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Data matrix for NodeJS
GS1 Data Matrix decoder for barcode scanner payloads
Data Matrix image processing library
Explore data matrix using scatterplot matrix
Explore data matrix using scatterplot matrix
Data Matrix Parser for Thai lottery tickets
Javascript Matrix and Vector library for High Performance WebGL apps
Matrix Client-Server SDK for Javascript
Matrix manipulation and computation library
2d transformation matrix functions written in ES6 syntax. Tree shaking ready!
WebAssembly bindings of the matrix-sdk-crypto encryption library
Canvas for Node.js with skia backend
Visualize relationships or network flow with an aesthetically-pleasing circular layout.
matrix for scena
JSON grammar for tree-sitter
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.
YAML grammar for tree-sitter
Search and Rewrite code at large scale using precise AST pattern
Python grammar for tree-sitter
View structured data in a matrix table (data grid), showing a value located at an X and Y coordinate.
React bindings for MobX. Create fully reactive components.
Chart.js module for creating matrix charts
Bash grammar for tree-sitter
This Ruby extension implements a DataMatrix encoder for Ruby. It is typically used to create semacodes, which are barcodes, that contain URLs. This encoder does not create image files or visual representations of the semacode. This is because it can be used for more than creating images, such as rendering semacodes to HTML, SVG, PDF or even stored in a database or file for later use.
Red Arrow GSL adds `Arrow::*Array#to_gsl`/`Arrow::Tensor#to_gsl` for Apache Arrow to GSL conversion. Red Arrow GSL adds `GSL::Vector#to_arrow`/`GSL::Vector::Int#to_arrow`/`GSL::Matrix::*#to_arrow` for GSL to Apache Arrow conversion.
Red Arrow NMatrix adds `Arrow::Tensor#to_nmatrix` for Apache Arrow to NMatrix conversion. Red Arrow NMatrix adds `NMatrix#to_arrow` for NMatrix to Apache Arrow conversion.
A thread-safe two dimensional sparse matrix data structure with C, Java and Ruby bindings. It was created to make loading and accessing medium sized (10GB+) matrices in boxed languages like Java/Scala or Ruby easier.
A thread-safe two dimensional sparse matrix data structure with C, Java and Ruby bindings. It was created to make loading and accessing medium sized (10GB+) matrices in boxed languages like Java/Scala or Ruby easier.
This is a ruby wrapper for libdmtx, which is a open source software for reading and writing Data Matrix barcodes.
An n-dim matrix data structure with fast loop, search and access
Gem for mathematics method and different data structure like complex and vector and matrix and some else you're been able to see his method on Homepage of this gem
Gaussian (http://gaussian.com/) is one of the most popular general purpose computational chemistry software packages. The output genearated by it contains a lot of data which should be structured properly before evaluation. The purpose of the Gaussian Parser is to perform routine operations for better and faster log data processing. Currently it's able to parse the output file and process the data for - Distance matrix - Molecular Orbital Coefficients - Harmonic frequencies
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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