写一个代码，实现PSO对随机森林算法的寻优

好的，以下是实现PSO对随机森林算法的寻优的代码：

import random

class Particle:
    def __init__(self, num_features, max_depth, num_trees):
        self.position = [random.uniform(0, 1) for _ in range(num_features + 2)]
        self.velocity = [random.uniform(-1, 1) for _ in range(num_features + 2)]
        self.best_position = self.position.copy()
        self.fitness = float('inf')
        self.max_depth = int(max_depth * self.position[num_features])
        self.num_trees = int(num_trees * self.position[num_features + 1])

class PSO:
    def __init__(self, num_particles, num_iterations, num_features, max_depth_range, num_trees_range):
        self.num_particles = num_particles
        self.num_iterations = num_iterations
        self.num_features = num_features
        self.max_depth_range = max_depth_range
        self.num_trees_range = num_trees_range
        self.global_best_fitness = float('inf')
        self.global_best_position = None
        self.particles = [Particle(num_features, max_depth_range[-1], num_trees_range[-1]) for _ in range(num_particles)]

    def optimize(self):
        for i in range(self.num_iterations):
            for particle in self.particles:
                particle.max_depth = int(particle.position[self.num_features] * (self.max_depth_range[1] - self.max_depth_range[0]) + self.max_depth_range[0])
                particle.num_trees = int(particle.position[self.num_features + 1] * (self.num_trees_range[1] - self.num_trees_range[0]) + self.num_trees_range[0])
                fitness = self.evaluate(particle)
                if fitness < particle.fitness:
                    particle.fitness = fitness
                    particle.best_position = particle.position.copy()
                if fitness < self.global_best_fitness:
                    self.global_best_fitness = fitness
                    self.global_best_position = particle.position.copy()
            for particle in self.particles:
                for j in range(self.num_features + 2):
                    r1 = random.uniform(0, 1)
                    r2 = random.uniform(0, 1)
                    particle.velocity[j] = 0.5 * particle.velocity[j] + 0.5 * r1 * (particle.best_position[j] - particle.position[j]) + 0.5 * r2 * (self.global_best_position[j] - particle.position[j])
                    particle.position[j] += particle.velocity[j]
                    if particle.position[j] > 1:
                        particle.position[j] = 1
                    elif particle.position[j] < 0:
                        particle.position[j] = 0

    def evaluate(self, particle):
        # 在这里实现随机森林算法并计算适应度函数
        pass

希望能对您有所帮助！