增加注释

README.md

@@ -6,6 +6,9 @@
 * 若有想扩充的功能请进群提意见，若是通用场景我会陆续补充，技术交流群：561433236
 ## 详细视频教程地址：
 * 视频教程地址：https://www.bilibili.com/video/av89134035
+## 测试素材下载
+链接:https://pan.baidu.com/s/1Vzwn3iMPBI-FAXBDrCSglg  
+密码:7juj
 ## 框架效果演示结果:
 * 因为是框架没有图像化界面，演示结果就是控制台输出的数据，只能用视频展示，想看演示结果请看教学视频
 ## 控制台输出演示

src/main/java/org/wlld/config/Classifier.java

@@ -1,7 +1,7 @@
 package org.wlld.config;
 
 public class Classifier {//分类器
-    public static final int LVQ = 1;//LVQ分类
+    public static final int LVQ = 1;//LVQ分类 你的训练模版量非常少 比如 一种只有几十一百张照片/分类少
-    public static final int DNN = 2; //使用DNN分类
+    public static final int DNN = 2; //使用DNN分类 训练量足够大，一个种类1500+训练图片
-    public static final int VAvg = 3;//使用特征向量均值分类
+    public static final int VAvg = 3;//使用特征向量均值分类 一种只有几十一百张照片
 }

src/test/java/coverTest/FoodTest.java

@@ -37,8 +37,6 @@ public class FoodTest {
         ModelParameter modelParameter2 = JSON.parseObject(ModelData.DATA3, ModelParameter.class);
         templeConfig.insertModel(modelParameter2);
         Operation operation = new Operation(templeConfig);
-
-
         // 一阶段
 //        for (int j = 0; j < 1; j++) {
 //            for (int i = 1; i < 1500; i++) {//一阶段
@@ -132,7 +130,7 @@ public class FoodTest {
             }
         }
         double wrongPoint = ArithUtil.div(wrong, allNub);
-        System.out.println("错误率1：" + (wrongPoint * 100) + "%");
+        System.out.println("错误率：" + (wrongPoint * 100) + "%");
         ModelParameter modelParameter = templeConfig.getModel();
         String model = JSON.toJSONString(modelParameter);
         System.out.println(model);

src/test/java/org/wlld/HelloWorld.java

@@ -40,6 +40,8 @@ public class HelloWorld {
     }
 
     public static void pictureDemo1() throws Exception {//图像学习DEMO
+        //easyAI 包持续更新，现阶段一直在优化
+        //
         Picture picture = new Picture();
         //使用精度计算
         TempleConfig templeConfig = new TempleConfig(false, true);
@@ -55,10 +57,10 @@ public class HelloWorld {
         for (int i = 1; i < 1900; i++) {//一阶段
             System.out.println("study1===================" + i);
             //读取本地URL地址图片,并转化成矩阵
-            Matrix a = picture.getImageMatrixByLocal("D:\\share\\picture/a" + i + ".jpg");
+            Matrix a = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/picture/a" + i + ".jpg");
-            Matrix b = picture.getImageMatrixByLocal("D:\\share\\picture/b" + i + ".jpg");
+            Matrix b = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/picture/b" + i + ".jpg");
-            Matrix c = picture.getImageMatrixByLocal("D:\\share\\picture/c" + i + ".jpg");
+            Matrix c = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/picture/c" + i + ".jpg");
-            Matrix d = picture.getImageMatrixByLocal("D:\\share\\picture/d" + i + ".jpg");
+            Matrix d = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/picture/d" + i + ".jpg");
             operation.learning(a, 1, false);
             operation.learning(b, 2, false);
             operation.learning(c, 3, false);
@@ -66,12 +68,12 @@ public class HelloWorld {
         }
 
         //二阶段学习
-        for (int i = 1; i < 1900; i++) {
+        for (int i = 1; i < 1900; i++) {//特征归一化
             System.out.println("avg==" + i);
-            Matrix a = picture.getImageMatrixByLocal("D:\\share\\picture/a" + i + ".jpg");
+            Matrix a = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/picture/a" + i + ".jpg");
-            Matrix b = picture.getImageMatrixByLocal("D:\\share\\picture/b" + i + ".jpg");
+            Matrix b = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/picture/b" + i + ".jpg");
-            Matrix c = picture.getImageMatrixByLocal("D:\\share\\picture/c" + i + ".jpg");
+            Matrix c = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/picture/c" + i + ".jpg");
-            Matrix d = picture.getImageMatrixByLocal("D:\\share\\picture/d" + i + ".jpg");
+            Matrix d = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/picture/d" + i + ".jpg");
             operation.normalization(a, templeConfig.getConvolutionNerveManager());
             operation.normalization(b, templeConfig.getConvolutionNerveManager());
             operation.normalization(c, templeConfig.getConvolutionNerveManager());