本文實例講述了基於Java實現的一層簡單人工神經網絡算法。分享給大家供大家參考，具體如下：
先來看看筆者繪製的算法圖：
2、數據類
import java.util.Arrays;public class Data { double[] vector; int dimention; int type; public double[] getVector() { return vector; } public void setVector(double[] vector) { this.vector = vector; } public int getDimention() { return dimention; } public void setDimention(int dimention) { this.dimention = dimention; } public int getType() { return type; } public void setType(int type) { this.type = type; } public Data(double[] vector, int dimention, int type) { super(); this.vector = vector; this.dimention = dimention; this.type = type; } public Data() { } @Override public String toString() { return "Data [vector=" + Arrays.toString(vector) + ", dimention=" + dimention + ", type=" + type + "]"; }}3、簡單人工神經網絡
package cn.edu.hbut.chenjie;import java.util.ArrayList;import java.util.List;import java.util.Random;import org.jfree.chart.ChartFactory;import org.jfree.chart.ChartFrame;import org.jfree.chart.JFreeChart;import org.jfree.data.xy.DefaultXYDataset;import org.jfree.ui.RefineryUtilities;public class ANN2 { private double eta;//學習率private int n_iter;//權重向量w[]訓練次數private List<Data> exercise;//訓練數據集private double w0 = 0;//閾值private double x0 = 1;//固定值private double[] weights;//權重向量，其長度為訓練數據維度+1，在本例中數據為2維，故長度為3 private int testSum = 0;//測試數據總數private int error = 0;//錯誤次數DefaultXYDataset xydataset = new DefaultXYDataset(); /** * 向圖表中增加同類型的數據* @param type 類型* @param a 所有數據的第一個分量* @param b 所有數據的第二個分量*/ public void add(String type,double[] a,double[] b) { double[][] data = new double[2][a.length]; for(int i=0;i<a.length;i++) { data[0][i] = a[i]; data[1][i] = b[i]; } xydataset.addSeries(type, data); } /** * 畫圖*/ public void draw() { JFreeChart jfreechart = ChartFactory.createScatterPlot("exercise", "x1", "x2", xydataset); ChartFrame frame = new ChartFrame("訓練數據", jfreechart); frame.pack(); RefineryUtilities.centerFrameOnScreen(frame); frame.setVisible(true); } public static void main(String[] args) { ANN2 ann2 = new ANN2(0.001,100);//構造人工神經網絡List<Data> exercise = new ArrayList<Data>();//構造訓練集//人工模擬1000條訓練數據，分界線為x2=x1+0.5 for(int i=0;i<1000000;i++) { Random rd = new Random(); double x1 = rd.nextDouble();//隨機產生一個分量double x2 = rd.nextDouble();//隨機產生另一個分量double[] da = {x1,x2};//產生數據向量Data d = new Data(da, 2, x2 > x1+0.5 ? 1 : -1);//構造數據exercise.add(d);//將訓練數據加入訓練集} int sum1 = 0;//記錄類型1的訓練記錄數int sum2 = 0;//記錄類型-1的訓練記錄數for(int i = 0; i < exercise.size(); i++) { if(exercise.get(i).getType()==1) sum1++; else if(exercise.get(i).getType()==-1) sum2++; } double[] x1 = new double[sum1]; double[] y1 = new double[sum1]; double[] x2 = new double[sum2]; double[] y2 = new double[sum2]; int index1 = 0; int index2 = 0; for(int i = 0; i < exercise.size(); i++) { if(exercise.get(i).getType()==1) { x1[index1] = exercise.get(i).vector[0]; y1[index1++] = exercise.get(i).vector[1]; } else if(exercise.get(i).getType()==-1) { x2[index2] = exercise.get(i).vector[0]; y2[index2++] = exercise.get(i).vector[1]; } } ann2.add("1", x1, y1); ann2.add("-1", x2, y2); ann2.draw(); ann2.input(exercise);//將訓練集輸入人工神經網絡ann2.fit();//訓練ann2.showWeigths();//顯示權重向量//人工生成一千條測試數據for(int i=0;i<10000;i++) { Random rd = new Random(); double x1_ = rd.nextDouble(); double x2_ = rd.nextDouble(); double[] da = {x1_,x2_}; Data test = new Data(da, 2, x2_ > x1_+0.5 ? 1 : -1); ann2.predict(test);//測試} System.out.println("總共測試" + ann2.testSum + "條數據,有" + ann2.error + "條錯誤,錯誤率:" + ann2.error * 1.0 /ann2.testSum * 100 + "%"); } /** * * @param eta 學習率* @param n_iter 權重分量學習次數*/ public ANN2(double eta, int n_iter) { this.eta = eta; this.n_iter = n_iter; } /** * 輸入訓練集到人工神經網絡* @param exercise */ private void input(List<Data> exercise) { this.exercise = exercise;//保存訓練集weights = new double[exercise.get(0).dimention + 1];//初始化權重向量，其長度為訓練數據維度+1 weights[0] = w0;//權重向量第一個分量為w0 for(int i = 1; i < weights.length; i++) weights[i] = 0;//其餘分量初始化為0 } private void fit() { for(int i = 0; i < n_iter; i++)//權重分量調整n_iter次{ for(int j = 0; j < exercise.size(); j++)//對於訓練集中的每條數據進行訓練{ int real_result = exercise.get(j).type;//y int calculate_result = CalculateResult(exercise.get(j));//y' double delta0 = eta * (real_result - calculate_result);//計算閾值更新w0 += delta0;//閾值更新weights[0] = w0;//更新w[0] for(int k = 0; k < exercise.get(j).getDimention(); k++)//更新權重向量其它分量{ double delta = eta * (real_result - calculate_result) * exercise.get(j).vector[k]; //Δw=η*(y-y')*X weights[k+1] += delta; //w=w+Δw } } } } private int CalculateResult(Data data) { double z = w0 * x0; for(int i = 0; i < data.dimention; i++) z += data.vector[i] * weights[i+1]; //z=w0x0+w1x1+...+WmXm //激活函數if(z>=0) return 1; else return -1; } private void showWeigths() { for(double w : weights) System.out.println(w); } private void predict(Data data) { int type = CalculateResult(data); if(type == data.getType()) { //System.out.println("預測正確"); } else { //System.out.println("預測錯誤"); error ++; } testSum ++; }}運行結果：
-0.22000000000000017-0.44168439828154530.442444202054685總共測試10000條數據,有17條錯誤,錯誤率:0.16999999999999998%
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