LevMar¶
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class
sherpa.optmethods.LevMar(name='levmar')[source] [edit on github]¶ Bases:
sherpa.optmethods.OptMethodLevenberg-Marquardt optimization method.
The Levenberg-Marquardt method is an interface to the MINPACK subroutine lmdif to find the local minimum of nonlinear least squares functions of several variables by a modification of the Levenberg-Marquardt algorithm [1].
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ftol¶ The function tolerance to terminate the search for the minimum; the default is FLT_EPSILON ~ 1.19209289551e-07, where FLT_EPSILON is the smallest number x such that 1.0 != 1.0 + x. The conditions are satisfied when both the actual and predicted relative reductions in the sum of squares are, at most, ftol.
Type: number
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xtol¶ The relative error desired in the approximate solution; default is FLT_EPSILON ~ 1.19209289551e-07, where FLT_EPSILON is the smallest number x such that 1.0 != 1.0 + x. The conditions are satisfied when the relative error between two consecutive iterates is, at most, xtol.
Type: number
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gtol¶ The orthogonality desired between the function vector and the columns of the jacobian; default is FLT_EPSILON ~ 1.19209289551e-07, where FLT_EPSILON is the smallest number x such that 1.0 != 1.0 + x. The conditions are satisfied when the cosine of the angle between fvec and any column of the jacobian is, at most, gtol in absolute value.
Type: number
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maxfev¶ The maximum number of function evaluations; the default value of None means to use 1024 * n, where n is the number of free parameters.
Type: int or None
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epsfcn¶ This is used in determining a suitable step length for the forward-difference approximation; default is FLT_EPSILON ~ 1.19209289551e-07, where FLT_EPSILON is the smallest number x such that 1.0 != 1.0 + x. This approximation assumes that the relative errors in the functions are of the order of epsfcn. If epsfcn is less than the machine precision, it is assumed that the relative errors in the functions are of the order of the machine precision.
Type: number
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factor¶ Used in determining the initial step bound; default is 100. The initial step bound is set to the product of factor and the euclidean norm of diag*x if nonzero, or else to factor itself. In most cases, factor should be from the interval (.1,100.).
Type: int
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verbose¶ The amount of information to print during the fit. The default is 0, which means no output.
Type: int
References
[1] J.J. More, “The Levenberg Marquardt algorithm: implementation and theory,” in Lecture Notes in Mathematics 630: Numerical Analysis, G.A. Watson (Ed.), Springer-Verlag: Berlin, 1978, pp.105-116. Attributes Summary
default_configThe default settings for the optimiser. Methods Summary
fit(statfunc, pars, parmins, parmaxes[, …])Run the optimiser. Attributes Documentation
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default_config¶ The default settings for the optimiser.
Methods Documentation
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fit(statfunc, pars, parmins, parmaxes, statargs=(), statkwargs={}) [edit on github]¶ Run the optimiser.
Parameters: - statfunc (function) – Given a list of parameter values as the first argument and,
as the remaining positional arguments,
statargsandstatkwargsas keyword arguments, return the statistic value. - pars (sequence) – The start position of the model parameter values.
- parmins (sequence) – The minimum allowed values for each model parameter. This
must match the length of
pars. - parmaxes (sequence) – The maximum allowed values for each model parameter. This
must match the length of
pars. - statargs (optional) – Additional positional arguments to send to
statfunc. - statkwargs (optional) – Additional keyword arguments to send to
statfunc.
Returns: newpars – The model parameters after the optimiser has run.
Return type: tuple
- statfunc (function) – Given a list of parameter values as the first argument and,
as the remaining positional arguments,
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