将期望矩阵的间隔变成1

src/main/java/org/wlld/imageRecognition/TempleConfig.java

@@ -53,6 +53,11 @@ public class TempleConfig {
     private boolean isShowLog = false;
     private ActiveFunction activeFunction = new Tanh();
     private double studyPoint = 0;
+    private double matrixWidth = 1;//期望矩阵间隔
+
+    public void setMatrixWidth(double matrixWidth) {
+        this.matrixWidth = matrixWidth;
+    }
 
     public boolean isAccurate() {
         return isAccurate;
@@ -257,7 +262,7 @@ public class TempleConfig {
             row = 5;
             column = (int) (d * row);
         }
-        initNerveManager(initPower, row * column, deep,studyPoint);
+        initNerveManager(initPower, row * column, deep, studyPoint);
     }
 
     private void initNerveManager(boolean initPower, int sensoryNerveNub
@@ -281,7 +286,7 @@ public class TempleConfig {
                 if (isThreeChannel) {
                     nub = nub * 3;
                 }
-                initNerveManager(true, nub, this.deep,studyPoint);
+                initNerveManager(true, nub, this.deep, studyPoint);
                 break;
             case Classifier.LVQ:
                 lvq = new LVQ(classificationNub, lvqNub);
@@ -293,10 +298,9 @@ public class TempleConfig {
         }
         //加载各识别分类的期望矩阵
         matrixMap.put(0, new Matrix(height, width));
-        double nub = 5;//每个分类期望参数的跨度
         for (int k = 1; k <= classificationNub; k++) {
             Matrix matrix = new Matrix(height, width);//初始化期望矩阵
-            double t = k * nub;//期望矩阵的分类参数数值
+            double t = k * matrixWidth;//期望矩阵的分类参数数值
             for (int i = 0; i < height; i++) {//给期望矩阵注入期望参数
                 for (int j = 0; j < width; j++) {
                     matrix.setNub(i, j, t);
@@ -316,7 +320,7 @@ public class TempleConfig {
     private NerveManager initNerveManager(Map<Integer, Matrix> matrixMap, boolean initPower, int deep) throws Exception {
         //初始化卷积神经网络
         NerveManager convolutionNerveManager = new NerveManager(1, 1,
-                1, deep - 1, new ReLu(), true, isAccurate,studyPoint);
+                1, deep - 1, new ReLu(), true, isAccurate, studyPoint);
         convolutionNerveManager.setMatrixMap(matrixMap);//给卷积网络管理器注入期望矩阵
         convolutionNerveManager.init(initPower, true, isShowLog);
         return convolutionNerveManager;