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#!/usr/bin/python3
from sklearn.datasets import *
from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import train_test_split
import numpy as np
# TODO schön machen und verweisen
SEED = 42
np.random.seed(SEED)
data = load_boston()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, train_size=0.85)
model = LinearRegression()
model.fit(data.data, data.target)
p=model.predict(X_test)
print(p)
print('diff')
print('')
print(y_test - p)
squares=[]
for n in range(len(p)):
squares.append((y_test[n] - p[n])**2)
print(np.mean(squares))
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