用bpso算法对红外光谱数据进行波段筛选的python代码

以下是一个基于BP-SO算法的红外光谱数据波段筛选Python代码：

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier

# 加载数据
data = np.loadtxt('infrared_spectra.csv', delimiter=',')

# 分割数据集
X = data[:, :-1]
y = data[:, -1]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 定义适应度函数
def fitness_function(X, y, selected_features):
    X_train_selected = X[:, selected_features]
    X_test_selected = X_test[:, selected_features]
    knn = KNeighborsClassifier(n_neighbors=3)
    knn.fit(X_train_selected, y_train)
    y_pred = knn.predict(X_test_selected)
    return accuracy_score(y_test, y_pred)

# 初始化粒子群
num_particles = 50
num_dimensions = X.shape[1]
particles_position = np.random.randint(2, size=(num_particles, num_dimensions))
particles_velocity = np.zeros((num_particles, num_dimensions))
particles_best_position = particles_position.copy()
particles_best_fitness = np.zeros(num_particles)

# 计算每个粒子的适应度
for i in range(num_particles):
    particles_best_fitness[i] = fitness_function(X_train, y_train, particles_best_position[i])

# 初始化全局最优解
global_best_fitness = particles_best_fitness.max()
global_best_position = particles_best_position[particles_best_fitness.argmax()]

# 定义算法参数
num_iterations = 100
w = 0.7
c1 = 1.5
c2 = 1.5

# 开始迭代
for t in range(num_iterations):
    for i in range(num_particles):
        # 更新速度
        particles_velocity[i] = w * particles_velocity[i] + c1 * np.random.rand() * (particles_best_position[i] - particles_position[i]) + c2 * np.random.rand() * (global_best_position - particles_position[i])
        # 更新位置
        particles_position[i] = np.logical_xor(particles_position[i], (np.random.rand(num_dimensions) < 1 / (1 + np.exp(-particles_velocity[i]))).astype(int))
        # 计算适应度
        fitness = fitness_function(X_train, y_train, particles_position[i])
        # 更新粒子的历史最优解
        if fitness > particles_best_fitness[i]:
            particles_best_fitness[i] = fitness
            particles_best_position[i] = particles_position[i].copy()
        # 更新全局最优解
        if fitness > global_best_fitness:
            global_best_fitness = fitness
            global_best_position = particles_position[i].copy()

# 打印最佳特征集和测试集精度
print('最佳特征集:', np.where(global_best_position)[0])
print('测试集精度:', global_best_fitness)

在这个代码中，我们使用了K最近邻分类器作为评估器，并使用准确率作为适应度函数。我们还使用了粒子群优化算法来搜索最佳特征子集。在每次迭代中，我们更新每个粒子的速度和位置，并计算适应度。我们还跟踪每个粒子的历史最优解和全局最优解。最终，我们打印出最佳特征子集和测试集精度。