vector_array.append(vector_create(line[1],word_r,word_n)) pass for line in lines_tr: line = line.split('\t',2) train_array.append(vector_create(line[2],word_r,word_n)) flag.append(line[1]) pass #svm clf = modle(train_array,flag) print "predicting" fileHandler_out = open ('data\\result.csv',"w") num = 800001 for pr in vector_array: tag = [] tag.append(str(num)) tag.append(predict(clf,pr)) result.append(tag) num += 1 pass #change the result # fileHandler = open('data\\pat1_2_3.3') # fileHandler.seek(0) # ch = fileHandler.readlines() # for x in ch: # for r in result: # if x == r[0]: # r[1]=1 # pass # pass # pass for r in result:
result = [] word_r, word_n = fileUtil.read_dict() print 'set train vector' for list1 in list_train: train_array.append(vector_create(list1[2], word_r, word_n)) flag.append(list1[1]) pass print 'set test vector' for list2 in list_test: test_array.append(vector_create(list2[2], word_r, word_n)) answer.append(list2[1]) pass print "read finished" # svm clf = modle(train_array, flag) result = predict(clf, test_array) sum_rubbish = 0 sum_normal = 0 pre_rubbish = 0 pre_normal = 0 right_rubbish = 0 right_normal = 0 r_pre_n = [] for x in xrange(0, len(result)-1): if result[x] == answer[x]: if result[x] == "1": sum_rubbish += 1 pre_rubbish += 1 right_rubbish += 1 else: sum_normal += 1