ML之SLR:简单线性回归;根据多组数据(x,y)模拟得到一次线性方程(斜率和截距),然后输入新的x来智能预测y值
目录
- 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
-
- def prefict(x,b0,b1):
- return b0+x*b1
- x=[1,6,2,9,30]
- y=[64,54,39,55,48]
-
- b0,b1=fitSLR(x, y)
- y_predict = prefict(6,b0,b1)
- print("y_predict:"+str(y_predict))
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ML之SLR:简单线性回归;根据多组数据(x,y)模拟得到一次线性方程(斜率和截距),然后输入新的x来智能预测y值
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