03_loss_functions.py

from sklearn.linear_model import LinearRegression
import numpy as np
from numpy.linalg import inv,lstsq
from numpy import dot, transpose

x = [[6], [8], [10], [14], [18]]
y = [[7], [9], [13], [17.5], [18]]

model = LinearRegression()
model.fit(x,y)

print ('Residual sum of squares = ',np.mean((model.predict(x)- y) ** 2))
print ('Variance = ',np.var([6, 8, 10, 14, 18], ddof=1))
print ('Co-variance = ',np.cov([6, 8, 10, 14, 18], [7, 9, 13, 17.5, 18])[0][1])