本文实例讲述了基于java实现的一层简单人工神经网络算法。分享给大家供大家参考，具体如下：
先来看看笔者绘制的算法图：
2、数据类
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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、简单人工神经网络
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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;//学习率 private int n_iter;//权重向量w[]训练次数 private list<data> exercise;//训练数据集 private double w0 = 0;//阈值 private double x0 = 1;//固定值 private double[] weights;//权重向量，其长度为训练数据维度+1，在本例中数据为2维，故长度为3 private int testsum = 0;//测试数据总数 private int error = 0;//错误次数 defaultxydataset xydataset = new defaultxydataset(); /** * 向图表中增加同类型的数据 * @param type 类型 * @param a 所有数据的第一个分量 * @param b 所有数据的第二个分量 */ 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); } /** * 画图 */ public void draw() { jfreechart jfreechart = chartfactory.createscatterplot("exercise", "x1", "x2", xydataset); chartframe frame = new chartframe("训练数据", jfreechart); frame.pack(); refineryutilities.centerframeonscreen(frame); frame.setvisible(true); } public static void main(string[] args) { ann2 ann2 = new ann2(0.001,100);//构造人工神经网络 list<data> exercise = new arraylist<data>();//构造训练集 //人工模拟1000条训练数据 ，分界线为x2=x1+0.5 for(int i=0;i<1000000;i++) { random rd = new random(); double x1 = rd.nextdouble();//随机产生一个分量 double x2 = rd.nextdouble();//随机产生另一个分量 double[] da = {x1,x2};//产生数据向量 data d = new data(da, 2, x2 > x1+0.5 ? 1 : -1);//构造数据 exercise.add(d);//将训练数据加入训练集 } int sum1 = 0;//记录类型1的训练记录数 int sum2 = 0;//记录类型-1的训练记录数 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);//将训练集输入人工神经网络 ann2.fit();//训练 ann2.showweigths();//显示权重向量 //人工生成一千条测试数据 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);//测试 } system.out.println("总共测试" + ann2.testsum + "条数据,有" + ann2.error + "条错误,错误率:" + ann2.error * 1.0 /ann2.testsum * 100 + "%"); } /** * * @param eta 学习率 * @param n_iter 权重分量学习次数 */ public ann2(double eta, int n_iter) { this.eta = eta; this.n_iter = n_iter; } /** * 输入训练集到人工神经网络 * @param exercise */ private void input(list<data> exercise) { this.exercise = exercise;//保存训练集 weights = new double[exercise.get(0).dimention + 1];//初始化权重向量，其长度为训练数据维度+1 weights[0] = w0;//权重向量第一个分量为w0 for(int i = 1; i < weights.length; i++) weights[i] = 0;//其余分量初始化为0 } private void fit() { for(int i = 0; i < n_iter; i++)//权重分量调整n_iter次 { for(int j = 0; j < exercise.size(); j++)//对于训练集中的每条数据进行训练 { int real_result = exercise.get(j).type;//y int calculate_result = calculateresult(exercise.get(j));//y' double delta0 = eta * (real_result - calculate_result);//计算阈值更新 w0 += delta0;//阈值更新 weights[0] = w0;//更新w[0] for(int k = 0; k < exercise.get(j).getdimention(); k++)//更新权重向量其它分量 { 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 //激活函数 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("预测正确"); } else { //system.out.println("预测错误"); error ++; } testsum ++; }} |
运行结果：
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-0.22000000000000017-0.44168439828154530.442444202054685总共测试10000条数据,有17条错误,错误率:0.16999999999999998% |
希望本文所述对大家java程序设计有所帮助。
原文链接：http://blog.csdn.net/csj941227/article/details/73325695
