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pyhrf.jde.nrl.gammagaussian module

class pyhrf.jde.nrl.gammagaussian.GamGaussMixtureParamsSampler(parameters=None, xmlHandler=None, xmlLabel=None, xmlComment=None)

Bases: pyhrf.jde.samplerbase.GibbsSamplerVariable

#TODO : comment

I_MEAN_CA = 0
I_VAR_CA = 1
I_VAR_CI = 2
NB_PARAMS = 3
PARAMS_NAMES = ['Shape_Activ', 'Scale_Activ', 'Var_Inactiv']
P_SAMPLE_FLAG = 'sampleFlag'
P_SCALE_CA_PR_ALPHA = 'scaleCAPrAlpha'
P_SCALE_CA_PR_BETA = 'scaleCAPrBeta'
P_SHAPE_CA_PR_MEAN = 'shapeCAPrMean'
P_VAL_INI = 'initialValue'
P_VAR_CI_PR_ALPHA = 'varCIPrAlpha'
P_VAR_CI_PR_BETA = 'varCIPrBeta'
checkAndSetInitValue(variables)
defaultParameters = {'initialValue': None, 'sampleFlag': 1, 'scaleCAPrAlpha': 2.5, 'scaleCAPrBeta': 1.5, 'shapeCAPrMean': 10.0, 'varCIPrAlpha': 2.5, 'varCIPrBeta': 0.5}
linkToData(dataInput)
sampleNextInternal(variables)
class pyhrf.jde.nrl.gammagaussian.InhomogeneousNRLSampler(parameters=None, xmlHandler=None, xmlLabel=None, xmlComment=None)

Bases: pyhrf.xmlio.Initable, pyhrf.jde.samplerbase.GibbsSamplerVariable

Class handling the Gibbs sampling of Neural Response Levels according to:

  • Makni, S., Ciuciu, P., Idier, J., & Poline, J.-B. (2005). Joint detection-estimation of brain activity in functional MRI: a Multichannel Deconvolution solution. IEEE Transactions on Signal Processing, 53(9), 3488–3502. https://doi.org/10.1109/TSP.2005.853303

Inherits the abstract class C{GibbsSamplerVariable}. #TODO : comment attributes

L_CA = 1
L_CI = 0
P_BETA = 'beta'
P_LABELS_COLORS = 'labelsColors'
P_LABELS_INI = 'labelsIni'
P_SAMPLE_FLAG = 'sampleFlag'
P_SAMPLE_LABELS = 'sampleLabels'
P_TRUE_LABELS = 'trueLabels'
P_VAL_INI = 'initialValue'
calcEnergy(voxIdx, label, cond)
checkAndSetInitValue(variables)
computeMean()
computeMeanClassApost(j, nrls, varXhj, rb)
computeVarYTilde(varXh)
computeVariablesApost(varCI, shapeCA, scaleCA, rb, varXh, varLambda)
countLabels()
defaultParameters = {'beta': 0.4, 'initialValue': None, 'labelsColors': array([ 0., 0.]), 'labelsIni': None, 'sampleFlag': 1, 'sampleLabels': 1}
finalizeSampling()
linkToData(dataInput)
sampleLabels(cond, varCI, varCA, meanCA)
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.

samplingWarmUp(variables)

#TODO : comment