用Python编写PSO优化的DELM代码，代码将使用纳斯达克ETF.xlsx文件内的'成交量','涨幅','开盘涨幅'来预测下一个时刻的'涨幅'

好的，我可以为您提供一个简单的PSO算法的DELM模型的Python实现，用于预测纳斯达克ETF的涨幅。

首先，我们需要导入所需的库：

import pandas as pd
import numpy as np
import random
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

然后，我们需要读取纳斯达克ETF.xlsx文件并对数据进行预处理：

# 读取数据
data = pd.read_excel('纳斯达克ETF.xlsx')

# 取出需要预测的列
y = data['涨幅'].values

# 取出用于预测的特征列
X = data[['成交量', '涨幅', '开盘涨幅']].values

# 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=42)

# 对特征进行标准化处理
X_mean = np.mean(X_train, axis=0)
X_std = np.std(X_train, axis=0)
X_train = (X_train - X_mean) / X_std
X_test = (X_test - X_mean) / X_std

接下来，我们定义DELM模型的基本结构：

class DELM:
    def __init__(self, input_size, hidden_size, output_size, lr):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.lr = lr

        # 初始化权重矩阵
        self.W1 = np.random.randn(self.input_size, self.hidden_size)
        self.b1 = np.zeros((1, self.hidden_size))
        self.W2 = np.random.randn(self.hidden_size, self.output_size)
        self.b2 = np.zeros((1, self.output_size))

    def forward(self, x):
        # 前向传播
        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
        self.y_hat = self.z2

        return self.y_hat

    def backward(self, x, y, y_hat):
        # 反向传播
        delta3 = y_hat - y
        dW2 = np.dot(self.a1.T, delta3)
        db2 = np.sum(delta3, axis=0, keepdims=True)
        delta2 = np.dot(delta3, self.W2.T) * (1 - np.power(self.a1, 2))
        dW1 = np.dot(x.T, delta2)
        db1 = np.sum(delta2, axis=0)

        # 更新权重矩阵
        self.W2 -= self.lr * dW2
        self.b2 -= self.lr * db2
        self.W1 -= self.lr * dW1
        self.b1 -= self.lr * db1

然后，我们定义PSO算法的粒子类：

class Particle:
    def __init__(self, input_size, hidden_size, output_size):
        self.position = np.random.randn(input_size * hidden_size + hidden_size * output_size + hidden_size + output_size)
        self.velocity = np.random.randn(input_size * hidden_size + hidden_size * output_size + hidden_size + output_size)
        self.best_position = self.position
        self.best_error = float('inf')

接着，我们定义PSO算法的主要部分：

class PSO:
    def __init__(self, input_size, hidden_size, output_size, num_particles, max_iterations, c1, c2):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.num_particles = num_particles
        self.max_iterations = max_iterations
        self.c1 = c1
        self.c2 = c2
        self.particles = [Particle(input_size, hidden_size, output_size) for i in range(num_particles)]
        self.global_best_position = np.random.randn(input_size * hidden_size + hidden_size * output_size + hidden_size + output_size)
        self.global_best_error = float('inf')

    def train(self, X_train, y_train):
        for i in range(self.max_iterations):
            for particle in self.particles:
                # 将粒子的位置转换为权重矩阵
                W1 = np.reshape(particle.position[:self.input_size * self.hidden_size], (self.input_size, self.hidden_size))
                b1 = np.reshape(particle.position[self.input_size * self.hidden_size:self.input_size * self.hidden_size + self.hidden_size], (1, self.hidden_size))
                W2 = np.reshape(particle.position[self.input_size * self.hidden_size + self.hidden_size:self.input_size * self.hidden_size + self.hidden_size + self.hidden_size * self.output_size], (self.hidden_size, self.output_size))
                b2 = np.reshape(particle.position[self.input_size * self.hidden_size + self.hidden_size + self.hidden_size * self.output_size:], (1, self.output_size))

                # 使用权重矩阵初始化DELM模型
                model = DELM(self.input_size, self.hidden_size, self.output_size, 0)

                # 计算模型在训练集上的均方误差
                y_hat = model.forward(X_train)
                error = mean_squared_error(y_train, y_hat)

                # 更新粒子的最佳位置和全局最佳位置
                if error < particle.best_error:
                    particle.best_position = particle.position
                    particle.best_error = error
                if error < self.global_best_error:
                    self.global_best_position = particle.position
                    self.global_best_error = error

                # 计算粒子的新速度和位置
                new_velocity = particle.velocity + self.c1 * random.random() * (particle.best_position - particle.position) + self.c2 * random.random() * (self.global_best_position - particle.position)
                new_position = particle.position + new_velocity

                # 更新粒子的速度和位置
                particle.velocity = new_velocity
                particle.position = new_position

最后，我们使用PSO算法训练DELM模型并在测试集上进行预测：

# 初始化PSO算法
pso = PSO(input_size=3, hidden_size=10, output_size=1, num_particles=30, max_iterations=100, c1=0.5, c2=0.5)

# 使用PSO算法训练DELM模型
pso.train(X_train, y_train)

# 将最佳位置转换为权重矩阵
W1 = np.reshape(pso.global_best_position[:3 * 10], (3, 10))
b1 = np.reshape(pso.global_best_position[3 * 10:3 * 10 + 10], (1, 10))
W2 = np.reshape(pso.global_best_position[3 * 10 + 10:3 * 10 + 10 + 10], (10, 1))
b2 = np.reshape(pso.global_best_position[3 * 10 + 10 + 10:], (1, 1))

# 使用权重矩阵初始化DELM模型
model = DELM(3, 10, 1, 0)

# 在测试集上进行预测
X_test = np.array(X_test)
y_test = np.array(y_test)
X_test = (X_test - X_mean) / X_std
y_hat = model.forward(X_test)

# 计算预测结果的均方误差
mse = mean_squared_error(y_test, y_hat)
print('均方误差：', mse)

以上就是使用PSO算法训练DELM模型进行预测的Python实现，希望能对您有所帮助！