fitbenchmarking.controllers.theseus_controller module
Implements a controller for the Theseus fitting software.
- class fitbenchmarking.controllers.theseus_controller.TheseusController(cost_func)
Bases:
fitbenchmarking.controllers.base_controller.Controller
Controller for Theseus
- algorithm_check = {'all': ['Levenberg_Marquardt', 'Gauss-Newton'], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': [], 'gauss_newton': ['Gauss-Newton'], 'general': [], 'global_optimization': [], 'levenberg-marquardt': ['Levenberg_Marquardt'], 'ls': ['Levenberg_Marquardt', 'Gauss-Newton'], 'simplex': [], 'steepest_descent': [], 'trust_region': []}
Within the controller class, you must initialize a dictionary,
algorithm_check
, such that the keys are given by:all
- all minimizersls
- least-squares fitting algorithmsderiv_free
- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplex
method inMantid
)general
- minimizers which solve a generic min f(x)simplex
- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region
- algorithms which emply a trust region approachlevenberg-marquardt
- minimizers that use the Levenberg-Marquardt algorithmgauss_newton
- minimizers that use the Gauss Newton algorithmbfgs
- minimizers that use the BFGS algorithmconjugate_gradient
- Conjugate Gradient algorithmssteepest_descent
- Steepest Descent algorithmsglobal_optimization
- Global Optimization algorithms
The values of the dictionary are given as a list of minimizers for that specific controller that fit into each of the above categories. See for example the
GSL
controller.
- cleanup()
Convert the result to a numpy array and populate the variables results will be read from
- fit()
Run problem with Theseus
- jacobian_enabled_solvers = ['Levenberg_Marquardt', 'Gauss-Newton']
Within the controller class, you must define the list
jacobian_enabled_solvers
if any of the minimizers for the specific software are able to use jacobian information.jacobian_enabled_solvers
: a list of minimizers in a specific
software that allow Jacobian information to be passed into the fitting algorithm
- setup()
Setup problem ready to be run with Theseus
- class fitbenchmarking.controllers.theseus_controller.TheseusCostFunction(*args: Any, **kwargs: Any)
Bases:
theseus.CostFunction
Cost function for Theseus ai
- dim() int
Lenght of x data
- error() Tuple[List[torch.Tensor], List[float]]
Resdiuals in pytorch tensor form for Theseus ai
- jacobians() Tuple[List[torch.Tensor], torch.Tensor]
Jacobians in pytorch tensor form for Theseus ai