SVM分类算法详解。

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
from sklearn.svm import SVC

生成模拟数据集

X, y = make_blobs(n_samples=100, centers=2, random_state=42)

创建SVM模型

model = SVC(kernel='linear')

训练模型

model.fit(X, y)

绘制决策边界和支持向量

plt.scatter(X[:, 0], X[:, 1], c=y, cmap='viridis')
ax = plt.gca()
xlim = ax.get_xlim()
ylim = ax.get_ylim()

创建网格来绘制决策边界

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 = model.decision_function(xy).reshape(XX.shape)

绘制决策边界和支持向量

ax.contour(XX, YY, Z, colors='k', levels=[-1, 0, 1], 
		   alpha=0.5, linestyles=['--', '-', '--'])
ax.scatter(model.support_vectors_[:, 0], model.support_vectors_[:, 1], 
		   s=100, linewidth=1, facecolors='none', edgecolors='k')
plt.xlabel('vector1')
plt.ylabel('vector2')
plt.title('SVM decision boundary')
plt.show()

svm0078.png