我想对具有资格和价格的汽车进行分类。我应该用MLP来做,但除了XOR示例之外没有任何其他示例。我有6个条件,我将它们转换为[1,0,0,0]加倍的双倍。(条件在我链接的uci集中。)
这是我的MLP代码,我想用uci数据集Dataset训练它如何使其适应此代码?
编辑:让我更清楚一点,我不是说除了XOR问题之外没有其他任何例子。我的意思是我需要一个输入集的例子,而不是像[1,0]我需要超过2个输入。
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
public class MultiLayerPerceptron implements Cloneable
{
protected double fLearningRate = 0.6;
protected Layer[] fLayers;
protected TransferFunction fTransferFunction;
public MultiLayerPerceptron(int[] layers, double learningRate, TransferFunction fun)
{
fLearningRate = learningRate;
fTransferFunction = fun;
fLayers = new Layer[layers.length];
for(int i = 0; i < layers.length; i++)
{
if(i != 0)
{
fLayers[i] = new Layer(layers[i], layers[i - 1]);
}
else
{
fLayers[i] = new Layer(layers[i], 0);
}
}
}
public double[] execute(double[] input)
{
int i;
int j;
int k;
double new_value;
double output[] = new double[fLayers[fLayers.length - 1].Length];
// Put input
for(i = 0; i < fLayers[0].Length; i++)
{
fLayers[0].Neurons[i].Value = input[i];
}
// Execute - hiddens + output
for(k = 1; k < fLayers.length; k++)
{
for(i = 0; i < fLayers[k].Length; i++)
{
new_value = 0.0;
for(j = 0; j < fLayers[k - 1].Length; j++)
new_value += fLayers[k].Neurons[i].Weights[j] * fLayers[k - 1].Neurons[j].Value;
new_value += fLayers[k].Neurons[i].Bias;
fLayers[k].Neurons[i].Value = fTransferFunction.evalute(new_value);
}
}
// Get output
for(i = 0; i < fLayers[fLayers.length - 1].Length; i++)
{
output[i] = fLayers[fLayers.length - 1].Neurons[i].Value;
}
return output;
}
public double backPropagateMultiThread(double[] input, double[] output, int nthread)
{
return 0.0;
}
public double backPropagate(double[] input, double[] output)
{
double new_output[] = execute(input);
double error;
int i;
int j;
int k;
/* doutput = correct output (output) */
for(i = 0; i < fLayers[fLayers.length - 1].Length; i++)
{
error = output[i] - new_output[i];
fLayers[fLayers.length - 1].Neurons[i].Delta = error * fTransferFunction.evaluteDerivate(new_output[i]);
}
for(k = fLayers.length - 2; k >= 0; k--)
{
//delta
for(i = 0; i < fLayers[k].Length; i++)
{
error = 0.0;
for(j = 0; j < fLayers[k + 1].Length; j++)
error += fLayers[k + 1].Neurons[j].Delta * fLayers[k + 1].Neurons[j].Weights[i];
fLayers[k].Neurons[i].Delta = error * fTransferFunction.evaluteDerivate(fLayers[k].Neurons[i].Value);
}
// success
for(i = 0; i < fLayers[k + 1].Length; i++)
{
for(j = 0; j < fLayers[k].Length; j++)
fLayers[k + 1].Neurons[i].Weights[j] += fLearningRate * fLayers[k + 1].Neurons[i].Delta *
fLayers[k].Neurons[j].Value;
fLayers[k + 1].Neurons[i].Bias += fLearningRate * fLayers[k + 1].Neurons[i].Delta;
}
}
// error
error = 0.0;
for(i = 0; i < output.length; i++)
{
error += Math.abs(new_output[i] - output[i]);
//System.out.println(output[i]+" "+new_output[i]);
}
error = error / output.length;
return error;
}
public boolean save(String path)
{
try
{
FileOutputStream fout = new FileOutputStream(path);
ObjectOutputStream oos = new ObjectOutputStream(fout);
oos.writeObject(this);
oos.close();
}
catch (Exception e)
{
return false;
}
return true;
}
public static MultiLayerPerceptron load(String path)
{
try
{
MultiLayerPerceptron net;
FileInputStream fin = new FileInputStream(path);
ObjectInputStream oos = new ObjectInputStream(fin);
net = (MultiLayerPerceptron) oos.readObject();
oos.close();
return net;
}
catch (Exception e)
{
return null;
}
}
public double getLearningRate()
{
return fLearningRate;
}
public void setLearningRate(double rate)
{
fLearningRate = rate;
}
public void setTransferFunction(TransferFunction fun)
{
fTransferFunction = fun;
}
public int getInputLayerSize()
{
return fLayers[0].Length;
}
public int getOutputLayerSize()
{
return fLayers[fLayers.length - 1].Length;
}
}
答案 0 :(得分:0)
使用ANN,XOR是非线性分类的简单基准。它有2个输入和1个输出(例如[0,1] =&gt; [1]),而且它不仅仅是示例。
对于您的问题,简单的答案是将其视为与实施XOR的神经网络(MLP)相同,
不同之处在于您需要5个输入和1个输出。(参考您的uci数据集)
您也可以尝试以下链接:
http://scikit-learn.org/dev/modules/neural_networks_supervised.html