二元线性回归

import numpy as np
import  matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
data=np.genfromtxt("Delivery.csv",delimiter=',')
x_data = data[:,[1,2]]
y_data = data[:,[-1]]

lr=0.000001
theta0=0
theta1=0
theta2=0
epochs=50
print (x_data[0])
def gredient_desent_runner(x_data,y_data,lr,theta0,theta1,theta2,epochs):
    m = len(x_data)
    for i in range(epochs):
        tmp_theta0=0
        tmp_theta1=0
        tmp_theta2=0
        for j in range(m):
            # print ()
            tmp_theta0+=theta0+theta1*x_data[j][0]+theta2*x_data[j][1]-y_data[j][0]
            tmp_theta1+=(theta0+theta1*x_data[j][0]+theta2*x_data[j][1]-y_data[j][0])*x_data[j][0]
            tmp_theta2+=(theta0+theta1*x_data[j][0]+theta2*x_data[j][1]-y_data[j][0])*x_data[j][1]
        theta0-=lr*tmp_theta0/m
        theta1-=lr*tmp_theta1/m
        theta2-=lr*tmp_theta2/m
    return theta0,theta1,theta2
theta0,theta1,theta2=gredient_desent_runner(x_data,y_data,lr,theta0,theta1,theta2,epochs)
ax=plt.figure().add_subplot(111,projection = '3d')
x0=x_data[:,0]
x1=x_data[:,1]
x0,x1 = np.meshgrid(x0,x1)
z=theta0+theta1*x0+theta2*x1
ax.plot_surface(x0,x1,z)
plt.show()

梯度下降法实现线性回归