pyhrf.jde.nrl.trigaussian.GGGNRLSampler(do_sampling=True, val_ini=None, contrasts={}, do_label_sampling=True, use_true_nrls=False, use_true_labels=False, labels_ini=None, ppm_proba_threshold=0.05, ppm_value_threshold=0, ppm_value_multi_threshold=array([ 0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1., 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2., 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4. ]), mean_activation_threshold=4, rescale_results=False, wip_variance_computation=False)¶Bases: pyhrf.jde.nrl.bigaussian.NRLSampler
CLASSES = array([0, 1, 2])¶CLASS_NAMES = ['inactiv', 'activ', 'deactiv']¶FALSE_NEG = 4¶FALSE_POS = 3¶L_CA = 1¶L_CD = 2¶L_CI = 0¶sampleLabels(cond, variables)¶pyhrf.jde.nrl.trigaussian.TriGaussMixtureParamsSampler(do_sampling=True, use_true_value=False, val_ini=None, hyper_prior_type='Jeffreys', activ_thresh=4.0, var_ci_pr_alpha=2.04, var_ci_pr_beta=0.5, var_ca_pr_alpha=2.01, var_ca_pr_beta=0.5, var_cd_pr_alpha=2.01, var_cd_pr_beta=0.5, mean_ca_pr_mean=5.0, mean_ca_pr_var=20.0, mean_cd_pr_mean=-20.0, mean_cd_pr_var=20.0)¶Bases: pyhrf.jde.nrl.bigaussian.BiGaussMixtureParamsSampler
I_MEAN_CD = 3¶I_VAR_CD = 4¶L_CD = 2¶NB_PARAMS = 5¶PARAMS_NAMES = ['Mean_Activ', 'Var_Activ', 'Var_Inactiv', 'Mean_Deactiv', 'Var_Deactiv']¶P_MEAN_CD_PR_MEAN = 'meanCDPrMean'¶P_MEAN_CD_PR_VAR = 'meanCDPrVar'¶P_VAR_CD_PR_ALPHA = 'varCDPrAlpha'¶P_VAR_CD_PR_BETA = 'varCDPrBeta'¶checkAndSetInitValue(variables)¶computeWithJeffreyPriors(j, cardCDj)¶finalizeSampling()¶getCurrentMeans()¶getCurrentVars()¶getOutputs()¶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.