使用SMO算法编写了SVM对iris数据进行分类的代码，其中iris数据集由本地txt文档导入

以下是使用SMO算法编写SVM对iris数据进行分类的Python代码：

import numpy as np

def load_data(filename):
    data = []
    label = []
    with open(filename, 'r') as f:
        for line in f.readlines():
            line = line.strip().split(',')
            data.append([float(x) for x in line[:-1]])
            label.append(int(line[-1]))
    return np.array(data), np.array(label)

def select_j(i, m):
    j = i
    while j == i:
        j = np.random.randint(0, m)
    return j

def clip_alpha(alpha, H, L):
    if alpha > H:
        alpha = H
    if alpha < L:
        alpha = L
    return alpha

def smo_simple(data, label, C, toler, max_iter):
    m, n = data.shape
    alpha = np.zeros(m)
    b = 0
    iter = 0

    while iter < max_iter:
        alpha_pairs_changed = 0

        for i in range(m):
            f_xi = float(np.dot((alpha*label).T, np.dot(data, data[i, :].T))) + b
            error_i = f_xi - float(label[i])
            if (label[i]*error_i < -toler and alpha[i] < C) or (label[i]*error_i > toler and alpha[i] > 0):
                j = select_j(i, m)
                f_xj = float(np.dot((alpha*label).T, np.dot(data, data[j, :].T))) + b
                error_j = f_xj - float(label[j])

                alpha_i_old, alpha_j_old = alpha[i], alpha[j]
                if label[i] != label[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 = 2.0 * np.dot(data[i, :], data[j, :].T) - np.dot(data[i, :], data[i, :].T) - np.dot(data[j, :], data[j, :].T)
                if eta >= 0:
                    continue

                alpha[j] -= label[j] * (error_i - error_j) / eta
                alpha[j] = clip_alpha(alpha[j], H, L)
                if abs(alpha[j] - alpha_j_old) < 1e-5:
                    continue

                alpha[i] += label[i] * label[j] * (alpha_j_old - alpha[j])
                b1 = b - error_i - label[i] * (alpha[i] - alpha_i_old) * np.dot(data[i, :], data[i, :].T) - label[j] * (alpha[j] - alpha_j_old) * np.dot(data[i, :], data[j, :].T)
                b2 = b - error_j - label[i] * (alpha[i] - alpha_i_old) * np.dot(data[i, :], data[j, :].T) - label[j] * (alpha[j] - alpha_j_old) * np.dot(data[j, :], data[j, :].T)
                if 0 < alpha[i] < C:
                    b = b1
                elif 0 < alpha[j] < C:
                    b = b2
                else:
                    b = (b1 + b2) / 2.0

                alpha_pairs_changed += 1

        if alpha_pairs_changed == 0:
            iter += 1
        else:
            iter = 0
    return alpha, b

def svm(data, label, C, toler, max_iter):
    alpha, b = smo_simple(data, label, C, toler, max_iter)
    w = np.dot((alpha*label).T, data)
    return w, b

if __name__ == '__main__':
    data, label = load_data('iris.txt')
    w, b = svm(data, label, 0.6, 0.001, 40)
    print('w:', w)
    print('b:', b)

其中，load_data函数用于从本地txt文件中加载iris数据集。select_j函数用于随机选择第二个alpha值的下标。clip_alpha函数用于调整alpha值使其满足KKT条件。smo_simple函数用于实现SMO算法。svm函数是对smo_simple函数的封装，返回计算出的权重w和偏移量b。最后，在main函数中调用svm函数进行训练并输出结果。