MonCar
- class sherpa.optmethods.MonCar(name='moncar')[source] [edit on github]
Bases:
OptMethodMonte Carlo optimization 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
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.- Type
number
- maxfev
The maximum number of function evaluations; the default value of
Nonemeans to use8192 * n, wherenis the number of free parameters.- Type
int or
None
- verbose
The amount of information to print during the fit. The default is
0, which means no output.- Type
- population_size
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
Nonemeans to use a value16 * n, wherenis the number of free parameters.- Type
int or
None
- xprob
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.
- Type
num
- weighting_factor
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.
- Type
num
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
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={}) [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 tuple contains: boolean indicating whether the optimization succeeded or not, the best fit parameters as a NumPy array, the statistic value at the best-fit location, a string message indicating the status, and a dictionary containing information about the optimisation (this depends on the optimiser).
- Return type