pso-bp多分类预测python

PSO-BP算法是一种常用的神经网络训练算法，结合了粒子群优化算法和反向传播算法，可以用于多分类预测问题。

以下是使用Python实现PSO-BP多分类预测的基本步骤：

1. 导入所需的库和数据集

import numpy as np
from sklearn.datasets import load_iris
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split

2. 数据预处理

# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target.reshape(-1, 1)

# 对标签进行One-hot编码
enc = OneHotEncoder()
y = enc.fit_transform(y).toarray()

# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

3. 定义PSO-BP算法

# 定义PSO-BP算法类
class PSO_BP():
    def __init__(self, n_input, n_hidden, n_output):
        self.n_input = n_input
        self.n_hidden = n_hidden
        self.n_output = n_output
        self.w1 = np.random.randn(n_input, n_hidden)
        self.b1 = np.random.randn(n_hidden)
        self.w2 = np.random.randn(n_hidden, n_output)
        self.b2 = np.random.randn(n_output)
        self.v_w1 = np.random.randn(n_input, n_hidden)
        self.v_b1 = np.random.randn(n_hidden)
        self.v_w2 = np.random.randn(n_hidden, n_output)
        self.v_b2 = np.random.randn(n_output)
        
    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))
    
    def softmax(self, x):
        exp_x = np.exp(x)
        return exp_x / np.sum(exp_x, axis=1, keepdims=True)
    
    def forward(self, X):
        self.z1 = np.dot(X, self.w1) + self.b1
        self.a1 = self.sigmoid(self.z1)
        self.z2 = np.dot(self.a1, self.w2) + self.b2
        self.a2 = self.softmax(self.z2)
        return self.a2
    
    def loss(self, X, y):
        y_pred = self.forward(X)
        loss = -np.sum(y * np.log(y_pred))
        return loss
    
    def accuracy(self, X, y):
        y_pred = self.predict(X)
        acc = np.sum(y_pred == np.argmax(y, axis=1)) / len(y)
        return acc
    
    def predict(self, X):
        y_pred = self.forward(X)
        return np.argmax(y_pred, axis=1)
    
    def backward(self, X, y):
        y_pred = self.forward(X)
        delta3 = y_pred - y
        delta2 = np.dot(delta3, self.w2.T) * self.a1 * (1 - self.a1)
        grad_w2 = np.dot(self.a1.T, delta3)
        grad_b2 = np.sum(delta3, axis=0)
        grad_w1 = np.dot(X.T, delta2)
        grad_b1 = np.sum(delta2, axis=0)
        return grad_w1, grad_b1, grad_w2, grad_b2
    
    def update(self, grad_w1, grad_b1, grad_w2, grad_b2, lr, w_decay, v_decay, v_scale):
        self.v_w1 = v_decay * self.v_w1 - lr * (grad_w1 + w_decay * self.w1)
        self.v_b1 = v_decay * self.v_b1 - lr * grad_b1
        self.v_w2 = v_decay * self.v_w2 - lr * (grad_w2 + w_decay * self.w2)
        self.v_b2 = v_decay * self.v_b2 - lr * grad_b2
        self.w1 += v_scale * self.v_w1
        self.b1 += v_scale * self.v_b1
        self.w2 += v_scale * self.v_w2
        self.b2 += v_scale * self.v_b2
    
    def train(self, X, y, lr=0.01, w_decay=0.0, v_decay=0.99, v_scale=0.1, n_epoch=1000):
        for i in range(n_epoch):
            grad_w1, grad_b1, grad_w2, grad_b2 = self.backward(X, y)
            self.update(grad_w1, grad_b1, grad_w2, grad_b2, lr, w_decay, v_decay, v_scale)
            if i % 100 == 0:
                loss = self.loss(X, y)
                acc = self.accuracy(X, y)
                print('Epoch {}: loss={}, accuracy={}'.format(i, loss, acc))

4. 训练模型并测试

# 初始化模型
model = PSO_BP(n_input=4, n_hidden=10, n_output=3)

# 训练模型
model.train(X_train, y_train, lr=0.01, w_decay=0.0, v_decay=0.99, v_scale=0.1, n_epoch=1000)

# 测试模型
acc = model.accuracy(X_test, y_test)
print('Test accuracy:', acc)

通过以上步骤，即可使用PSO-BP算法进行多分类预测。需要注意的是，不同的数据集和模型可能需要不同的参数设置，需要进行适当的调整。