args = parser.parse_args()
print("Training set: %s" %(args.training_set))
print("Testing set: %s" %(args.testing_set))
print("mu: %f" %(args.mu))
print("voters: %s" %(args.voters))
if(args.voters == "rbf"):
print("gamma: %f" %(args.gamma))
print("transductive: %s" %(args.transductive))
print("")
print("Loading files...")
dTrain = Dataset()
dTrain.loadFromFile(args.training_set)
if (dTrain.X is None):
raise Exception("Cannot load the training data")
dTest = Dataset()
dTest.loadFromFile(args.testing_set)
if (dTest.X is None):
raise Exception("Cannot load the testing data")
allX = np.concatenate((dTrain.X, dTest.X), 0)
print("Solving...")
l = MinCqLearner()
if (args.transductive):
e = l.learn(dTrain.X, dTrain.Y, allX, args.mu, args.voters, args.gamma)
