PSO-SVM多分类python并输出参数以及评价指标

首先需要导入所需的库：

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

然后加载数据集并进行数据预处理：

# 加载数据集
iris = datasets.load_iris()
X = iris.data
y = iris.target

# 数据预处理
scaler = StandardScaler()
X = scaler.fit_transform(X)

接下来，定义PSO算法的类：

class PSO:
    def __init__(self, n_particles, max_iter, c1, c2, w, dim, lb, ub):
        self.n_particles = n_particles
        self.max_iter = max_iter
        self.c1 = c1
        self.c2 = c2
        self.w = w
        self.dim = dim
        self.lb = lb
        self.ub = ub
        self.g_best_fitness = np.inf
        self.g_best_pos = None
        self.particles_fitness = np.empty(self.n_particles)
        self.particles_pos = np.random.uniform(low=self.lb, high=self.ub, size=(self.n_particles, self.dim))
        self.particles_vel = np.zeros((self.n_particles, self.dim))
    
    def optimize(self, function):
        for i in range(self.max_iter):
            for j in range(self.n_particles):
                fitness = function(self.particles_pos[j])
                if fitness < self.particles_fitness[j]:
                    self.particles_fitness[j] = fitness
                    if fitness < self.g_best_fitness:
                        self.g_best_fitness = fitness
                        self.g_best_pos = self.particles_pos[j]
                
                r1 = np.random.uniform(size=self.dim)
                r2 = np.random.uniform(size=self.dim)
                self.particles_vel[j] = self.w * self.particles_vel[j] + self.c1 * r1 * (self.particles_pos[j] - self.particles_pos[j]) + self.c2 * r2 * (self.g_best_pos - self.particles_pos[j])
                self.particles_pos[j] = np.clip(self.particles_pos[j] + self.particles_vel[j], self.lb, self.ub)
                
        return self.g_best_pos

接下来，定义适应度函数和SVM分类器：

def fitness_function(params):
    C = params[0]
    gamma = params[1]
    kernel = params[2]
    
    clf = SVC(C=C, gamma=gamma, kernel=kernel)
    clf.fit(X_train, y_train)
    y_pred = clf.predict(X_test)
    acc = accuracy_score(y_test, y_pred)
    
    return 1 - acc

# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 定义搜索范围
lb = [1e-3, 1e-3, 'linear']
ub = [100, 10, 'rbf']
dim = 3

# 使用PSO算法进行参数优化
pso = PSO(n_particles=20, max_iter=50, c1=2, c2=2, w=0.7, dim=dim, lb=lb, ub=ub)
params = pso.optimize(fitness_function)

# 使用优化后的参数训练SVM分类器
C = params[0]
gamma = params[1]
kernel = params[2]

clf = SVC(C=C, gamma=gamma, kernel=kernel)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)

# 输出评价指标
print('Accuracy:', accuracy_score(y_test, y_pred))
print('Confusion Matrix:\n', confusion_matrix(y_test, y_pred))
print('Classification Report:\n', classification_report(y_test, y_pred))

这就是PSO-SVM多分类python并输出参数以及评价指标的实现过程。