pso-bp python实现

PSO-BP 是结合了粒子群优化算法和 BP 算法的神经网络算法，可以用于解决回归和分类问题。以下是 PSO-BP 的 Python 实现步骤：

1. 导入必要的库，包括 numpy、pandas 和 sklearn：

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split

2. 读取数据，将数据集分为训练集和测试集：

data = pd.read_csv('data.csv')
X = data.iloc[:,:-1].values
y = data.iloc[:,-1].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

3. 定义神经网络模型的参数，包括输入层、隐藏层和输出层的神经元个数，以及 PSO 算法的参数：

input_layer_size = X_train.shape[1]
hidden_layer_size = 10
output_layer_size = 1
swarm_size = 20
max_iter = 100
w = 0.7
c1 = 1.4
c2 = 1.4

4. 初始化粒子群和速度矩阵，随机初始化权重和偏置：

def initialize():
    swarm = np.zeros((swarm_size, (input_layer_size + 1) * hidden_layer_size + \
                       (hidden_layer_size + 1) * output_layer_size))
    velocity = np.zeros((swarm_size, (input_layer_size + 1) * hidden_layer_size + \
                        (hidden_layer_size + 1) * output_layer_size))
    for i in range(swarm_size):
        theta1 = np.random.rand(input_layer_size+1, hidden_layer_size)
        theta2 = np.random.rand(hidden_layer_size+1, output_layer_size)
        swarm[i,:] = np.hstack((theta1.ravel(), theta2.ravel()))
    return swarm, velocity

5. 计算粒子的适应度，即神经网络的误差：

def fitness(swarm):
    fitness = np.zeros(swarm_size)
    for i in range(swarm_size):
        theta1 = swarm[i,:hidden_layer_size * (input_layer_size + 1)].reshape((input_layer_size+1, hidden_layer_size))
        theta2 = swarm[i,hidden_layer_size * (input_layer_size + 1):].reshape((hidden_layer_size+1, output_layer_size))
        a1 = np.hstack((np.ones((X_train.shape[0],1)), X_train))
        z2 = np.dot(a1, theta1)
        a2 = np.hstack((np.ones((z2.shape[0],1)), sigmoid(z2)))
        z3 = np.dot(a2, theta2)
        h = sigmoid(z3)
        J = np.sum((h - y_train)**2)/2/X_train.shape[0]
        fitness[i] = J
    return fitness

6. 更新粒子的位置和速度，计算新的适应度：

def update(swarm, velocity, pbest, gbest):
    for i in range(swarm_size):
        r1 = np.random.rand((input_layer_size+1) * hidden_layer_size + \
                            (hidden_layer_size+1) * output_layer_size)
        r2 = np.random.rand((input_layer_size+1) * hidden_layer_size + \
                            (hidden_layer_size+1) * output_layer_size)
        velocity[i,:] = w * velocity[i,:] + c1 * r1 * (pbest[i,:] - swarm[i,:]) + \
                         c2 * r2 * (gbest - swarm[i,:])
        swarm[i,:] = swarm[i,:] + velocity[i,:]
    fitness_new = fitness(swarm)
    return swarm, velocity, fitness_new

7. 运行 PSO-BP 算法，得到最优权重和偏置：

swarm, velocity = initialize()
fitness_current = fitness(swarm)
pbest = swarm.copy()
gbest_index = np.argmin(fitness_current)
gbest = swarm[gbest_index,:]
for i in range(max_iter):
    swarm, velocity, fitness_new = update(swarm, velocity, pbest, gbest)
    pbest_index = np.where(fitness_new < fitness_current)
    pbest[pbest_index,:] = swarm[pbest_index,:]
    fitness_current[pbest_index] = fitness_new[pbest_index]
    gbest_index = np.argmin(fitness_current)
    gbest = swarm[gbest_index,:]

8. 使用最优权重和偏置构建神经网络模型，并在测试集上进行预测：

theta1 = gbest[:hidden_layer_size * (input_layer_size + 1)].reshape((input_layer_size+1, hidden_layer_size))
theta2 = gbest[hidden_layer_size * (input_layer_size + 1):].reshape((hidden_layer_size+1, output_layer_size))
a1 = np.hstack((np.ones((X_test.shape[0],1)), X_test))
z2 = np.dot(a1, theta1)
a2 = np.hstack((np.ones((z2.shape[0],1)), sigmoid(z2)))
z3 = np.dot(a2, theta2)
h = sigmoid(z3)
y_pred = np.round(h)

完整的 PSO-BP 神经网络实现代码如下：