使用Java语言写一个预测价格的bp神经网路算法
时间: 2024-03-12 09:47:16 浏览: 23
好的,我可以为您提供一个基本的Java语言实现BP神经网络的代码框架,但是预测价格的具体实现需要根据您的数据和需求进行调整和优化。以下是示例代码:
```java
import java.util.Arrays;
import java.util.Random;
public class BPNeuralNetwork {
private int inputSize;
private int hiddenSize;
private int outputSize;
private double[][] inputHiddenWeights;
private double[][] hiddenOutputWeights;
private double[] hiddenBiases;
private double[] outputBiases;
private Random random;
public BPNeuralNetwork(int inputSize, int hiddenSize, int outputSize) {
this.inputSize = inputSize;
this.hiddenSize = hiddenSize;
this.outputSize = outputSize;
this.inputHiddenWeights = new double[inputSize][hiddenSize];
this.hiddenOutputWeights = new double[hiddenSize][outputSize];
this.hiddenBiases = new double[hiddenSize];
this.outputBiases = new double[outputSize];
this.random = new Random();
// 初始化权重和偏差
initializeWeightsAndBiases();
}
// 初始化权重和偏差
private void initializeWeightsAndBiases() {
// 从 -1 到 1 之间随机生成权重和偏差
for (int i = 0; i < inputSize; i++) {
for (int j = 0; j < hiddenSize; j++) {
inputHiddenWeights[i][j] = random.nextDouble() * 2 - 1;
}
}
for (int i = 0; i < hiddenSize; i++) {
for (int j = 0; j < outputSize; j++) {
hiddenOutputWeights[i][j] = random.nextDouble() * 2 - 1;
}
}
for (int i = 0; i < hiddenSize; i++) {
hiddenBiases[i] = random.nextDouble() * 2 - 1;
}
for (int i = 0; i < outputSize; i++) {
outputBiases[i] = random.nextDouble() * 2 - 1;
}
}
// sigmoid函数
private double sigmoid(double x) {
return 1 / (1 + Math.exp(-x));
}
// 前向传播
private double[] forward(double[] input) {
double[] hidden = new double[hiddenSize];
double[] output = new double[outputSize];
// 计算隐藏层的输出
for (int i = 0; i < hiddenSize; i++) {
double sum = 0;
for (int j = 0; j < inputSize; j++) {
sum += input[j] * inputHiddenWeights[j][i];
}
sum += hiddenBiases[i];
hidden[i] = sigmoid(sum);
}
// 计算输出层的输出
for (int i = 0; i < outputSize; i++) {
double sum = 0;
for (int j = 0; j < hiddenSize; j++) {
sum += hidden[j] * hiddenOutputWeights[j][i];
}
sum += outputBiases[i];
output[i] = sigmoid(sum);
}
return output;
}
// 反向传播
private void backward(double[] input, double[] output, double[] target, double learningRate) {
double[] hidden = new double[hiddenSize];
double[] hiddenError = new double[hiddenSize];
double[] outputError = new double[outputSize];
// 计算输出层的误差
for (int i = 0; i < outputSize; i++) {
outputError[i] = (target[i] - output[i]) * output[i] * (1 - output[i]);
}
// 计算隐藏层的误差
for (int i = 0; i < hiddenSize; i++) {
double sum = 0;
for (int j = 0; j < outputSize; j++) {
sum += outputError[j] * hiddenOutputWeights[i][j];
}
hiddenError[i] = hidden[i] * (1 - hidden[i]) * sum;
}
// 更新隐藏层到输出层的权重和偏差
for (int i = 0; i < hiddenSize; i++) {
for (int j = 0; j < outputSize; j++) {
hiddenOutputWeights[i][j] += learningRate * outputError[j] * hidden[i];
}
}
for (int i = 0; i < outputSize; i++) {
outputBiases[i] += learningRate * outputError[i];
}
// 更新输入层到隐藏层的权重和偏差
for (int i = 0; i < inputSize; i++) {
for (int j = 0; j < hiddenSize; j++) {
inputHiddenWeights[i][j] += learningRate * hiddenError[j] * input[i];
}
}
for (int i = 0; i < hiddenSize; i++) {
hiddenBiases[i] += learningRate * hiddenError[i];
}
}
// 训练网络
public void train(double[][] inputs, double[][] targets, int epochs, double learningRate) {
for (int epoch = 0; epoch < epochs; epoch++) {
double error = 0;
for (int i = 0; i < inputs.length; i++) {
double[] input = inputs[i];
double[] target = targets[i];
double[] output = forward(input);
backward(input, output, target, learningRate);
for (int j = 0; j < outputSize; j++) {
error += (target[j] - output[j]) * (target[j] - output[j]);
}
}
System.out.println("Epoch " + epoch + ", error: " + error);
}
}
// 预测
public double[] predict(double[] input) {
return forward(input);
}
public static void main(String[] args) {
// 示例数据
double[][] inputs = {
{0.1, 0.2, 0.3},
{0.4, 0.5, 0.6},
{0.7, 0.8, 0.9}
};
double[][] targets = {
{0.4},
{0.7},
{1.0}
};
// 创建神经网络
BPNeuralNetwork neuralNetwork = new BPNeuralNetwork(3, 4, 1);
// 训练神经网络
neuralNetwork.train(inputs, targets, 1000, 0.1);
// 预测
double[] input = {0.3, 0.5, 0.7};
double[] output = neuralNetwork.predict(input);
System.out.println("Predicted output: " + Arrays.toString(output));
}
}
```
这个代码框架可以根据您的需求进行修改和扩展,例如调整层数、节点数、激活函数、损失函数等。同时,您需要根据您的数据进行数据预处理,例如归一化、标准化等,以提高训练效果。
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