Firefox 17-26 iterators throw a StopIteration object to indicate "done". This normalizes it.
An ESnext spec-compliant iterator helpers shim/polyfill/replacement that works as far down as ES3.
Get an iterator for any JS language value. Works robustly across all environments, all versions.
Iterate over promises serially
Iterate any JS iterator. Works robustly in all environments, all versions.
Map over an array or object of values in parallel or series, passing each through the async iterator, with optional lifecycle hooks.
Async iterator utilities for Metorial. Provides programmable async iterators for creating controlled data streams and managing async iteration flow.
Minimal async jobs utility library, with streams support
Iterate over a collection, invoking a function for each element.
Higher order iterator library for JavaScript/TypeScript.
Extended iterable class, providing lazy array-like methods with automatic async and return/throw forwarding
Iterator abstraction based on ES6 specification
Iterate any iterable JS value. Works robustly in all environments, all versions.
Walk up ancester's dir up to root
A finite state machine iterator for JavaScript
Small, performant & immutable iteration utilities for Arrays and Objects
Convert an argument into a valid iterator. Based on the `.makeIterator()` implementation in mout https://github.com/mout/mout.
Abstraction for DynamoDB queries and scans that handles pagination and parallel worker coordination
Maximize the parallel calls of an iterator supporting asyncIterator interface
Creates an async iterator for a variety of inputs in the browser and node. Supports fetch, node-fetch, and cross-fetch
Turn an abstract-leveldown iterator into a readable stream
[](http://www.typescriptlang.org/) [](https://www.npmjs.com/package/@n1ru4l/push-pull-async
ES async interator wrapper for node streams
Framework-independent loaders for 3D graphics formats
Manage start-stop timeframes and a series of timeframes for pattern matching and iterating.
== Synopsys Ruby Enumerable extension. Main idea is lazy computations within enumerators. == Usage Install as a gem: sudo gem install deferred_enum This gem introduces DeferredEnumerator class: ary = [1, 2, 3, 4] deferred = ary.defer # #<DeferredEnumerator: [1, 2, 3, 4]:each> DeferredEnumerator brings some optimizations to all?, any? and none? predicates deferred.all?(&:even?) # Will stop iteration after first false-result = 1 iteration deferred.none?(&:even?) # 2 iterations deferred.any?(&:even?) # 2 iterations It also introduces lazy versions of Enumerable's #select, #map and #reject methods deferred.map { |i| i + 1 } # #<DeferredEnumerator: #<Enumerator::Generator>:each> deferred.select { |i| i.even? } # #<DeferredEnumerator: #<Enumerator::Generator>:each> deferred.reject { |i| i.odd? } # #<DeferredEnumerator: #<Enumerator::Generator>:each> So you can safely chain your filters, they won't be treated as arrays: deferred.map(&:succ).select(&:even?) # #<DeferredEnumerator: #<Enumerator::Generator>:each> You can build chains of Enumerables: deferred.concat([2]).to_a # [1, 2, 3, 4, 2] Or append elements to the end of enumerator: deferred << 2 You can even remove duplicates from enumerator, though this operation can be tough: deferred.uniq # #<DeferredEnumerator: #<Enumerator::Generator>:each> There are many other methods in DeferredEnumerator, please refer to documentation.
A quick and easy benchmarking library for Ruby. Useful for benchmarking only part of an iteration, and accumulating the data to report later in the code.
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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