def optimize(w, params, spl_params): assert(spl_params.spl_mode == 0) init_latent_variables(w, params) for iter in range(params.max_outer_iter): print "SSVM" w = SPLInnerLoop.optimize(w, params, spl_params) #if spl_params.spl_mode == 0 then this just calls SSVM.optimize() print "imputing h" HImputation.impute(w, params, spl_params) #this may interact with SPL at some point if has_converged(w, params, spl_params): break
def optimize(w, globalSPLVars, params):
bestObj = numpy.inf
wBest= CommonApp.PsiObject(params,False)
initLatentVariables(w, params)
utils.dumpCurrentLatentVariables(params, "%s.%s"%(params.latentVariableFile, 'init'))
iter = 0
while True:
logging.debug("SSVM iteration %d" % (iter))
w,optState = SPLInnerLoop.optimize(w, globalSPLVars, params, iter)
(converged, bestObj, wBest,newObj) = checkConvergence(w, globalSPLVars, params, bestObj, w,iter)
logging.debug("Objective after optimizing w: %f" % newObj)
lastedLongEnough= (iter > params.minOuterIters) and ((params.splParams.splMode=='CCCP' ) or (params.splParams.splMode!='CCCP' and iter > params.splParams.splInitIters and globalSPLVars.fraction >= 1.0))
logging.debug("Breaking because of convergence")
if (converged and lastedLongEnough):
logging.debug("Breaking because of convergence")
break
elif iter>params.maxOuterIters:
logging.debug("Breaking because its been 10 iterations")
break
elif params.supervised:
logging.debug("Only one run because we are in supervised mode")
break
HImputation.impute(optState, params)
(converged, bestObj, wBest,newObj) = checkConvergence(w, globalSPLVars, params, bestObj, w,iter)
logging.debug("Objective after updating latents: %f" % newObj)
logging.debug("Objective (best so far) %f" % bestObj)
CacheObj.cacheObject(params.modelFile + "."+str(iter), w)
utils.dumpCurrentLatentVariables(params, "%s.%d"%(params.latentVariableFile, iter))
iter += 1
utils.dumpCurrentLatentVariables(params, params.latentVariableFile)
logging.debug("Best objective attained is %f" % bestObj)
return wBest
def initLatentVariables(w, params): if params.initialModelFile: HImputation.impute(w, params)
