#!/usr/bin/env python
import numpy as np
import sys
import csv
from sklearn.feature_extraction.text import CountVectorizer
from numpy import loadtxt
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
tsv_file = open("test-A/in.tsv")
read_tsv = csv.reader(tsv_file)
listatesting = []
for line in read_tsv:
	listatesting.append(line[0])

lista = []

for line in sys.stdin:
	lista.append(line)
	
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(lista)
Y = loadtxt("train/expected.tsv")
seed = 1
param = {
'objective':'binary:logistic'}

X_train = X
X_test = Y 
Y_train =  vectorizer.fit_transform(listatesting)
model = XGBClassifier()
model.fit(X_train, X_test)
y_pred = model.predict_proba(X_train)
predictions = [value for value in y_pred]
for a in predictions:
	print(a[0])