leastsq

SeparableModelResult.leastsq(params=None, **kws)

Use Levenberg-Marquardt minimization to perform a fit.

It assumes that the input Parameters have been initialized, and a function to minimize has been properly set up. When possible, this calculates the estimated uncertainties and variable correlations from the covariance matrix.

This method calls scipy.optimize.leastsq. By default, numerical derivatives are used, and the following arguments are set:

leastsq() arg	Default Value	Description
xtol	1.e-7	Relative error in the approximate solution
ftol	1.e-7	Relative error in the desired sum of squares
maxfev	2000*(nvar+1)	Maximum number of function calls (nvar= # of variables)
Dfun	None	Function to call for Jacobian calculation

Parameters:

• params (Parameters, optional) – Parameters to use as starting point.
• **kws (dict, optional) – Minimizer options to pass to scipy.optimize.leastsq.

Returns:

Object containing the optimized parameter and several goodness-of-fit statistics.

Return type:

MinimizerResult

Changed in version 0.9.0: Return value changed to MinimizerResult.