sentiments = ['Virtue', 'Weak', 'HU', 'Hostile', 'EnlTot', 'ComForm',
'Passive', 'Pstv', 'Ngtv', 'PowTot', 'Strong', 'Positiv',
'IAV', 'Active', 'Negativ']
sentiments.reverse()
classifiers = {}
print "DB connexion"
con = None
try:
db, usr, pwd = load_database('database.properties')
con = psycopg2.connect(database=db, user=usr, host='localhost')
print "Loading the Training Set"
fe = FeatureExtractor(tokenizer, con, sentiments)
mySet = s.load(fe, args.number_pos, args.number_pos, args.number_neut)
training, testing = s.splitTrainingAndTestingSet(mySet, .8)
print "Training the Models"
# RBF: gamma varies
classifier_rbf_gamma_01 = svm.SVC(kernel='rbf', gamma=.1)
classifier_rbf_gamma_02 = svm.SVC(kernel='rbf', gamma=.2)
classifier_rbf_gamma_03 = svm.SVC(kernel='rbf', gamma=.3)
classifier_rbf_gamma_05 = svm.SVC(kernel='rbf', gamma=.5)
classifier_rbf_gamma_08 = svm.SVC(kernel='rbf', gamma=.8)
classifier_rbf_gamma_15 = svm.SVC(kernel='rbf', gamma=1.5)
classifier_rbf_gamma_3 = svm.SVC(kernel='rbf', gamma=3)
classifier_rbf_gamma_10 = svm.SVC(kernel='rbf', gamma=10)
classifier_rbf_gamma_25 = svm.SVC(kernel='rbf', gamma=25)
