import numpy as np
import pandas as pd
from scipy.sparse import data
from sklearn import linear_model
from sklearn import preprocessing
from sklearn.pipeline import make_pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import linear_model
import csv
import pandas as pd

regression = linear_model.LinearRegression()  
    
train_file = pd.read_csv('train/train.tsv', delimiter='\t', names=['price', 'mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
train_data_frame = pd.DataFrame(train_file, columns=['price', 'mileage', 'year', 'brand', 'engineType', 'engineCapacity'])

Y = train_data_frame[['price']]
X = train_data_frame[['year', 'mileage', 'engineCapacity']]

regression.fit(X, Y)

in_file = pd.read_csv('test-A/in.tsv', delimiter='\t', names=['mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
in_data_frame = pd.DataFrame(in_file, columns=['mileage', 'year', 'brand', 'engineType', 'engineCapacity'])

reshape = in_data_frame[['year', 'mileage', 'engineCapacity']]

y_predict = regression.predict(reshape)
y_predict = np.concatenate(y_predict)


labels = np.array2string(y_predict, separator='\n', suppress_small=True)
        
file_out = open("test-A/out.tsv", 'w')
file_out.write(labels[1:-1])

with open("test-A/out.tsv", 'r') as fix_space:
    lines = fix_space.readlines()

    lines = [line.replace(' ', '') for line in lines]
with open("test-A/out.tsv", 'w') as fix_space:
    fix_space.writelines(lines)