Likelihood Module
- EMCqMRI.core.base.base_likelihood_model.Likelihood.likelihood(self, signal, modeled_signal)
Computes the loss, or error, based on the negative log likelihood function.
- Parameters
signal ([torch.Tensor]) – Measured, input signal.
modeled_signal ([torch.Tensor]) – Tensor containing a simulated signal, generated with a signal model.
- Raises
NotImplementedError – When the subclass does not override this method.
- Returns
A scalar loss (i.e. error)
- Return type
([torch.Float])
- EMCqMRI.core.base.base_likelihood_model.Likelihood.gradients(self, signal, kappa, *extra_args)
Computes the gradient of the signal model parameters with respect to the likelihood function. This function can be overriden if you want to define your own gradients (e.g. analytical, different shapes, etc.)
- Parameters
signal ([torch.Tensor]) – Measured, input signal.
kappa ([list]) – A list of torch.Tensor parameters.
*extra_args ([tuple]) – Any additional parameters required by the signal model or likelihood model
- Raises
TypeError – When kappa is not a list of torch.Tensor.
- Returns
list of torch.Tensor with same number of elements as Kappa. Each element of the list is the gradient of each parameter with respect to the likelihood function.
- Return type
([list])