fitAlgs.leastsq module¶
Author: | Dominic Hunt |
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-
class
fitAlgs.leastsq.
Leastsq
(method=u'dogbox', jacobian_method=u'3-point', **kwargs)[source]¶ Bases:
fitAlgs.fitAlg.FitAlg
Fits data based on the least squared optimizer scipy.optimize.least_squares
Not properly developed and will not be documented until upgrade
Parameters: - fit_sim (fitAlgs.fitSims.FitSim instance, optional) – An instance of one of the fitting simulation methods. Default
fitAlgs.fitSims.FitSim
- fit_measure (string, optional) – The name of the function used to calculate the quality of the fit.
The value it returns provides the fitter with its fitting guide. Default
-loge
- fit_measure_args (dict, optional) – The parameters used to initialise fit_measure and extra_fit_measures. Default
None
- extra_fit_measures (list of strings, optional) – List of fit measures not used to fit the model, but to provide more information. Any arguments needed for these
measures should be placed in fit_measure_args. Default
None
- bounds (dictionary of tuples of length two with floats, optional) – The boundaries for methods that use bounds. If unbounded methods are
specified then the bounds will be ignored. Default is
None
, which translates to boundaries of (0, np.inf) for each parameter. - boundary_excess_cost (basestring or callable returning a function, optional) – The function is used to calculate the penalty for exceeding the boundaries.
Default is
boundFunc.scalarBound()
- boundary_excess_cost_properties (dict, optional) – The parameters for the boundary_excess_cost function. Default {}
- method ({‘trf’, ‘dogbox’, ‘lm’}, optional) – Algorithm to perform minimization. Default
dogbox
-
Name
¶ The name of the fitting method
Type: string
See also
fitAlgs.fitAlg.fitAlg
- The general fitting method class, from which this one inherits
fitAlgs.fitSims.fitSim
- The general fitting class
scipy.optimize.least_squares
- The fitting class this wraps around
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fit
(simulator, model_parameter_names, model_initial_parameters)[source]¶ Runs the model through the fitting algorithms and starting parameters and returns the best one.
Parameters: - simulator (function) – The function used by a fitting algorithm to generate a fit for given model parameters. One example is fitAlg.fitness
- model_parameter_names (list of strings) – The list of initial parameter names
- model_initial_parameters (list of floats) – The list of the initial parameters
Returns: - fitParams (list of floats) – The best fitting parameters
- fit_quality (float) – The quality of the fit as defined by the quality function chosen.
- testedParams (tuple of two lists) – The two lists are a list containing the parameter values tested, in the order they were tested, and the fit qualities of these parameters.
See also
fitAlgs.fitAlg.fitness()
- fit_sim (fitAlgs.fitSims.FitSim instance, optional) – An instance of one of the fitting simulation methods. Default