def runBatchVB(workQueue, lockingPost):
for (W, K, docs, alpha, maxiters, thr, hbb) in iter(workQueue.get, "none"):
#read current parameter value (and copy)
lam = copy.deepcopy(lockingPost.value())
#calculate correction
batchVB = batchvb.BatchLDA(W, K, docs, alpha, lam, useHBBBound=hbb)
lam = batchVB.train(maxiters, thr)
ss = (lam - batchVB._eta)
#apply correction
lockingPost.increment(ss)
workQueue.task_done()
def runBatchVB(W, K, docs, alpha, lam, maxiters, thr, hbb):
batchVB = batchvb.BatchLDA(W, K, docs, alpha, lam, useHBBBound=hbb)
lam = batchVB.train(maxiters, thr)
return lam
def getSuffStats(vocab, K, docs, alpha, lam):
batchVB = batchvb.BatchLDA(vocab, K, docs, alpha, lam)
batchVB.set_lambda(copy.deepcopy(lam))
return batchVB.do_e_step()
def update_lambda(self, docs):
batchVB = batchvb.BatchLDA(self._W, self._K, docs, self._alpha, self._lambda, self._useHBBBound)
self._lambda = batchVB.train(self._maxiters, self._threshold) #1E-4)
return (self._alpha,self._lambda)