def update_global_params_VB(self, SS,
mergeCompA=None, mergeCompB=None,
**kwargs):
''' Update global parameters.
'''
self.K = SS.K
if not hasattr(self, 'rho') or self.rho.size != SS.K:
# Big change from previous model is being proposed.
# We'll init rho from scratch, and need more iters to improve.
nGlobalIters = self.nGlobalItersBigChange
else:
# Small change required. Current rho is good initialization.
nGlobalIters = self.nGlobalIters
# Special update case for merges:
# Fast, heuristic update for new rho given original value
if mergeCompA is not None:
beta = OptimizerRhoOmega.rho2beta_active(self.rho)
beta[mergeCompA] += beta[mergeCompB]
beta = np.delete(beta, mergeCompB, axis=0)
self.rho = OptimizerRhoOmega.beta2rho(beta, SS.K)
omega = self.omega
omega[mergeCompA] += omega[mergeCompB]
self.omega = np.delete(omega, mergeCompB, axis=0)
# TODO think about smarter init for rho/omega??
# Update theta with recently updated info from suff stats
self.transTheta, self.startTheta = self._calcTheta(SS)
for giter in range(nGlobalIters):
# Update rho, omega through numerical optimization
self.rho, self.omega = self.find_optimum_rhoOmega(**kwargs)
# Update theta again to reflect the new rho, omega
self.transTheta, self.startTheta = self._calcTheta(SS)
def _convert_beta2rhoomega(self, beta, nDoc=10):
''' Find vectors rho, omega that are probable given beta
Returns
--------
rho : 1D array, size K
omega : 1D array, size K
'''
assert abs(np.sum(beta) - 1.0) < 0.001
rho = OptimizerRhoOmega.beta2rho(beta, self.K)
omega = (nDoc + self.gamma) * np.ones(rho.size)
return rho, omega