fitbenchmarking.controllers.ralfit_controller module
Implements a controller for RALFit https://github.com/ralna/RALFit
- class fitbenchmarking.controllers.ralfit_controller.RALFitController(cost_func)
Bases:
fitbenchmarking.controllers.base_controller.Controller
Controller for the RALFit fitting software.
- algorithm_check = {'all': ['gn', 'hybrid', 'gn_reg', 'hybrid_reg'], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': [], 'gauss_newton': ['gn', 'gn_reg'], 'general': [], 'global_optimization': [], 'levenberg-marquardt': ['gn', 'gn_reg'], 'ls': ['gn', 'hybrid', 'gn_reg', 'hybrid_reg'], 'simplex': [], 'steepest_descent': [], 'trust_region': ['gn', 'hybrid']}
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 RALFit.
- hes_eval(params, r)
Function to ensure correct inputs and outputs are used for the RALFit hessian evaluation
- param params
parameters
- type params
numpy array
- param r
residuals, required by RALFit to be passed for hessian evaluation
- type r
numpy array
- return
hessian 2nd order term: sum_{i=1}^m r_i
- abla^2 r_i
- rtype
numpy array
- hessian_enabled_solvers = ['hybrid', 'hybrid_reg']
Within the controller class, you must define the list
hessian_enabled_solvers
if any of the minimizers for the specific software are able to use hessian information.hessian_enabled_solvers
: a list of minimizers in a specific
software that allow Hessian information to be passed into the fitting algorithm
- jacobian_enabled_solvers = ['gn', 'hybrid', 'gn_reg', 'hybrid_reg']
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 for RALFit