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

下面是绘制在简化版SMO算法下生成的分类超平面的Python代码示例：

import numpy as np
import matplotlib.pyplot as plt

# 定义简化版SMO算法生成的分类超平面函数
def plot_smo_simple():
    # 生成数据集
    X = np.array([[3, 3], [4, 3], [1, 1]])
    y = np.array([1, 1, -1])
    
    # 简化版SMO算法训练模型
    alpha = np.zeros(len(X))
    b = 0
    m, n = X.shape
    while True:
        alpha_pairs_changed = 0
        for i in range(m):
            g_i = np.sum(alpha * y * np.dot(X, X[i])) + b
            if y[i] * g_i < 1:
                alpha[i] += 1
                b += y[i]
                alpha_pairs_changed += 1
        if alpha_pairs_changed == 0:
            break
    
    # 绘制数据点
    plt.scatter(X[:, 0], X[:, 1], c=y)
    
    # 绘制分类超平面（即 w·x + b = 0）
    w = np.sum(alpha * y * X.T, axis=1)
    x0 = np.linspace(0, 5)
    y0 = (-b - w[0] * x0) / w[1]
    plt.plot(x0, y0)
    
    plt.show()

# 调用简化版SMO算法生成分类超平面
plot_smo_simple()

下面是绘制在完整版SMO算法下生成的分类超平面的Python代码示例：

import numpy as np
import matplotlib.pyplot as plt

# 定义完整版SMO算法生成的分类超平面函数
def plot_smo_full():
    # 生成数据集
    X = np.array([[3, 3], [4, 3], [1, 1]])
    y = np.array([1, 1, -1])
    
    # 定义核函数
    def kernel(x_i, x_j):
        return np.dot(x_i, x_j)
    
    # 完整版SMO算法训练模型
    C = 1.0
    tol = 0.001
    max_iter = 50
    alpha = np.zeros(len(X))
    b = 0
    m, n = X.shape
    K = np.zeros((m, m))
    for i in range(m):
        for j in range(m):
            K[i, j] = kernel(X[i], X[j])
    for iter in range(max_iter):
        alpha_pairs_changed = 0
        for i in range(m):
            E_i = np.sum(alpha * y * K[i]) + b - y[i]
            if (y[i] * E_i < -tol and alpha[i] < C) or (y[i] * E_i > tol and alpha[i] > 0):
                j = np.random.choice(list(range(i)) + list(range(i+1, m)))
                E_j = np.sum(alpha * y * K[j]) + b - y[j]
                alpha_i_old, alpha_j_old = alpha[i], alpha[j]
                if y[i] != y[j]:
                    L = max(0, alpha[j] - alpha[i])
                    H = min(C, C + alpha[j] - alpha[i])
                else:
                    L = max(0, alpha[i] + alpha[j] - C)
                    H = min(C, alpha[i] + alpha[j])
                if L == H:
                    continue
                eta = 2 * K[i, j] - K[i, i] - K[j, j]
                if eta >= 0:
                    continue
                alpha[j] -= y[j] * (E_i - E_j) / eta
                alpha[j] = np.clip(alpha[j], L, H)
                if abs(alpha[j] - alpha_j_old) < tol:
                    alpha[j] = alpha_j_old
                    continue
                alpha[i] += y[i] * y[j] * (alpha_j_old - alpha[j])
                b1 = b - E_i - y[i] * (alpha[i] - alpha_i_old) * K[i, i] - y[j] * (alpha[j] - alpha_j_old) * K[i, j]
                b2 = b - E_j - y[i] * (alpha[i] - alpha_i_old) * K[i, j] - y[j] * (alpha[j] - alpha_j_old) * K[j, j]
                if 0 < alpha[i] < C:
                    b = b1
                elif 0 < alpha[j] < C:
                    b = b2
                else:
                    b = (b1 + b2) / 2
                alpha_pairs_changed += 1
        if alpha_pairs_changed == 0:
            break
    
    # 绘制数据点
    plt.scatter(X[:, 0], X[:, 1], c=y)
    
    # 绘制分类超平面（即 w·x + b = 0）
    w = np.sum(alpha * y * X.T, axis=1)
    x0 = np.linspace(0, 5)
    y0 = (-b - w[0] * x0) / w[1]
    plt.plot(x0, y0)
    
    plt.show()

# 调用完整版SMO算法生成分类超平面
plot_smo_full()

注意：以上代码只是示例，实际使用时需要根据自己的数据集和问题进行修改。