| class NearEngine( OrderEngine ) | [source] |
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The NearEngine searches the nearest neighbour (NN) for a point and goes there first.
This is NOT a random engine as it mostly(??) steps uphill.
It belongs to the class of generalized travelling salesman problems where the parameters of the problem is an ordered list.
The walker is kept when the logLikelihood > lowLhood
Attributes from Engine
walkers, errdis, maxtrials, nstep, slow, rng, report, phantoms, verbose
Author Do Kester.
| NearEngine( walkers, errdis, copy=None, **kwargs ) | [source] |
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Constructor.
Parameters
- walkers : SampleList
walkers to be diffused - errdis : ErrorDistribution
error distribution to be used - copy : NearEngine
to be copied - kwargs : dict for Engine
"phantoms", "slow", "seed", "verbose"
| copy( ) | [source] |
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| executeOnce( kw, lowLhood, dims=[0,1] ) | [source] |
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TBC: This engine seems to take a lot of CPU.
Parameters
- kw : int
id of walker to diffuse - lowLhood : float
lower limit in logLikelihood - dims : list of 2 ints
dimensions to process over
| Methods inherited from OrderEngine |
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| Methods inherited from Engine |
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- setWalker( kw, problem, allpars, logL, walker=None, fitIndex=None )
- domain2Unit( problem, dval, kpar=None )
- unit2Domain( problem, uval, kpar=None )
- startJourney( unitStart )
- calcJourney( unitDistance )
- reportJourney( )
- makeIndex( np, val )
- reportCall( )
- reportSuccess( )
- reportReject( )
- reportFailed( )
- reportBest( )
- printReport( best=False )
- successRate( )
- getUnitMinmax( problem, lowLhood, nap )
- getUnitRange( problem, lowLhood, nap )
- printIter( iteration=0, repiter=1000 )