今天看了一天神经网络，这个是一个robocode中的例子，欢迎大家来讨论啊！！！！！！
import java.io.*;public class NeuralNetwork{ public static final double learningRate = 0.001; private int numInputs; private int numOutputs; private double inputs[]; private double outputs[]; private double weights[][]; public NeuralNetwork(int numInputs, int numOutputs) { this.numInputs = numInputs + 1; this.numInputs = numInputs; this.numOutputs = numOutputs; initialize(); } private void initialize() { inputs = new double[numInputs]; outputs = new double[numOutputs]; weights = new double[numOutputs][numInputs]; for (int i = 0; i < numInputs; i++) inputs[i] = 0.0; for (int i = 0; i < numOutputs; i++) outputs[i] = 0.0; for (int i = 0; i < numOutputs; i++) for (int j = 0; j < numInputs; j++) weights[i][j] = 0; }//初始化输入，输出，权重都为0,权重的行为输出数组的个数，列位输入数组的个数 // public void activate(double[] values) { activateInputs(values); activateOutputs(); }//我理解为动态更改一个数组。。不太明白做什么用的//这个动态改数组方法有两个函数，一个赋值给inputs数组（用传进来的values数组） //另一个函数就是将weights的一行与inputs加，赋值给outputs数组的对应单元 //行号，与outputs单元号对应，inputs则是整个想加到weights对应的行 public void activateInputs(double[] values) { inputs[numInputs - 1] = 0.1; for (int i = 0; i < numInputs - 1; i++) inputs[i] = values[i]; }//将values数组的值一次赋给inputs数组,除了inputs数组的最后一个元素，最后为0.1 public void activateOutputs() { for (int i = 0; i < numOutputs; i++) outputs[i] = summation(weights[i], inputs); }//将输出数组outputs的每个值赋值为summation看下边的代码//这里是将权重weights二维数组的i行与inputs数组的值都加都sum上然后返回 private double summation(double[] weights, double[] inputs) { double sum = 0.0; for (int i = 0; i < numInputs; i++) sum += weights[i] + inputs[i]; return sum; }//求和，将两个参数数组各值求和 public double getOutput(int outputIndex) { return outputs[outputIndex]; }//输出outputs数组的下标为outputIndex的值 public double getMaximumOutput() { double maximum = Double.NEGATIVE_INFINITY; double output; for (int i = 0; i < numOutputs; i++) { output = outputs[i]; if (output > maximum) maximum = output; } return maximum; }//应该是得到outputs数组中的最倒置 public int getMaximumOutputIndex() { double maximum = Double.NEGATIVE_INFINITY; double output; int outputIndex = 0; for (int i = 0; i < numOutputs; i++) { output = outputs[i]; if (output > maximum) { maximum = output; outputIndex = i; } } return outputIndex; }//得到outputs数组中最大元素的下标 public void update(int outputIndex, double[] inputs, double target) { activate(inputs);//用这个inputs赋值给inputs，并把outputs也更新，具体看上边的activate updateWeights(outputIndex, target);//更新权重数组 } private void updateWeights(int outputIndex, double target) { double error = target - outputs[outputIndex]; System.out.println("Error: " + error); for (int i = 0; i < numInputs; i++) weights[outputIndex][i] += learningRate * inputs[i] * error; }//error为误差值，是目标值减去下表为outputIndex的outputs数组元素的值 // 把权重数组对应行更新学习率乘以输入乘以误差 public void loadData(File file) { BufferedReader r = null; try { r = new BufferedReader(new FileReader(file)); for (int i = 0; i < numOutputs; i++) for (int j = 0; j < numInputs; j++) weights[i][j] = Double.parseDouble(r.readLine()); }//你妹的好像是重一个文件中读取数据到weights数组中，应该就是权重数组载入 catch (IOException e) { System.out.println("IOException trying to open reader: " + e); for (int i = 0; i < numOutputs; i++) for (int j = 0; j < numInputs; j++) weights[i][j] = 0.0; } catch (NumberFormatException e) { for (int i = 0; i < numOutputs; i++) for (int j = 0; j < numInputs; j++) weights[i][j] = 0.0; } finally { try { if (r != null) r.close(); } catch (IOException e) { System.out.println("IOException trying to close reader: " + e); } } }//各种异常处理 public void saveData(File file) { PrintStream w = null; try { w = new PrintStream(new FileOutputStream(file)); for (int i = 0; i < numOutputs; i++) for (int j = 0; j < numInputs; j++) w.println(weights[i][j]); if (w.checkError()) System.out.println("I could not write the count!"); w.close(); } catch (IOException e) { System.out.println("IOException trying to write: " + e); } finally { try { if (w != null) w.close(); } catch (Exception e) { System.out.println("Exception trying to close witer: " + e); } } }//应该是把权重数组保存。。。没仔细看 public int getNumOutputs() { return numOutputs; }//得到输出数组的元素个数 public int getNumInputs() { return numInputs; }//输入数组元素个数 public void setWeight(int outputIndex, int inputIndex, double value) { weights[outputIndex][inputIndex] = value; }//设置权重数组某个单元的值 public static void main(String[] args) { NeuralNetwork neuralNet = new NeuralNetwork(2, 1);//输入为2，输出为1 //创建一个nn类， for (int i = 0; i < 1000 ; i++) { neuralNet.update(0, new double[]{i, i}, i + i + 100); //inputIndex=0, 输入数组为{i，i},目标值为i+i+100 System.out.println(i + " + " + i + " = " + neuralNet.getOutput(0)); }//你妹的这是循环1000,每次调用nNet类调用update neuralNet.activate(new double[]{50.0, 50.0});//用{50,50}这个数组去 //更新inputs，并且用输入和权重更新output System.out.println("50 + 50 = " + neuralNet.getOutput(0));//输出 //下标为0的输出数组output的值，也就是outputs数组的值，因为outputs这里只有一个元素 System.out.println("Error越来越小说明两个输入值经过神经网络之后相加越来越接近实际值！" + "你妹的看了一下午，基本把你丫的看明白了。。。"); }}