from pandas import DataFrame
from pandas import DataFrame
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets,linear_model
 
X=[]
Y=[]
with open("C:\\Users\\www\\ex1data1.txt","r") as f:  #讀取txt文件。
 for line in f:
  p_tmp, E_tmp = [float(i) for i in line.split(',')]
  X.append(p_tmp)
  Y.append(E_tmp)
 #'data=np.loadtxt('ex1data1.txt',delimiter=',')

# X=data[0]

# Y=data[1] 

data=DataFrame(X,columns={'a'})
data['b']=b
X=DataFrame(X)
 
fig = plt.figure()         
ax1 = fig.add_subplot(1,1,1)
plt.scatter(data['a'],data['b'])   #顯示X，Y的散點(diǎn)圖
 
def linear_model_main(X,Y,predict_value): #定義一個使用線性回歸的函數(shù)
 regr=linear_model.LinearRegression()
 regr.fit(X,Y)  #訓(xùn)練模型
 predict_output=regr.predict(predict_value) #預(yù)測
 predictions={}        #用一個集合裝以下元素
 predictions['intercept']=regr.intercept_  #截距
 predictions['codfficient']=regr.coef_   #斜率（參數(shù)）
 predictions['predict_value']=predict_output  #預(yù)測值
 return predictions
 
result = linear_model_main(X,Y,1500)    #調(diào)用函數(shù)
print(result['predict_value'])
 
 
def show_predict(X,Y):
 regr=linear_model.LinearRegression()
 regr.fit(X,Y) 
 plt.scatter(X,Y,color='blue')
 plt.plot(X,regr.predict(X),color='red')
 
show_predict(X,Y)