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()¶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()¶