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class ConjugateGradientFitter( ScipyFitter )	Source

Conjugate Gradient Method of Polak and Ribiere.

Syntactic sugar for
     ScipyFitter( ..., method='CG', ... )

See ScipyFitter

ConjugateGradientFitter( 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

• fit( data, weights=None, par0=None, keep=None, limits=None,
• collectVectors( par )

Methods inherited from MaxLikelihoodFitter

• makeFuncs( data, weights=None, index=None, ret=3 )
• getScale( )
• getLogLikelihood( autoscale=False, var=1.0 )
• normalize( normdfdp, normdata, weight=1.0 )
• testGradient( par, at, data, weights=None )

Methods inherited from IterativeFitter

• setParameters( params )
• doPlot( param, force=False )
• fitprolog( ydata, weights=None, accuracy=None, keep=None )
• report( verbose, param, chi, more=None, force=False )

Methods inherited from BaseFitter

• setMinimumScale( scale=0 )
• fitpostscript( ydata, plot=False )
• keepFixed( keep=None )
• insertParameters( fitpar, index=None, into=None )
• modelFit( ydata, weights=None, keep=None )
• limitsFit( ydata, weights=None, keep=None )
• checkNan( ydata, weights=None, accuracy=None )
• getVector( ydata, index=None )
• getHessian( params=None, weights=None, index=None )
• getInverseHessian( params=None, weights=None, index=None )
• getCovarianceMatrix( )
• makeVariance( scale=None )
• getDesign( params=None, xdata=None, index=None )
• chiSquared( ydata, params=None, weights=None )
• getStandardDeviations( )
• monteCarloError( xdata=None, monteCarlo=None)
• getEvidence( limits=None, noiseLimits=None )
• getLogZ( limits=None, noiseLimits=None )
• plotResult( xdata=None, ydata=None, model=None, residuals=True,