A zero-boilerplate higher-order reducer for managing normalized relational data
Resolve a URI relative to an optional base URI
Reduce a list of values using promises into a promise for a value
A JavaScript library for efficient immutable updates
Pack RGBA color channels into uint32 and back
Easing functions for smooth animation.
No description provided.
Newline character converter
Mouse wheel normalization across multiple multiple browsers.
A normalized and configurable cache exchange for urql
Normalizes data that can be found in package.json files.
Cross platform normalization of process.argv
Turn any flavor of allowable package.json bin into a normalized object
append AST into power-assert context
A library implementing different string similarity
A Redux binding for React Router v4 and v5
normalized JS Object and JSON message and event protocol for ES6+ node.js, browsers, electron, vanialla js, react.js, components, actions, stores and dispatchers
reducer for the Shift AST format
React useReducer with async actions
Contains common CLI functionality like logging and prompting. This package is used by both cli and create-amplify to provide a normalized CLI experience
Create a normalized catalogs config from pnpm-workspace.yaml contents.
Sequence your effects naturally and purely by returning them from your reducers.
Contains core logic that needs to be normalized across many packages in the repo. This includes things like error classes, parsing stack name and BackendIdentifiers, and other cross-cutting concerns. Over time, this package will probably have a tendency t
Publishing createReducer from https://redux.js.org/recipes/reducing-boilerplate#generating-reducers
A quick command line interface to lighthouse. The goal is to reduce overhead of tracking tickets inline with normal workflow. The effect is achieved by setting conventions.
A quick command line interface to lighthouse. The goal is to reduce overhead of tracking tickets inline with normal workflow. The effect is achieved by setting conventions.
FSelector is a Ruby gem that aims to integrate various feature selection algorithms and related functions into one single package. Welcome to contact me (need47@gmail.com) if you'd like to contribute your own algorithms or report a bug. FSelector allows user to perform feature selection by using either a single algorithm or an ensemble of multiple algorithms, and other common tasks including normalization and discretization on continuous data, as well as replace missing feature values with certain criterion. FSelector acts on a full-feature data set in either CSV, LibSVM or WEKA file format and outputs a reduced data set with only selected subset of features, which can later be used as the input for various machine learning softwares such as LibSVM and WEKA. FSelector, as a collection of filter methods, does not implement any classifier like support vector machines or random forest.