spyrit.core.noise.Gaussian

class spyrit.core.noise.Gaussian(sigma: float = 0.1)

Bases: Module

Simulate measurements corrupted by additive Gaussian noise

\[y \sim \mathcal{N}\left(\mu = z, \sigma^2\right),\]

where \(\mathcal{N}(\mu, \sigma^2)\) is a Gaussian distribution with mean \(\mu\) and variance \(\sigma^2\) and \(z\) are the noiseless measurements.

The class is constructed from the standard deviation of the noise \(\sigma\).

Args:

sigma (float): Standard deviation of the noise \(\sigma\)

Example:

>>> noise = Gaussian(1.0)
>>> z = torch.tensor([1., 3., 6.])
>>> y = noise(z)
>>> print(y)
tensor([...])

Methods

forward(z)

Simulates measurements corrupted by additive Gaussian noise