Random Number Generator

This tool provides a configurable random number generator with seed control and distribution options to generate reproducible data for simulations, testing, and educational demos. It targets developers, data scientists, QA engineers, researchers, and instructors who require deterministic sequences and quick variability analysis. Use cases include Monte Carlo simulations, unit tests relying on randomized inputs, and demonstrations of statistical concepts. When reproducibility is essential, this tool offers a transparent seeding mechanism and explicit distribution selection.\n\nCore Logic & Features: Core logic implements seeded pseudo-random generation with multiple engines (Mersenne Twister, PCG, XorShift) and distribution samplers (uniform, normal, Poisson, binomial, exponential). Required inputs include range bounds, sample size, and distribution; optional inputs include seed, decimals, and algorithm choice. Advanced features include per-run reproducibility, batch generation, and export-ready outputs in CSV or JSON formats. This section differentiates core capabilities from optional enhancements.\n\nInputs & Outputs: Inputs include seed (integer, optional), min_value (number), max_value (number), distribution (enum), sample_size (integer), decimals (integer, optional), engine (enum, optional). Outputs include numbers (array of numbers), actual_min, actual_max, distribution, sample_size, seed_used, and summary statistics when requested. Validation enforces min <= max, sample_size > 0, and numeric types; outputs are formatted to specified decimals.\n\nAlgorithms & Calculations: Uniform distribution uses simple rescaling of U(0,1) to [min, max]. Normal distribution uses mean = (min+max)/2 and stddev derived from width to approximate a bounded normal via clipping. Poisson and binomial use appropriate rate and trials parameters; decimal scaling applies after sampling. The generator updates internal state with the chosen engine and seeding policy; results are deterministic for the same seed.\n\nError & Edge Cases: Invalid ranges (min > max) raise errors; missing required inputs raise errors; extremely large sample sizes may affect memory; non-numeric inputs fail validation; distribution-specific parameter constraints apply (e.g., Poisson rate must be >0). When using a seed, results are reproducible across environments unless engine implementation differs.\n\nIndustry/Region & Localization: Uses standard numeric formats with dot as decimal separator; no currency or locale-specific formatting by default; scalable across regions; no regulatory constraints apply beyond standard scientific practice; ensure consistent decimal places across reports if exported.\n\nAssumptions & Exclusions: Assumes non-cryptographic randomness suitable for simulations and tests; not recommended for security or cryptographic purposes; does not guarantee cryptographic strength; no live streaming feed; does not access external data sources unless explicitly provided by user.