dipas.optimize module

class dipas.optimize.JacobianAdapter(f_compute, *, ref_data, verbose=False, history=False)

Bases: object

This class allows combination with external optimizers which require the Jacobian.

Can be used together with scipy.optimize.least_squares for example.

Parameters

f_compute (callable) – This function should compute the desired quantity, given a tensor of inputs. It will be called with a single argument, a tensor of shape (N,) and must output a single tensor of shape (M,).
ref_data (torch.Tensor, shape (M,)) – The reference data to compute the residuals w.r.t. the output of f_compute. Must have the same shape as the output of f_compute.
verbose (bool, optional) – If True then at every iteration the current mean squared error is printed to sys.stdout.
history (bool, optional) – If True then at every iteration the current estimate and residuals are saved in the history attribute.

step

The current step during the optimization. This attribute is incremented by one for each __call__ of the adapter.

history

If the history parameter is set to true then this list is appended the current parameter estimate and residuals as a tuple on every iteration.

class Progress(estimate, residual)

Bases: tuple