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pyhrf.jde.wsampler module

class pyhrf.jde.wsampler.WSampler(do_sampling=True, use_true_value=False, val_ini=None, pr_sigmoid_slope=1.0, pr_sigmoid_thresh=0.0)

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

CLASSES = array([0, 1])
CLASS_NAMES = ['inactiv', 'activ']
L_CA = 1
L_CI = 0
checkAndSetInitValue(variables)
computeProbW1(Qgj, gTQgj, rb, moyqj, t1, t2, mCAj, vCIj, vCAj, j, cardClassCAj)

ProbW1 is the probability that condition is relevant It is a vecteur on length nbcond

computeVarXhtQ(h, matXQ)
computemoyq(cardClassCA, nbVoxels)

Compute mean of labels in ROI

finalizeSampling()
getOutputs()
initObservables()
linkToData(dataInput)
sampleNextInternal(variables)

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

saveCurrentValue(it)
saveObservables(it)
threshold_W(meanW, thresh)
updateObsersables()
class pyhrf.jde.wsampler.W_Drift_Sampler(do_sampling=True, use_true_value=False, val_ini=None, pr_sigmoid_slope=1.0, pr_sigmoid_thresh=0.0)

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

CLASSES = array([0, 1])
CLASS_NAMES = ['inactiv', 'activ']
L_CA = 1
L_CI = 0
checkAndSetInitValue(variables)
computeProbW1(gj, gTgj, rb, t1, t2, mCAj, vCIj, vCAj, j, cardClassCAj)

ProbW1 is the probability that condition is relevant It is a vecteur on length nbcond

computemoyq(cardClassCA, nbVoxels)

Compute mean of labels in ROI

finalizeSampling()
getOutputs()
initObservables()
linkToData(dataInput)
sampleNextInternal(variables)

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

saveCurrentValue(it)
saveObservables(it)
threshold_W(meanW, thresh)
updateObsersables()