def testSHM(): threshold = 150 #Load data pkl_file = open('data.plk', 'rb') pos, neg, Ni, Nr = pickle.load(pkl_file) idx = np.random.permutation(len(Ni)) train_len = round(len(Ni) * 0.8) train_idx = idx[:train_len] test_idx = idx[train_len:] x_train = [Ni[i] for i in train_idx] y_train = [Nr[i] for i in train_idx] x_test = [Ni[i] for i in test_idx] y_test = [Nr[i] for i in test_idx] #CS model test cs = CS() cs.train(x_train, y_train) cs.predict(x_test) print "RSE: %f" % cs.RSE(y_test) print "precision: %f, recall: %f" % cs.PR(y_test,threshold) #LN model test ln = LN() ln.train(x_train, y_train) ln.predict(x_test) print "RSE: %f" % ln.RSE(y_test) print "precision: %f, recall: %f" % ln.PR(y_test,threshold)
#CreatData('../../corpus_g2','data.plk',threshold) #just run once the pos: 425, the neg: 682 #Load data pkl_file = open('sequence.plk', 'rb') _ ,time = pickle.load(pkl_file) idx = np.random.permutation(len(Ni)) train_len = round(len(Ni) * 0.8) train_idx = idx[:train_len] test_idx = idx[train_len:] x_train = [Ni[i] for i in train_idx] y_train = [Nr[i] for i in train_idx] x_test = [Ni[i] for i in test_idx] y_test = [Nr[i] for i in test_idx] #CS model test cs = CS() cs.train(x_train, y_train) cs.predict(x_test) print "RSE: %f" % cs.RSE(y_test) print "precision: %f, recall: %f" % cs.PR(y_test,threshold) #LN model test ln = LN() ln.train(x_train, y_train) ln.predict(x_test) print "RSE: %f" % ln.RSE(y_test) print "precision: %f, recall: %f" % ln.PR(y_test,threshold) print 'exit'