n_nfeat = ndatas.size(2) ndatas = scaleEachUnitaryDatas(ndatas) # trans-mean-sub ndatas = centerDatas(ndatas) print("size of the datas...", ndatas.size()) # switch to cuda ndatas = ndatas.cuda() labels = labels.cuda() #MAP lam = 10 model = GaussianModel(n_ways, lam) model.initFromLabelledDatas() alpha = 0.2 optim = MAP(alpha) optim.verbose = False optim.progressBar = True acc_test = optim.loop(model, n_epochs=20) print( "final accuracy found {:0.2f} +- {:0.2f}".format(*(100 * x for x in acc_test)))
ndatas = QRreduction(ndatas) n_nfeat = ndatas.size(2) ndatas = scaleEachUnitaryDatas(ndatas) # trans-mean-sub ndatas = centerDatas(ndatas) print("size of the datas...", ndatas.size()) # switch to cuda ndatas = ndatas.cuda() labels = labels.cuda() #MAP lam = LAMBDA model = GaussianModel(n_ways, lam) model.initFromLabelledDatas() alpha = ALPHA optim = MAP(alpha) optim.verbose=False optim.progressBar=True acc_test = optim.loop(model, n_epochs=N_STEPS) print("final accuracy found {:0.2f} +- {:0.2f}".format(*(100*x for x in acc_test)))