D3 plugin which computes a Weighted Voronoi tesselation
A dead-simple module for picking a random item with weights.
Miscellaneous graph metrics for graphology.
Miscellaneous indices for graphology.
A random weighted item chooser with custom seed option for JavaScript and TypeScript.
Node.js module to make a random choice among weighted elements of table.
Takes a FeatureCollection of points and calculates the median center.
Balena specific semver utility methods
Small weighted-probability list library (node.js port fork)
Calculates the weighted mean of an array of numbers
MCP server for diagrammatic thinking and spatial representation
Produce a random integer based on weights
Exponentially Weighted Moving Average
Protobuf and gRPC definitions for microservice-based argumentation machines
AdiaUI A2UI validator — JSON Schema structural validation plus catalog-aware semantic validation (component exists, props match YAML). Split out from the compose engine so non-compose tooling (tests, MCP validator tools, CI gates) can depend on validation
Select randomly from a list of weighted values.
The FinTech utility collections of simple, cumulative, and exponential moving averages.
Weighted Damerau-Levenshtein and Levenshtein edit distance for Node.js with configurable operation costs.
A library for recording opus encoded audio
Edmond's weighted maximum matching algorithm (Blossom algorithm) ported from http://jorisvr.nl/maximummatching.html
Collection of ~170 lightweight, composable transducers, reducers, generators, iterators for functional data transformations
Simple weighted round robin implementation using Redis list and sorted set
Pick a random item from a weighted list.
TypeScript definitions for weighted
Weighted argumentation frameworks with Dunne et al. 2011 inconsistency-budget semantics
Weighted bipolar argumentation: Amgoud et al. 2008 composition of argumentation-weighted and argumentation-bipolar with Dunne 2011 budget
Bridge between encounter social interactions and argumentation schemes
Nake is light-weight and highly flexible Rake replacement with much better arguments parsing
Multi-agent deliberation equipment with Pathos/Logos/Ethos weighting for consensus building in the Cocapn fleet. The Consensus Engine implements a sophisticated multi-agent deliberation framework based on the classical rhetorical tripartite of Pathos, Logos, and Ethos. This equipment enables AI systems to make well-rounded decisions by considering multiple perspectives before reaching consensus. ## The Tripartite Framework - **Pathos** (πάθος) - Appeals to emotion, intent, and human experience - **Logos** (λόγος) - Appeals to logic, reason, and rational argument - **Ethos** (ἦθος) - Appeals to ethics, credibility, and moral character
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.