meshgrid usage of python and 3D library mpl_toolkits.mplot3d and PolynomialFeatures polynomial library learning

meshgrid

 

. 1 Import numpy AS NP
 2  from matplotlib Import pyplot AS PLT
 . 3  from mpl_toolkits.mplot3d Import Axes3D
 . 4 X = np.array ([ 0 , . 1 , 2 ])
 . 5 Y = np.array ([ 0 , . 1 ])
 . 6 X-, = the Y np.meshgrid (X, Y) X-#, expanded into the Y matrix,
 . 7  Print (X-)
 . 8  Print (the Y)
 . 9 theta0, Theta1, Theta2 = 2 , . 3 , . 4 
10 AX = Axes3D (plt.figure ( )) used to draw three-dimensional map #
 11* = Theta0 + Theta1 the Z + X-* Theta2 the Y value # z request
 12 is plt.plot (X-, the Y, ' R & lt. ' ) # Then you will find a drawing is 3 * 2 points, which constitute a network grid, the coordinates of each point is cut * X- the Y Cartesian product
 13 is  ax.plot_surface (X-, the Y, the Z) used to draw three-dimensional map #
 14 plt.show ()

Specific can refer to this blog

mpl_toolkits.mplot3d

Blog about 3D graphics

The method is used when drawing paneled

Use add_subplot

 

import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 100)
fig = plt.figure()
ax1 = fig.add_subplot(221)
ax1.plot(x, x)
ax2 = fig.add_subplot(222)
ax2.plot(x, -x)
ax3 = fig.add_subplot(223)
ax3.plot(x, x ** 2)
ax4 = fig.add_subplot(224)
ax4.plot(x, np.log(x))
plt.show()

 

Use subplot method

import numpy as np
from matplotlib import pyplot as plt
x = np.arange(10)
plt.subplot(221)
plt.plot(x,x)
plt.subplot(223)
plt.plot(x,-x)
plt.show()

 

 

 

PolynomialFeatures

 1 import numpy as np
 2 import matplotlib.pyplot as plt
 3 from sklearn.preprocessing import PolynomialFeatures#多项式
 4 from sklearn.linear_model import LinearRegression
 5 
 6 # 载入数据
 7 data = np.genfromtxt("job.csv", delimiter=",")
 8 x_data = data[1:,1]
 9 y_data = data[1:,2]
10 plt.scatter(x_data,y_data)
11 plt.show ()
 12 is  # dimension must be two-dimensional
 13 is x_data = x_data [:, np.newaxis]
 14 y_data = y_data [:, np.newaxis]
 15  # polynomial regression defined, can adjust the value of degree polynomial features
 16 poly PolynomialFeatures = (Degree = . 4 )
 . 17  # feature processing
 18 is x_poly = poly.fit_transform (x_data)
 . 19  # define the regression model
 20 is model = LinearRegression ()
 21 is  # training model
 22 is  model.fit (x_poly, y_data)
 23 is plt.plot (x_data , y_data, ' B. ' )
 24 plt.plot (x_data, model.predict (poly.fit_transform (x_data)),'r')
25 plt.show()