DoglegFitter = class DoglegFitter(ScipyFitter) |
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DoglegFitter(xdata, model, gradient=True, **kwargs)
Dog-leg trust-region algorithm.
Syntactic sugar for
ScipyFitter( ..., method='DOGLEG', ... )
See ScipyFitter |
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- Method resolution order:
- DoglegFitter
- ScipyFitter
- MaxLikelihoodFitter
- IterativeFitter
- BaseFitter
- builtins.object
Constructor:
- DoglegFitter( xdata, model, gradient=True, **kwargs )
- Constructor.
Create a class, providing inputs and model.
Parameters
----------
xdata : array_like
array of independent input values
model : Model
a model function to be fitted (linear or nonlinear)
gradient : bool or None or callable gradient( par )
if True use gradient calculated from model. It is the default.
if False/None dont use gradient (use numeric approximation in stead)
if callable use the method as gradient
kwargs : dict
Possibly includes keywords from
ScipyFitter: gradient, hessp
MaxLikelihoodFitter : errdis, scale, power
IterativeFitter : maxIter, tolerance, verbose
BaseFitter : map, keep, fixedScale
Methods inherited from ScipyFitter:
- collectVectors( par )
- fit( data, weights=None, par0=None, keep=None, limits=None,
maxiter=None, tolerance=None, constraints=(), verbose=0, accuracy=None,
plot=False, callback=None, **options)
Methods inherited from MaxLikelihoodFitter:
Methods inherited from IterativeFitter:
Methods inherited from BaseFitter:
- checkNan( ydata, weights=None, accuracy=None )
- chiSquared( ydata, params=None, weights=None )
- fitpostscript( ydata, plot=False )
- getCovarianceMatrix()
- getDesign( params=None, xdata=None, index=None )
- getEvidence( limits=None, noiseLimits=None )
- getHessian( params=None, weights=None, index=None )
- getInverseHessian( params=None, weights=None, index=None )
- getLogZ( limits=None, noiseLimits=None )
- getStandardDeviations()
- getVector( ydata, index=None )
- insertParameters( fitpar, index=None, into=None )
- keepFixed( keep=None )
- limitsFit( fitmethod, ydata, weights=None, keep=None )
- makeVariance( scale=None )
- modelFit( ydata, weights=None, keep=None )
- monteCarloError( xdata=None, monteCarlo=None )
- plotResult( xdata=None, ydata=None, model=None, residuals=True, confidence=False, show=True )
- setMinimumScale( scale=0)
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