| class NestedSolver( NestedSampler ) | Source |
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NestedSolver is an extension of NestedSampler. It uses the likelihood-climbing technique to find a solution in an ordering problem. The negative value of the costfunction, commonly defined in ordering problems, is maximised. In this sense the costfunction is acting as the logLikelihood.
For more information about this technique see NestedSampler.
For the random walk of the parameters 4 so-called engines are written. By default only he first is switched on.
MoveEngine : insert a snippet of parameters at another location ReverseEngine : reverse the order of a snippet of parameters ShuffleEngine : shuffle part of the parameter list SwitchEngine : switch two elements LoopEngine : uncross a crossing loop NearEngine : find the nearest location and go there first.
The last 2 engines are not random. They are mostly (?) taking steps uphill. Mixing them with other engines maintain detailed balance in an overall sense.
Attributes
-
xdata : array_like
array of independent input values -
model : Model
the model function to be fitted -
ydata : array_like
array of dependent (to be fitted) data -
weights : array_like (None)
weights pertaining to ydata -
distribution : ErrorDistribution
to calculate the loglikelihood -
ensemble : int (100)
number of walkers -
discard : int (1)
number of walkers to be replaced each generation -
rng : RandomState
random number generator -
seed : int (80409)
seed of rng -
rate : float (1.0)
speed of exploration -
maxsize : None or int
maximum size of the resulting sample list (None : no limit) -
end : float (2.0)
stopping criterion -
verbose : int
level of blabbering -
walkers : SampleList
ensemble of Samples that explore the likelihood space -
samples : SampleList
Samples resulting from the exploration -
engines : list of Engine
Engine that move the walkers around within the given constraint: logL > lowLogL -
initialEngine : Engine
Engine that distributes the walkers over the available space -
restart : StopStart (TBW)
write intermediate results to (optionally) start from.
Author Do Kester.
| NestedSolver( problem, distribution=None, keep=None, ensemble=100, discard=1, seed=80409, rate=1.0, engines=None, maxsize=None, threads=False, verbose=1 ) |
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Create a new class, providing inputs and model.
Parameters
- problem : OrderProblem
Problem with integer parameters - keep : None or dict of {int:float}
None : none of the model parameters are kept fixed.
Dictionary of indices (int) to be kept at a fixed value (float).
Hyperparameters follow model parameters.
The values will override those at initialization.
They are used in this instantiation, unless overwritten at the call to sample() - distribution : None or String or ErrorDistribution
None : DistanceCostFunction is chosen.
"distance" : DistanceCostFunction no hyperpar
errdis : A class inheriting from ErrorDistribution
which implements logLikelihood
When the hyperpar(s) are not to be kept fixed, they need Prior and maybe limits.
- ensemble : int (100)
number of walkers - discard : int (1)
number of walkers to be replaced each generation - seed : int (80409)
seed of rng - rate : float (1.0)
speed of exploration - engines : None or (list of) string or (list of) Engine
to randomly move the walkers around, within the likelihood bound.
"move" : insert a snippet of parameters at another location
"reverse" : reverse the order of a snippet of parameters
"shuffle" : shuffle part of the parameter list
"switch" : switch two elements
"loop" : find two paths that cross, then uncross them
"near" : find the nearest location and go there first.
None : take default [all of above].
engine : a class inheriting from Engine. At least implementing
execute( walker, lowLhood )
- maxsize : None or int
maximum size of the resulting sample list (None : no limit) - threads : bool (False)
Use Threads to distribute the diffusion of discarded samples over the available cores. - verbose : int (1)
0 : silent
1 : basic information
2 : more about every 100th iteration
3 : more about every iteration
| solve( keep=None, plot=False ) |
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Return the last sample, representing the best solution.
The more sammples (with solutions) can be found in the sample list.
Parameters
- keep : None or dict of {int:float}
Dictionary of indices (int) to be kept at a fixed value (float)
Hyperparameters follow model parameters
The values will override those at initialization.
They are only used in this call of fit. - plot : bool
Show a plot of the results
| setErrorDistribution( name=None, scale=1.0, power=2.0 ) |
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Parameters
- name : string
name of distribution - scale : float
fixed scale of distribution - power : float
fixed power of distribution
| setEngines( engines=None, enginedict=None ) |
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Parameters
- engines : list of string
list of engine names - enginedict : dictionary of { str : Engine }
connecting names to Engines
| initWalkers( ensemble, allpars, fitIndex, startdict=None ) |
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Parameters
- ensemble : int
length od the walkers list - allpars : array_like
array of parameters - fitIndex : array_like
indices of allpars to be fitted - startdict : dictionary of { str : Engine }
connecting a name to a StartEngine
| Methods inherited from NestedSampler |
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- sample( keep=None, plot=False )
- initSample( ensemble=None, keep=None )
- walkerLogL( w )
- makeFitlist( keep=None )
- doIterPlot( plot )
- doLastPlot( plot )
- initReport( keep=None )
- iterReport( kw, tail, plot=False )
- printIterRep( kw, parfmt="%s", tail=0, max=None, indent=0, end="\n" )
- lastReport( kw, plot=False )
- plotLast( )
- getMaxIter( )
- nextIteration( )
- optionalRestart( )
- optionalSave( )
- updateEvidence( worst )
- copyWalker( worst )
- copyWalkerFromPhantoms( worst )
- copyWalkerFromDynamicPhantoms( worst )
- updateWalkers( explorer, worst )
- setProblem( name, model=None, xdata=None, ydata=None, weights=None,
- setInitialEngine( ensemble, allpars, fitIndex, startdict=None )
- plotResult( walker, iter, plot=0 )
- report( )