python matlibplot 3D 도형 그리 기

from mpl_toolkits.mplot3d import Axes3D
import numpy as np
from matplotlib import pyplot as plt


#   3D    

mu_vec1 = np.array([0,0,0]) #     
cov_mat1 = np.array([[1,0,0],[0,1,0],[0,0,1]]) #      

class1_sample = np.random.multivariate_normal(mu_vec1, cov_mat1, 20)
class2_sample = np.random.multivariate_normal(mu_vec1 + 1, cov_mat1, 20)
class3_sample = np.random.multivariate_normal(mu_vec1 + 2, cov_mat1, 20)


# class1_sample.shape -> (20, 3), 20 rows, 3 columns


fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(111, projection='3d')

ax.scatter(class1_sample[:,0], class1_sample[:,1], class1_sample[:,2],
   marker='x', color='blue', s=40, label='class 1')
ax.scatter(class2_sample[:,0], class2_sample[:,1], class2_sample[:,2],
   marker='o', color='green', s=40, label='class 2')
ax.scatter(class3_sample[:,0], class3_sample[:,1], class3_sample[:,2],
   marker='^', color='red', s=40, label='class 3')

ax.set_xlabel('variable X')
ax.set_ylabel('variable Y')
ax.set_zlabel('variable Z')

plt.title('3D Scatter Plot')

plt.show()

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
from itertools import product, combinations
fig = plt.figure(figsize=(7,7))
ax = fig.gca(projection='3d')
ax.set_aspect("equal")


#   

 

#       

X_inside = np.array([[0,0,0],[0.2,0.2,0.2],[0.1, -0.1, -0.3]])

X_outside = np.array([[-1.2,0.3,-0.3],[0.8,-0.82,-0.9],[1, 0.6, -0.7],
      [0.8,0.7,0.2],[0.7,-0.8,-0.45],[-0.3, 0.6, 0.9],
      [0.7,-0.6,-0.8]])

for row in X_inside:
 ax.scatter(row[0], row[1], row[2], color="r", s=50, marker='^')

for row in X_outside:
 ax.scatter(row[0], row[1], row[2], color="k", s=50)


#     

h = [-0.5, 0.5]
for s, e in combinations(np.array(list(product(h,h,h))), 2):
 if np.sum(np.abs(s-e)) == h[1]-h[0]:
  ax.plot3D(*zip(s,e), color="g")

ax.set_xlim(-1.5, 1.5)
ax.set_ylim(-1.5, 1.5)
ax.set_zlim(-1.5, 1.5)

plt.show()