from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
M = [[0] * len(x) for i in range(len(y))]
lr2 = LinearRegression()
lr2.fit(x, y)
y2 = lr2.predict(x)
plt.figure()
#plt.plot(xx[f:t], y[f:t], color='r', linewidth=4, label='y')
#plt.plot(xx[f:t], y2[f:t], color='b', linewidth=2, label='predicted y')
#plt.ylabel('Target label')
#plt.xlabel('Line number in dataset')
#plt.legend(loc=4)
plt.show()