from?numpy?import?*
from?numpy.linalg?import?inv
import?operator
import?pandas?as?pd

dataset?=?pd.read_csv('data.csv')
temp=dataset.iloc[:,0:3]
Y=dataset.iloc[:,3]
X=temp
X['b']=1
#print?(Y)

##最小二乘
#theta=inv(X.T*X)*X.T*Y
theta=dot(dot(inv(dot(X.T,X)),X.T),Y)
print(theta)

#梯度下降
alpha=0.01
theta1=array([1.,?1.,?1.,?1.]).reshape(4,?1)
X1=X.iloc[:,?0]
X2=X.iloc[:,?1]
X3=X.iloc[:,?2]
B=X.iloc[:,?3]
t=theta1
print(X2)
for?i?in?range(10000):
????t[0]?=?theta1[0]?-?alpha?*?sum((dot(X,?theta1)-Y)*X1)/200.
????t[1]?=?theta1[1]?-?alpha?*?sum((dot(X,?theta1)?-?Y)?*?X2)?/?200.
????t[2]?=?theta1[2]?-?alpha?*?sum((dot(X,?theta1)?-?Y)?*?X3)?/?200.
????t[3]?=?theta1[3]?-?alpha?*?sum((dot(X,?theta1)?-?Y)?*?B)?/?200.
????theta1?=?t