Random Module Overview

The np.random namespace provides NumPy-compatible random number generation with two APIs:

Legacy API — Global functions like np.random.seed(), np.random.rand(), np.random.normal(), etc. Uses MT19937 (Mersenne Twister), matching NumPy’s legacy random module.
Modern API — The Generator class created via np.random.default_rng(). Uses PCG64 with SeedSequence, matching NumPy’s recommended modern approach.

Both APIs produce statistically correct distributions and can be seeded for reproducibility.

Seeding and State

seed

Set the global seed for the legacy random number generator.

function seed(seedValue?: number | null): void

Name	Type	Default	Description
`seedValue`	`number \| null`	`null`	Seed value (integer). If `null` or omitted, a time-based random seed is used.

Returns: void

import * as np from 'numpy-ts';

np.random.seed(42);
console.log(np.random.random()); // Reproducible value

get_state

Get the internal state of the legacy MT19937 random number generator. The returned object can be passed to set_state to restore the RNG to this exact point.

function get_state(): { mt: number[]; mti: number; }

Returns: { mt: number[]; mti: number } — Object containing the MT19937 state array and index.

import * as np from 'numpy-ts';

np.random.seed(0);
const state = np.random.get_state();
const a = np.random.random();

np.random.set_state(state);
const b = np.random.random();
// a === b

set_state

Restore the internal state of the legacy random number generator from a previously saved state object.

function set_state(state: { mt: number[]; mti: number; }): void

Name	Type	Default	Description
`state`	`{ mt: number[]; mti: number }`	—	State object obtained from `get_state()`.

Returns: void

get_bit_generator

Get the current bit generator object. Returns an object with name and state properties.

function get_bit_generator(): BitGenerator

Returns: BitGenerator — The current bit generator descriptor.

set_bit_generator

Set the bit generator used by the legacy random functions.

function set_bit_generator(bitgen: BitGenerator): void

Name	Type	Default	Description
`bitgen`	`BitGenerator`	—	Bit generator descriptor.

Returns: void

Reproducibility

numpy-ts implements the same algorithms as NumPy for both the legacy and modern APIs:

API	Bit Generator	Initialization
Legacy (`np.random.seed`)	MT19937	Direct seed init
Modern (`np.random.default_rng`)	PCG64	SeedSequence expansion

NumPy Compatibility

Category	Match Level	Notes
Uniform `[0, 1)`	Exact	Identical 53-bit float generation
Normal / Gaussian	Exact (seeded)	Box-Muller transform, same pairing
Integer generation	Exact	Same range mapping
PCG64 (Generator)	Exact	Same SeedSequence, same state advance order
Gamma, Beta, Chi-square	Statistical	Same algorithms, minor float differences
Poisson, Binomial	Statistical	Same algorithm selection thresholds
Multivariate Normal	Statistical	Cholesky-based, same approach

When using the legacy API with np.random.seed(), uniform and normal outputs match NumPy exactly for the same seed. Other distributions use the same algorithms as NumPy and produce statistically equivalent results, though individual values may differ due to floating-point ordering.

default_rng and Generator

The modern API uses default_rng to create a Generator instance backed by PCG64. See the Generator API page for full details.

import * as np from 'numpy-ts';

const rng = np.random.default_rng(12345);
const arr = rng.random([3, 3]);
const ints = rng.integers(0, 100, [5]);

Overview

NDArray

Array Creation

Math

Array Manipulation

Reductions & Statistics

Linear Algebra

Logic & Comparison

Sorting & Searching

Set Operations

Bitwise

Indexing

Gradient & Differences

Polynomials

Random (np.random)

FFT (np.fft)

I/O

Utilities

Random Module Overview

Seeding and State

seed

get_state

set_state

get_bit_generator

set_bit_generator

Reproducibility

NumPy Compatibility

default_rng and Generator

​Seeding and State

​seed

​get_state

​set_state

​get_bit_generator

​set_bit_generator

​Reproducibility

​NumPy Compatibility

​default_rng and Generator

Seeding and State

seed

get_state

set_state

get_bit_generator

set_bit_generator

Reproducibility

NumPy Compatibility

default_rng and Generator