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pyhrf.jde.nrl.habituation module

pyhrf.jde.nrl.habituation.LaplacianPdf(beta, r0Hab, a, b, N=1)
class pyhrf.jde.nrl.habituation.NRLwithHabSampler

Bases: pyhrf.jde.nrl.bigaussian.NRLSampler

Class handling the Gibbs sampling of Neural Response Levels in combination with habituation speed factor sampling. The underlying model is exponential decaying #TODO : comment attributes

P_HABITS_INI = 'habitIni'
P_HAB_ALGO_PARAM = 'paramLexp'
P_OUTPUT_RATIO = 'outputRatio'
P_SAMPLE_HABITS = 'sampleHabit'
P_TRUE_HABITS = 'trueHabits'
checkAndSetInitHabit(variables)
checkAndSetInitValue(variables)
cleanMemory()
cleanObservables()
computeComponentsApost(variables, j, XhtQXh)
computeVarXhtQ(Q)
computeVarYTildeHab(varXh)
computeVarYTildeHabOld(varXh)
finalizeSampling()
getOutputs()
habitCondSampler(j, rb, varHRF)
habitCondSamplerParallel(rb, h)
habitCondSamplerSerial(rb, h)
initObservables()
linkToData(dataInput)
parametersComments = {'contrasts': 'Define contrasts as arithmetic expressions.\nCondition names used in expressions must be consistent with those specified in session data above', 'paramLexp': 'lambda-like parameter of the Laplacian distribution in habit sampling\n recommended between 1. and 10.'}
sampleNextAlt(variables)

Define the behaviour of the variable at each sampling step when its sampling is not activated.

sampleNextInternal(variables)

Define the behaviour of the variable at each sampling step when its sampling is not activated. Must be overriden in child classes.

sampleNrlsParallel(rb, h, varLambda, varCI, varCA, meanCA, varXhtQXh, variables)
sampleNrlsSerial(varXh, rb, h, varCI, varCA, meanCA, variables)
sampleNrlsSerial_bak(rb, h, varLambda, varCI, varCA, meanCA, varXhtQXh, variables)
samplingWarmUp(variables)

#TODO : comment

saveCurrentValue()
setupGamma()
setupTimeNrls()
spExtract(spInd, mtrx, cond)
updateGammaTimeNRLs(nc, nv)
updateObsersables()
updateXh(varHRF)
updateYtilde()
pyhrf.jde.nrl.habituation.sparsedot(X, A, mask, taille)
pyhrf.jde.nrl.habituation.sparsedotdimun(X, A, mask, lenght)
pyhrf.jde.nrl.habituation.subcptGamma(nrl, habit, nbTrials, deltaOns)