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SGD：最大间隔分离超平面#

使用线性支持向量机分类器在一个可分的双类数据集中绘制最大间隔分离超平面，该分类器使用随机梯度下降（SGD）进行训练。

import matplotlib.pyplot as plt
import numpy as np

from sklearn.datasets import make_blobs
from sklearn.linear_model import SGDClassifier

# 我们创建50个可分离点
X, Y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=0.60)

# 拟合模型
clf = SGDClassifier(loss="hinge", alpha=0.01, max_iter=200)

clf.fit(X, Y)

# 绘制直线、点和最近的向量到平面
xx = np.linspace(-1, 5, 10)
yy = np.linspace(-1, 5, 10)

X1, X2 = np.meshgrid(xx, yy)
Z = np.empty(X1.shape)
for (i, j), val in np.ndenumerate(X1):
    x1 = val
    x2 = X2[i, j]
    p = clf.decision_function([[x1, x2]])
    Z[i, j] = p[0]
levels = [-1.0, 0.0, 1.0]
linestyles = ["dashed", "solid", "dashed"]
colors = "k"
plt.contour(X1, X2, Z, levels, colors=colors, linestyles=linestyles)
plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired, edgecolor="black", s=20)

plt.axis("tight")
plt.show()