sklearn支持向量机（待完成）

from sklearn.datasets import make_circles
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import numpy as np, matplotlib.pyplot as mp
# 创建随机样本、数据标准化
X, y = make_circles(noise=.1, factor=.4)
X = StandardScaler().fit_transform(X)
# 建模、训练
for e, kernel in enumerate(['linear', 'rbf'], 1):
    clf = SVC(kernel=kernel)
    clf.fit(X, y)
    # 可视化
    mp.subplot(1, 2, e)  # 散点图
    mp.scatter(X[:, 0], X[:, 1], s=40, c=y)
    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
    xx, yy = np.meshgrid(np.arange(x_min, x_max, .01), np.arange(y_min, y_max, .01))
    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)
    mp.contourf(xx, yy, Z, 0, alpha=.2)  # 等高线图
mp.show()