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

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()