2-class 2-D SVM sample with iris dataset

iris_svm.py

#!/usr/bin/env python3
#-*- coding: utf-8 -*-

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.svm import LinearSVC

iris = load_iris()
mask = iris.target != 2
X = iris.data[mask][:,[1,0]]
Y = iris.target[mask]
xmin, ymin = np.min(X, axis=0) - 0.1
xmax, ymax = np.max(X, axis=0) + 0.1

clf = LinearSVC(loss="hinge", C=100000)
clf.fit(X, Y)
a, b = clf.coef_[0]
c = clf.intercept_

plt.plot(X[Y==0,0], X[Y==0,1], "b_")
plt.plot(X[Y==1,0], X[Y==1,1], "r+")
x = np.linspace(xmin, xmax, 1000)
for d in [-1, 0, 1]:
  y = -(a*x+c+d)/b
  plt.plot(x, y, "g-" if d == 0 else "g--")
plt.show()