def sampleNextInternal(self, variables):
reps = self.get_variable('noise_var').currentValue
smplVarARp = self.get_variable('noise_var')
invAutoCorrNoise = smplVarARp.InvAutoCorrNoise
varNrls = self.get_variable('nrl').currentValue
smplVarh = self.get_variable('hrf')
varXh = smplVarh.varXh
eta = self.get_variable('drift_var').currentValue
invSigma = empty((self.dimDrift, self.dimDrift), dtype=float)
datamPredict = empty((self.ny), dtype=float)
self.updateVarYmDrift()
self.computeVarYTilde(varNrls, varXh)
if 1:
logger.debug('Computing PtDeltaP and PtDeltaY in C fashion')
tSQSOptimIni = time.time()
intensivecalc.computePtLambdaARModel(self.P, invAutoCorrNoise,
varNrls, varXh,
self.dataInput.varMBY, reps,
self.varPtLambdaP,
self.varPtLambdaYmP)
logger.debug(
'Computing PtDeltaP and PtDeltaY in C fashion'
' done in %1.3f sec',
time.time() - tSQSOptimIni)
for v in xrange(self.nbVox):
invSigma = eye(self.dimDrift, dtype=float) / \
eta + self.varPtLambdaP[:, :, v]
self.currentValue[:,
v] = sampleDrift(invSigma,
self.varPtLambdaYmP[:, v],
self.dimDrift)
logger.debug('Sampling drift in C fashion done in %1.3f sec',
time.time() - tSQSOptimIni)
if 0:
logger.debug('Computing PtDeltaP and PtDeltaY in Numpy fashion')
tSQSOptimIni = time.time()
for v in xrange(self.nbVox):
projCovNoise = dot(self.P.transpose(),
invAutoCorrNoise[:, :, v]) / reps[v]
invSigma = dot(projCovNoise, self.P)
assert numpy.allclose(invSigma, self.varPtLambdaP[:, :, v])
invSigma += eye(self.dimDrift, dtype=float) / eta
repNRLv = repmat(varNrls[:, v], self.ny, 1)
avjXjh = repNRLv * varXh
datamPredict = self.dataInput.varMBY[:, v] - avjXjh.sum(axis=1)
datamPredict = dot(projCovNoise, datamPredict)
assert numpy.allclose(datamPredict, self.varPtLambdaYmP[:, v])
self.currentValue[:, v] = sampleDrift(invSigma, datamPredict,
self.dimDrift)
logger.debug('Sampling drift in Numpy fashion done in %1.3f sec',
time.time() - tSQSOptimIni)
logger.debug('drift params :')
logger.debug(numpy.array2string(self.currentValue, precision=3))
self.updateNorm()
self.updateVarYmDrift()
self.computeVarYTilde(varNrls, varXh)
def sampleNextInternal(self, variables):
reps = self.get_variable('noise_var').currentValue
smplVarARp = self.get_variable('noise_var')
invAutoCorrNoise = smplVarARp.InvAutoCorrNoise
varNrls = self.get_variable('nrl').currentValue
smplVarh = self.get_variable('hrf')
varXh = smplVarh.varXh
eta = self.get_variable('drift_var').currentValue
invSigma = empty((self.dimDrift, self.dimDrift), dtype=float)
datamPredict = empty((self.ny), dtype=float)
self.updateVarYmDrift()
self.computeVarYTilde(varNrls, varXh)
if 1:
logger.debug('Computing PtDeltaP and PtDeltaY in C fashion')
tSQSOptimIni = time.time()
intensivecalc.computePtLambdaARModel(self.P,
invAutoCorrNoise,
varNrls,
varXh,
self.dataInput.varMBY,
reps,
self.varPtLambdaP,
self.varPtLambdaYmP)
logger.debug('Computing PtDeltaP and PtDeltaY in C fashion'
' done in %1.3f sec', time.time() - tSQSOptimIni)
for v in xrange(self.nbVox):
invSigma = eye(self.dimDrift, dtype=float) / \
eta + self.varPtLambdaP[:, :, v]
self.currentValue[:, v] = sampleDrift(invSigma,
self.varPtLambdaYmP[
:, v],
self.dimDrift)
logger.debug('Sampling drift in C fashion done in %1.3f sec',
time.time() - tSQSOptimIni)
if 0:
logger.debug('Computing PtDeltaP and PtDeltaY in Numpy fashion')
tSQSOptimIni = time.time()
for v in xrange(self.nbVox):
projCovNoise = dot(
self.P.transpose(), invAutoCorrNoise[:, :, v]) / reps[v]
invSigma = dot(projCovNoise, self.P)
assert numpy.allclose(invSigma, self.varPtLambdaP[:, :, v])
invSigma += eye(self.dimDrift, dtype=float) / eta
repNRLv = repmat(varNrls[:, v], self.ny, 1)
avjXjh = repNRLv * varXh
datamPredict = self.dataInput.varMBY[:, v] - avjXjh.sum(axis=1)
datamPredict = dot(projCovNoise, datamPredict)
assert numpy.allclose(datamPredict, self.varPtLambdaYmP[:, v])
self.currentValue[:, v] = sampleDrift(
invSigma, datamPredict, self.dimDrift)
logger.debug('Sampling drift in Numpy fashion done in %1.3f sec',
time.time() - tSQSOptimIni)
logger.debug('drift params :')
logger.debug(
numpy.array2string(self.currentValue, precision=3))
self.updateNorm()
self.updateVarYmDrift()
self.computeVarYTilde(varNrls, varXh)
def sampleNextInternal(self, variables):
reps = variables[self.samplerEngine.I_NOISE_VAR].currentValue
smplVarARp = variables[self.samplerEngine.I_NOISE_ARP]
invAutoCorrNoise = smplVarARp.InvAutoCorrNoise
varNrls = variables[self.samplerEngine.I_NRLS].currentValue
smplVarh = variables[self.samplerEngine.I_HRF]
varXh = smplVarh.varXh
eta = variables[self.samplerEngine.I_ETA].currentValue
invSigma= empty( ( self.dimDrift, self.dimDrift), dtype=float )
datamPredict = empty(( self.ny), dtype=float)
self.updateVarYmDrift()
self.computeVarYTilde(varNrls, varXh)
if 1:
pyhrf.verbose(6, 'Computing PtDeltaP and PtDeltaY in C fashion')
tSQSOptimIni = time.time()
# print self.dataInput.varMBY.shape
# print "dim VarXh", varXh.shape
# print "dim VarNRLs", varNrls.shape
# return
intensivecalc.computePtLambdaARModel(self.P,
invAutoCorrNoise,
varNrls,
varXh,
self.dataInput.varMBY,
reps,
self.varPtLambdaP,
self.varPtLambdaYmP)
pyhrf.verbose(6, 'Computing PtDeltaP and PtDeltaY in C fashion'+\
' done in %1.3f sec' %(time.time()-tSQSOptimIni))
for v in xrange(self.nbVox):
invSigma = eye(self.dimDrift,dtype=float)/eta + self.varPtLambdaP[:,:,v]
# pyhrf.verbose.printNdarray(6, invSigma )
self.currentValue[:,v] = sampleDrift( invSigma,
self.varPtLambdaYmP[:,v],
self.dimDrift )
pyhrf.verbose(6, 'Sampling drift in C fashion'+\
' done in %1.3f sec' %(time.time()-tSQSOptimIni))
if 0:
pyhrf.verbose(6, 'Computing PtDeltaP and PtDeltaY in Numpy fashion')
tSQSOptimIni = time.time()
for v in xrange(self.nbVox):
projCovNoise= dot( self.P.transpose(), invAutoCorrNoise[:,:,v] )/reps[v]
# invSigma = self.dataInput.varPtP/eta + dot( projCovNoise, self.P)
invSigma = dot( projCovNoise, self.P)
assert numpy.allclose(invSigma, self.varPtLambdaP[:,:,v])
invSigma += eye(self.dimDrift,dtype=float)/eta
# pyhrf.verbose.printNdarray(6, invSigma )
# self.updateDatamPredict(v, varNrls, varXh)
repNRLv = repmat(varNrls[:,v], self.ny, 1)
avjXjh = repNRLv * varXh
datamPredict = self.dataInput.varMBY[:,v] - avjXjh.sum(axis=1)
datamPredict = dot(projCovNoise, datamPredict)
assert numpy.allclose(datamPredict, self.varPtLambdaYmP[:,v])
self.currentValue[:,v] = sampleDrift(invSigma, datamPredict, self.dimDrift )
pyhrf.verbose(6, 'Sampling drift in Numpy fashion'+\
' done in %1.3f sec' %(time.time()-tSQSOptimIni))
pyhrf.verbose(6, 'drift params :')
pyhrf.verbose(6,
numpy.array2string(self.currentValue,precision=3))
self.updateNorm()
self.updateVarYmDrift()
self.computeVarYTilde(varNrls, varXh)
