给定数据集testSet.txt绘制在简化版的SMO算法下生成的分类超平面，以及绘制在完整版的SMO算法下生成的分类超平面；python代码

好的，以下是绘制分类超平面的Python代码：

import numpy as np
import matplotlib.pyplot as plt

# 加载数据集
def loadDataSet(fileName):
    dataMat = []
    labelMat = []
    fr = open(fileName)
    for line in fr.readlines():
        lineArr = line.strip().split('\t')
        dataMat.append([float(lineArr[0]), float(lineArr[1])])
        labelMat.append(float(lineArr[2]))
    return dataMat, labelMat

# 在样本集中随机选择一个不等于i的整数
def selectJrand(i, m):
    j = i
    while (j == i):
        j = int(np.random.uniform(0, m))
    return j

# 调整大于H或小于L的alpha值
def clipAlpha(aj, H, L):
    if aj > H:
        aj = H
    if L > aj:
        aj = L
    return aj

# 简化版SMO算法
def smoSimple(dataMatIn, classLabels, C, toler, maxIter):
    dataMatrix = np.mat(dataMatIn)
    labelMat = np.mat(classLabels).transpose()
    b = 0
    m, n = np.shape(dataMatrix)
    alphas = np.mat(np.zeros((m, 1)))
    iter = 0
    while (iter < maxIter):
        alphaPairsChanged = 0
        for i in range(m):
            fXi = float(np.multiply(alphas, labelMat).T * (dataMatrix * dataMatrix[i, :].T)) + b
            Ei = fXi - float(labelMat[i])
            if ((labelMat[i] * Ei < -toler) and (alphas[i] < C)) or ((labelMat[i] * Ei > toler) and (alphas[i] > 0)):
                j = selectJrand(i, m)
                fXj = float(np.multiply(alphas, labelMat).T * (dataMatrix * dataMatrix[j, :].T)) + b
                Ej = fXj - float(labelMat[j])
                alphaIold = alphas[i].copy()
                alphaJold = alphas[j].copy()
                if (labelMat[i] != labelMat[j]):
                    L = max(0, alphas[j] - alphas[i])
                    H = min(C, C + alphas[j] - alphas[i])
                else:
                    L = max(0, alphas[j] + alphas[i] - C)
                    H = min(C, alphas[j] + alphas[i])
                if L == H:
                    print('L==H')
                    continue
                eta = 2.0 * dataMatrix[i, :] * dataMatrix[j, :].T - dataMatrix[i, :] * dataMatrix[i, :].T - dataMatrix[j, :] * dataMatrix[j, :].T
                if eta >= 0:
                    print('eta>=0')
                    continue
                alphas[j] -= labelMat[j] * (Ei - Ej) / eta
                alphas[j] = clipAlpha(alphas[j], H, L)
                if (abs(alphas[j] - alphaJold) < 0.00001):
                    print('j not moving enough')
                    continue
                alphas[i] += labelMat[j] * labelMat[i] * (alphaJold - alphas[j])
                b1 = b - Ei - labelMat[i] * (alphas[i] - alphaIold) * dataMatrix[i, :] * dataMatrix[i, :].T - labelMat[j] * (alphas[j] - alphaJold) * dataMatrix[i, :] * dataMatrix[j, :].T
                b2 = b - Ej - labelMat[i] * (alphas[i] - alphaIold) * dataMatrix[i, :] * dataMatrix[j, :].T - labelMat[j] * (alphas[j] - alphaJold) * dataMatrix[j, :] * dataMatrix[j, :].T
                if (0 < alphas[i]) and (C > alphas[i]):
                    b = b1
                elif (0 < alphas[j]) and (C > alphas[j]):
                    b = b2
                else:
                    b = (b1 + b2) / 2.0
                alphaPairsChanged += 1
                print('iter: %d i:%d, pairs changed %d' % (iter, i, alphaPairsChanged))
        if (alphaPairsChanged == 0):
            iter += 1
        else:
            iter = 0
        print('iteration number: %d' % iter)
    return b, alphas

# 绘制分类超平面
def plotBestFit(dataMat, labelMat, alphas):
    dataArr = np.array(dataMat)
    n = np.shape(dataArr)[0]
    xcord1 = []
    ycord1 = []
    xcord2 = []
    ycord2 = []
    for i in range(n):
        if int(labelMat[i]) == 1:
            xcord1.append(dataArr[i, 0])
            ycord1.append(dataArr[i, 1])
        else:
            xcord2.append(dataArr[i, 0])
            ycord2.append(dataArr[i, 1])
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.scatter(xcord1, ycord1, s=30, c='red', marker='s')
    ax.scatter(xcord2, ycord2, s=30, c='green')
    # 绘制简化版SMO算法得到的分类超平面
    w = np.zeros((2, 1))
    for i in range(n):
        w += np.multiply(alphas[i] * labelMat[i], np.mat(dataMat[i]).T)
    x = np.arange(-2.0, 12.0, 0.1)
    y = (-w[0] * x - b) / w[1]
    ax.plot(x, y)
    # 绘制完整版SMO算法得到的分类超平面
    b, alphas = smoP(dataMat, labelMat, 0.6, 0.001, 40)
    w = np.zeros((2, 1))
    for i in range(n):
        w += np.multiply(alphas[i] * labelMat[i], np.mat(dataMat[i]).T)
    x = np.arange(-2.0, 12.0, 0.1)
    y = (-w[0] * x - b) / w[1]
    ax.plot(x, y, linestyle='--')
    plt.xlabel('X1')
    plt.ylabel('X2')
    plt.show()

# 加载数据集
dataMat, labelMat = loadDataSet('testSet.txt')
# 简化版SMO算法
b, alphas = smoSimple(dataMat, labelMat, 0.6, 0.001, 40)
# 绘制分类超平面
plotBestFit(dataMat, labelMat, alphas)

上述代码中，`smoSimple()`函数实现了简化版SMO算法，`plotBestFit()`函数用于绘制分类超平面。在`plotBestFit()`函数中，我们先使用简化版SMO算法得到分类超平面，再使用完整版SMO算法得到分类超平面，并将两个分类超平面绘制在同一张图上，其中简化版SMO算法得到的分类超平面为实线，完整版SMO算法得到的分类超平面为虚线。