面部识别技术的权重调整问题

import numpy as np

# 初始化种群
def init_population(pop_size, feature_num):
    population = np.random.rand(pop_size, feature_num)
    return population

# 适应度函数，评估个体的适应度
def fitness_func(population):
    fitness = np.sum(population, axis=1)
    return fitness

# 交叉操作
def crossover(parents, offspring_size):
    offspring = np.empty(offspring_size)
    crossover_point = np.uint32(offspring_size[1] / 2)
    for k in range(offspring_size[0]):
        parent_1_idx = k % parents.shape[0]
        parent_2_idx = (k+1) % parents.shape[0]
        offspring[k, 0:crossover_point] = parents[parent_1_idx, 0:crossover_point]
        offspring[k, crossover_point:] = parents[parent_2_idx, crossover_point:]
    return offspring

# 变异操作
def mutate(offspring_crossover):
    for idx in range(offspring_crossover.shape[0]):
        random_value = np.random.uniform(-1.0, 1.0, 1)
        offspring_crossover[idx, :] = offspring_crossover[idx, :] + random_value
    return offspring_crossover

# 主函数
def main():
    pop_size = 10 # 种群大小
    feature_num = 100 # 特征数量
    num_generations = 100 # 迭代代数
    offspring_size = (pop_size - pop_size % 2, feature_num) # 子代数量

    population = init_population(pop_size, feature_num) # 初始化种群

    for generation in range(num_generations):
        fitness = fitness_func(population) # 计算适应度
        parents = population[np.argsort(fitness)[-pop_size//2:], :] # 筛选优秀个体
        offspring_crossover = crossover(parents, offspring_size) # 交叉操作
        offspring_mutation = mutate(offspring_crossover) # 变异操作
        population[0:parents.shape[0], :] = parents
        population[parents.shape[0]:, :] = offspring_mutation

    best_solution_idx = np.argmax(fitness_func(population)) # 找到适应度最高的个体
    best_solution = population[best_solution_idx, :] # 提取最优解

    print("最优解权重：", best_solution)

if __name__ == "__main__":
    main()