3D Plotting with linear SVM Python 3.10

 (.env) [boris@Server35fedora PLOT3DSVM]$ cat plotting3DSVM1.py

from sklearn.svm import SVC

import numpy as np

import matplotlib.pyplot as plt

from sklearn import svm, datasets

from mpl_toolkits.mplot3d import Axes3D

iris = datasets.load_iris()

X = iris.data[:, :3]  # we only take the first three features.

Y = iris.target

#make it binary classification problem

X = X[np.logical_or(Y==0,Y==1)]

Y = Y[np.logical_or(Y==0,Y==1)]

model = svm.SVC(kernel='linear')

clf = model.fit(X, Y)

# The Equation the separating plane given by 

# all x so that np.dot(svc.coef_[0],x)+b = 0

# Solved for w3 (z)

z=lambda x,y: \

  (-clf.intercept_[0]-clf.coef_[0][0]*x -clf.coef_[0][1]*y) \

  / clf.coef_[0][2]

tmp = np.linspace(-5,5,30)

x,y = np.meshgrid(tmp,tmp)

fig = plt.figure()

ax  = fig.add_subplot(111, projection='3d')

ax.plot3D(X[Y==0,0], X[Y==0,1], X[Y==0,2],'ob')

ax.plot3D(X[Y==1,0], X[Y==1,1], X[Y==1,2],'sr')

ax.plot_surface(x, y, z(x,y))

ax.view_init(30, 60)

plt.show()

(.env) [boris@Server35fedora PLOT3DSVM]$ cat plotting3DSVM4.py

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

from sklearn.svm import SVC

rs = np.random.RandomState(1234)

# Generate some fake data.

n_samples = 200

# X is the input features by row.

X = np.zeros((200,3))

X[:n_samples//2] = rs.multivariate_normal( np.ones(3), np.eye(3), size=n_samples//2)

X[n_samples//2:] = rs.multivariate_normal(-np.ones(3), np.eye(3), size=n_samples//2)

# Y is the class labels for each row of X.

Y = np.zeros(n_samples); Y[n_samples//2:] = 1

# Fit the data with an svm

svc = SVC(kernel='linear')

svc.fit(X,Y)

# The equation of the separating plane is given by all x in R^3 such that:

# np.dot(svc.coef_[0], x) + b = 0. We should solve for the last coordinate

# to plot the plane in terms of x and y.

z = lambda x,y: \

    (-svc.intercept_[0]-svc.coef_[0][0]*x-svc.coef_[0][1]*y) \

    / svc.coef_[0][2]

tmp = np.linspace(-2,2,51)

x,y = np.meshgrid(tmp,tmp)

# Plot stuff.

fig = plt.figure()

ax  = fig.add_subplot(111, projection='3d')

ax.plot_surface(x, y, z(x,y))

ax.plot3D(X[Y==0,0], X[Y==0,1], X[Y==0,2],'ob')

ax.plot3D(X[Y==1,0], X[Y==1,1], X[Y==1,2],'sr')

plt.show()