def calcMargLik(self, SS):
''' Calculate marginal likelihood of assignments, summed over all comps
'''
theta = self.gamma / SS.K + SS.N
cPrior = gammaln(self.gamma) - SS.K * gammaln(self.gamma / SS.K)
cPost = gammaln(np.sum(theta)) - np.sum(gammaln(theta))
return cPrior - cPost
def E_logpV( self ):
logNormC = gammaln(self.alpha0 + self.alpha1) \
- gammaln(self.alpha0) - gammaln(self.alpha1)
logBetaPDF = (self.alpha1-1)*self.ElogV + (self.alpha0-1)*self.Elog1mV
if self.truncType == 'z':
return self.K*logNormC + logBetaPDF.sum()
elif self.truncType == 'v':
return self.K*logNormC + logBetaPDF[:-1].sum()
def log_pdf_dirichlet(self, wvec=None, avec=None):
''' Return scalar log probability for Dir(wvec | avec)
'''
if wvec is None:
wvec = self.w
if avec is None:
avec = (self.gamma / self.K) * np.ones(self.K)
logC = gammaln(np.sum(avec)) - np.sum(gammaln(avec))
return logC + np.sum((avec - 1.0) * np.log(wvec + 1e-100))
def E_logqV( self ):
logNormC = gammaln(self.qalpha0 + self.qalpha1) \
- gammaln(self.qalpha0) - gammaln(self.qalpha1)
logBetaPDF = (self.qalpha1-1)*self.ElogV + (self.qalpha0-1)*self.Elog1mV
if self.truncType == 'z':
return logNormC.sum() + logBetaPDF.sum()
elif self.truncType == 'v':
# skip last entry because entropy of Beta(1,0) = 0
return logNormC[:-1].sum() + logBetaPDF[:-1].sum()
def log_pdf_dirichlet(self, wvec=None, avec=None):
""" Return scalar log probability for Dir(wvec | avec)
"""
if wvec is None:
wvec = self.w
if avec is None:
avec = self.alpha0 * np.ones(self.K)
logC = gammaln(np.sum(avec)) - np.sum(gammaln(avec))
return logC + np.sum((avec - 1.0) * np.log(wvec))
def calcMargLik(self, SS):
""" Calculate marginal likelihood of assignments, summed over all comps
"""
mask = SS.N > 0
Nvec = SS.N[mask]
K = Nvec.size
return gammaln(self.gamma0) \
+ K * np.log(self.gamma0) \
+ np.sum(gammaln(Nvec)) \
- gammaln(np.sum(Nvec) + self.gamma0)
def c_Beta(a1, a0):
''' Evaluate cumulant function of the Beta distribution
When input is vectorized, we compute sum over all entries.
Returns
-------
c : scalar real
'''
return np.sum(gammaln(a1 + a0)) - np.sum(gammaln(a1)) - np.sum(gammaln(a0))
def log_pdf_dirichlet(PiMat, alphavec):
''' Return scalar log probability for Dir(PiMat | alphavec)
'''
PiMat = as2D(PiMat + 1e-100)
J, K = PiMat.shape
if isinstance(alphavec, float):
alphavec = alphavec * np.ones(K)
elif alphavec.ndim == 0:
alphavec = alphavec * np.ones(K)
assert alphavec.size == K
cDir = gammaln(np.sum(alphavec)) - np.sum(gammaln(alphavec))
return K * cDir + np.sum(np.dot(np.log(PiMat), alphavec - 1.0))
def L_alloc_no_slack(self):
''' Compute allocation term of objective function, without slack term
Returns
-------
L : scalar float
'''
N = self.theta.shape[0]
K = self.K
prior_cDir = N * (gammaln(self.alpha) - K * gammaln(self.alpha / K))
post_cDir = np.sum(gammaln(np.sum(self.theta, axis=1))) - \
np.sum(gammaln(self.theta))
return prior_cDir - post_cDir
def c_Beta_ReturnVec(eta1, eta0):
''' Evaluate cumulant of Beta distribution for vector of parameters
Parameters
-------
eta1 : 1D array, size K
represents ON pseudo-count parameter of the Beta
eta0 : 1D array, size K
represents OFF pseudo-count parameter of the Beta
Returns
-------
cvec : 1D array, size K
'''
return gammaln(eta1 + eta0) - gammaln(eta1) - gammaln(eta0)
def c_Beta(eta1, eta0):
''' Evaluate cumulant function of Beta distribution
Parameters
-------
eta1 : 1D array, size K
represents ON pseudo-count parameter of the Beta
eta0 : 1D array, size K
represents OFF pseudo-count parameter of the Beta
Returns
-------
c : float
= \sum_k c_B(eta1[k], eta0[k])
'''
return np.sum(gammaln(eta1 + eta0) - gammaln(eta1) - gammaln(eta0))
def elbo_alloc(self):
K = self.K
normPinit = gammaln(self.K * self.startAlpha) \
- self.K * gammaln(self.startAlpha)
normQinit = gammaln(np.sum(self.startTheta)) \
- np.sum(gammaln(self.startTheta))
normPtrans = K * gammaln(K * self.transAlpha + self.kappa) - \
self.K * (self.K - 1) * gammaln(self.transAlpha) - \
self.K * gammaln(self.transAlpha + self.kappa)
normQtrans = np.sum(gammaln(np.sum(self.transTheta, axis=1))) \
- np.sum(gammaln(self.transTheta))
return normPinit + normPtrans - normQinit - normQtrans
def E_cDalphabeta_surrogate(alpha, rho, omega):
''' Compute expected value of cumulant function of alpha * beta.
Returns
-------
csur : scalar float
'''
K = rho.size
eta1 = rho * omega
eta0 = (1 - rho) * omega
digammaBoth = digamma(eta1 + eta0)
ElogU = digamma(eta1) - digammaBoth
Elog1mU = digamma(eta0) - digammaBoth
OFFcoef = kvec(K)
calpha = gammaln(alpha) + (K + 1) * np.log(alpha)
return calpha + np.sum(ElogU) + np.inner(OFFcoef, Elog1mU)
def get_log_norm_const(self):
''' Calculate log normalization constant (aka log partition function)
for this Gauss-Gamma distribution.
p(mu,Lam) = NormalGamma( mu, Lam | a, b, m, kappa)
= 1/Z f(mu|Lam) g(Lam), where Z is const w.r.t mu,Lam
Normalization constant = Z = \int f() g() dmu dLam
Returns
--------
logZ : float
'''
D = self.D
a = self.a
b = self.b
logNormConstNormal = 0.5 * D * (LOGTWOPI - np.log(self.kappa))
logNormConstGamma = np.sum(gammaln(a)) - np.inner(a, np.log(b))
return logNormConstNormal + logNormConstGamma
def get_log_norm_const(self):
''' Calculate log normalization constant (aka log partition function)
for this Gauss-Gamma distribution.
p(mu,Lam) = NormalGamma( mu, Lam | a, b, m, kappa)
= 1/Z f(mu|Lam) g(Lam), where Z is const w.r.t mu,Lam
Normalization constant = Z = \int f() g() dmu dLam
Returns
--------
logZ : float
'''
D = self.D
a = self.a
b = self.b
logNormConstNormal = 0.5 * D * (LOGTWOPI - np.log(self.kappa))
logNormConstGamma = np.sum(gammaln(a)) - np.inner(a, np.log(b))
return logNormConstNormal + logNormConstGamma
def calcMergeTermsFromSeparateLP(
Data=None,
LPa=None, SSa=None,
LPb=None, SSb=None,
mUIDPairs=None):
''' Compute merge terms that combine two comps from separate LP dicts.
Returns
-------
Mdict : dict of key, array-value pairs
'''
M = len(mUIDPairs)
m_sumLogPi = np.zeros(M)
m_gammalnTheta = np.zeros(M)
m_slackTheta = np.zeros(M)
m_Hresp = np.zeros(M)
assert np.allclose(LPa['digammaSumTheta'], LPb['digammaSumTheta'])
for m, (uidA, uidB) in enumerate(mUIDPairs):
kA = SSa.uid2k(uidA)
kB = SSb.uid2k(uidB)
m_resp = LPa['resp'][:, kA] + LPb['resp'][:, kB]
if hasattr(Data, 'word_count') and \
Data.nUniqueToken == m_resp.shape[0]:
m_Hresp[m] = -1 * calcRlogRdotv(
m_resp[:,np.newaxis], Data.word_count)
else:
m_Hresp[m] = -1 * calcRlogR(m_resp[:,np.newaxis])
DTC_vec = LPa['DocTopicCount'][:, kA] + LPb['DocTopicCount'][:, kB]
theta_vec = LPa['theta'][:, kA] + LPb['theta'][:, kB]
m_gammalnTheta[m] = np.sum(gammaln(theta_vec))
ElogPi_vec = digamma(theta_vec) - LPa['digammaSumTheta']
m_sumLogPi[m] = np.sum(ElogPi_vec)
# slack = (Ndm - theta_dm) * E[log pi_dm]
slack_vec = ElogPi_vec
slack_vec *= (DTC_vec - theta_vec)
m_slackTheta[m] = np.sum(slack_vec)
return dict(
Hresp=m_Hresp,
gammalnTheta=m_gammalnTheta,
slackTheta=m_slackTheta,
sumLogPi=m_sumLogPi)
def c_Func(avec, K=0):
''' Evaluate cumulant function of the Dirichlet distribution
Returns
-------
c : scalar real
'''
if isinstance(avec, float) or avec.ndim == 0:
assert K > 0
avec = avec * np.ones(K)
return gammaln(np.sum(avec)) - np.sum(gammaln(avec))
elif avec.ndim == 1:
return gammaln(np.sum(avec)) - np.sum(gammaln(avec))
else:
return np.sum(gammaln(np.sum(avec, axis=1))) - np.sum(gammaln(avec))
def c_Dir(AMat, arem=None):
''' Evaluate cumulant function of the Dir distribution
When input is vectorized, we compute sum over all entries.
Returns
-------
c : scalar real
'''
AMat = np.asarray(AMat)
D = AMat.shape[0]
if arem is None:
if AMat.ndim == 1:
return gammaln(np.sum(AMat)) - np.sum(gammaln(AMat))
else:
return np.sum(gammaln(np.sum(AMat, axis=1))) \
- np.sum(gammaln(AMat))
return np.sum(gammaln(np.sum(AMat, axis=1) + arem)) \
- np.sum(gammaln(AMat)) \
- D * np.sum(gammaln(arem))
def calc_log_norm_const(cls, a, b, m, kappa):
logNormConstNormal = 0.5 * D * (LOGTWOPI + np.log(kappa))
logNormConstGamma = np.sum(gammaln(a)) - np.inner(a, np.log(b))
return logNormConstNormal + logNormConstGamma
def E_logpW( self ):
''' Bishop PRML eq. 10.73
'''
return gammaln(self.K*self.alpha0) \
- self.K*gammaln(self.alpha0) + (self.alpha0-1)*self.Elogw.sum()
def c_Dir(tvec):
return gammaln(tvec.sum()) - gammaln(tvec).sum()
def E_logqW(self):
""" Bishop PRML eq. 10.76
"""
return gammaln(self.alpha.sum()) - gammaln(self.alpha).sum() + np.inner((self.alpha - 1), self.Elogw)
def E_logpW(self):
''' Bishop PRML eq. 10.73
'''
return gammaln(self.gamma) \
- self.K * gammaln(self.gamma/self.K) + \
(self.gamma / self.K - 1) * self.Elogw.sum()
def E_logqW(self):
''' Bishop PRML eq. 10.76
'''
return gammaln(self.theta.sum()) - gammaln(self.theta).sum() \
+ np.inner((self.theta - 1), self.Elogw)
def E_logpW(self):
""" Bishop PRML eq. 10.73
"""
return gammaln(self.K * self.alpha0) - self.K * gammaln(self.alpha0) + (self.alpha0 - 1) * self.Elogw.sum()
def calcELBO_NonlinearTerms(Data=None, SS=None, LP=None, todict=0,
rho=None, Ebeta=None, alpha=None,
resp=None,
nDoc=None, DocTopicCount=None,
theta=None, thetaRem=None,
ElogPi=None, ElogPiRem=None,
sumLogPi=None,
sumLogPiRem=None, sumLogPiRemVec=None,
Hresp=None, slackTheta=None, slackThetaRem=None,
gammalnTheta=None, gammalnSumTheta=None,
gammalnThetaRem=None,
thetaEmptyComp=None, ElogPiEmptyComp=None,
ElogPiOrigComp=None,
gammalnThetaOrigComp=None, slackThetaOrigComp=None,
returnMemoizedDict=0, **kwargs):
""" Calculate ELBO objective terms non-linear in suff stats.
"""
if resp is not None:
N, K = resp.shape
elif LP is not None:
if 'resp' in LP:
N, K = LP['resp'].shape
else:
N, K = LP['spR'].shape
if Ebeta is None:
Ebeta = rho2beta(rho, returnSize='K+1')
if LP is not None:
DocTopicCount = LP['DocTopicCount']
nDoc = DocTopicCount.shape[0]
theta = LP['theta']
thetaRem = LP['thetaRem']
ElogPi = LP['ElogPi']
ElogPiRem = LP['ElogPiRem']
sumLogPi = np.sum(ElogPi, axis=0)
sumLogPiRem = np.sum(ElogPiRem)
if 'thetaEmptyComp' in LP:
thetaEmptyComp = LP['thetaEmptyComp']
ElogPiEmptyComp = LP['ElogPiEmptyComp']
ElogPiOrigComp = LP['ElogPiOrigComp']
gammalnThetaOrigComp = LP['gammalnThetaOrigComp']
slackThetaOrigComp = LP['slackThetaOrigComp']
HrespOrigComp = LP['HrespOrigComp']
elif SS is not None:
sumLogPi = SS.sumLogPi
nDoc = SS.nDoc
if hasattr(SS, 'sumLogPiRemVec'):
sumLogPiRemVec = SS.sumLogPiRemVec
else:
sumLogPiRem = SS.sumLogPiRem
if DocTopicCount is not None and theta is None:
theta = DocTopicCount + alpha * Ebeta[:-1]
thetaRem = alpha * Ebeta[-1]
if theta is not None and ElogPi is None:
digammasumtheta = digamma(theta.sum(axis=1) + thetaRem)
ElogPi = digamma(theta) - digammasumtheta[:, np.newaxis]
ElogPiRem = digamma(thetaRem) - digammasumtheta[:, np.newaxis]
if sumLogPi is None and ElogPi is not None:
sumLogPi = np.sum(ElogPi, axis=0)
sumLogPiRem = np.sum(ElogPiRem)
if Hresp is None:
if SS is not None and SS.hasELBOTerm('Hresp'):
Hresp = SS.getELBOTerm('Hresp')
else:
if hasattr(Data, 'word_count') and N == Data.word_count.size:
if resp is not None:
Hresp = -1 * NumericUtil.calcRlogRdotv(
resp, Data.word_count)
elif 'resp' in LP:
Hresp = -1 * NumericUtil.calcRlogRdotv(
LP['resp'], Data.word_count)
elif 'spR' in LP:
Hresp = calcSparseRlogRdotv(
v=Data.word_count,
**LP)
else:
raise ValueError("Missing resp assignments!")
else:
if resp is not None:
Hresp = -1 * NumericUtil.calcRlogR(resp)
elif 'resp' in LP:
Hresp = -1 * NumericUtil.calcRlogR(LP['resp'])
elif 'spR' in LP:
assert 'nnzPerRow' in LP
Hresp = calcSparseRlogR(**LP)
else:
raise ValueError("Missing resp assignments!")
if slackTheta is None:
if SS is not None and SS.hasELBOTerm('slackTheta'):
slackTheta = SS.getELBOTerm('slackTheta')
slackThetaRem = SS.getELBOTerm('slackThetaRem')
else:
slackTheta = DocTopicCount - theta
slackTheta *= ElogPi
slackTheta = np.sum(slackTheta, axis=0)
slackThetaRem = -1 * np.sum(thetaRem * ElogPiRem)
if gammalnTheta is None:
if SS is not None and SS.hasELBOTerm('gammalnTheta'):
gammalnSumTheta = SS.getELBOTerm('gammalnSumTheta')
gammalnTheta = SS.getELBOTerm('gammalnTheta')
gammalnThetaRem = SS.getELBOTerm('gammalnThetaRem')
else:
sumTheta = np.sum(theta, axis=1) + thetaRem
gammalnSumTheta = np.sum(gammaln(sumTheta))
gammalnTheta = np.sum(gammaln(theta), axis=0)
gammalnThetaRem = theta.shape[0] * gammaln(thetaRem)
if thetaEmptyComp is not None:
gammalnThetaEmptyComp = nDoc * gammaln(thetaEmptyComp) - \
gammalnThetaOrigComp
slackThetaEmptyComp = -np.sum(thetaEmptyComp * ElogPiEmptyComp) - \
slackThetaOrigComp
if returnMemoizedDict:
Mdict = dict(Hresp=Hresp,
slackTheta=slackTheta,
slackThetaRem=slackThetaRem,
gammalnTheta=gammalnTheta,
gammalnThetaRem=gammalnThetaRem,
gammalnSumTheta=gammalnSumTheta)
if thetaEmptyComp is not None:
Mdict['HrespEmptyComp'] = -1 * HrespOrigComp
Mdict['gammalnThetaEmptyComp'] = gammalnThetaEmptyComp
Mdict['slackThetaEmptyComp'] = slackThetaEmptyComp
return Mdict
# First, compute all local-only terms
Lentropy = np.sum(Hresp)
Lslack = slackTheta.sum() + slackThetaRem
LcDtheta = -1 * (gammalnSumTheta - gammalnTheta.sum() - gammalnThetaRem)
# For stochastic (soVB), we need to scale up these terms
# Only used when --doMemoELBO is set to 0 (not recommended)
if SS is not None and SS.hasAmpFactor():
Lentropy *= SS.ampF
Lslack *= SS.ampF
LcDtheta *= SS.ampF
# Next, compute the slack term
alphaEbeta = alpha * Ebeta
Lslack_alphaEbeta = np.sum(alphaEbeta[:-1] * sumLogPi)
if sumLogPiRemVec is not None:
Ebeta_gt = 1 - np.cumsum(Ebeta[:-1])
Lslack_alphaEbeta += alpha * np.inner(Ebeta_gt, sumLogPiRemVec)
else:
Lslack_alphaEbeta += alphaEbeta[-1] * sumLogPiRem
Lslack += Lslack_alphaEbeta
if todict:
return dict(
Lslack=Lslack,
Lentropy=Lentropy,
LcDtheta=LcDtheta,
Lslack_alphaEbeta=Lslack_alphaEbeta)
return LcDtheta + Lslack + Lentropy
def calcSummaryStats(Dslice,
LP=None,
alpha=None,
alphaEbeta=None,
doTrackTruncationGrowth=0,
doPrecompEntropy=0,
doPrecompMergeEntropy=0,
mergePairSelection=None,
mPairIDs=None,
trackDocUsage=0,
**kwargs):
""" Calculate summary from local parameters for given data slice.
Parameters
-------
Data : bnpy data object
LP : local param dict with fields
resp : Data.nObs x K array,
where resp[n,k] = posterior resp of comp k
doPrecompEntropy : boolean flag
indicates whether to precompute ELBO terms in advance
used for memoized learning algorithms (moVB)
Returns
-------
SS : SuffStatBag with K components
Relevant fields
* nDoc : scalar float
Counts total documents available in provided data.
* sumLogPi : 1D array, size K
Entry k equals \sum_{d in docs} E[ \log \pi_{dk} ]
* sumLogPiRem : scalar float
Equals sum over docs of probability of inactive topics.
Also has optional ELBO field when precompELBO is True
* Hvec : 1D array, size K
Vector of entropy contributions from each comp.
Hvec[k] = \sum_{n=1}^N H[q(z_n)], a function of 'resp'
"""
if mPairIDs is None:
M = 0
else:
M = len(mPairIDs)
K = LP['DocTopicCount'].shape[1]
if 'digammaSumTheta' not in LP:
digammaSumTheta = digamma(LP['theta'].sum(axis=1) + LP['thetaRem'])
LP['digammaSumTheta'] = digammaSumTheta # Used for merges
if 'ElogPi' not in LP:
LP['ElogPiRem'] = digamma(LP['thetaRem']) - LP['digammaSumTheta']
LP['ElogPi'] = digamma(LP['theta']) - \
LP['digammaSumTheta'][:, np.newaxis]
SS = SuffStatBag(K=K, D=Dslice.dim, M=M)
SS.setField('nDoc', Dslice.nDoc, dims=None)
SS.setField('sumLogPi', np.sum(LP['ElogPi'], axis=0), dims='K')
if 'ElogPiEmptyComp' in LP:
sumLogPiEmptyComp = np.sum(LP['ElogPiEmptyComp']) - \
np.sum(LP['ElogPiOrigComp'])
SS.setField('sumLogPiEmptyComp', sumLogPiEmptyComp, dims=None)
if doTrackTruncationGrowth:
remvec = np.zeros(K)
remvec[K - 1] = np.sum(LP['ElogPiRem'])
SS.setField('sumLogPiRemVec', remvec, dims='K')
else:
SS.setField('sumLogPiRem', np.sum(LP['ElogPiRem']), dims=None)
if doPrecompEntropy:
Mdict = calcELBO_NonlinearTerms(Data=Dslice,
LP=LP,
returnMemoizedDict=1)
if type(Mdict['Hresp']) == float:
# SPARSE HARD ASSIGNMENTS
SS.setELBOTerm('Hresp', Mdict['Hresp'], dims=None)
else:
SS.setELBOTerm('Hresp', Mdict['Hresp'], dims=('K', ))
SS.setELBOTerm('slackTheta', Mdict['slackTheta'], dims='K')
SS.setELBOTerm('gammalnTheta', Mdict['gammalnTheta'], dims='K')
if 'ElogPiEmptyComp' in LP:
SS.setELBOTerm('slackThetaEmptyComp', Mdict['slackThetaEmptyComp'])
SS.setELBOTerm('gammalnThetaEmptyComp',
Mdict['gammalnThetaEmptyComp'])
SS.setELBOTerm('HrespEmptyComp', Mdict['HrespEmptyComp'])
else:
SS.setELBOTerm('gammalnSumTheta',
Mdict['gammalnSumTheta'],
dims=None)
SS.setELBOTerm('slackThetaRem', Mdict['slackThetaRem'], dims=None)
SS.setELBOTerm('gammalnThetaRem',
Mdict['gammalnThetaRem'].sum(),
dims=None)
if doPrecompMergeEntropy:
if mPairIDs is None:
raise NotImplementedError("TODO: all pairs for merges")
m_Hresp = calcHrespForSpecificMergePairs(LP, Dslice, mPairIDs)
if m_Hresp is not None:
SS.setMergeTerm('Hresp', m_Hresp, dims=('M'))
m_sumLogPi = np.zeros(M)
m_gammalnTheta = np.zeros(M)
m_slackTheta = np.zeros(M)
for m, (kA, kB) in enumerate(mPairIDs):
theta_vec = LP['theta'][:, kA] + LP['theta'][:, kB]
ElogPi_vec = digamma(theta_vec) - LP['digammaSumTheta']
m_gammalnTheta[m] = np.sum(gammaln(theta_vec))
m_sumLogPi[m] = np.sum(ElogPi_vec)
# slack = (Ndm - theta_dm) * E[log pi_dm]
slack_vec = ElogPi_vec
slack_vec *= -1 * (alphaEbeta[kA] + alphaEbeta[kB])
m_slackTheta[m] = np.sum(slack_vec)
SS.setMergeTerm('gammalnTheta', m_gammalnTheta, dims=('M'))
SS.setMergeTerm('sumLogPi', m_sumLogPi, dims=('M'))
SS.setMergeTerm('slackTheta', m_slackTheta, dims=('M'))
# Uncomment this for verification of merge calculations.
# for (kA, kB) in mPairIDs:
# self.verifySSForMergePair(Data, SS, LP, kA, kB)
# .... end merge computations
# Selection terms (computes doc-topic correlation)
if mergePairSelection is not None:
if mergePairSelection.count('corr') > 0:
Tmat = LP['DocTopicCount']
SS.setSelectionTerm('DocTopicPairMat',
np.dot(Tmat.T, Tmat),
dims=('K', 'K'))
SS.setSelectionTerm('DocTopicSum', np.sum(Tmat, axis=0), dims='K')
if trackDocUsage:
# Track num of times a topic appears nontrivially in a doc
DocUsage = np.sum(LP['DocTopicCount'] > 0.01, axis=0)
SS.setSelectionTerm('DocUsageCount', DocUsage, dims='K')
Pi = LP['theta'] / LP['theta'].sum(axis=1)[:, np.newaxis]
SumPi = np.sum(Pi, axis=0)
SS.setSelectionTerm('SumPi', SumPi, dims='K')
return SS
def calc_log_norm_const(cls, a, b, m, kappa): logNormConstNormal = 0.5 * D * (LOGTWOPI + np.log(kappa)) logNormConstGamma = np.sum(gammaln(a)) - np.inner(a, np.log(b)) return logNormConstNormal + logNormConstGamma
