def __init__(self,
do_sampling=True,
use_true_value=False,
val_ini=None,
hyper_prior_type='Jeffreys',
activ_thresh=4.,
var_ci_pr_alpha=2.04,
var_ci_pr_beta=.5,
var_ca_pr_alpha=2.01,
var_ca_pr_beta=.5,
var_cd_pr_alpha=2.01,
var_cd_pr_beta=.5,
mean_ca_pr_mean=5.,
mean_ca_pr_var=20.,
mean_cd_pr_mean=-20.,
mean_cd_pr_var=20.):
BiGaussMixtureParamsSampler.__init__(self, do_sampling, use_true_value,
val_ini, hyper_prior_type,
activ_thresh, var_ci_pr_alpha,
var_ci_pr_beta, var_ca_pr_alpha,
var_ca_pr_beta, mean_ca_pr_mean,
mean_ca_pr_var)
self.varCDPrAlpha = var_cd_pr_alpha
self.varCDPrBeta = var_cd_pr_beta
self.meanCDPrMean = mean_cd_pr_mean
self.meanCDPrVar = mean_cd_pr_var
def checkAndSetInitValue(self, variables):
if self.currentValue is None:
curValWasNone = True
else:
curValWasNone = False
BiGaussMixtureParamsSampler.checkAndSetInitValue(self, variables)
# TODO : retrieve simulated components ...
if curValWasNone:
if not self.useTrueValue:
nc = self.nbConditions
self.currentValue[self.I_MEAN_CD] = zeros(nc) - 10.
self.currentValue[self.I_VAR_CD] = zeros(nc) + 1.
def checkAndSetInitValue(self, variables):
if self.currentValue is None:
curValWasNone = True
else:
curValWasNone = False
BiGaussMixtureParamsSampler.checkAndSetInitValue(self, variables)
# TODO : retrieve simulated components ...
if curValWasNone:
if not self.useTrueValue:
nc = self.nbConditions
self.currentValue[self.I_MEAN_CD] = zeros(nc) - 10.
self.currentValue[self.I_VAR_CD] = zeros(nc) + 1.
def linkToData(self, dataInput):
BiGaussMixtureParamsSampler.linkToData(self, dataInput)
self.nrlCD = range(self.nbConditions)
if self.dataInput.simulData is not None:
mixtures = self.dataInput.simulData.nrls.getMixture()
itemsCond = mixtures.items()
meanCD = zeros(self.nbConditions, dtype=float)
varCD = zeros(self.nbConditions, dtype=float)
for cn, mixt in mixtures.iteritems():
genDeactiv = mixt.generators['deactiv']
indCond = self.dataInput.simulData.nrls.condIds[cn]
meanCD[indCond] = genDeactiv.mean
varCD[indCond] = genDeactiv.std ** 2
self.trueValue[self.I_MEAN_CD] = meanCD
self.trueValue[self.I_VAR_CD] = varCD
def linkToData(self, dataInput):
BiGaussMixtureParamsSampler.linkToData(self, dataInput)
self.nrlCD = range(self.nbConditions)
if self.dataInput.simulData is not None:
mixtures = self.dataInput.simulData.nrls.getMixture()
itemsCond = mixtures.items()
meanCD = zeros(self.nbConditions, dtype=float)
varCD = zeros(self.nbConditions, dtype=float)
for cn, mixt in mixtures.iteritems():
genDeactiv = mixt.generators['deactiv']
indCond = self.dataInput.simulData.nrls.condIds[cn]
meanCD[indCond] = genDeactiv.mean
varCD[indCond] = genDeactiv.std**2
self.trueValue[self.I_MEAN_CD] = meanCD
self.trueValue[self.I_VAR_CD] = varCD
def __init__(self, do_sampling=True, use_true_value=False,
val_ini=None, hyper_prior_type='Jeffreys', activ_thresh=4.,
var_ci_pr_alpha=2.04, var_ci_pr_beta=.5,
var_ca_pr_alpha=2.01, var_ca_pr_beta=.5,
var_cd_pr_alpha=2.01, var_cd_pr_beta=.5,
mean_ca_pr_mean=5., mean_ca_pr_var=20.,
mean_cd_pr_mean=-20., mean_cd_pr_var=20.):
BiGaussMixtureParamsSampler.__init__(self, do_sampling, use_true_value,
val_ini, hyper_prior_type,
activ_thresh, var_ci_pr_alpha,
var_ci_pr_beta, var_ca_pr_alpha,
var_ca_pr_beta, mean_ca_pr_mean,
mean_ca_pr_var)
self.varCDPrAlpha = var_cd_pr_alpha
self.varCDPrBeta = var_cd_pr_beta
self.meanCDPrMean = mean_cd_pr_mean
self.meanCDPrVar = mean_cd_pr_var
def finalizeSampling(self):
BiGaussMixtureParamsSampler.finalizeSampling(self)
del self.nrlCD
def sampleNextInternal(self, variables):
nrlsSmpl = variables[self.samplerEngine.I_NRLS]
cardCD = nrlsSmpl.cardClass[self.L_CD, :]
if self.hyperPriorFlag:
for j in xrange(self.nbConditions):
vICD = nrlsSmpl.voxIdx[nrlsSmpl.L_CD][j]
self.nrlCD[j] = nrlsSmpl.currentValue[j, vICD]
if cardCD[j] > 0:
etaj = mean(self.nrlCD[j])
nrlCDCentered = self.nrlCD[j] - etaj
nuj = .5 * dot(nrlCDCentered, nrlCDCentered)
varCDj = 1.0 / random.gamma(0.5 * cardCD[j] + self.varCDPrAlpha,
1 / (nuj + self.varCDPrBeta))
else:
etaj = 0.0
varCDj = 1.0 / \
random.gamma(self.varCDPrAlpha, 1 / self.varCDPrBeta)
invVarLikelihood = cardCD[j] / varCDj
meanCDVarAPost = 1 / (invVarLikelihood + 1 / self.meanCDPrVar)
rPrMV = self.meanCDPrMean / self.meanCDPrVar
meanCDMeanAPost = meanCDVarAPost * \
(etaj * invVarLikelihood + rPrMV)
meanCDj = random.normal(meanCDMeanAPost, meanCDVarAPost ** 0.5)
self.currentValue[self.I_MEAN_CD, j] = meanCDj
self.currentValue[self.I_VAR_CD, j] = varCDj
else:
nrlsSmpl = variables[self.samplerEngine.I_NRLS]
for j in random.permutation(self.nbConditions):
ca = nrlsSmpl.cardClass[self.L_CA, j]
ci = nrlsSmpl.cardClass[self.L_CI, j]
cd = nrlsSmpl.cardClass[self.L_CD, j]
vICI = nrlsSmpl.voxIdx[nrlsSmpl.L_CI][j]
vICA = nrlsSmpl.voxIdx[nrlsSmpl.L_CA][j]
vICD = nrlsSmpl.voxIdx[nrlsSmpl.L_CD][j]
self.nrlCI[j] = nrlsSmpl.currentValue[j, vICI]
self.nrlCA[j] = nrlsSmpl.currentValue[j, vICA]
self.nrlCD[j] = nrlsSmpl.currentValue[j, vICD]
r = BiGaussMixtureParamsSampler.computeWithJeffreyPriors(self, j,
ci, ca)
varCIj, meanCAj, varCAj = r
meanCD, varCD = self.computeWithJeffreyPriors(j, cd)
self.currentValue[self.I_VAR_CI, j] = varCIj
# absolute(meanCAj)
self.currentValue[self.I_MEAN_CA, j] = meanCAj
self.currentValue[self.I_VAR_CA, j] = varCAj
self.currentValue[self.I_MEAN_CD, j] = meanCD
self.currentValue[self.I_VAR_CD, j] = varCD
logger.info('meanCD,%d = %f', j,
self.currentValue[self.I_MEAN_CD, j])
logger.info(
'varCD,%d = %f', j, self.currentValue[self.I_VAR_CD, j])
def finalizeSampling(self):
BiGaussMixtureParamsSampler.finalizeSampling(self)
del self.nrlCD
def sampleNextInternal(self, variables):
nrlsSmpl = variables[self.samplerEngine.I_NRLS]
cardCD = nrlsSmpl.cardClass[self.L_CD, :]
if self.hyperPriorFlag:
for j in xrange(self.nbConditions):
vICD = nrlsSmpl.voxIdx[nrlsSmpl.L_CD][j]
self.nrlCD[j] = nrlsSmpl.currentValue[j, vICD]
if cardCD[j] > 0:
etaj = mean(self.nrlCD[j])
nrlCDCentered = self.nrlCD[j] - etaj
nuj = .5 * dot(nrlCDCentered, nrlCDCentered)
varCDj = 1.0 / random.gamma(
0.5 * cardCD[j] + self.varCDPrAlpha, 1 /
(nuj + self.varCDPrBeta))
else:
etaj = 0.0
varCDj = 1.0 / \
random.gamma(self.varCDPrAlpha, 1 / self.varCDPrBeta)
invVarLikelihood = cardCD[j] / varCDj
meanCDVarAPost = 1 / (invVarLikelihood + 1 / self.meanCDPrVar)
rPrMV = self.meanCDPrMean / self.meanCDPrVar
meanCDMeanAPost = meanCDVarAPost * \
(etaj * invVarLikelihood + rPrMV)
meanCDj = random.normal(meanCDMeanAPost, meanCDVarAPost**0.5)
self.currentValue[self.I_MEAN_CD, j] = meanCDj
self.currentValue[self.I_VAR_CD, j] = varCDj
else:
nrlsSmpl = variables[self.samplerEngine.I_NRLS]
for j in random.permutation(self.nbConditions):
ca = nrlsSmpl.cardClass[self.L_CA, j]
ci = nrlsSmpl.cardClass[self.L_CI, j]
cd = nrlsSmpl.cardClass[self.L_CD, j]
vICI = nrlsSmpl.voxIdx[nrlsSmpl.L_CI][j]
vICA = nrlsSmpl.voxIdx[nrlsSmpl.L_CA][j]
vICD = nrlsSmpl.voxIdx[nrlsSmpl.L_CD][j]
self.nrlCI[j] = nrlsSmpl.currentValue[j, vICI]
self.nrlCA[j] = nrlsSmpl.currentValue[j, vICA]
self.nrlCD[j] = nrlsSmpl.currentValue[j, vICD]
r = BiGaussMixtureParamsSampler.computeWithJeffreyPriors(
self, j, ci, ca)
varCIj, meanCAj, varCAj = r
meanCD, varCD = self.computeWithJeffreyPriors(j, cd)
self.currentValue[self.I_VAR_CI, j] = varCIj
# absolute(meanCAj)
self.currentValue[self.I_MEAN_CA, j] = meanCAj
self.currentValue[self.I_VAR_CA, j] = varCAj
self.currentValue[self.I_MEAN_CD, j] = meanCD
self.currentValue[self.I_VAR_CD, j] = varCD
logger.info('meanCD,%d = %f', j,
self.currentValue[self.I_MEAN_CD, j])
logger.info('varCD,%d = %f', j,
self.currentValue[self.I_VAR_CD, j])
