机器学习(新手入门)-线性回归 #房价预测-CSDN博客

题目给定数据集dataSet每一行代表一组数据记录,每组数据记录中第一个值为房屋面积单位平方英尺第二个值为房屋中的房间数第三个值为房价单位千美元试用梯度下降法构造损失函数在函数gradientDescent中实现房价price关于房屋面积area和房间数rooms的线性回归返回值为线性方程𝑝𝑟𝑖𝑐𝑒=𝜃0+𝜃1∗𝑎𝑟𝑒𝑎+𝜃2∗𝑟𝑜𝑜𝑚𝑠中系数𝜃𝑖(𝑖=0,1,2)的列表。

%matplotlib inline

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from numpy import genfromtxt
dataPath = r"./Input/data1.csv"
dataSet = pd.read_csv(dataPath,header=None)
print(dataSet)
price = []
rooms = []
area = []
for data in range(0,len(dataSet)):
    area.append(dataSet[0][data])
    rooms.append(dataSet[1][data])
    price.append(dataSet[2][data])
print(area)

执行结果

def gradientDescent(rooms, price, area):
    epochs = 500
    alpha = 0.00000001
    theta_gradient = [0,0,0]
    const = [1,1,1,1,1]
    theta = [1,2,1]
    loss = []
    
    for i in range(epochs):
        
        theta0 = np.dot(theta[0],const)
        theta1 = np.dot(theta[1],area)
        theat2 = np.dot(theta[2],rooms) 
        predict_tmp = np.add(theta0,theta1)
        predict = np.add(predict_tmp,theat2) 
        loss_ = predict - price
        theta_gradient[0] = (theta_gradient[0] + np.dot(const,loss_.transpose()))/5
        theta_gradient[1] = (theta_gradient[1] + np.dot(area,loss_.transpose()))/5
        theta_gradient[2] = (theta_gradient[2] + np.dot(rooms,loss_.transpose()))/5
        loss_t = np.sum(np.divide(np.square(loss_),2))/5
        if i%50==0:
            print("loss_t:",loss_t)
        loss.append(loss_t)
        theta[0] = theta[0] - alpha * theta_gradient[0]
        theta[1] = theta[1] - alpha * theta_gradient[1]
        theta[2] = theta[2] - alpha * theta_gradient[2]
    plt.plot(loss,c='b')
    plt.show()
    return theta

def demo_GD():
    
    theta_list = gradientDescent(rooms, price, area)
demo_GD()

j结果展示