Results for predict-next-random

Predicts the next number in a sequence of Math.random() numbers

Generate random numbers from various distributions.

A functional typescript implementation of the PCG family random number generators

TypeScript definitions for d3-random

Fastest random ID and random string generation for Node.js

Operation with random numbers for ngraph.*

A Node.js module for efficient sampling from a discrete probability distribution using the alias method.

URL and cookie safe UIDs

Use the random function in CSS

Require Node module and immediately remove it from module cache to force loading again next time

An alias package for `crypto.randomBytes` in Node.js and/or browsers

Generate a cryptographically strong random string

Collision-resistant ids optimized for horizontal scaling and performance. For node and browsers.

Predict the output of Math.random

Random utility functions for ethers.

A Pulumi package to safely use randomness in Pulumi programs.

A small implementation of `crypto.getRandomValues` for React Native. This is useful to polyfill for libraries like [uuid](https://www.npmjs.com/package/uuid) that depend on it.

Provides functions for detecting if the host environment supports the WebCrypto API

Generate a random integer

Statistical routines and probability distributions.

XML ID utilities for tracespace projects

math-random is an isomorphic, drop-in replacement for `Math.random` that uses cryptographically secure random number generation, where available

A very fast and accurate text and line wrapping util

ARIMA, SARIMA, SARIMAX and AutoARIMA models for time series analysis and forecasting

This Ruby gem leverages Machine Learning(ML) techniques to make predictions(forecasts) and classifications in various applications. It provides capabilities such as predicting next month's billing, forecasting upcoming sales orders, identifying patient's potential findings(like Diabetes), determining user approval status, classifying text, generating similarity scores, and making recommendations. It uses Python3 under the hood, powered by popular machine learning techniques including NLP(Natural Language Processing), Decision Tree, K-Nearest Neighbors and Logistic Regression, Random Forest and Linear Regression algorithms.