def calcHardMergeGap(self, SS, kA, kB):
''' Calculate scalar improvement in ELBO for hard merge of comps kA, kB
Does *not* include any entropy.
Returns
---------
L : scalar
'''
m_K = SS.K - 1
m_SS = SuffStatBag(K=SS.K, D=0)
m_SS.setField('StartStateCount', SS.StartStateCount.copy(), dims='K')
m_SS.setField('TransStateCount',
SS.TransStateCount.copy(),
dims=('K', 'K'))
m_SS.mergeComps(kA, kB)
# Create candidate beta vector
m_beta = StickBreakUtil.rho2beta(self.rho)
m_beta[kA] += m_beta[kB]
m_beta = np.delete(m_beta, kB, axis=0)
# Create candidate rho and omega vectors
m_rho = StickBreakUtil.beta2rho(m_beta, m_K)
m_omega = np.delete(self.omega, kB)
# Create candidate startTheta
m_startTheta = self.startAlpha * m_beta.copy()
m_startTheta[:m_K] += m_SS.StartStateCount
# Create candidate transTheta
m_transTheta = self.alpha * np.tile(m_beta, (m_K, 1))
if self.kappa > 0:
m_transTheta[:, :m_K] += self.kappa * np.eye(m_K)
m_transTheta[:, :m_K] += m_SS.TransStateCount
# Evaluate objective func. for both candidate and current model
Lcur = calcELBO_LinearTerms(SS=SS,
rho=self.rho,
omega=self.omega,
startTheta=self.startTheta,
transTheta=self.transTheta,
alpha=self.alpha,
startAlpha=self.startAlpha,
gamma=self.gamma,
kappa=self.kappa)
Lprop = calcELBO_LinearTerms(SS=m_SS,
rho=m_rho,
omega=m_omega,
startTheta=m_startTheta,
transTheta=m_transTheta,
alpha=self.alpha,
startAlpha=self.startAlpha,
gamma=self.gamma,
kappa=self.kappa)
# Note: This gap relies on fact that all nonlinear terms are entropies,
return Lprop - Lcur
