def runAndreaFeatures(path_train,path_test, path_features, dataset,n=1000,iter_num=5):
features=[]
with open(path_features,'r') as f_in:
for line in f_in:
for token in line.split():
features.append(token)
model = Perceptron.train_from_disk(path_train,dataset.get_classes_list() ,features[0:n], iter_num)
print Perceptron.test_from_disk(model, path_test,features[0:n])
def runRainbowFeatures(path_train,path_test, path_features, dataset,n=1000,iter_num=5):
features=[]
with open(path_features,'r') as f_in:
for line in f_in:
features.append(line.split()[1])
model = Perceptron.train_from_disk(path_train,dataset.get_classes_list() ,features[0:n], iter_num)
for k,v in model.items():
print k,v
print Perceptron.test_from_disk(model, path_test,features[0:n])