!58 修改二叉树回归的误差bug

Merge pull request !58 from 逐光/test

src/main/java/org/wlld/regressionForest/Forest.java

@@ -59,6 +59,20 @@ public class Forest extends Frequency {
         this.resultVariance = resultVariance;
     }
 
+    public double getMappingFeature(Matrix feature) throws Exception {//获取映射后的特征
+        double nub;
+        if (feature.isRowVector()) {
+            if (isOldG) {//使用原有基
+                nub = feature.getNumber(0, oldGId);
+            } else {
+                nub = transG(pc1, feature, gNorm);
+            }
+        } else {
+            throw new Exception("feature is not a rowVector");
+        }
+        return nub;
+    }
+
     private double[] findG() throws Exception {//寻找新的切入维度
         // 先尝试从原有维度切入
         int xSize = conditionMatrix.getX();

src/main/java/org/wlld/regressionForest/RegressionForest.java

@@ -52,22 +52,15 @@ public class RegressionForest extends Frequency {
         }
     }
 
-    public double getDist(double[] feature, double result) {//获取特征误差结果
-        Forest forestFinish;
-        if (result <= min) {//直接找下边界区域
-            forestFinish = getLimitRegion(forest, false);
-        } else if (result >= max) {//直接找到上边界区域
-            forestFinish = getLimitRegion(forest, true);
-        } else {
-            forestFinish = getRegion(forest, result);
-        }
+    public double getDist(Matrix featureMatrix, double result) throws Exception {//获取特征误差结果
+        Forest forestFinish = getRegion(forest, featureMatrix);
         //计算误差
         double[] w = forestFinish.getW();
         double sigma = 0;
         for (int i = 0; i < w.length; i++) {
             double nub;
             if (i < w.length - 1) {
-                nub = w[i] * feature[i];
+                nub = w[i] * featureMatrix.getNumber(0, i);
             } else {
                 nub = w[i];
             }
@@ -76,8 +69,9 @@ public class RegressionForest extends Frequency {
         return Math.abs(result - sigma);
     }
 
-    private Forest getRegion(Forest forest, double result) {
+    private Forest getRegion(Forest forest, Matrix matrix) throws Exception {
         double median = forest.getMedian();
+        double result = forest.getMappingFeature(matrix);
         if (result > median && forest.getForestRight() != null) {//向右走
             forest = forest.getForestRight();
         } else if (result <= median && forest.getForestLeft() != null) {//向左走
@@ -85,7 +79,7 @@ public class RegressionForest extends Frequency {
         } else {
             return forest;
         }
-        return getRegion(forest, result);
+        return getRegion(forest, matrix);
     }
 
     private Forest getLimitRegion(Forest forest, boolean isMax) {
@@ -202,7 +196,7 @@ public class RegressionForest extends Frequency {
         }
     }
 
-    private void pruning() throws Exception {//剪枝
+    private void pruning() {//剪枝
         //先获取当前最大id
         int max = forestMap.lastKey();
         int layersNub = (int) (Math.log(max) / Math.log(2));//当前的层数

src/main/java/org/wlld/regressionForest/RgbFilter.java

@@ -0,0 +1,35 @@
+package org.wlld.regressionForest;
+
+import org.wlld.MatrixTools.Matrix;
+import org.wlld.imageRecognition.ThreeChannelMatrix;
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description rgb 替换为权重
+ */
+public class RgbFilter {
+
+    public void filter(ThreeChannelMatrix threeChannelMatrix, RegressionForest regressionForest) throws Exception {
+        Matrix matrixR = threeChannelMatrix.getMatrixR();
+        Matrix matrixG = threeChannelMatrix.getMatrixG();
+        Matrix matrixB = threeChannelMatrix.getMatrixB();
+        int x = matrixR.getX();
+        int y = matrixR.getY();
+        for (int i = 0; i < x; i++) {
+            for (int j = 0; j < y; j++) {
+                double[] feature = new double[]{matrixR.getNumber(i, j) / 255, matrixG.getNumber(i, j) / 255};
+                double result = matrixB.getNumber(i, j);
+                regressionForest.insertFeature(feature, result);
+            }
+        }
+        regressionForest.startStudy();
+        for (int i = 0; i < x; i++) {
+            for (int j = 0; j < y; j++) {
+                double[] feature = new double[]{matrixR.getNumber(i, j) / 255, matrixG.getNumber(i, j) / 255};
+                double result = matrixB.getNumber(i, j);
+            }
+        }
+    }
+}

src/test/java/coverTest/CoverTest.java

@@ -1,18 +1,14 @@
 package coverTest;
 
-import com.alibaba.fastjson.JSON;
 import com.alibaba.fastjson.JSONObject;
 import org.wlld.MatrixTools.Matrix;
 import org.wlld.ModelData;
 import org.wlld.config.Classifier;
 import org.wlld.config.RZ;
 import org.wlld.config.StudyPattern;
-import org.wlld.function.Sigmod;
 import org.wlld.imageRecognition.*;
 import org.wlld.nerveEntity.ModelParameter;
-import org.wlld.tools.ArithUtil;
 
-import java.util.Arrays;
 import java.util.HashMap;
 import java.util.Map;
 

src/test/java/coverTest/ForestTest.java

@@ -1,6 +1,18 @@
 package coverTest;
 
-import org.wlld.randomForest.Tree;
+import org.wlld.MatrixTools.Matrix;
+import org.wlld.config.Classifier;
+import org.wlld.config.StudyPattern;
+import org.wlld.imageRecognition.Convolution;
+import org.wlld.imageRecognition.Picture;
+import org.wlld.imageRecognition.TempleConfig;
+import org.wlld.imageRecognition.ThreeChannelMatrix;
+import org.wlld.imageRecognition.segmentation.RegionBody;
+import org.wlld.imageRecognition.segmentation.Specifications;
+import org.wlld.imageRecognition.segmentation.Watershed;
+import org.wlld.nerveEntity.ModelParameter;
+import org.wlld.param.Cutting;
+import org.wlld.param.Food;
 import org.wlld.regressionForest.RegressionForest;
 
 import java.util.*;
@@ -12,6 +24,8 @@ import java.util.*;
  * @Description
  */
 public class ForestTest {
+    private static Convolution convolution = new Convolution();
+
     public static void main(String[] args) throws Exception {
         test();
         //int a = (int) (Math.log(4) / Math.log(2));//id22是第几层
@@ -20,6 +34,66 @@ public class ForestTest {
 
     }
 
+    public static void testPic() throws Exception {
+        Picture picture = new Picture();
+        TempleConfig templeConfig = getTemple(null);
+        List<Specifications> specificationsList = new ArrayList<>();
+        Specifications specifications = new Specifications();
+        specifications.setMinWidth(100);
+        specifications.setMinHeight(100);
+        specifications.setMaxWidth(1000);
+        specifications.setMaxHeight(1000);
+        specificationsList.add(specifications);
+        ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix("/Users/lidapeng/Desktop/myDocument/d.jpg");
+        Watershed watershed = new Watershed(threeChannelMatrix, specificationsList, templeConfig);
+        List<RegionBody> regionBodies = watershed.rainfall();
+        RegionBody regionBody = regionBodies.get(0);
+        int minX = regionBody.getMinX();
+        int minY = regionBody.getMinY();
+        int maxX = regionBody.getMaxX();
+        int maxY = regionBody.getMaxY();
+        int xSize = maxX - minX;
+        int ySize = maxY - minY;
+        ThreeChannelMatrix threeChannelMatrix1 = convolution.getRegionMatrix(threeChannelMatrix, minX, minY, xSize, ySize);
+
+    }
+
+    public static void picDo(ThreeChannelMatrix threeChannelMatrix) {//进行lbp特征提取
+
+
+    }
+
+    public static TempleConfig getTemple(ModelParameter modelParameter) throws Exception {
+        TempleConfig templeConfig = new TempleConfig();
+        //templeConfig.isShowLog(true);//是否打印日志
+        Cutting cutting = templeConfig.getCutting();
+        Food food = templeConfig.getFood();
+        //
+        cutting.setMaxRain(360);//切割阈值
+        cutting.setTh(0.6);
+        cutting.setRegionNub(200);
+        cutting.setMaxIou(0.5);
+        //knn参数
+        templeConfig.setKnnNub(1);
+        //池化比例
+        templeConfig.setPoolSize(2);//缩小比例
+        //聚类
+        templeConfig.setFeatureNub(3);//聚类特征数量
+        //菜品识别实体类
+        food.setShrink(10);//缩紧像素
+        food.setTimes(2);//聚类数据增强
+        food.setRowMark(0.1);//0.12
+        food.setColumnMark(0.1);//0.25
+        food.setRegressionNub(20000);
+        food.setTrayTh(0.08);
+        templeConfig.setClassifier(Classifier.KNN);
+        templeConfig.init(StudyPattern.Cover_Pattern, true, 400, 400, 3);
+        if (modelParameter != null) {
+            templeConfig.insertModel(modelParameter);
+        }
+        return templeConfig;
+    }
+
     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);
@@ -41,18 +115,22 @@ public class ForestTest {
         double sigma = 0;
         for (int i = 0; i < 1000; i++) {
             double[] feature = a1.get(i);
-            double[] test = new double[]{feature[0], feature[1]};
+            Matrix test = new Matrix(1, 3);
+            test.setNub(0, 0, feature[0]);
+            test.setNub(0, 1, feature[1]);
+            test.setNub(0, 2, feature[2]);
             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]};
+            Matrix test = new Matrix(1, 3);
+            test.setNub(0, 0, feature[0]);
+            test.setNub(0, 1, feature[1]);
+            test.setNub(0, 2, feature[2]);
             double dist = regressionForest.getDist(test, feature[2]);
             sigma = sigma + dist;
         }