支持向量机使用rbf函数时如何输出超平面，请给出用python编写的代码

在Python中使用SVM输出超平面需要使用SVM模型的decision_function()方法。具体步骤如下：

1. 导入必要的库：

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

2. 加载数据集：

iris = datasets.load_iris()
X = iris.data[:, :2]
y = iris.target

3. 创建SVM模型：

clf = SVC(kernel='rbf')
clf.fit(X, y)

4. 定义绘制超平面的函数：

def plot_decision_function(model, ax=None, plot_support=True):
    if ax is None:
        ax = plt.gca()
    xlim = ax.get_xlim()
    ylim = ax.get_ylim()
    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)
    ax.contour(XX, YY, Z, colors='k', levels=[-1, 0, 1], alpha=0.5, linestyles=['--', '-', '--'])
    if plot_support:
        ax.scatter(model.support_vectors_[:, 0], model.support_vectors_[:, 1], s=300, linewidth=1, facecolors='none', edgecolors='k')
    ax.set_xlim(xlim)
    ax.set_ylim(ylim)

5. 绘制样本点和超平面：

plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')
plot_decision_function(clf)

完整代码如下：

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

iris = datasets.load_iris()
X = iris.data[:, :2]
y = iris.target

clf = SVC(kernel='rbf')
clf.fit(X, y)

def plot_decision_function(model, ax=None, plot_support=True):
    if ax is None:
        ax = plt.gca()
    xlim = ax.get_xlim()
    ylim = ax.get_ylim()
    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)
    ax.contour(XX, YY, Z, colors='k', levels=[-1, 0, 1], alpha=0.5, linestyles=['--', '-', '--'])
    if plot_support:
        ax.scatter(model.support_vectors_[:, 0], model.support_vectors_[:, 1], s=300, linewidth=1, facecolors='none', edgecolors='k')
    ax.set_xlim(xlim)
    ax.set_ylim(ylim)

plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')
plot_decision_function(clf)
plt.show()

运行上述代码，即可绘制出样本点和超平面。