Pick random data from array or object with specific filter
Generate random numbers from various distributions.
TypeScript definitions for d3-random
Fastest random ID and random string generation for Node.js
An alias package for `crypto.randomBytes` in Node.js and/or browsers
Statistical routines and probability distributions.
Temporary file and directory creator
Generate a cryptographically strong random string
Provides functions for detecting if the host environment supports the WebCrypto API
A Pulumi package to safely use randomness in Pulumi programs.
URL and cookie safe UIDs
Use the random function in CSS
prints a dependency graph in dot format for your typescript or react project
Random utility functions for ethers.
A node module for Google's Universal Analytics tracking
[](https://discord.gg/poimandres)
A functional typescript implementation of the PCG family random number generators
Pseudo-random number generators w/ unified API, distributions, weighted choices, ID generation
Filter object keys and values into a new object
Random number generator using xorshift128+
Plugin utilities for Rolldown
GRC's UHE PRNG in node (Ultra-High Entropy Pseudo-Random Number Generator by Gibson Research Corporation)
Universal Module for Secure Random Generator in JavaScript
A mathematically correct random number generator library for JavaScript.
CLI based DNS propagation check tool
Filter through NYC's GreenThumb gardens by borough, ZIP code, parkname, or ID number. Pull reports or search at random.
Library to build random text strings from rules defined as ruby hashes. Rules consist of Builders, which have Items which are componsed of Segments. Generated text can be modified with filters.
DH Movie Manager finds local movie files, queries IMDB, and stores the data in a sqlite3 database that you can use to run commands. Search for specific actors, directors, and genres. Filter results by audience score. Update your filenames to reflect the correct title. Feeling indecisive? use the 'play unseen [genre]' command to play a random movie that suits your mood.
Noisy sensor data, approximations in the equations that describe the system evolution, and external factors that are not accounted for all place limits on how well it is possible to determine the system's state. The Kalman filter deals effectively with the uncertainty due to noisy sensor data and to some extent also with random external factors. The Kalman filter produces an estimate of the state of the system as an average of the system's predicted state and of the new measurement using a weighted average. The purpose of the weights is that values with better (i.e., smaller) estimated uncertainty are "trusted" more. The weights are calculated from the covariance, a measure of the estimated uncertainty of the prediction of the system's state. The result of the weighted average is a new state estimate that lies between the predicted and measured state, and has a better estimated uncertainty than either alone. This process is repeated at every time step, with the new estimate and its covariance informing the prediction used in the following iteration. This means that the Kalman filter works recursively and requires only the last "best guess", rather than the entire history, of a system's state to calculate a new state.
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.