コード例 #1
0
                bestscore = score
                besttau = tau
                bestwfa = copy.deepcopy(wfa)

            print "Tau: ",tau, " Score: ", score

            avruntime += wfa.buildtime
            nummodelsmade += 1

        if metric == "WER":
            bestscore = bestwfa.get_WER(testdata)

        iohelpers.write_results(RESULTS_DIR+"co-"+wfatype+"-"+str(maxbasissize)+"-pautomac="+problem+"-"+metric+".txt", problem, wfatype+", "+"tau= "+str(besttau)+", basis size="+str(len(basisdict)), metric, bestscore, avruntime/float(nummodelsmade))

        if probabilistic:
            iohelpers.write_pnfa_model(MODEL_DIR+"co-"+wfatype+"-"+str(maxbasissize)+"-pautomac="+problem+"-"+metric+".fsm", bestwfa)


    else:
        

        RESULTS_DIR = "/home/williamleif/Dropbox/icml2014-experiments/results/real/"

        if problem == "tree":
            traindata = iohelpers.parse_file("/home/williamleif/Dropbox/icml2014-experiments/datasets/treebankdata.obs")
            validdata = traindata[0:5000]
            testdata = traindata[5000:10000]
            traindata = traindata[10000:len(traindata)]


        maxK = 500
コード例 #2
0
            if not success:
                break

            if metric == "WER":
                score = wfa.get_WER(validdata)
            else:
                score = wfa.get_perplexity(validdata)

            if bestsize == 0:
                bestscore = score
                bestsize = i
                bestwfa = copy.deepcopy(wfa)
            elif score < bestscore and abs(score-1000) > 0.1:
                bestscore = score
                bestsize = i
                bestwfa = copy.deepcopy(wfa)

            print "Model size: ", i, " Score: ", score
            avruntime += wfa.buildtime
            nummodelsmade += 1

        if metric == "WER":
            bestscore = bestwfa.get_WER(testdata)
        else:
            bestscore = bestwfa.get_perplexity(testdata)

        iohelpers.write_results(RESULTS_DIR+"tensor-"+metric+".txt", problem,"size= "+str(bestsize)+", basis size="+str(basislength), metric, bestscore, 0)
        iohelpers.write_pnfa_model(MODEL_DIR+"tensor-"+str(bestsize)+"-pautomac="+problem+"-"+metric+".fsm", bestwfa)