python的meshgrid用法和3D库 mpl_toolkits.mplot3d 与PolynomialFeatures多项式库学习

meshgrid

 import numpy as np
 from matplotlib import pyplot as plt
 from mpl_toolkits.mplot3d import  Axes3D
 x = np.array([,,])
 y = np.array([,])
 X,Y = np.meshgrid(x,y)#X,Y扩展成了矩阵，
 print(X)
 print(Y)
 theta0, theta1, theta2 = , ,
 ax = Axes3D(plt.figure())#用来画三维图
 Z = theta0 + theta1*X + theta2*Y#求z值
 plt.plot(X,Y,'r.')#此时你会发现绘画出的是3*2个点，这些点组成一个网格，切每个点的坐标是X*Y的笛卡尔积
 ax.plot_surface(X,Y,Z)#用来画三维图
 plt.show()

具体也可以参考这篇博客

mpl_toolkits.mplot3d

关于3D绘图的博客

绘图时用到划分面板的方法

使用add_subplot

import numpy as np
import matplotlib.pyplot as plt
x = np.arange(, )
fig = plt.figure()
ax1 = fig.add_subplot()
ax1.plot(x, x)
ax2 = fig.add_subplot()
ax2.plot(x, -x)
ax3 = fig.add_subplot()
ax3.plot(x, x ** )
ax4 = fig.add_subplot()
ax4.plot(x, np.log(x))
plt.show()

使用subplot方法

import numpy as np
from matplotlib import pyplot as plt
x = np.arange()
plt.subplot()
plt.plot(x,x)
plt.subplot()
plt.plot(x,-x)
plt.show()

PolynomialFeatures

 import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.preprocessing import PolynomialFeatures#多项式
 from sklearn.linear_model import LinearRegression

 # 载入数据
 data = np.genfromtxt("job.csv", delimiter=",")
 x_data = data[:,]
 y_data = data[:,]
 plt.scatter(x_data,y_data)
 plt.show()
 #维度必须是二维
 x_data = x_data[:,np.newaxis]
 y_data = y_data[:,np.newaxis]
 # 定义多项式回归,degree的值可以调节多项式的特征
 poly = PolynomialFeatures(degree=)
 # 特征处理
 x_poly = poly.fit_transform(x_data)
 # 定义回归模型
 model = LinearRegression()
 # 训练模型
 model.fit(x_poly,y_data)
 plt.plot(x_data,y_data,'b.')
 plt.plot(x_data,model.predict(poly.fit_transform(x_data)),'r')
 plt.show()