【深度学习基础-11】简单线性回归（下）--实例及python代码实现

版权声明：本文为博主原创文章，未经允许，不得转载！欢迎留言附带链接转载！ https://blog.csdn.net/qq_15698613/article/details/86307009

比如有5组数据，让你去做简单线性回归。 

python代码实现上述过程

 

[code]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", numerator)
print("dinominator", dinominator)
b1 = numerator/float(dinominator)
b0 = np.mean(y)/float(np.mean(x))

return b0, b1

def predict(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("intercept:", b0 , "  slope:", b1)

x_test = 6

y_test = predict(6, b0, b1)

print("y_test: ", y_test)

运行结果如下