import sys
from collections import defaultdict
from train_perceptron import create_features, predict_one

with open(sys.argv[1], "r") as model_file, open(sys.argv[2], "r") as input_file, open("test_result.txt", "w") as fout:
	w = defaultdict(int)
	for line in model_file:
		splited_line = line.strip().split("\t")
		w[splited_line[0]] = int(splited_line[1])
	for x in input_file:
		phi = create_features(x.strip())
		y_2 = predict_one(w, phi)
		fout.write(f"{y_2}\n")

# Accuracy = 80.517180%
7aecea94981b3b8ad8b71e88f2a39b56850af48e
<mask token>

<mask token>
with open(sys.argv[1], 'r') as model_file, open(sys.argv[2], 'r'
    ) as input_file, open('test_result.txt', 'w') as fout:
    w = defaultdict(int)
    for line in model_file:
        splited_line = line.strip().split('\t')
        w[splited_line[0]] = int(splited_line[1])
    for x in input_file:
        phi = create_features(x.strip())
        y_2 = predict_one(w, phi)
        fout.write(f'{y_2}\n')

import sys
from collections import defaultdict
from train_perceptron import create_features, predict_one
with open(sys.argv[1], 'r') as model_file, open(sys.argv[2], 'r'
    ) as input_file, open('test_result.txt', 'w') as fout:
    w = defaultdict(int)
    for line in model_file:
        splited_line = line.strip().split('\t')
        w[splited_line[0]] = int(splited_line[1])
    for x in input_file:
        phi = create_features(x.strip())
        y_2 = predict_one(w, phi)
        fout.write(f'{y_2}\n')

import sys
from collections import defaultdict
from train_perceptron import create_features, predict_one

with open(sys.argv[1], "r") as model_file, open(sys.argv[2], "r") as input_file, open("test_result.txt", "w") as fout:
	w = defaultdict(int)
	for line in model_file:
		splited_line = line.strip().split("\t")
		w[splited_line[0]] = int(splited_line[1])
	for x in input_file:
		phi = create_features(x.strip())
		y_2 = predict_one(w, phi)
		fout.write(f"{y_2}\n")

# Accuracy = 80.517180%