import numpy as np
import sklearn.linear_model as lm
import sklearn.metrics as sm
import matplotlib.pyplot as mp
X,Y = [],[]
with open('dd.txt','r') as f:
for line in f.readlines():
data = [float(substr)
for substr in line.split(',')]
x.append(data[:-1])
y.append(data[-1])
x = np.array(x)
y = np.array(y)
#创建线性回归器
model = lm.LinearRegression()
#训练线性回归其
model.fit(x,y)
#预测输出
pred_y = model.predict(x)
mp.figure(num='Linear Regerssion',facecolor='lightgray')
mp.title('Linear Regerssion',fontsize = 20)
mp.xtable('x',fontsize=14)
mp.ylable('y',fontsize = 14)
mp.tick_params(labelsize=10)
mp.grid(linestyle=':')
mp.scatter(x,y,c='dodgerblue',s = 60,label='Sample')
mp.plot(x,pred_y,c='orangered',label='Prediction')
mp.legend()
mp.show()
ML3: sklearn 线性回归
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转载自blog.csdn.net/weixin_38246633/article/details/80582101
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