python线性回归可视化_python多元线性回归及三维可视化
参考链接：https://www.jianshu.com/p/d2b926c458d9
目标函数：Y=A*X1+B*X2+C
代码：
import pandas as pd
import numpy as np
from io import StringIO
from urllib import request
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import ssl
import numpy as np
def costFunc(X,Y,theta):
inner = np.power((X*theta.T)-Y,2)
return np.sum(inner)/(2*len(X))
def gradientDescent(X,Y,theta,alpha,iters):
temp = np.mat(np.zeros(theta.shape))
cost = np.zeros(iters)
thetaNums = int(theta.shape[1])
print(thetaNums)
for i in range(iters):
error = (X*theta.T-Y)
for j in range(thetaNums):
derivativeInner = np.multiply(error,X[:,j])
temp[0,j] = theta[0,j] - (alpha*np.sum(derivativeInner)/len(X))
theta = temp
cost[i] = costFunc(X,Y,theta)
return theta,cost
#
ssl._create_default_https_context = ssl._create_unverified_context
names =["mpg","cylinders","displacement","horsepower",
"weight","acceleration","model year","origin","car name"]
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data'
s = request.urlopen(url).read().decode('utf8')
dataFile = StringIO(s)
cReader = pd.read_csv(dataFile,delim_whitespace=True,names=names)
ax = plt.subplot(111, projection='3d') # 创建一个三维的绘图工程
ax.scatter(cReader["mpg"][:100],cReader["displacement"][:100],cReader["acceleration"][:100],c='y')
ax.scatter(cReader["mpg"][100:250],cReader["displacement"][100:250],cReader["acceleration"][100:250],c='r')
ax.scatter(cReader["mpg"][250:],cReader["displacement"][250:],cReader["acceleration"][250:],c='b')
ax.set_zlabel('acceleration') # 坐标轴
ax.set_ylabel('displacement')
ax.set_xlabel('mpg')
plt.show()
plt.scatter(cReader["mpg"],cReader["displacement"])
plt.xlabel('mpg')
plt.ylabel('displacement')
plt.show()
trainData = cReader[['mpg','displacement','acceleration']]
trainData.insert(0,'ones',1)
print(trainData.head(5))
cols = trainData.shape[1]
X = trainData.iloc[:,0:cols-1]
Y = trainData.iloc[:,cols-1:cols]
X = np.mat(X.values)
Y = np.mat(Y.values)
for i in range(1,3):
X[:,i] = (X[:,i] - min(X[:,i])) / (max(X[:,i]) - min(X[:,i]))
print(X[:5:,:3])
#Y[:,0] = (Y[:,0] - min(Y[:,0])) / (max(Y[:,0]) - min(Y[:,0]))
print(Y[:5,0])
theta_n = (X.T*X).I*X.T*Y
print(theta_n)
theta = np.mat([0,0,0])
iters = 100000
alpha = 0.001
finalTheta,cost = gradientDescent(X,Y,theta,alpha,iters)
print(finalTheta)
print(cost)
x1 = np.linspace(X[:,1].min(),X[:,1].max(),100)
x2 = np.linspace(X[:,2].min(),X[:,2].max(),100)
x1,x2 = np.meshgrid(x1,x2)
f = finalTheta[0,0] + finalTheta[0,1]*x1 + finalTheta[0,2]*x2
fig = plt.figure()
Ax = Axes3D(fig)
Ax.plot_surface(x1, x2, f, rstride=1, cstride=1, cmap=cm.viridis,label='prediction')
Ax.scatter(X[:100,1],X[:100,2],Y[:100,0],c='y')
Ax.scatter(X[100:250,1],X[100:250,2],Y[100:250,0],c='r')
Ax.scatter(X[250:,1],X[250:,2],Y[250:,0],c='b')
Ax.set_zlabel('acceleration') # 坐标轴
Ax.set_ylabel('displacement')
Ax.set_xlabel('mpg')
plt.show()
fig, bx = plt.subplots(figsize=(8,6))
bx.plot(np.arange(iters), cost, 'r')
bx.set_xlabel('Iterations')
bx.set_ylabel('Cost')
bx.set_title('Error vs. Training Epoch')
plt.show()