mooc机器学习第八天-线性回归 linear_model

from sklearn import linear_model
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif']=['SimHei']
#切分读取数据分类保存在list

datasets_X=[]
datasets_Y=[]
fr=open('./prices.txt',
        'r')
lines=fr.readlines()
for line in lines:
    items = line.strip().split(',')
    datasets_X.append(int(items[0]))
    datasets_Y.append(int(items[1]))

length=len(datasets_X)
datasets_X=np.array(datasets_X).reshape([length,1])
datasets_Y=np.array(datasets_Y)
# print(datasets_X)

maxX=np.max(datasets_X)
minX=np.min(datasets_X)
X=np.arange(minX,maxX).reshape([-1,1])

#调用算法

linear=linear_model.LinearRegression()
linear.fit(datasets_X,datasets_Y)

#绘散点图和直线

plt.scatter(datasets_X,datasets_Y,edgecolors='r',color='y')
plt.plot(X,linear.predict(X),color='blue')
plt.title('房屋面积与价格的线性回归')
plt.xlabel('Area')
plt.ylabel('price')
plt.show()