增加输出神经元回调，及API文档说明

ApiDocument.txt

@@ -0,0 +1,38 @@
+//MyBrain-JAVA 神经网络api说明文档
+//构建一个神经网络管理器，参数：(感知神经元个数，隐层神经元个数，输出神经元个数，隐层神经元深度)
+//一个神经网络管理管理一个神经网络学习内容，所以当初始化一个神经网络管理器，请将它置于静态对象
+NerveManager nerveManager =
+new NerveManager(int sensoryNerveNub,int hiddenNerverNub,int outNerveNub,int hiddenDepth);
+//开始构建神经网络，参数（各神经元是否进行权重及阈值的初始化），若不进行初始化，则用户需将之前保存的训练数据加载进神经元
+nerveManager.init(boolean initPower);
+//设置学习率(取值范围是0-1开区间)，若不设置默认为0.1
+nerveManager.setStudyPoint(0.2);
+//添加判断回调输出类，参数：创建一个类 这将这类实现OutBack接口和其类方法,检测状态下的输出神经元，会将输出结果回调到这个类
+//若神经网络是学习状态，可不设置。检测状态若不设置会报错
+nerveManager.setOutBack(new Test());
+//获取感知神经元集合
+List<SensoryNerve> sensoryNerves = nerveManager.getSensoryNerves();
+//从集合里拿出一个感知神经元进行输入，参数：事件ID，输入参数，是否是学习状态，期望结果
+sensoryNerves.get(i).postMessage(long eventId, double parameter, boolean isStudy, double E);
+//实现OutBack 接口的类 回调 getBack() 参数：该输出神经元输出值，该神经元ID，该事件ID
+public void getBack(double out, int id, long eventId) {
+        System.out.println("out==" + out + ",id==" + id + ",eventId==" + eventId);
+    }
+神经管理器说明
+private List<SensoryNerve> sensoryNerves = new ArrayList<>();//感知神经元
+private List<List<Nerve>> depthNerves = new ArrayList<>();//隐层神经元
+private List<Nerve> outNevers = new ArrayList<>();//输出神经元
+学习结束通过GET方法 获取三类神经元 集合并通过Nerve类 的
+public Map<Integer, Double> getDendrites() {//各参数权重
+        return dendrites;
+    }
+     public double getThreshold() {//该神经元阈值
+            return threshold;
+        }
+获取这两个参数 并保存数据库，等下次服务启动 将对应数据在通过其SET方法 加载进对应神经元，此为上次学习结果
+参数说明：
+eventId：事件ID,进行一次神经网络判断，为一次事件ID,ID唯一，可通过IdCreator.get().nextId 生成
+其目的就是 回调的时候 通过这个ID 判断 输出的结果是对应哪一个检测及输入事件
+parameter:就是输入参数
+isStudy:是否是学习状态，判断状态传FALSE,模板学习传TRUE,注意：学习状态输出神经元不输出结果
+E:期望结果，检测状态随便写值没有影响，学习状态是，该模板期望的结果，通常为0或者1，即分类值

src/main/java/org/wlld/App.java

@@ -14,12 +14,20 @@ public class App {
     }
 
     public static void createNerveTest() throws Exception {
+        //构建一个神经网络管理器，参数：(感知神经元个数，隐层神经元个数，输出神经元个数，输出神经元深度)
+        //一个神经网络管理管理一个神经网络学习内容，
         NerveManager nerveManager =
                 new NerveManager(2, 3, 1, 2);
+        //开始构建神经网络，参数为是否初始化权重及阈值,若
         nerveManager.init(true);
+        nerveManager.setStudyPoint(0.2);//设置学习率(取值范围是0-1开区间)，若不设置默认为0.1
+        nerveManager.setOutBack(new Test());//添加判断回调输出类
         List<SensoryNerve> sensoryNerves = nerveManager.getSensoryNerves();
         for (int i = 0; i < sensoryNerves.size(); i++) {
             sensoryNerves.get(i).postMessage(1, 2 + i, true, 1);
         }
+        for (int i = 0; i < sensoryNerves.size(); i++) {
+            sensoryNerves.get(i).postMessage(1, 2 + i, false, 1);
+        }
     }
 }

src/main/java/org/wlld/Test.java

@@ -0,0 +1,15 @@
+package org.wlld;
+
+import org.wlld.i.OutBack;
+
+/**
+ * @author lidapeng
+ * @description
+ * @date 1:19 下午 2019/12/24
+ */
+public class Test implements OutBack {
+    @Override
+    public void getBack(double out, int id, long eventId) {
+        System.out.println("out==" + out + ",id==" + id + ",eventId==" + eventId);
+    }
+}

src/main/java/org/wlld/i/OutBack.java

@@ -0,0 +1,11 @@
+package org.wlld.i;
+
+/**
+ * @author lidapeng
+ * @将神经元的输出回调
+ * @date 1:07 下午 2019/12/24
+ */
+public interface OutBack {
+    //输出回调
+    void getBack(double out, int id, long eventId);
+}

src/main/java/org/wlld/nerveCenter/NerveManager.java

@@ -1,5 +1,6 @@
 package org.wlld.nerveCenter;
 
+import org.wlld.i.OutBack;
 import org.wlld.nerveEntity.HiddenNerve;
 import org.wlld.nerveEntity.Nerve;
 import org.wlld.nerveEntity.OutNerve;
@@ -23,6 +24,20 @@ public class NerveManager {
     private List<List<Nerve>> depthNerves = new ArrayList<>();//隐层神经元
     private List<Nerve> outNevers = new ArrayList<>();//输出神经元
     private boolean initPower;
+    private OutBack outBack;
+    private double studyPoint = 0.1;//学习率
+
+    public double getStudyPoint() {
+        return studyPoint;
+    }
+
+    public void setStudyPoint(double studyPoint) throws Exception {
+        if (studyPoint < 1 && studyPoint > 0) {
+            this.studyPoint = studyPoint;
+        } else {
+            throw new Exception("studyPoint Values range from 0 to 1");
+        }
+    }
 
     public NerveManager(int sensoryNerveNub, int hiddenNerverNub, int outNerveNub
             , int hiddenDepth) {
@@ -32,6 +47,18 @@ public class NerveManager {
         this.hiddenDepth = hiddenDepth;
     }
 
+    public void setOutBack(OutBack outBack) {//设置回调类
+        this.outBack = outBack;
+        for (Nerve nerve : outNevers) {
+            OutNerve outNerve = (OutNerve) nerve;
+            outNerve.setOutBack(outBack);
+        }
+    }
+
+    public OutBack getOutBack() {//获取回调类的引用
+        return outBack;
+    }
+
     public int getHiddenNerverNub() {
         return hiddenNerverNub;
     }
@@ -68,7 +95,7 @@ public class NerveManager {
         List<Nerve> lastNeveList = depthNerves.get(depthNerves.size() - 1);
         //初始化输出神经元
         for (int i = 1; i < outNerveNub + 1; i++) {
-            OutNerve outNerve = new OutNerve(i, hiddenNerverNub, 0, initPower);
+            OutNerve outNerve = new OutNerve(i, hiddenNerverNub, 0, studyPoint, initPower);
             //输出层神经元连接最后一层隐层神经元
             outNerve.connectFathor(lastNeveList);
             outNevers.add(outNerve);
@@ -104,7 +131,7 @@ public class NerveManager {
                 } else {
                     downNub = hiddenNerverNub;
                 }
-                HiddenNerve hiddenNerve = new HiddenNerve(j, i + 1, upNub, downNub, initPower);
+                HiddenNerve hiddenNerve = new HiddenNerve(j, i + 1, upNub, downNub, studyPoint, initPower);
                 hiddenNerveList.add(hiddenNerve);
             }
             depthNerves.add(hiddenNerveList);

src/main/java/org/wlld/nerveEntity/HiddenNerve.java

@@ -3,8 +3,6 @@ package org.wlld.nerveEntity;
 import org.apache.logging.log4j.LogManager;
 import org.apache.logging.log4j.Logger;
 
-import java.util.Map;
-
 /**
  * @author lidapeng
  * 隐层神经元
@@ -14,8 +12,8 @@ public class HiddenNerve extends Nerve {
     private int depth;//所处深度
     static final Logger logger = LogManager.getLogger(HiddenNerve.class);
 
-    public HiddenNerve(int id, int depth, int upNub, int downNub, boolean init) {//隐层神经元
-        super(id, upNub, "HiddenNerve", downNub, init);
+    public HiddenNerve(int id, int depth, int upNub, int downNub, double studyPoint, boolean init) {//隐层神经元
+        super(id, upNub, "HiddenNerve", downNub, studyPoint, init);
         this.depth = depth;
     }
 

src/main/java/org/wlld/nerveEntity/Nerve.java

@@ -28,7 +28,7 @@ public abstract class Nerve {
     protected double outNub;//输出数值（ps:只有训练模式的时候才可保存输出过的数值）
     protected double E;//模板期望值
     protected double gradient;//当前梯度
-    protected final double studyPoint = 0.1;
+    protected double studyPoint;
     protected double sigmaW;//对上一层权重与上一层梯度的积进行求和
     private int backNub = 0;//当前节点被反向传播的次数
 
@@ -48,11 +48,13 @@ public abstract class Nerve {
         this.threshold = threshold;
     }
 
-    protected Nerve(int id, int upNub, String name, int downNub, boolean init) {//该神经元在同层神经元中的编号
+    protected Nerve(int id, int upNub, String name, int downNub,
+                    double studyPoint, boolean init) {//该神经元在同层神经元中的编号
         this.id = id;
         this.upNub = upNub;
         this.name = name;
         this.downNub = downNub;
+        this.studyPoint = studyPoint;
         if (init) {
             initPower();//生成随机权重
         }

src/main/java/org/wlld/nerveEntity/OutNerve.java

@@ -2,11 +2,9 @@ package org.wlld.nerveEntity;
 
 import org.apache.logging.log4j.LogManager;
 import org.apache.logging.log4j.Logger;
+import org.wlld.i.OutBack;
 import org.wlld.tools.ArithUtil;
 
-import java.util.List;
-import java.util.Map;
-
 /**
  * @author lidapeng
  * 输出神经元
@@ -14,9 +12,14 @@ import java.util.Map;
  */
 public class OutNerve extends Nerve {
     static final Logger logger = LogManager.getLogger(OutNerve.class);
+    private OutBack outBack;
+
+    public OutNerve(int id, int upNub, int downNub, double studyPoint, boolean init) {
+        super(id, upNub, "OutNerve", downNub, studyPoint, init);
+    }
 
-    public OutNerve(int id, int upNub, int downNub, boolean init) {
-        super(id, upNub, "OutNerve", downNub, init);
+    public void setOutBack(OutBack outBack) {
+        this.outBack = outBack;
     }
 
     @Override
@@ -33,8 +36,13 @@ public class OutNerve extends Nerve {
                 gradient = outGradient();//当前梯度变化
                 //调整权重 修改阈值 并进行反向传播
                 updatePower(eventId);
-            } else {
+            } else {//获取最后输出
                 destoryParameter(eventId);
+                if (outBack != null) {
+                    outBack.getBack(out, getId(), eventId);
+                } else {
+                    throw new Exception("not find outBack");
+                }
             }
         }
     }

src/main/java/org/wlld/nerveEntity/SensoryNerve.java

@@ -10,7 +10,7 @@ import java.util.List;
 public class SensoryNerve extends Nerve {
 
     public SensoryNerve(int id, int upNub) {
-        super(id, upNub, "SensoryNerve", 0, false);
+        super(id, upNub, "SensoryNerve", 0, 0.1, false);
     }
 
     public void postMessage(long eventId, double parameter, boolean isStudy, double E) throws Exception {//感知神经元输出