机器学习-简单线性回归(二)

# -*- coding:utf-8 -*-

#简单线性回归：只有一个自变量 y=k*x+b 预测使得(y-y*)^2最小

import numpy as np

def fitSLR(x, y):
    n = len(x)
    dinominator = 0 #分母
    numerator = 0  #分子
    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)-(b1*(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)
print("b0:",b0,"b1:",b1)

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

numerator:20.0
dinominator:4.0
b0: 10.0 b1: 5.0
y_predict:40.0