module documentation
Parameters and Options for CMA-ES.
| Class | |
a dictionary with the available options and their default values for class CMAEvolutionStrategy. |
| Class | |
strategy parameters like population size and learning rates. |
| Class | |
collection of many meta parameters. |
| Function | amend |
return a dict with 'maxrestarts' and 'maxfevals' as keys. |
| Function | cma |
use this function to get keyword completion for CMAOptions. |
| Function | integer |
target probability for an integer mutation assuming a centered mean |
| Function | integer |
can be reassigned/overwritten like a global "parameter setting" |
| Function | is |
default to check feasibility of f-values. |
| Function | safe |
return a string safe to eval or raise an exception. |
| Variable | cma |
Undocumented |
| Variable | cma |
Undocumented |
| Variable | cma |
Undocumented |
| Variable | default |
Undocumented |
| Variable | integer |
None or True or a function float->bool, None -> limit only negative updates |
| Variable | integer |
factor used in integer_std_lower_bound as multiplier to mueff/N |
| Variable | integer |
factor used in integer_std_lower_bound as multiplier to integer_std_lower_bound_limit_when_mu_is_large |
| Variable | integer |
limit coordinate stds of solutions in C update, by default off, may go away |
| Variable | options |
Undocumented |
return a dict with 'maxrestarts' and 'maxfevals' as keys.
restarts is a parameter to cma.fmin*, see cma.fmin.
def cma_default_options_(AdaptSigma='True # or False or any CMAAdaptSigmaBase class e.g. CMAAdaptSigmaTPA, CMAAdaptSigmaCSA', CMA_active='True # negative update, conducted after the original update', CMA_active_injected='0 #v weight multiplier for negative weights of injected solutions', CMA_cmean='1 # learning rate for the mean value', CMA_const_trace='False # normalize trace, 1, True, "arithm", "geom", "aeig", "geig" are valid', CMA_diagonal='0*100*N/popsize**0.5 # nb of iterations with diagonal covariance matrix, True for always', CMA_diagonal_decoding='0 # learning rate multiplier for additional diagonal update', CMA_eigenmethod='np.linalg.eigh # or cma.utilities.math.eig or pygsl.eigen.eigenvectors', CMA_elitist='False #v or "initial" or True, elitism likely impairs global search performance', CMA_injections_threshold_keep_len='1 #v keep length if Mahalanobis length is below the given relative threshold', CMA_mirrors='popsize < 6 # values <0.5 are interpreted as fraction, values >1 as numbers (rounded), for `True` about 0.16 is used', CMA_mirrormethod='2 # 0=unconditional, 1=selective, 2=selective with delay', CMA_mu='None # parents selection parameter, default is popsize // 2', CMA_on='1 # multiplier for all covariance matrix updates', CMA_sampler='None # a class or instance that implements the interface of `cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass`', CMA_sampler_options='{} # options passed to `CMA_sampler` class init as keyword arguments', CMA_rankmu='1.0 # multiplier for rank-mu update learning rate of covariance matrix', CMA_rankone='1.0 # multiplier for rank-one update learning rate of covariance matrix', CMA_recombination_weights='None # a list, see class RecombinationWeights, overwrites CMA_mu and popsize options', CMA_dampsvec_fac='np.inf # tentative and subject to changes, 0.5 would be a "default" damping for sigma vector update', CMA_dampsvec_fade='0.1 # tentative fading out parameter for sigma vector update', CMA_teststds='None # factors for non-isotropic initial distr. of C, mainly for test purpose, see CMA_stds for production', CMA_stds='None # multipliers for sigma0 in each coordinate (not represented in C), or use `cma.ScaleCoordinates` instead', CSA_dampfac='1 #v positive multiplier for step-size damping, 0.3 is close to optimal on the sphere', CSA_damp_mueff_exponent='None # exponent for mueff/N, by default 0.5 and 1 if CSA_squared, zero means no dependency of damping on mueff, useful with CSA_disregard_length option', CSA_disregard_length='False #v True is untested, also changes respective parameters', CSA_clip_length_value='None #v poorly tested, [0, 0] means const length N**0.5, [-1, 1] allows a variation of +- N/(N+2), etc.', CSA_squared='False #v use squared length for sigma-adaptation ', CSA_invariant_path='False #v pc is invariant and ps (default) is unbiased', stall_sigma_change_on_divergence_iterations='False #v number of iterations of median worsenings threshold at which the sigma change is stalled; the default may become 2', BoundaryHandler='BoundTransform # or BoundPenalty, unused when ``bounds in (None, [None, None])``', bounds='[None, None] # lower (=bounds[0]) and upper domain boundaries, each a scalar or a list/vector', conditioncov_alleviate='[1e8, 1e12] # when to alleviate the condition in the coordinates and in main axes', eval_final_mean='True # evaluate the final mean, which is a favorite return candidate', fixed_variables='None # dictionary with index-value pairs like {0:1.1, 2:0.1} that are not optimized', ftarget='-inf #v target function value, minimization', integer_variables='[] # index list, invokes basic integer handling by setting minstd of integer variables if it was not given and by integer centering', is_feasible='is_feasible #v a function that computes feasibility, by default lambda x, f: f not in (None, np.nan)', maxfevals='inf #v maximum number of function evaluations', maxiter='100 + 150 * (N+3)**2 // popsize**0.5 #v maximum number of iterations', mean_shift_line_samples='False #v sample two new solutions colinear to previous mean shift', mindx='0 #v minimal std in any arbitrary direction, cave interference with tol*', minstd='0 #v minimal std (scalar or vector) in any coordinate direction, cave interference with tol*', maxstd='None #v maximal std (scalar or vector) in any coordinate direction', maxstd_boundrange='1/3 # maximal std relative to bound_range per coordinate, overruled by maxstd', pc_line_samples='False #v one line sample along the evolution path pc', popsize='4 + 3 * np.log(N) # population size, AKA lambda, int(popsize) is the number of new solution per iteration', popsize_factor='1 # multiplier for popsize, convenience option to increase default popsize', randn='np.random.randn #v randn(lam, N) must return an np.array of shape (lam, N), see also cma.utilities.math.randhss', scaling_of_variables='None # deprecated, rather use fitness_transformations.ScaleCoordinates instead (or CMA_stds). WAS: Scale for each variable in that effective_sigma0 = sigma0*scaling. Internally the variables are divided by scaling_of_variables and sigma is unchanged, default is `np.ones(N)`', seed='time # random number seed for `numpy.random`; `None` and `0` equate to `time`, `np.nan` means "do nothing", see also option "randn"', signals_filename='cma_signals.in # read versatile options from this file (use `None` or `""` for no file) which contains a single options dict, e.g. ``{"timeout": 0}`` to stop, string-values are evaluated, e.g. "np.inf" is valid', termination_callback='[] #v a function or list of functions returning True for termination, called in `stop` with `self` as argument, could be abused for side effects', timeout='inf #v stop if timeout seconds are exceeded, the string "2.5 * 60**2" evaluates to 2 hours and 30 minutes', tolconditioncov='1e14 #v stop if the condition of the covariance matrix is above `tolconditioncov`', tolfacupx='1e3 #v termination when step-size increases by tolfacupx (diverges). That is, the initial step-size was chosen far too small and better solutions were found far away from the initial solution x0', tolupsigma='1e20 #v sigma/sigma0 > tolupsigma * max(eivenvals(C)**0.5) indicates "creeping behavior" with usually minor improvements', tolflatfitness='1 #v iterations tolerated with flat fitness before termination', tolfun='1e-11 #v termination criterion: tolerance in function value, quite useful', tolfunhist='1e-12 #v termination criterion: tolerance in function value history', tolfunrel='0 #v termination criterion: relative tolerance in function value: Delta f current < tolfunrel * (median0 - median_min)', tolstagnation='int(100 + 100 * N**1.5 / popsize) #v termination if no improvement over tolstagnation iterations', tolxstagnation='[1e-9, 20, 0.1] #v termination thresholds for Delta of [mean, iterations, iterations fraction], the latter two are summed; trigger termination if Dmean stays below the threshold over Diter iterations, pass `False` or a negative value to turn off tolxstagnation', tolx='1e-11 #v termination criterion: tolerance in x-changes', transformation='None # deprecated, use a wrapper like those in cma.fitness_transformations instead.', typical_x='None # deprecated, use `cma.fitness_transformations.Shifted` instead', updatecovwait='None #v number of iterations without distribution update, name is subject to future changes', verbose='3 #v verbosity e.g. of initial/final message, -1 is very quiet, -9 maximally quiet, may not be fully implemented', verb_append='0 # initial evaluation counter, if append, do not overwrite output files', verb_disp='100 #v verbosity: display console output every verb_disp iteration', verb_disp_overwrite='inf #v start overwriting after given iteration', verb_filenameprefix=CMADataLogger.default_prefix+' # output path (folder) and filenames prefix', verb_log='1 #v verbosity: write data to files every verb_log iteration, writing can be time critical on fast to evaluate functions', verb_log_expensive='N * (N <= 50) # allow to execute eigendecomposition for logging every verb_log_expensive iteration, 0 or False for never', verb_plot='0 #v in fmin2(): plot() is called every verb_plot iteration', verb_time='True #v output timings on console', vv='{} #? versatile set or dictionary for hacking purposes, value found in self.opts["vv"]'):
¶
use this function to get keyword completion for CMAOptions.
cma.CMAOptions('substr') provides even substring search.
returns default options as a dict (not a cma.CMAOptions dict).
target probability for an integer mutation assuming a centered mean
and no boundaries (two-tailed).
2 / (N + N_int) should keep at least 37% of the solutions unaffected.
Details: from ptarget we can compute sigma = 1 / PPF(ptarget/2) / 2 where PPF is the quantile function of the standard normal distribution and the first 2 accounts for two-sided sampling of the tails and the second 2 is needed because the distance to the value domain bound is 1/2.
can be reassigned/overwritten like a global "parameter setting"
This function returns the minimum of three bounds, an absolute bound (default 0.2), mueff / N, and a bound computed from the normal quantile (when the mean is assumed to be in the domain middle) which takes ptarget=integer_lower_bound_target_probability(N, N_int) (which should be roughly 1/N) as input. This bound becomes < 0.2 only when ptarget < 1/152 (==> < -2.5-sigma).
default to check feasibility of f-values.
Used for rejection sampling in method ask_and_eval.
| See Also | |
| CMAOptions, CMAOptions('feas'). |
return a string safe to eval or raise an exception.
Selected words and chars are considered safe such that all default
string-type option values from CMAOptions() pass. This function is
implemented for convenience, to keep the default option format
backwards compatible, and to be able to pass, for example, 3 * N.
Function or class names other than those from the default values cannot
be passed as strings (any more) but only as the function or class
themselves.
integer_active_limit_recombination_weight_condition =
¶
None or True or a function float->bool, None -> limit only negative updates
factor used in integer_std_lower_bound as multiplier to
integer_std_lower_bound_limit_when_mu_is_large