MonCar

class sherpa.optmethods.MonCar(name='moncar')

Bases: sherpa.optmethods.OptMethod

Monte Carlo optimzation method.

This is an implementation of the differential-evolution algorithm from Storn and Price (1997) [1]. A population of fixed size - which contains n-dimensional vectors, where n is the number of free parameters - is randomly initialized. At each iteration, a new n-dimensional vector is generated by combining vectors from the pool of population, the resulting trial vector is selected if it lowers the objective function.

ftol

number – The function tolerance to terminate the search for the minimum; the default is sqrt(DBL_EPSILON) ~ 1.19209289551e-07, where DBL_EPSILON is the smallest number x such that 1.0 != 1.0 + x.

maxfev

int or None – The maximum number of function evaluations; the default value of None means to use 8192 * n, where n is the number of free parameters.

verbose

int – The amount of information to print during the fit. The default is 0, which means no output.

seed

int – The seed for the random number generator.

population_size

int or None – The population of potential solutions is allowed to evolve to search for the minimum of the fit statistics. The trial solution is randomly chosen from a combination from the current population, and it is only accepted if it lowers the statistics. A value of None means to use a value 16 * n, where n is the number of free parameters.

xprob

num – The crossover probability should be within the range [0.5,1.0]; default value is 0.9. A high value for the crossover probability should result in a faster convergence rate; conversely, a lower value should make the differential evolution method more robust.

weighting_factor

num – The weighting factor should be within the range [0.5, 1.0]; default is 0.8. Differential evolution is more sensitive to the weighting_factor then the xprob parameter. A lower value for the weighting_factor, coupled with an increase in the population_size, gives a more robust search at the cost of efficiency.

References

[1]	Storn, R. and Price, K. “Differential Evolution: A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces.” J. Global Optimization 11, 341-359, 1997. http://www.icsi.berkeley.edu/~storn/code.html

Attributes Summary

default_config

The default settings for the optimiser.

Methods Summary

fit(statfunc, pars, parmins, parmaxes[, …])

Run the optimiser.

Attributes Documentation

default_config

The default settings for the optimiser.

Methods Documentation

fit(statfunc, pars, parmins, parmaxes, statargs=(), statkwargs={})

Run the optimiser.

Parameters:

• statfunc (function) – Given a list of parameter values as the first argument and, as the remaining positional arguments, statargs and statkwargs as 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