fitbenchmarking.controllers.ceres_controller module
Implements a controller for the Ceres fitting software.
- class fitbenchmarking.controllers.ceres_controller.CeresController(cost_func)
Bases:
Controller
Controller for Ceres Solver
- algorithm_check = {'MCMC': [], 'all': ['Levenberg_Marquardt', 'Dogleg', 'BFGS', 'LBFGS', 'steepest_descent', 'Fletcher_Reeves', 'Polak_Ribiere', 'Hestenes_Stiefel'], 'bfgs': ['BFGS', 'LBFGS'], 'conjugate_gradient': ['Fletcher_Reeves', 'Polak_Ribiere', 'Hestenes_Stiefel'], 'deriv_free': [], 'gauss_newton': [], 'general': [], 'global_optimization': [], 'levenberg-marquardt': [], 'ls': ['Levenberg_Marquardt', 'Dogleg', 'BFGS', 'LBFGS', 'steepest_descent', 'Fletcher_Reeves', 'Polak_Ribiere', 'Hestenes_Stiefel'], 'simplex': [], 'steepest_descent': ['steepest_descent'], 'trust_region': ['Levenberg_Marquardt', 'Dogleg']}
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 algorithmsMCMC
- Markov Chain Monte Carlo 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 Ceres solver
- jacobian_enabled_solvers = ['Levenberg_Marquardt', 'Dogleg', 'BFGS', 'LBFGS', 'steepest_descent', 'Fletcher_Reeves', 'Polak_Ribiere', 'Hestenes_Stiefel']
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 Ceres solver