Machine Learning (8) - Simple linear regression

import numpy as np

def fitSLR(x,y):
    n=len(x)
    dinominator = 0
    numerator=0
    for i in range(0,n):
        numerator += (x[i]-np.mean(x))*(y[i]-np.mean(y))
        dinominator += (x[i]-np.mean(x))**2
        
    print("numerator:"+str(numerator))
    print("dinominator:"+str(dinominator))
    
    b1 = numerator/float(dinominator)
    b0 = np.mean(y)/float(np.mean(x))
    
    return b0,b1


# y= b0+x*b1
def prefict(x,b0,b1):
    return b0+x*b1

x=[1,3,2,1,3]
y=[14,24,18,17,27]

b0,b1=fitSLR(x, y)
y_predict = prefict(6,b0,b1)
print("y_predict:"+str(y_predict))