best_aro = sys.maxint for i in range(50): train_ids = ids random.shuffle(train_ids) all_ids = train_ids[141:] train_ids = train_ids[0:140] # calcultae valence and arousal find_a_v_mens val_mean, aro_mean = find_a_v_mens_va(train_ids, valence, arousal) train_feat = find_in_dict(feat, train_ids) test_feat = find_in_dict(feat, all_ids) # use regression X_v, X_a = regression(train_feat, val_mean, aro_mean) # calculating features for whole dataset #print all_feat.shape # use regresion function to calculate v and a all_val = np.sum(np.array(test_feat) * X_v, axis=1) all_aro = np.sum(test_feat * X_a, axis=1) #print all_val.shape #print all_aro.shape print "ATTEMPT" + str(i) avg = average_distance_va(all_val, all_aro, valence, arousal, all_ids) nearest = nearest_dist_average_va(all_val, all_aro, valence, arousal,
Xtrain = X[1:trainlen] Yvtrain = Yv[1:trainlen] Yatrain = Ya[1:trainlen] Xtest = X[trainlen + 1:] Yvtest = Yv[trainlen + 1:] Yatest = Ya[trainlen + 1:] idstest = all_ids[trainlen + 1:] print "check 5" print X.shape print len(Yv) print len(Ya) rv, ra = regression(Xtrain, Yvtrain, Yatrain) Yvpred = np.sum(Xtest * rv, axis=1) Yapred = np.sum(Xtest * ra, axis=1) # print len(Yvpred) # print len(Yvtest) # print Yvpred.shape # print Yvtest.shape # avg = averagedist(Yvpred, Yapred, Yvtest, Yatest) avg = average_distance_va(Yvpred, Yapred, valence, arousal, idstest) if avg < best_avg: best_avg = avg print "BEST" print 'Average distance: ' + str(best_avg)
for i in range(50): train_ids = ids random.shuffle(train_ids) all_ids = train_ids[141:] train_ids = train_ids[0:140] # calcultae valence and arousal find_a_v_mens val_mean, aro_mean = find_a_v_mens_va(train_ids, valence, arousal) train_feat = find_in_dict(feat, train_ids) test_feat = find_in_dict(feat, all_ids) # use regression X_v, X_a = regression(train_feat, val_mean, aro_mean) # calculating features for whole dataset #print all_feat.shape # use regresion function to calculate v and a all_val = np.sum(np.array(test_feat) * X_v, axis=1) all_aro = np.sum(test_feat * X_a, axis=1) #print all_val.shape #print all_aro.shape print "ATTEMPT" + str(i) avg = average_distance_va(all_val, all_aro, valence, arousal, all_ids) nearest = nearest_dist_average_va(all_val, all_aro, valence, arousal, all_ids)
Xtrain = X[1:trainlen] Yvtrain = Yv[1:trainlen] Yatrain = Ya[1:trainlen] Xtest = X[trainlen + 1 :] Yvtest = Yv[trainlen + 1 :] Yatest = Ya[trainlen + 1 :] idstest = all_ids[trainlen + 1 :] print "check 5" print X.shape print len(Yv) print len(Ya) rv, ra = regression(Xtrain, Yvtrain, Yatrain) Yvpred = np.sum(Xtest * rv, axis=1) Yapred = np.sum(Xtest * ra, axis=1) # print len(Yvpred) # print len(Yvtest) # print Yvpred.shape # print Yvtest.shape # avg = averagedist(Yvpred, Yapred, Yvtest, Yatest) avg = average_distance_va(Yvpred, Yapred, valence, arousal, idstest) if avg < best_avg: best_avg = avg print "BEST" print "Average distance: " + str(best_avg)