diff --git a/src/main/java/org/wlld/imageRecognition/border/LVQ.java b/src/main/java/org/wlld/imageRecognition/border/LVQ.java
index b6863bc..a5540b7 100644
--- a/src/main/java/org/wlld/imageRecognition/border/LVQ.java
+++ b/src/main/java/org/wlld/imageRecognition/border/LVQ.java
@@ -92,7 +92,7 @@ public class LVQ {
                 //修正矩阵与原矩阵的范数差
                 double dist = vectorEqual(modelMatrix, matrix);
                 if (distEnd == 0 || dist < distEnd) {
-                    id = matrixBody.getId();
+                    id = modelBody.getId();
                     distEnd = dist;
                 }
             }
diff --git a/src/test/java/org/wlld/HelloWorld.java b/src/test/java/org/wlld/HelloWorld.java
index 5f59345..2348ad5 100644
--- a/src/test/java/org/wlld/HelloWorld.java
+++ b/src/test/java/org/wlld/HelloWorld.java
@@ -31,32 +31,30 @@ public class HelloWorld {
     public static void test() throws Exception {
         Picture picture = new Picture();
         TempleConfig templeConfig = new TempleConfig();
-        // templeConfig.setHavePosition(true);
+        //templeConfig.setHavePosition(true);
 //        Frame frame = new Frame();
 //        frame.setWidth(3024);
 //        frame.setHeight(4032);
 //        frame.setLengthHeight(100);
 //        frame.setLengthWidth(100);
 //        templeConfig.setFrame(frame);
-        //ModelParameter modelParameter1 = JSONObject.parseObject(ModelData.DATA, ModelParameter.class);
+        ModelParameter modelParameter = JSONObject.parseObject(ModelData.DATA, ModelParameter.class);
         templeConfig.init(StudyPattern.Accuracy_Pattern, true, 1076, 1436, 1);
+        templeConfig.insertModel(modelParameter);
         Operation operation = new Operation(templeConfig);
-        Matrix right = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/s1.jpeg");
-        operation.learning(right, 1, false);
 
-//        for (int i = 1; i < 120; i++) {//faster rcnn神经网络学习
-//            System.out.println("study==" + i);
-//            //读取本地URL地址图片,并转化成矩阵
-//            Matrix right = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/c/c" + i + ".png");
-//            Matrix wrong = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/b/b" + i + ".png");
-//            //将图像矩阵和标注加入进行学习，Accuracy_Pattern 模式 进行第二次学习
-//            //第二次学习的时候，第三个参数必须是 true
-//            operation.learning(right, 1, true);
-//            operation.learning(wrong, 0, true);
-//        }
-//        templeConfig.boxStudy();//边框聚类
+        for (int i = 1; i < 100; i++) {//faster rcnn神经网络学习
+            System.out.println("study==" + i);
+            //读取本地URL地址图片,并转化成矩阵
+            Matrix right = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/c/c" + i + ".png");
+            Matrix wrong = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/b/b" + i + ".png");
+            //将图像矩阵和标注加入进行学习，Accuracy_Pattern 模式 进行第二次学习
+            //第二次学习的时候，第三个参数必须是 true
+            operation.learning(right, 1, true);
+            operation.learning(wrong, 0, true);
+        }
 //        //精准模式在全部学习结束的时候一定要使用此方法，速度模式不要调用此方法
-//        templeConfig.startLvq();//原型向量量化
+        templeConfig.startLvq();//原型向量量化
 //        templeConfig.boxStudy();//边框回归
 //        for (int j = 1; j < 2; j++) {
 //            Matrix right = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/c/c" + j + ".png");