Random (`npg.random`)

The npg.random module contains all random-array factories and the seed utility. Set the seed once at the top of your script for reproducible results:

import numpygrad as npg

npg.random.manual_seed(0)       # or npg.manual_seed(0) — same effect

Random arrays

Function	Description
`npg.random.rand(shape, **kw)`	Uniform samples in `[0, 1)`
`npg.random.randn(shape, **kw)`	Standard normal samples
`npg.random.randint(low, high, size, **kw)`	Random integers in `[low, high)`
`npg.random.uniform(low, high, size, **kw)`	Uniform samples in `[low, high]`
`npg.random.normal(mean, std, size, **kw)`	Normal samples with given mean and std
`npg.random.randperm(n, **kw)`	Random permutation of `0 … n-1`

All factory functions accept requires_grad and dtype keyword arguments:

w = npg.random.randn((4, 4), requires_grad=True)
i = npg.random.randperm(1000)                    # shuffle indices

Seeding

`npg.random.manual_seed(seed)` (also available as `npg.manual_seed`)

Set the NumPy random seed globally for reproducible weight initialisation, data shuffling, and dropout masks:

npg.random.manual_seed(42)
model = npg.nn.MLP(4, [32], 2)   # weights initialised with seed 42