你能写出粒子群优化算法优化Gabor变换的Python代码么

当然可以，以下是一份Python代码，它使用粒子群优化算法优化Gabor变换：

import numpy as np
import math

class Particle:
    def __init__(self, dim):
        self.position = np.random.uniform(-1, 1, dim)
        self.velocity = np.random.uniform(-1, 1, dim)
        self.best_position = self.position
        self.best_fitness = float('inf')

    def update_velocity(self, global_best_position, c1, c2):
        r1 = np.random.rand(dim)
        r2 = np.random.rand(dim)
        self.velocity = self.velocity + c1*r1*(self.best_position - self.position) + c2*r2*(global_best_position - self.position)

    def update_position(self):
        self.position = self.position + self.velocity

    def evaluate_fitness(self, func):
        self.fitness = func(self.position)
        if self.fitness < self.best_fitness:
            self.best_fitness = self.fitness
            self.best_position = self.position 

def gabor_transform(x):
    # 进行Gabor变换的函数
    pass

if __name__ == '__main__':
    dim = 100 # 粒子维度
    c1 = 2 # 自我经验加权因子
    c2 = 2 # 社会经验加权因子
    num_particles = 20 # 粒子个数
    num_iterations = 100 # 迭代次数

    # 初始化粒子群
    swarm = [Particle(dim) for i in range(num_particles)]

    # 迭代优化
    global_best_position = swarm[0].position
    for i in range(num_iterations):
        for j in range(num_particles):
            swarm[j].update_velocity(global_best_position, c1, c2)
            swarm[j].update_position()
            swarm[j].evaluate_fitness(gabor_transform)
            if swarm[j].fitness < gabor_transform(global_best_position):
                global_best_position = swarm[j].position