MonCar

class sherpa.optmethods.MonCar(name: str = 'moncar', **kwargs)[source] [edit on github]

Bases: OptMethod

Monte 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.

Attributes:

ftolnumber: 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.
maxfevint 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.
seedint: The seed for the random number generator.
population_sizeint 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.
xprobnum: 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.
numcoresint: The number of CPU cores to use. The default is 1.

Methods

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

Run the optimiser.

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. https://cse.engineering.nyu.edu/~mleung/CS909/s04/Storn95-012.pdf

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: Callable[[Sequence[float] | ndarray], tuple[float, ndarray]], pars: Sequence[float] | ndarray, parmins: Sequence[float] | ndarray, parmaxes: Sequence[float] | ndarray, statargs: Any = None, statkwargs: Any = None) → tuple[bool, ndarray, float, str, dict[str, Any]] [edit on github]

Run the optimiser.

Changed in version 4.18.0: The statargs and statkwargs arguments are now ignored.

Changed in version 4.16.0: The statkwargs argument now defaults to None rather than {}.

Parameters:

statfuncfunction: 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.
parssequence: The start position of the model parameter values.
parminssequence: The minimum allowed values for each model parameter. This must match the length of pars.
parmaxessequence: The maximum allowed values for each model parameter. This must match the length of pars.
statargsoptional: This is currently unused.
statkwargsdict, optional: This is currently unused.

Returns:

newparstuple: 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).