Simple_LinearRegression

Simple_LinearRegression_Test

一个简单的线性回归程序

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

 1 import numpy as np
 2 
 3 def fitSLR(x, y):
 4     num = len(x)
 5     dinominator = 0  # 分母
 6     numerator = 0    # 分子
 7     for i in range(0, num):
 8         numerator += (x[i] - np.mean(x)) * (y[i] - np.mean(y))
 9         dinominator += (x[i] - np.mean(x)) ** 2
10     b1 = numerator / float(dinominator)
11     b0 = np.mean(y) - b1 * np.mean(x)
12     return b1, b0
13 
14 def test(x, b1, b0):
15     y = b0 + b1 * x
16     print(y)
17 
18 x = [1, 3, 2, 1, 3]
19 y = [14, 24, 18, 17, 27]
20 b1, b0 = fitSLR(x, y)
21 x_input = float(input("请输入要测试的数据："))
22 test(x_input, b1, b0)