numpy-random

What problem does it solve?

This Skill provides modern, high-quality random number generation capabilities for simulations, data shuffling, and reproducible experiments, moving beyond legacy methods.

Core Features & Use Cases

• Modern RNG: Utilizes NumPy's Generator API for better statistical properties and performance.
• Parallel Streams: Supports creating independent random streams for parallel processing using SeedSequence.
• Reproducibility: Enables reproducible results through proper seeding.
• Use Case: Ensure your machine learning model training is reproducible by using a fixed seed for random data shuffling and augmentation.

Quick Start

Use the numpy-random skill to generate a reproducible random sample with a seed of 42.