stimator.estimation.DeODEOptimizer¶

class stimator.estimation.DeODEOptimizer(model, optSettings, tcs, weights=None, aMsgTicker=None, anEndComputationTicker=None, dump_generations=None, dump_predictions=False, initial='init', maxGenerations_noimprovement=20)¶

Overides energy function and report functions.

The energy function solves ODEs and computes a least-squares score. Ticker functions are called on completion of a generation and when optimization finishes.

Methods

Best1Bin(candidate)
Best1Exp(candidate)
Best2Bin(candidate)
Best2Exp(candidate)
EnergyFunction(trial)
GetClassRandomFloatBetweenZeroAndOne()
GetClassRandomIntegerBetweenZeroAndParameterCount()
GetClassRandomIntegerBetweenZeroAndPopulationSize()
GetRandFloatIn01()
GetRandIntInPars()
GetRandIntInPop()
Rand1Bin(candidate)
Rand1Exp(candidate)
Rand2Bin(candidate)
Rand2Exp(candidate)
RandToBest1Bin(candidate)
RandToBest1Exp(candidate)
SelectSamples(candidate, n)	Select n different members of population which are different from candidate.
SetupClassRandomNumberMethods()
computeGeneration()
computeSolution(i, trial[, dense])	Computes solution for timecourse i, given parameters trial.
externalEnergyFunction(trial)
finalize()
genIndxOfGenesToXover()
generateOptimumData()
generation_string(generation)
reportFinal()
reportFinalString()
reportGeneration()
reportGenerationString()
reportInitial()
reportInitialString()
run()

__init__(model, optSettings, tcs, weights=None, aMsgTicker=None, anEndComputationTicker=None, dump_generations=None, dump_predictions=False, initial='init', maxGenerations_noimprovement=20)¶

Methods

Best1Bin(candidate)
Best1Exp(candidate)
Best2Bin(candidate)
Best2Exp(candidate)
EnergyFunction(trial)
GetClassRandomFloatBetweenZeroAndOne()
GetClassRandomIntegerBetweenZeroAndParameterCount()
GetClassRandomIntegerBetweenZeroAndPopulationSize()
GetRandFloatIn01()
GetRandIntInPars()
GetRandIntInPop()
Rand1Bin(candidate)
Rand1Exp(candidate)
Rand2Bin(candidate)
Rand2Exp(candidate)
RandToBest1Bin(candidate)
RandToBest1Exp(candidate)
SelectSamples(candidate, n)	Select n different members of population which are different from candidate.
SetupClassRandomNumberMethods()
__init__(model, optSettings, tcs[, weights, ...])
computeGeneration()
computeSolution(i, trial[, dense])	Computes solution for timecourse i, given parameters trial.
externalEnergyFunction(trial)
finalize()
genIndxOfGenesToXover()
generateOptimumData()
generation_string(generation)
reportFinal()
reportFinalString()
reportGeneration()
reportGenerationString()
reportInitial()
reportInitialString()
run()

Attributes

exitCodeStrings