pso-bp多分类python

PSO-BP 是一种结合了粒子群优化算法和 BP 算法的神经网络模型，可以用于多分类问题。在 Python 中实现 PSO-BP 多分类模型，可以参考以下步骤：

1. 导入必要的库：

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

2. 载入数据集：

iris = load_iris()
X = iris['data']
y = iris['target']

3. 划分训练集和测试集：

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

4. 定义神经网络模型：

class NeuralNetwork:
    def __init__(self, input_dim, hidden_dim, output_dim):
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        self.W1 = np.random.randn(self.input_dim, self.hidden_dim)
        self.b1 = np.zeros((1, self.hidden_dim))
        self.W2 = np.random.randn(self.hidden_dim, self.output_dim)
        self.b2 = np.zeros((1, self.output_dim))
    
    def forward(self, X):
        self.z1 = np.dot(X, self.W1) + self.b1
        self.a1 = np.tanh(self.z1)
        self.z2 = np.dot(self.a1, self.W2) + self.b2
        exp_scores = np.exp(self.z2)
        self.probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)
        return self.probs
    
    def predict(self, X):
        return np.argmax(self.forward(X), axis=1)

5. 定义 PSO-BP 算法：

class PSO_BP:
    def __init__(self, n_particles, max_iter, learning_rate, input_dim, hidden_dim, output_dim):
        self.n_particles = n_particles
        self.max_iter = max_iter
        self.learning_rate = learning_rate
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        self.neural_networks = [NeuralNetwork(self.input_dim, self.hidden_dim, self.output_dim) for i in range(self.n_particles)]
        self.global_best_loss = float('inf')
        self.global_best_weights = None
        self.global_best_bias = None
        self.particle_best_loss = [float('inf') for i in range(self.n_particles)]
        self.particle_best_weights = [self.neural_networks[i].W1 for i in range(self.n_particles)]
        self.particle_best_bias = [self.neural_networks[i].b1 for i in range(self.n_particles)]
        self.velocities = [(np.random.randn(self.input_dim, self.hidden_dim), np.random.randn(1, self.hidden_dim), np.random.randn(self.hidden_dim, self.output_dim), np.random.randn(1, self.output_dim)) for i in range(self.n_particles)]
    
    def fitness(self, X, y, weights, bias):
        nn = NeuralNetwork(self.input_dim, self.hidden_dim, self.output_dim)
        nn.W1 = weights[0]
        nn.b1 = bias[0]
        nn.W2 = weights[1]
        nn.b2 = bias[1]
        probs = nn.forward(X)
        log_likelihood = -np.log(probs[range(len(y)), y])
        loss = np.sum(log_likelihood) / len(y)
        return loss
    
    def train(self, X_train, y_train):
        for i in range(self.max_iter):
            for j in range(self.n_particles):
                nn = self.neural_networks[j]
                weights = (nn.W1, nn.W2)
                bias = (nn.b1, nn.b2)
                loss = self.fitness(X_train, y_train, weights, bias)
                if loss < self.particle_best_loss[j]:
                    self.particle_best_loss[j] = loss
                    self.particle_best_weights[j] = nn.W1
                    self.particle_best_bias[j] = nn.b1
                if loss < self.global_best_loss:
                    self.global_best_loss = loss
                    self.global_best_weights = nn.W1
                    self.global_best_bias = nn.b1
                v_weights = self.velocities[j][0]
                v_bias = self.velocities[j][1]
                r1 = np.random.rand(self.input_dim, self.hidden_dim)
                r2 = np.random.rand(1, self.hidden_dim)
                r3 = np.random.rand(self.hidden_dim, self.output_dim)
                r4 = np.random.rand(1, self.output_dim)
                v_weights = self.learning_rate * (v_weights + r1 * (self.particle_best_weights[j] - nn.W1) + r2 * (self.global_best_weights - nn.W1))
                v_bias = self.learning_rate * (v_bias + r3 * (self.particle_best_bias[j] - nn.b1) + r4 * (self.global_best_bias - nn.b1))
                nn.W1 = nn.W1 + v_weights
                nn.b1 = nn.b1 + v_bias
    
    def predict(self, X_test):
        nn = NeuralNetwork(self.input_dim, self.hidden_dim, self.output_dim)
        nn.W1 = self.global_best_weights
        nn.b1 = self.global_best_bias
        return nn.predict(X_test)

6. 训练模型并预测：

pso_bp = PSO_BP(n_particles=10, max_iter=100, learning_rate=0.01, input_dim=X_train.shape[1], hidden_dim=10, output_dim=len(np.unique(y_train)))
pso_bp.train(X_train, y_train)
y_pred = pso_bp.predict(X_test)

这样，就可以使用 PSO-BP 算法实现多分类模型了。需要注意的是，这里的代码仅供参考，具体的实现方式可能因具体问题而异。