Home       Trees       Indices       Help   
Package comocma :: Module como :: Class CmaKernel
[hide private]
[frames] | no frames]

Class CmaKernel

source code

                             object --+        
                                      |        
             cma.interfaces.OOOptimizer --+    
                                          |    
cma.evolution_strategy.CMAEvolutionStrategy --+
                                              |
                                             CmaKernel

inheriting from the `cma.CMAEvolutionStrategy` class, by adding the property `incumbent`, the attributes `objective_values` and `_last_offspring_f_values`.

Instance Methods [hide private]
 
__init__(self, x0, sigma0, inopts=None)
Arguments ========= `x0` initial solution, starting point.		 source code
 
_copy_light(self, sigma=None, inopts=None)
tentative copy of self, versatile (interface and functionalities may change).		 source code

Inherited from cma.evolution_strategy.CMAEvolutionStrategy: alleviate_conditioning, alleviate_conditioning_in_coordinates, ask, ask_and_eval, ask_geno, disp, disp_annotation, feed_for_resume, get_mirror, get_selective_mirrors, inject, mahalanobis_norm, manage_plateaus, plot, random_rescale_to_mahalanobis, repair_genotype, result_pretty, stop, tell

Inherited from cma.evolution_strategy.CMAEvolutionStrategy (private): _prepare_injection_directions, _random_rescaling_factor_to_mahalanobis_size, _set_x0, _tfg, _tfp, _updateBDfromSM

Inherited from cma.interfaces.OOOptimizer: initialize, optimize

Inherited from cma.interfaces.OOOptimizer (private): _force_final_logging, _prepare_callback_list

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __str__, __subclasshook__

Properties [hide private]
  incumbent
it gives the 'repaired' mean of a cma-es.

Inherited from cma.evolution_strategy.CMAEvolutionStrategy: condition_number, isotropic_mean_shift, popsize, result

Inherited from object: __class__

Method Details [hide private]

__init__(self, x0, sigma0, inopts=None)
(Constructor)

source code  

Arguments
=========
`x0`
    initial solution, starting point. `x0` is given as "phenotype"
    which means, if::

        opts = {'transformation': [transform, inverse]}

    is given and ``inverse is None``, the initial mean is not
    consistent with `x0` in that ``transform(mean)`` does not
    equal to `x0` unless ``transform(mean)`` equals ``mean``.
`sigma0`
    initial standard deviation.  The problem variables should
    have been scaled, such that a single standard deviation
    on all variables is useful and the optimum is expected to
    lie within about `x0` +- ``3*sigma0``. See also options
    `scaling_of_variables`. Often one wants to check for
    solutions close to the initial point. This allows,
    for example, for an easier check of consistency of the
    objective function and its interfacing with the optimizer.
    In this case, a much smaller `sigma0` is advisable.
`inopts`
    options, a dictionary with optional settings,
    see class `cma.CMAOptions`.
Overrides: object.__init__

_copy_light(self, sigma=None, inopts=None)

source code 

tentative copy of self, versatile (interface and functionalities may change).

This may not work depending on the used sampler.

Copy mean and sample distribution parameters and input options.

Do not copy evolution paths, termination status or other state variables.

Overrides: cma.evolution_strategy.CMAEvolutionStrategy._copy_light

Property Details [hide private]

incumbent

it gives the 'repaired' mean of a cma-es. For a problem with bound constraints, `self.incumbent` in inside the bounds.

Get Method:
unreachable.incumbent(self) - it gives the 'repaired' mean of a cma-es.

   Home       Trees       Indices       Help   
Generated by Epydoc 3.0.1 on Sat Apr 18 16:48:09 2020 http://epydoc.sourceforge.net