Python中的简单线性回归

Question

我正试图实现这个算法来找到单变量的截距和斜率：

这是我的Python代码来更新截距和斜率。但它并没有收敛。RSS是随着迭代而增加，而不是减少，经过一段迭代后，它变得无限。我没有发现实现算法的任何错误。如何解决此问题？我也附上了csv文件。 这是密码。

import pandas as pd
import numpy as np

#Defining gradient_decend
#This Function takes X value, Y value and vector of w0(intercept),w1(slope)
#INPUT FEATURES=X(sq.feet of house size)
#TARGET VALUE=Y (Price of House)
#W=np.array([w0,w1]).reshape(2,1)
#W=[w0,
#    w1]

def gradient_decend(X,Y,W):
    intercept=W[0][0]
    slope=W[1][0]

    #Here i will get a list
    #list is like this
    #gd=[sum(predicted_value-(intercept+slope*x)),
    #     sum(predicted_value-(intercept+slope*x)*x)]
    gd=[sum(y-(intercept+slope*x) for x,y in zip(X,Y)),
        sum(((y-(intercept+slope*x))*x) for x,y in zip(X,Y))]
    return np.array(gd).reshape(2,1)

#Defining Resudual sum of squares
def RSS(X,Y,W):
    return sum((y-(W[0][0]+W[1][0]*x))**2 for x,y in zip(X,Y))


#Reading Training Data
training_data=pd.read_csv("kc_house_train_data.csv")

#Defining fixed parameters
#Learning Rate
n=0.0001
iteration=1500
#Intercept
w0=0
#Slope
w1=0

#Creating 2,1 vector of w0,w1 parameters
W=np.array([w0,w1]).reshape(2,1)

#Running gradient Decend
for i in range(iteration):
     W=W+((2*n)*    (gradient_decend(training_data["sqft_living"],training_data["price"],W)))
     print RSS(training_data["sqft_living"],training_data["price"],W)

Here是CSV文件。