# Extended Generator¶

The `ExtendedGenerator` provides access to a small number of distributions that are not present in NumPy. The default bit generator used by `ExtendedGenerator` is `PCG64`. The bit generator can be changed by passing an instantized bit generator to `ExtendedGenerator`. It is also possible to share a bit generator with an instance of NumPy’s `numpy.random.Generator`.

class randomgen.generator.ExtendedGenerator(bit_generator=None)

Additional random value generator using a bit generator source.

`ExtendedGenerator` exposes methods for generating random numbers from some distributions that are not in numpy.random.Generator.

Parameters
bit_generatorBitGenerator, optional

Bit generator to use as the core generator. If none is provided, uses PCG64(variant=”cm-dxsm”).

`numpy.random.Generator`

The primary generator of random variates.

Examples

```>>> from randomgen import ExtendedGenerator
>>> rg = ExtendedGenerator()
>>> rg.complex_normal()
-0.203 + .936j  # random
```

Using a specific generator

```>>> from randomgen import MT19937
>>> rg = ExtendedGenerator(MT19937())
```

Share a bit generator with numpy

```>>> from numpy.random import Generator, PCG64
>>> pcg = PCG64()
>>> gen = Generator(pcg)
>>> eg = ExtendedGenerator(pcg)
```

## Seed and State Manipulation¶

 `state` Get or set the bit generator's state `bit_generator` Gets the bit generator instance used by the generator

## Distributions¶

 `uintegers`([size, bits]) Return random unsigned integers `random`([size, dtype, out]) Return random floats in the half-open interval [0.0, 1.0). `complex_normal`([loc, gamma, relation, size]) Draw random samples from a complex normal (Gaussian) distribution. `multivariate_normal`(mean, cov[, size, ...]) Draw random samples from a multivariate normal distribution. `multivariate_complex_normal`(loc[, gamma, ...]) Draw random samples from a multivariate complex normal (Gaussian) distribution. `standard_wishart`(df, dim[, size]) Draw samples from the Standard Wishart and Pseudo-Wishart distributions `wishart`(df, scale[, size, check_valid, tol, ...]) Draw samples from the Wishart and pseudo-Wishart distributions.