Ds.append(Di)
Ys.append(Yi)
D = Ds[0]
Y = Ys[0]
for i,Di in enumerate(Ds[1:]):
D = np.vstack( (D,Di) )
Y = np.concatenate( (Y,Ys[i+1]) )
print D.shape
print Y.shape
print "accuracy per fold is outputted"
print "there is a lot of data here, this will take hours"
ab_svm.kfoldcv_svm(D,Y,10,cores=1,innerCores=8,useLibLinear=True,useL1R=False)
#res=ab_svm.SVMLinear(Dtrain,np.int32(Ytrain),Dtest)
#tp=np.sum(res==Ytest)
#print 'Accuracy is %.1f%%' % ((np.float64(tp)/Dtest.shape[0])*100)
#
pool.join()
if args.onlyfeaturize:
sys.exit(0)
# Step 2: Compute Action Bank Embedding of the Videos
# Load the bank itself
AB = ActionBank(args.bank)
if (args.bankfactor != 1):
AB.factor = args.bankfactor
# Apply the bank
# do not do it asynchronously, as the individual bank elements are done that way
for fi,f in enumerate(files):
print "\b\b\b\b\b %02d%%" % (100*fi/len(files))
bank_and_save(AB,f,f.replace(args.input,args.output),args.cores)
if not args.testsvm:
sys.exit(0)
# Step 3: Try a k-fold cross-validation classification with an SVM in the simple set-up data set case.
import ab_svm
(D,Y) = ab_svm.load_simpleone(args.output)
ab_svm.kfoldcv_svm(D,Y,10,cores=args.cores)
# Nothing else to do here
