern
A fully persistent balanced binary search tree
JSON grammar for tree-sitter
Filter that lets you change RGBA via a 5x4 matrix transformation
YAML grammar for tree-sitter
Matrix Widget API SDK
A practical functional library for JavaScript programmers.
Python grammar for tree-sitter
ESLint rules to promote functional programming in TypeScript.
Chart.js module for creating matrix charts
React bindings for MobX. Create fully reactive components.
Functional programming in TypeScript
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
A Ruby gem for vector and matrix operations. Provides methods to calculate: - Matrix determinant: Determinant(matrix) input: matrix - Array of arrays size of nxn output: res[Int] - simple Integer - Scalar product of vectors scalar_prod(a, b) input: a[Array], b[Array]- vectors a and b output: res[Int] - simple Integer as a result of scalar prod - Cross product for 3D vectors cross_prod(a, b) input: a[Array], b[Array] - vectors a and b with dimension n = 3; output: res[Array] - vector with the size = 3 (its dimension) as a result of cross prod - Help function help() output: String with info about gem funcs Includes comprehensive error handling and input validation. Designed for educational use and basic linear algebra computations. Ruby-гем для операций с векторами и матрицами. Предоставляет методы для вычисления: - Определителя матрицы Determinant(matrix) input: matrix - матрица (массив массивов) размера nxn output: res[Int] - целое число - Скалярного произведения векторов scalar_prod(a, b) input: a[Array], b[Array] - векторы (массивы) a и b output: res[Int] - целое число как результат скалярного произведения - Векторного произведения для 3D векторов cross_prod(a, b) input: a[Array], b[Array] - векторы (массивы) a и b размером n = 3; output: res[Array] - вектор (массив) с размером = 3 (его размерность) как результат векторного произведения векторов - Функция "помощь" help() output: Строка с информацией про математические методы гема Включает обработку ошибок и валидацию входных данных. Разработан для образовательных целей и базовых вычислений линейной алгебры.
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.