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)

sampleNextInternal(variables)

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)

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)