D3 plugin which computes a Weighted Voronoi tesselation
A random weighted item chooser with custom seed option for JavaScript and TypeScript.
CSS selector engine supporting jQuery selectors
View docs [here](https://radix-ui.com/primitives/docs/components/select).
a CSS selector compiler/engine
A dead-simple module for picking a random item with weights.
Miscellaneous graph metrics for graphology.
Inquirer select/list prompt
A Select control built with and for ReactJS
Miscellaneous indices for graphology.
Select randomly from a list of weighted values.
Select protocol using first bytes of incoming data and hose stuff to the handler
Node.js module to make a random choice among weighted elements of table.
two functions: One that returns true, one that returns false
tree-select ui component for react
Balena specific semver utility methods
Takes a FeatureCollection of points and calculates the median center.
React Select
Selectors decision tree - choose matching selectors, fast
hast utility for `querySelector`, `querySelectorAll`, and `matches`
Small weighted-probability list library (node.js port fork)
Provides some base functions needed by a css-select adapter so that you don't have to implement the whole thing.
Simulate react-select events for react-testing-library
Calculates the weighted mean of an array of numbers
futures::stream::Select with weights
Select type from weighted index
Gem for selecting items by weight
Simple, weighted selection of items.
A simple library for obtaining weighted randomized selections
When the method `select_from_hash_list` is given an array of hashes where the value of each hash is a weight, the gem will take that into account and select a value at random.
Provides a method to select a element by weighted randomization from a hash with weights.
Selectivity.js is a modular and light-weight selection library for jQuery and Zepto.js. This gem integrates Selectivity.js with Ruby on Rails.
Proper related posts plugin for Jekyll - uses document correlation matrix on TF-IDF (optionally with Latent Semantic Indexing). Each document is tokenized and stemmed, every word found is treated as keyword for analysis (except for some stop words). TF-IDF matrix for the whole site is calculated (including extra provided weights), then if given accuraccy is lower than 1.0, LSI algorithm is used to compute new simplified vector space. Document correlation matrix is created using dot product of the matrix and its transpose. For each of the post' related documents are inserted into priority queue (sorted by score from document correlation matrix), assuming the score is greater than minimal required score. Selected few bests related posts are retrieven from the queue. Liquid template for each post is rendered and <related-posts /> is replaced with the outcomes of algorithm.
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