先来绘制散点图：
from sklearn.datasets import make_circles
X,y = make_circles(100, factor=0.1, noise=.1) # 100个样本，factor:内圈和外圈的距离之比，noise:噪声
X.shape
y.shape
plt.scatter(X[:,0],X[:,1],c=y,s=50,cmap="rainbow")
plt.show()
接下来绘制决策边界：
clf = SVC(kernel = "rbf").fit(X,y) # 高斯核
plt.scatter(X[:,0],X[:,1],c=y,s=50,cmap="rainbow")
def plot_svc_decision_function(model,ax=None):if ax is None:ax = plt.gca()xlim = ax.get_xlim()ylim = ax.get_ylim()x = np.linspace(xlim[0],xlim[1],30)y = np.linspace(ylim[0],ylim[1],30)Y,X = np.meshgrid(y,x) xy = np.vstack([X.ravel(), Y.ravel()]).TP = model.decision_function(xy).reshape(X.shape)ax.contour(X, Y, P,colors="k",levels=[-1,0,1],alpha=0.5,linestyles=["--","-","--"])ax.set_xlim(xlim)ax.set_ylim(ylim)
plot_svc_decision_function(clf)
