5-1-1. SVM 실습

print(__doc__)



import numpy as np

import matplotlib.pyplot as plt

from sklearn import svm

from sklearn.datasets import make_blobs




# we create 40 separable points

X, y = make_blobs(n_samples=40, centers=2, random_state=6)



# fit the model, don't regularize for illustration purposes

clf = svm.SVC(kernel='linear', C=1000)

clf.fit(X, y)



plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)



# plot the decision function

ax = plt.gca()

xlim = ax.get_xlim()

ylim = ax.get_ylim()



# create grid to evaluate model

xx = np.linspace(xlim[0], xlim[1], 30)

yy = np.linspace(ylim[0], ylim[1], 30)

YY, XX = np.meshgrid(yy, xx)

xy = np.vstack([XX.ravel(), YY.ravel()]).T

Z = clf.decision_function(xy).reshape(XX.shape)



# plot decision boundary and margins

ax.contour(XX, YY, Z, colors='k', levels=[-1, 0, 1], alpha=0.5,

           linestyles=['--', '-', '--'])

# plot support vectors

ax.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=100,

           linewidth=1, facecolors='none', edgecolors='k')

plt.show()

 

참조 : scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane.html#sphx-glr-auto-examples-svm-plot-separating-hyperplane-py

SVM: Maximum margin separating hyperplane — scikit-learn 0.23.2 documentation