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With generated data, I am trying to plot 3d decision boundary of QDA in 3d spaces. I used sklearn library to calculate QDA, but couldn't plot 3d QDA decision boundary.

Here is my code:

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.datasets import make_classification

# Generate a random dataset for classification
X, y = make_classification(n_features=3, n_informative=2, n_redundant=0, n_repeated=0, random_state=0)

# Create and fit a QDA classifier
qda = QuadraticDiscriminantAnalysis()
qda.fit(X, y)

# Plot the decision boundary of the QDA classifier
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
z_min, z_max = X[:, 2].min() - 1, X[:, 2].max() + 1

xx, yy, zz = np.meshgrid(np.arange(x_min, x_max, 0.1),
                        np.arange(y_min, y_max, 0.1),
                        np.arange(z_min, z_max, 0.1))

X_grid = np.c_[xx.ravel(), yy.ravel(), zz.ravel()]

Z = qda.predict(X_grid)
Z = Z.reshape(xx.shape)

ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=plt.cm.Paired)
ax.set_xlabel('X axis')
ax.set_ylabel('Y axis')
ax.set_zlabel('Z axis')

ax.plot_surface(xx, yy, zz, facecolors=plt.cm.Paired(Z), alpha=0.2)

plt.show()

Above code saying zz must be 2d instead of 3d, but I don't really get why it has to be 2d?

At the end I want to see figure with scatter plot of binary classes and decision boundary of QDA on data points.

Sunwoo Kwak
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1 Answers1

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With respect to z, this also needs to be a 2D array since Axes3D.surface_plot maps each element of the 2D array z with the 2D grid defined by x and y.

Hence, when you use your own x, y and z make sure that you use numpy.meshgrid for x and y and, then, define z = f(x,y) (e.g. the function flux_qubit_potential you show).

maybe help you: Link

Tornike Kharitonishvili
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