!57 增加二叉树回归

Merge pull request !57 from 逐光/test

src/main/java/org/wlld/MatrixTools/MatrixOperation.java

@@ -243,7 +243,7 @@ public class MatrixOperation {
             double nub = 0;
             for (int i = 0; i < matrix.getX(); i++) {
                 for (int j = 0; j < matrix.getY(); j++) {
-                    nub = ArithUtil.add(Math.pow(matrix.getNumber(i, j), 2), nub);
+                    nub = Math.pow(matrix.getNumber(i, j), 2) + nub;
                 }
             }
             return Math.sqrt(nub);

src/main/java/org/wlld/imageRecognition/border/Knn.java

@@ -22,6 +22,22 @@ public class Knn {//KNN分类器
         featureMap.remove(type);
     }
 
+    public void revoke(int type, int nub) {//撤销一个类别最新的
+        List<Matrix> list = featureMap.get(type);
+        for (int i = 0; i < nub; i++) {
+            list.remove(list.size() - 1);
+        }
+    }
+
+    public int getNub(int type) {//获取该分类模型的数量
+        int nub = 0;
+        List<Matrix> list = featureMap.get(type);
+        if (list != null) {
+            nub = list.size();
+        }
+        return nub;
+    }
+
     public void insertMatrix(Matrix vector, int tag) throws Exception {
         if (vector.isVector() && vector.isRowVector()) {
             if (featureMap.size() == 0) {

src/main/java/org/wlld/imageRecognition/segmentation/Watershed.java

@@ -277,13 +277,13 @@ public class Watershed {
                 regionBodies.add(regionBody);
             }
         }
-//        for (RegionBody regionBody : regionBodies) {
+        for (RegionBody regionBody : regionBodies) {
-//            int minX = regionBody.getMinX();
+            int minX = regionBody.getMinX();
-//            int maxX = regionBody.getMaxX();
+            int maxX = regionBody.getMaxX();
-//            int minY = regionBody.getMinY();
+            int minY = regionBody.getMinY();
-//            int maxY = regionBody.getMaxY();
+            int maxY = regionBody.getMaxY();
-//            System.out.println("minX==" + minX + ",minY==" + minY + ",maxX==" + maxX + ",maxY==" + maxY);
+            System.out.println("minX==" + minX + ",minY==" + minY + ",maxX==" + maxX + ",maxY==" + maxY);
-//        }
+        }
         return iou(regionBodies);
     }
 

src/main/java/org/wlld/tools/Frequency.java

@@ -99,4 +99,23 @@ public abstract class Frequency {//统计频数
         }
         return ArithUtil.div(my, all);
     }
+
+    public double[] getLimit(double[] m) {//获取数组中的最大值和最小值，最小值在前，最大值在后
+        double[] limit = new double[2];
+        double max = 0;
+        double min = -1;
+        int l = m.length;
+        for (int i = 0; i < l; i++) {
+            double nub = m[i];
+            if (min == -1 || nub < min) {
+                min = nub;
+            }
+            if (nub > max) {
+                max = nub;
+            }
+        }
+        limit[0] = min;
+        limit[1] = max;
+        return limit;
+    }
 }

src/test/java/coverTest/FoodTest.java

@@ -61,7 +61,7 @@ public class FoodTest {
         Food food = templeConfig.getFood();
         //
         cutting.setMaxRain(360);//切割阈值
-        cutting.setTh(0.3);
+        cutting.setTh(0.6);
         cutting.setRegionNub(200);
         cutting.setMaxIou(2.0);
         //knn参数
@@ -73,8 +73,8 @@ public class FoodTest {
         //菜品识别实体类
         food.setShrink(20);//缩紧像素
         food.setTimes(2);//聚类数据增强
-        food.setRowMark(0.1);//0.12
+        food.setRowMark(0.12);//0.12
-        food.setColumnMark(0.1);//0.25
+        food.setColumnMark(0.12);//0.25
         food.setRegressionNub(20000);
         food.setTrayTh(0.08);
         templeConfig.setClassifier(Classifier.KNN);
@@ -99,18 +99,11 @@ public class FoodTest {
         ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix("/Users/lidapeng/Desktop/myDocument/d.jpg");
         operation.setTray(threeChannelMatrix);
         for (int i = 1; i <= 1; i++) {
-            ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("/Users/lidapeng/Desktop/test/a1.jpg");
+            ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("/Users/lidapeng/Desktop/test/test.jpg");
-            ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix("/Users/lidapeng/Desktop/test/b.jpg");
-            ThreeChannelMatrix threeChannelMatrix3 = picture.getThreeMatrix("/Users/lidapeng/Desktop/test/c.jpg");
             operation.colorStudy(threeChannelMatrix1, 1, specificationsList);
-            operation.colorStudy(threeChannelMatrix2, 2, specificationsList);
-            operation.colorStudy(threeChannelMatrix3, 3, specificationsList);
 
         }
-
+        //    test2(templeConfig);
-//        minX==301,minY==430,maxX==854,maxY==920
-//        minX==497,minY==1090,maxX==994,maxY==1520
-        test2(templeConfig);
     }
 
     public static void study() throws Exception {

src/test/java/coverTest/ForestTest.java

@@ -0,0 +1,78 @@
+package coverTest;
+
+import org.wlld.randomForest.Tree;
+import org.wlld.regressionForest.RegressionForest;
+
+import java.util.*;
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description
+ */
+public class ForestTest {
+    public static void main(String[] args) throws Exception {
+        test();
+        //int a = (int) (Math.log(4) / Math.log(2));//id22是第几层
+        //double a = Math.pow(2, 5) - 1; 第五层的第一个数
+        // System.out.println("a==" + a);
+
+    }
+
+    public static void test() throws Exception {//对分段回归进行测试
+        int size = 2000;
+        RegressionForest regressionForest = new RegressionForest(size, 3, 0.01, 200);
+        regressionForest.setCosSize(40);
+        List<double[]> a = fun(0.1, 0.2, 0.3, size, 2, 1);
+        List<double[]> b = fun(0.7, 0.3, 0.1, size, 2, 2);
+        for (int i = 0; i < 1000; i++) {
+            double[] featureA = a.get(i);
+            double[] featureB = b.get(i);
+            double[] testA = new double[]{featureA[0], featureA[1]};
+            double[] testB = new double[]{featureB[0], featureB[1]};
+            regressionForest.insertFeature(testA, featureA[2]);
+            regressionForest.insertFeature(testB, featureB[2]);
+        }
+        regressionForest.startStudy();
+        ///
+        List<double[]> a1 = fun(0.1, 0.2, 0.3, size, 2, 1);
+        List<double[]> b1 = fun(0.7, 0.3, 0.1, size, 2, 2);
+        double sigma = 0;
+        for (int i = 0; i < 1000; i++) {
+            double[] feature = a1.get(i);
+            double[] test = new double[]{feature[0], feature[1]};
+            double dist = regressionForest.getDist(test, feature[2]);
+            sigma = sigma + dist;
+        }
+        double avs = sigma / size;
+        System.out.println("a误差：" + avs);
+//        a误差：0.0017585065712555645
+//        b误差：0.00761733737464547
+        sigma = 0;
+        for (int i = 0; i < 1000; i++) {
+            double[] feature = b1.get(i);
+            double[] test = new double[]{feature[0], feature[1]};
+            double dist = regressionForest.getDist(test, feature[2]);
+            sigma = sigma + dist;
+        }
+        double avs2 = sigma / size;
+        System.out.println("b误差：" + avs2);
+
+    }
+
+    public static List<double[]> fun(double w1, double w2, double w3, int size, int region, int index) {//生成假数据
+        List<double[]> list = new ArrayList<>();
+        Random random = new Random();
+        int nub = (index - 1) * 100;
+        double max = region * 100;
+        for (int i = 0; i < size; i++) {
+            double b = (double) (random.nextInt(100) + nub) / max;
+            double a = random.nextDouble();
+            double c = w1 * a + w2 * b + w3;
+            double[] data = new double[]{a, b, c};
+            list.add(data);
+        }
+        return list;
+    }
+}