粒子群算法优化SVM的python代码

以下是使用粒子群算法优化SVM的简单示例代码，使用Python编写：

import numpy as np
from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 粒子群算法参数
n_particles = 20  # 粒子数量
max_iter = 100    # 迭代次数
w = 0.5           # 惯性权重
c1 = 2            # 学习因子1
c2 = 2            # 学习因子2

# SVM参数范围
C_range = (-5, 5)       # 惩罚系数范围
gamma_range = (-5, 5)   # 核函数参数范围

# 数据准备（假设已有特征向量X和标签y）
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 定义适应度函数（分类准确率）
def fitness_function(params):
    C = 2 ** params[0]
    gamma = 2 ** params[1]
    clf = svm.SVC(C=C, gamma=gamma)
    clf.fit(X_train, y_train)
    y_pred = clf.predict(X_test)
    return accuracy_score(y_test, y_pred)

# 初始化粒子群位置和速度
particle_pos = np.random.uniform(low=[C_range[0], gamma_range[0]], high=[C_range[1], gamma_range[1]], size=(n_particles, 2))
particle_vel = np.zeros((n_particles, 2))

# 初始化全局最优位置和适应度值
global_best_pos = particle_pos[0]
global_best_fitness = fitness_function(particle_pos[0])

# 初始化个体最优位置和适应度值
personal_best_pos = particle_pos.copy()
personal_best_fitness = np.zeros(n_particles)
for i in range(n_particles):
    personal_best_fitness[i] = fitness_function(particle_pos[i])

# 粒子群算法迭代
for _ in range(max_iter):
    for i in range(n_particles):
        # 更新粒子速度和位置
        particle_vel[i] = w * particle_vel[i] + c1 * np.random.rand() * (personal_best_pos[i] - particle_pos[i]) + c2 * np.random.rand() * (global_best_pos - particle_pos[i])
        particle_pos[i] = particle_pos[i] + particle_vel[i]

        # 边界处理
        particle_pos[i] = np.clip(particle_pos[i], [C_range[0], gamma_range[0]], [C_range[1], gamma_range[1]])

        # 更新个体最优位置和适应度值
        fitness = fitness_function(particle_pos[i])
        if fitness > personal_best_fitness[i]:
            personal_best_fitness[i] = fitness
            personal_best_pos[i] = particle_pos[i]

        # 更新全局最优位置和适应度值
        if fitness > global_best_fitness:
            global_best_fitness = fitness
            global_best_pos = particle_pos[i]

# 构建最优的SVM模型
best_C = 2 ** global_best_pos[0]
best_gamma = 2 ** global_best_pos[1]
best_clf = svm.SVC(C=best_C, gamma=best_gamma)
best_clf.fit(X_train, y_train)
y_pred = best_clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("最优分类准确率：", accuracy)

请注意，以上代码仅为示例，具体应用中可能需要根据问题的需求进行修改和调整。另外，代码中使用了scikit-learn库中的SVM实现，如果没有安装该库，可以使用`pip install scikit-learn`进行安装。