diff --git a/src/main/java/org/wlld/MatrixTools/MatrixOperation.java b/src/main/java/org/wlld/MatrixTools/MatrixOperation.java
index dff2b67..6b4f15e 100644
--- a/src/main/java/org/wlld/MatrixTools/MatrixOperation.java
+++ b/src/main/java/org/wlld/MatrixTools/MatrixOperation.java
@@ -93,7 +93,7 @@ public class MatrixOperation {
         }
     }
 
-    public static double errorNub(Matrix matrix, Matrix avgMatrix, int excNub) throws Exception {//求均方误差
+    public static double errorNub(Matrix matrix, Matrix avgMatrix) throws Exception {//求均方误差
         int y = matrix.getY();
         if (matrix.isRowVector() && avgMatrix.isRowVector() && y == avgMatrix.getY()) {
             double[] subAll = new double[y];
@@ -103,12 +103,11 @@ public class MatrixOperation {
                 double sub = Math.pow(avg - mySelf, 2);
                 subAll[j] = sub;
             }
-            Arrays.sort(subAll);
             double sigma = 0;
-            for (int i = 0; i < y - excNub; i++) {
+            for (int i = 0; i < y; i++) {
                 sigma = sigma + subAll[i];
             }
-            return sigma / (y - excNub);
+            return sigma / y;
         } else {
             throw new Exception("this matrix is not  rowVector or length different");
         }
diff --git a/src/main/java/org/wlld/imageRecognition/Convolution.java b/src/main/java/org/wlld/imageRecognition/Convolution.java
index 83c6a24..f01eab2 100644
--- a/src/main/java/org/wlld/imageRecognition/Convolution.java
+++ b/src/main/java/org/wlld/imageRecognition/Convolution.java
@@ -7,8 +7,6 @@ import org.wlld.imageRecognition.border.Border;
 import org.wlld.imageRecognition.border.Frame;
 import org.wlld.imageRecognition.border.FrameBody;
 import org.wlld.imageRecognition.segmentation.ColorFunction;
-import org.wlld.imageRecognition.segmentation.DimensionMapping;
-import org.wlld.param.Food;
 import org.wlld.pso.PSO;
 import org.wlld.tools.ArithUtil;
 import org.wlld.tools.Frequency;
@@ -100,62 +98,44 @@ public class Convolution extends Frequency {
 
 
     public List<Double> getCenterColor(ThreeChannelMatrix threeChannelMatrix, TempleConfig templeConfig,
-                                       boolean isStudy, int tag) throws Exception {
+                                       int sqNub) throws Exception {
         Matrix matrixR = threeChannelMatrix.getMatrixR();
         Matrix matrixG = threeChannelMatrix.getMatrixG();
         Matrix matrixB = threeChannelMatrix.getMatrixB();
+        MeanClustering meanClustering = new MeanClustering(sqNub, templeConfig, true);
         int maxX = matrixR.getX();
         int maxY = matrixR.getY();
         ColorFunction colorFunction = new ColorFunction(threeChannelMatrix);
         int[] minBorder = new int[]{0, 0};
-        int[] maxBorder = new int[]{maxX, maxY};
+        int[] maxBorder = new int[]{maxX - 1, maxY - 1};
         //创建粒子群
-        PSO pso = new PSO(2, minBorder, maxBorder, 100, 100,
+        PSO pso = new PSO(2, minBorder, maxBorder, 200, 200,
                 colorFunction, 0.2, 1, 0.5, true, 10, 1);
-        List<double[]> positions = pso.start(0, 0);
-        List<double[]> feature = new ArrayList<>();//像素特征
+        List<double[]> positions = pso.start();
         for (int i = 0; i < positions.size(); i++) {
             double[] parameter = positions.get(i);
             //获取取样坐标
             int x = (int) parameter[0];
             int y = (int) parameter[1];
-            double[] rgb = new double[]{matrixR.getNumber(x, y) / 255, matrixG.getNumber(x, y) / 255,
-                    matrixB.getNumber(x, y) / 255};
-            feature.add(rgb);
+            double[] rgb = new double[]{matrixR.getNumber(x, y), matrixG.getNumber(x, y),
+                    matrixB.getNumber(x, y)};
+            meanClustering.setColor(rgb);
         }
-        if (isStudy) {//进行新维度映射排序
-            pso = new PSO(3, null, null, 200, 50,
-                    new DimensionMapping(feature), 0.5, 2, 2, true, 0.5, 0.001);
-            List<double[]> mappings = pso.start(0, 0);//最小映射集合
-            double[] sigmaMapping = new double[3];//最小映射
-            for (int i = 0; i < mappings.size(); i++) {
-                double[] mapping = mappings.get(i);
-                for (int j = 0; j < sigmaMapping.length; j++) {
-                    sigmaMapping[j] = sigmaMapping[j] + mapping[j];
-                }
-            }
-            for (int j = 0; j < sigmaMapping.length; j++) {
-                sigmaMapping[j] = sigmaMapping[j] / mappings.size();
-            }
-            //保存类别及映射
-            Food food = templeConfig.getFood();
-            Map<Integer, double[]> allMappings = food.getMappings();
-            if (allMappings == null) {
-                allMappings = new HashMap<>();
-                food.setMappings(allMappings);
+        meanClustering.start(true);
+        List<RGBNorm> rgbNorms = meanClustering.getMatrices();
+        List<Double> features = new ArrayList<>();
+        for (int i = 0; i < sqNub; i++) {
+            double[] rgb = rgbNorms.get(i).getRgb();
+            for (int j = 0; j < rgb.length; j++) {
+                features.add(rgb[j]);
             }
-            allMappings.put(tag, sigmaMapping);
-            //根据映射值进行排序
-
-        } else {
-
         }
 
-        return null;
+        return features;
     }
 
-    public List<Double> getCenterTexture(ThreeChannelMatrix threeChannelMatrix, int size, int poolSize, TempleConfig templeConfig
-            , int sqNub, int tag) throws Exception {
+    public List<Double> getCenterTexture(ThreeChannelMatrix threeChannelMatrix, int size, TempleConfig templeConfig
+            , int sqNub, boolean isStudy) throws Exception {
         MeanClustering meanClustering = new MeanClustering(sqNub, templeConfig, true);
         Matrix matrixR = threeChannelMatrix.getMatrixR();
         Matrix matrixG = threeChannelMatrix.getMatrixG();
@@ -166,8 +146,8 @@ public class Convolution extends Frequency {
         //局部特征选区筛选
         int nub = size * size;
         int twoNub = nub * 2;
-        for (int i = 0; i <= xn - size; i++) {
-            for (int j = 0; j <= yn - size; j++) {
+        for (int i = 0; i <= xn - size; i += 3) {
+            for (int j = 0; j <= yn - size; j += 3) {
                 Matrix sonR = matrixR.getSonOfMatrix(i, j, size, size);
                 Matrix sonG = matrixG.getSonOfMatrix(i, j, size, size);
                 Matrix sonB = matrixB.getSonOfMatrix(i, j, size, size);
@@ -204,14 +184,29 @@ public class Convolution extends Frequency {
         List<Double> features = new ArrayList<>();
         for (int i = 0; i < sqNub; i++) {
             double[] rgb = rgbNorms.get(i).getRgb();
+            if (!isStudy) {
+                rgb = rgbMapping(rgb, i, templeConfig);
+            }
             for (int j = 0; j < rgb.length; j++) {
                 features.add(rgb[j]);
             }
+
         }
         // System.out.println(features);
         return features;
     }
 
+    private double[] rgbMapping(double[] rgb, int index, TempleConfig templeConfig) {//进行映射
+        double[] mapping = templeConfig.getFood().getMappingParameter();
+        int size = rgb.length;
+        int allSize = mapping.length / 2;
+        double[] mappingFeature = new double[size];
+        for (int i = 0; i < size; i++) {
+            int myIndex = size * index + i;
+            mappingFeature[i] = rgb[i] * mapping[myIndex] + mapping[allSize + myIndex];
+        }
+        return mappingFeature;
+    }
 
     private void normalization(Matrix matrix) throws Exception {
         for (int i = 0; i < matrix.getX(); i++) {
diff --git a/src/main/java/org/wlld/imageRecognition/MappingSort.java b/src/main/java/org/wlld/imageRecognition/MappingSort.java
new file mode 100644
index 0000000..a3efc1a
--- /dev/null
+++ b/src/main/java/org/wlld/imageRecognition/MappingSort.java
@@ -0,0 +1,23 @@
+package org.wlld.imageRecognition;
+
+import org.wlld.imageRecognition.modelEntity.MappingBody;
+
+import java.util.Comparator;
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description
+ */
+public class MappingSort implements Comparator<MappingBody> {
+    @Override
+    public int compare(MappingBody o1, MappingBody o2) {
+        if (o1.getMappingNub() > o2.getMappingNub()) {
+            return -1;
+        } else if (o1.getMappingNub() < o2.getMappingNub()) {
+            return 1;
+        }
+        return 0;
+    }
+}
diff --git a/src/main/java/org/wlld/imageRecognition/Operation.java b/src/main/java/org/wlld/imageRecognition/Operation.java
index 6e7b088..3fa5093 100644
--- a/src/main/java/org/wlld/imageRecognition/Operation.java
+++ b/src/main/java/org/wlld/imageRecognition/Operation.java
@@ -93,10 +93,8 @@ public class Operation {//进行计算
             int ySize = maxY - minY;
             //convolution.imgNormalization(threeChannelMatrix);
             ThreeChannelMatrix threeChannelMatrix1 = convolution.getRegionMatrix(threeChannelMatrix, minX, minY, xSize, ySize);
-//                List<Double> feature = convolution.getCenterColor(threeChannelMatrix1, templeConfig.getPoolSize(),
-//                        templeConfig.getFeatureNub(), templeConfig);
-            List<Double> feature = convolution.getCenterTexture(threeChannelMatrix1, templeConfig.getFood().getRegionSize(),
-                    templeConfig.getPoolSize(), templeConfig, templeConfig.getFeatureNub(), tag);
+            // List<Double> feature = convolution.getCenterColor(threeChannelMatrix1, templeConfig, templeConfig.getFeatureNub());
+            List<Double> feature = convolution.getCenterTexture(threeChannelMatrix1, templeConfig.getFood().getRegionSize(), templeConfig, templeConfig.getFeatureNub(), true);
             if (templeConfig.isShowLog()) {
                 System.out.println(tag + ":" + feature);
             }
@@ -169,10 +167,9 @@ public class Operation {//进行计算
             int xSize = maxX - minX;
             int ySize = maxY - minY;
             ThreeChannelMatrix threeChannelMatrix1 = convolution.getRegionMatrix(threeChannelMatrix, minX, minY, xSize, ySize);
-//            List<Double> feature = convolution.getCenterColor(threeChannelMatrix1, templeConfig.getPoolSize(),
-//                    templeConfig.getFeatureNub(), templeConfig);
-            List<Double> feature = convolution.getCenterTexture(threeChannelMatrix1, templeConfig.getFood().getRegionSize(),
-                    templeConfig.getPoolSize(), templeConfig, templeConfig.getFeatureNub(), 0);
+            //List<Double> feature = convolution.getCenterColor(threeChannelMatrix1, templeConfig, templeConfig.getFeatureNub());
+            List<Double> feature = convolution.getCenterTexture(threeChannelMatrix1, templeConfig.getFood().getRegionSize(), templeConfig, templeConfig.getFeatureNub(), false);
+
             if (templeConfig.isShowLog()) {
                 System.out.println(feature);
             }
@@ -202,7 +199,7 @@ public class Operation {//进行计算
                     break;
             }
             regionBody.setType(id);
-            //System.out.println("类别" + id);
+            System.out.println("类别" + id);
         }
         return regionList;
     }
diff --git a/src/main/java/org/wlld/imageRecognition/border/Knn.java b/src/main/java/org/wlld/imageRecognition/border/Knn.java
index 6ffa2fd..bcca245 100644
--- a/src/main/java/org/wlld/imageRecognition/border/Knn.java
+++ b/src/main/java/org/wlld/imageRecognition/border/Knn.java
@@ -14,6 +14,10 @@ public class Knn {//KNN分类器
         this.nub = nub;
     }
 
+    public void setFeatureMap(Map<Integer, List<Matrix>> featureMap) {
+        this.featureMap = featureMap;
+    }
+
     public Map<Integer, List<Matrix>> getFeatureMap() {
         return featureMap;
     }
@@ -96,8 +100,8 @@ public class Knn {//KNN分类器
             int type = entry.getKey();
             List<Matrix> matrices = entry.getValue();
             for (Matrix matrix : matrices) {
-                double dist = MatrixOperation.errorNub(vector, matrix, 0);
-                // double dist = MatrixOperation.getEDist(matrix, vector);
+                //double dist = MatrixOperation.errorNub(vector, matrix);
+                double dist = MatrixOperation.getEDist(matrix, vector);
                 compare(dists, types, dist, type);
             }
         }
diff --git a/src/main/java/org/wlld/imageRecognition/modelEntity/MappingBody.java b/src/main/java/org/wlld/imageRecognition/modelEntity/MappingBody.java
index dd61a68..27f83a1 100644
--- a/src/main/java/org/wlld/imageRecognition/modelEntity/MappingBody.java
+++ b/src/main/java/org/wlld/imageRecognition/modelEntity/MappingBody.java
@@ -10,19 +10,13 @@ public class MappingBody {
     private double[] feature;//特征
     private double mappingNub;//映射好的值
 
-    public MappingBody(double[] mapping, double[] feature) {
+    public MappingBody(double[] feature) {
         this.feature = feature;
         double sigma = 0;
-        for (int i = 0; i < mapping.length; i++) {
-            sigma = sigma + Math.pow(mapping[i], 2);
+        for (int i = 0; i < feature.length; i++) {
+            sigma = sigma + Math.pow(feature[i], 2);
         }
-        sigma = Math.sqrt(sigma);//映射维度的莫
-        double s = 0;
-        for (int j = 0; j < feature.length; j++) {
-            s = s + feature[j] * mapping[j];
-        }
-        mappingNub = s / sigma;
-
+        mappingNub = Math.sqrt(sigma);
     }
 
     public double[] getFeature() {
diff --git a/src/main/java/org/wlld/imageRecognition/segmentation/DimensionMapping.java b/src/main/java/org/wlld/imageRecognition/segmentation/DimensionMapping.java
deleted file mode 100644
index 68b3b15..0000000
--- a/src/main/java/org/wlld/imageRecognition/segmentation/DimensionMapping.java
+++ /dev/null
@@ -1,41 +0,0 @@
-package org.wlld.imageRecognition.segmentation;
-
-import org.wlld.i.PsoFunction;
-import org.wlld.tools.Frequency;
-
-import java.util.List;
-
-/**
- * @param
- * @DATA
- * @Author LiDaPeng
- * @Description 维度映射
- */
-public class DimensionMapping extends Frequency implements PsoFunction {
-    private List<double[]> features;
-    private int size;
-
-    public DimensionMapping(List<double[]> features) {
-        this.features = features;
-        size = features.size();
-    }
-
-    @Override
-    public double getResult(double[] parameter, int id) throws Exception {
-        double sigma = 0;
-        for (int i = 0; i < parameter.length; i++) {
-            sigma = sigma + Math.pow(parameter[i], 2);
-        }
-        sigma = Math.sqrt(sigma);
-        double[] mapping = new double[size];//在新维度的映射集合
-        for (int i = 0; i < size; i++) {
-            double[] feature = features.get(i);
-            double s = 0;
-            for (int j = 0; j < feature.length; j++) {
-                s = s + feature[j] * parameter[j];
-            }
-            mapping[i] = s / sigma;
-        }
-        return variance(mapping);
-    }
-}
diff --git a/src/main/java/org/wlld/imageRecognition/segmentation/DimensionMappingStudy.java b/src/main/java/org/wlld/imageRecognition/segmentation/DimensionMappingStudy.java
new file mode 100644
index 0000000..04eab49
--- /dev/null
+++ b/src/main/java/org/wlld/imageRecognition/segmentation/DimensionMappingStudy.java
@@ -0,0 +1,153 @@
+package org.wlld.imageRecognition.segmentation;
+
+import org.wlld.MatrixTools.Matrix;
+import org.wlld.i.PsoFunction;
+import org.wlld.imageRecognition.RGBNorm;
+import org.wlld.imageRecognition.TempleConfig;
+import org.wlld.pso.PSO;
+
+import java.util.*;
+
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description 维度映射
+ */
+public class DimensionMappingStudy {
+    private double getDist(Matrix data1, Matrix data2) throws Exception {
+        int size = data1.getY();
+        double sigma = 0;
+        for (int i = 0; i < size; i++) {
+            sigma = sigma + Math.pow(data1.getNumber(0, i) - data2.getNumber(0, i), 2);
+        }
+        return Math.sqrt(sigma);
+    }
+
+    private double compareSame(List<Matrix> testFeatures) throws Exception {//比较同类找最小值
+        int size = testFeatures.size();
+        double max = 0;
+        for (int i = 0; i < size; i++) {
+            Matrix feature = testFeatures.get(i);
+            double min = -1;
+            for (int j = 0; j < size; j++) {
+                if (i != j) {
+                    double dist = getDist(feature, testFeatures.get(j));
+                    if (min == -1 || dist < min) {
+                        min = dist;
+                    }
+                }
+            }
+            if (min > max) {
+                max = min;
+            }
+        }
+        return max;
+    }
+
+    private double compareDifferent(List<Matrix> featureSame, List<Matrix> featureDifferent) throws Exception {
+        double min = -1;//比较异类取最小值
+        for (Matrix myFeature : featureSame) {
+            for (Matrix feature : featureDifferent) {
+                double dist = getDist(myFeature, feature);
+                if (min < 0 || dist < min) {
+                    min = dist;
+                }
+            }
+        }
+        return min;
+    }
+
+    class MinType {
+        private double minDist;
+        private int type;
+    }
+
+    public void selfTest(TempleConfig templeConfig) throws Exception {//对模型数据进行自检测
+        Map<Integer, List<Matrix>> featureMap = templeConfig.getKnn().getFeatureMap();
+        Map<Integer, Double> maxMap = new HashMap<>();//保存与该类别最大间距
+        Map<Integer, List<MinType>> minMap = new HashMap<>();//保存与该类别最相似的类别,及距离值
+        for (Map.Entry<Integer, List<Matrix>> entry : featureMap.entrySet()) {
+            int key = entry.getKey();
+            List<MinType> minTypes = new ArrayList<>();
+            List<Matrix> myFeatures = entry.getValue();
+            double sameMax = compareSame(myFeatures);//同类之间的最大值
+            maxMap.put(key, sameMax);
+            for (Map.Entry<Integer, List<Matrix>> entry2 : featureMap.entrySet()) {
+                int testKey = entry2.getKey();
+                if (testKey != key) {//找出最小距离
+                    List<Matrix> features = entry2.getValue();
+                    MinType minType = new MinType();
+                    double minDist = compareDifferent(myFeatures, features);//该类别的最小值
+                    if (minDist < sameMax) {
+                        minType.minDist = minDist;
+                        minType.type = testKey;
+                        minTypes.add(minType);
+                    }
+                }
+            }
+            minMap.put(key, minTypes);
+        }
+        for (Map.Entry<Integer, Double> entry : maxMap.entrySet()) {
+            int key = entry.getKey();
+            double maxValue = entry.getValue();
+            System.out.println("=============================================");
+            System.out.println("类别：" + key + ",最大类间距为：" + maxValue);
+            List<MinType> minTypes = minMap.get(key);
+            for (int i = 0; i < minTypes.size(); i++) {
+                MinType minType = minTypes.get(i);
+                System.out.println("相近类:" + minType.type + ",最小类间距为：" + minType.minDist);
+            }
+        }
+    }
+
+    public void start(TempleConfig templeConfig) throws Exception {
+        Map<Integer, List<Matrix>> featureMap = templeConfig.getKnn().getFeatureMap();
+        FeatureMapping featureMapping = new FeatureMapping(featureMap);
+        int dimensionNub = templeConfig.getFeatureNub() * 3 * 2;//PSO维度
+        //创建粒子群
+        PSO pso = new PSO(dimensionNub, null, null, 200, 100,
+                featureMapping, 0.4, 2, 2, true, 0.2, 0.01);
+        List<double[]> mappings = pso.start();
+        int size = mappings.size();
+        double[] mappingSigma = new double[dimensionNub];
+        for (int i = 0; i < size; i++) {
+            double[] mapping = mappings.get(i);
+            for (int j = 0; j < mapping.length; j++) {
+                mappingSigma[j] = mappingSigma[j] + mapping[j];
+            }
+        }
+        for (int i = 0; i < mappingSigma.length; i++) {
+            mappingSigma[i] = mappingSigma[i] / size;
+        }
+        templeConfig.getFood().setMappingParameter(mappingSigma);
+        //还要把所有已存在的knn特征完成映射
+        featureMapping(featureMap, mappingSigma, templeConfig);
+    }
+
+    private void featureMapping(Map<Integer, List<Matrix>> featureMap, double[] mapping
+            , TempleConfig templeConfig) throws Exception {
+        Map<Integer, List<Matrix>> featureMap2 = new HashMap<>();
+        for (Map.Entry<Integer, List<Matrix>> entry : featureMap.entrySet()) {
+            int key = entry.getKey();
+            List<Matrix> features = entry.getValue();
+            List<Matrix> mappingFeatures = new ArrayList<>();
+            for (Matrix matrix : features) {
+                mappingFeatures.add(mapping(matrix, mapping));
+            }
+            featureMap2.put(key, mappingFeatures);
+        }
+        templeConfig.getKnn().setFeatureMap(featureMap2);
+    }
+
+    private Matrix mapping(Matrix feature, double[] mapping) throws Exception {
+        int size = feature.getY();
+        Matrix matrix = new Matrix(1, size);
+        for (int i = 0; i < size; i++) {
+            double nub = feature.getNumber(0, i) * mapping[i] + mapping[size + i];
+            matrix.setNub(0, i, nub);
+        }
+        return matrix;
+    }
+}
diff --git a/src/main/java/org/wlld/imageRecognition/segmentation/FeatureMapping.java b/src/main/java/org/wlld/imageRecognition/segmentation/FeatureMapping.java
new file mode 100644
index 0000000..b71516a
--- /dev/null
+++ b/src/main/java/org/wlld/imageRecognition/segmentation/FeatureMapping.java
@@ -0,0 +1,115 @@
+package org.wlld.imageRecognition.segmentation;
+
+import org.wlld.MatrixTools.Matrix;
+import org.wlld.i.PsoFunction;
+import org.wlld.tools.Frequency;
+
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description 特征映射
+ */
+public class FeatureMapping extends Frequency implements PsoFunction {
+    private Map<Integer, List<Matrix>> featureMap;//特征
+
+    public FeatureMapping(Map<Integer, List<Matrix>> featureMap) {
+        this.featureMap = featureMap;
+    }
+
+    @Override
+    public double getResult(double[] parameter, int id) throws Exception {
+
+        return featuresMapping(parameter);
+    }
+
+    private double[] mapping(Matrix matrix, double[] parameter) throws Exception {
+        int size = matrix.getY();
+        double[] mapping = new double[size];
+        for (int i = 0; i < size; i++) {
+            mapping[i] = matrix.getNumber(0, i) * parameter[i] + parameter[size + i];
+        }
+        return mapping;
+    }
+
+    private double getDist(double[] data1, double[] data2) {
+        int size = data1.length;
+        double sigma = 0;
+        for (int i = 0; i < size; i++) {
+            sigma = sigma + Math.pow(data1[i] - data2[i], 2);
+        }
+        return Math.sqrt(sigma);
+    }
+
+    private double compareSame(List<double[]> testFeatures) {//比较同类找最大值
+        int size = testFeatures.size();
+        double max = 0;
+        for (int i = 0; i < size; i++) {
+            double min = -1;
+            double[] feature = testFeatures.get(i);
+            for (int j = 0; j < size; j++) {
+                if (i != j) {
+                    double dist = getDist(feature, testFeatures.get(j));
+                    if (min < 0 || dist < min) {
+                        min = dist;
+                    }
+                }
+            }
+            if (min > max) {
+                max = min;
+            }
+        }
+        return max;
+    }
+
+    private double compareDifferent(List<double[]> featureSame, List<double[]> featureDifferent) {
+        double min = -1;//比较异类取最小值
+        for (double[] myFeature : featureSame) {
+            for (double[] feature : featureDifferent) {
+                double dist = getDist(myFeature, feature);
+                if (min < 0 || dist < min) {
+                    min = dist;
+                }
+            }
+        }
+        return min;
+    }
+
+    private double compareDist(Map<Integer, List<double[]>> mapping) {//比较距离
+        double sigma = 0;
+        for (Map.Entry<Integer, List<double[]>> entry : mapping.entrySet()) {
+            int key = entry.getKey();
+            List<double[]> myFeature = entry.getValue();
+            List<double[]> featureDifferent = new ArrayList<>();
+            for (Map.Entry<Integer, List<double[]>> entry2 : mapping.entrySet()) {
+                if (entry2.getKey() != key) {
+                    featureDifferent.addAll(entry2.getValue());
+                }
+            }
+            double sameMax = compareSame(myFeature);//同类最大值
+            double differentMin = compareDifferent(myFeature, featureDifferent);//异类最小值
+            double sub = differentMin - sameMax;
+            sigma = sigma + sub;
+        }
+        return sigma;
+    }
+
+    private double featuresMapping(double[] parameter) throws Exception {//对当前所有数据进行映射
+        Map<Integer, List<double[]>> mapping = new HashMap<>();
+        for (Map.Entry<Integer, List<Matrix>> entry : featureMap.entrySet()) {
+            int key = entry.getKey();
+            List<Matrix> features = entry.getValue();//当前类别的所有特征
+            List<double[]> featureList = new ArrayList<>();
+            for (Matrix matrix : features) {
+                featureList.add(mapping(matrix, parameter));
+            }
+            mapping.put(key, featureList);
+        }
+        return compareDist(mapping);
+    }
+}
diff --git a/src/main/java/org/wlld/param/Food.java b/src/main/java/org/wlld/param/Food.java
index 025e11e..7c2adf6 100644
--- a/src/main/java/org/wlld/param/Food.java
+++ b/src/main/java/org/wlld/param/Food.java
@@ -24,14 +24,14 @@ public class Food {
     private int step = 1;//特征取样步长
     private int speciesNub = 24;//种类数
     private KNerveManger kNerveManger;
-    private Map<Integer, double[]> mappings;//类别映射集合
+    private double[] mappingParameter;//特征映射
 
-    public Map<Integer, double[]> getMappings() {
-        return mappings;
+    public double[] getMappingParameter() {
+        return mappingParameter;
     }
 
-    public void setMappings(Map<Integer, double[]> mappings) {
-        this.mappings = mappings;
+    public void setMappingParameter(double[] mappingParameter) {
+        this.mappingParameter = mappingParameter;
     }
 
     public KNerveManger getkNerveManger() {
diff --git a/src/main/java/org/wlld/pso/PSO.java b/src/main/java/org/wlld/pso/PSO.java
index 874f3d6..c10ef53 100644
--- a/src/main/java/org/wlld/pso/PSO.java
+++ b/src/main/java/org/wlld/pso/PSO.java
@@ -66,7 +66,7 @@ public class PSO {
         this.allPar = allPar;
     }
 
-    public List<double[]> start(int fatherX, int fatherY) throws Exception {//开始进行迭代
+    public List<double[]> start() throws Exception {//开始进行迭代
         int size = allPar.size();
         for (int i = 0; i < times; i++) {
             for (int j = 0; j < size; j++) {
diff --git a/src/test/java/coverTest/FoodTest.java b/src/test/java/coverTest/FoodTest.java
index 8474709..a6f68f8 100644
--- a/src/test/java/coverTest/FoodTest.java
+++ b/src/test/java/coverTest/FoodTest.java
@@ -8,10 +8,7 @@ import org.wlld.config.Classifier;
 import org.wlld.config.RZ;
 import org.wlld.config.StudyPattern;
 import org.wlld.imageRecognition.*;
-import org.wlld.imageRecognition.segmentation.KNerveManger;
-import org.wlld.imageRecognition.segmentation.RegionBody;
-import org.wlld.imageRecognition.segmentation.RegionMapping;
-import org.wlld.imageRecognition.segmentation.Specifications;
+import org.wlld.imageRecognition.segmentation.*;
 import org.wlld.nerveEntity.ModelParameter;
 import org.wlld.param.Cutting;
 import org.wlld.param.Food;
@@ -68,14 +65,14 @@ public class FoodTest {
         cutting.setRegionNub(100);
         cutting.setMaxIou(2);
         //knn参数
-        templeConfig.setKnnNub(15);
+        templeConfig.setKnnNub(1);
         //池化比例
         templeConfig.setPoolSize(2);//缩小比例
         //聚类
         templeConfig.setFeatureNub(5);//聚类特征数量
         //菜品识别实体类
         food.setShrink(5);//缩紧像素
-        food.setRegionSize(5);
+        food.setRegionSize(6);
         KNerveManger kNerveManger = new KNerveManger(12, 24, 6000);
         food.setkNerveManger(kNerveManger);
         food.setRowMark(0.15);//0.12
@@ -156,59 +153,65 @@ public class FoodTest {
             operation.colorStudy(threeChannelMatrix23, 23, specificationsList, name);
             operation.colorStudy(threeChannelMatrix24, 24, specificationsList, name);
         }
-        System.out.println("========================");
-        kNerveManger.startStudy();
-        int i = 3;
-//        ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix(name + "a" + i + ".jpg");
-//        ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix(name + "b" + i + ".jpg");
-//        ThreeChannelMatrix threeChannelMatrix3 = picture.getThreeMatrix(name + "c" + i + ".jpg");
-//        ThreeChannelMatrix threeChannelMatrix4 = picture.getThreeMatrix(name + "d" + i + ".jpg");
-//        ThreeChannelMatrix threeChannelMatrix5 = picture.getThreeMatrix(name + "e" + i + ".jpg");
-//        ThreeChannelMatrix threeChannelMatrix6 = picture.getThreeMatrix(name + "f" + i + ".jpg");
-//        ThreeChannelMatrix threeChannelMatrix7 = picture.getThreeMatrix(name + "g" + i + ".jpg");
-//        ThreeChannelMatrix threeChannelMatrix8 = picture.getThreeMatrix(name + "h" + i + ".jpg");
-//        ThreeChannelMatrix threeChannelMatrix9 = picture.getThreeMatrix(name + "i" + i + ".jpg");
-//        ThreeChannelMatrix threeChannelMatrix10 = picture.getThreeMatrix(name + "j" + i + ".jpg");
+        DimensionMappingStudy dimensionMappingStudy = new DimensionMappingStudy();
+        //System.out.println("映射前检查========================");
+        dimensionMappingStudy.selfTest(templeConfig);//检查
+        dimensionMappingStudy.start(templeConfig);//完成映射
+        System.out.println("映射后检查========================");
+        dimensionMappingStudy.selfTest(templeConfig);//检查
+        //System.out.println("========================");
+        // kNerveManger.startStudy();
+        int i = 4;
+        ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix(name + "a" + i + ".jpg");
+        ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix(name + "b" + i + ".jpg");
+        ThreeChannelMatrix threeChannelMatrix3 = picture.getThreeMatrix(name + "c" + i + ".jpg");
+        ThreeChannelMatrix threeChannelMatrix4 = picture.getThreeMatrix(name + "d" + i + ".jpg");
+        ThreeChannelMatrix threeChannelMatrix5 = picture.getThreeMatrix(name + "e" + i + ".jpg");
+        ThreeChannelMatrix threeChannelMatrix6 = picture.getThreeMatrix(name + "f" + i + ".jpg");
+        ThreeChannelMatrix threeChannelMatrix7 = picture.getThreeMatrix(name + "g" + i + ".jpg");
+        ThreeChannelMatrix threeChannelMatrix8 = picture.getThreeMatrix(name + "h" + i + ".jpg");
+        ThreeChannelMatrix threeChannelMatrix9 = picture.getThreeMatrix(name + "i" + i + ".jpg");
+        ThreeChannelMatrix threeChannelMatrix10 = picture.getThreeMatrix(name + "j" + i + ".jpg");
         ThreeChannelMatrix threeChannelMatrix11 = picture.getThreeMatrix(name + "k" + i + ".jpg");
-//        ThreeChannelMatrix threeChannelMatrix12 = picture.getThreeMatrix(name + "l" + i + ".jpg");
+        ThreeChannelMatrix threeChannelMatrix12 = picture.getThreeMatrix(name + "l" + i + ".jpg");
         ThreeChannelMatrix threeChannelMatrix13 = picture.getThreeMatrix(name + "m" + i + ".jpg");
-//        ThreeChannelMatrix threeChannelMatrix14 = picture.getThreeMatrix(name + "n" + i + ".jpg");
+        ThreeChannelMatrix threeChannelMatrix14 = picture.getThreeMatrix(name + "n" + i + ".jpg");
 //
-//        ThreeChannelMatrix threeChannelMatrix15 = picture.getThreeMatrix(name + "o" + i + ".jpg");
-//        ThreeChannelMatrix threeChannelMatrix16 = picture.getThreeMatrix(name + "p" + i + ".jpg");
-//        ThreeChannelMatrix threeChannelMatrix17 = picture.getThreeMatrix(name + "q" + i + ".jpg");
-//        ThreeChannelMatrix threeChannelMatrix18 = picture.getThreeMatrix(name + "r" + i + ".jpg");
-//        ThreeChannelMatrix threeChannelMatrix19 = picture.getThreeMatrix(name + "s" + i + ".jpg");
-//        ThreeChannelMatrix threeChannelMatrix20 = picture.getThreeMatrix(name + "t" + i + ".jpg");
-//        ThreeChannelMatrix threeChannelMatrix21 = picture.getThreeMatrix(name + "u" + i + ".jpg");
-//        ThreeChannelMatrix threeChannelMatrix22 = picture.getThreeMatrix(name + "v" + i + ".jpg");
-//        ThreeChannelMatrix threeChannelMatrix23 = picture.getThreeMatrix(name + "w" + i + ".jpg");
-        // ThreeChannelMatrix threeChannelMatrix24 = picture.getThreeMatrix(name + "x" + i + ".jpg");
-//        operation.colorLook(threeChannelMatrix1, specificationsList);
-//        operation.colorLook(threeChannelMatrix2, specificationsList);
-//        operation.colorLook(threeChannelMatrix3, specificationsList);
-//        operation.colorLook(threeChannelMatrix4, specificationsList);
-//        operation.colorLook(threeChannelMatrix5, specificationsList);
-//        operation.colorLook(threeChannelMatrix6, specificationsList);
-//        operation.colorLook(threeChannelMatrix7, specificationsList);
-//        operation.colorLook(threeChannelMatrix8, specificationsList);
-//        operation.colorLook(threeChannelMatrix9, specificationsList);
-//        operation.colorLook(threeChannelMatrix10, specificationsList);
+        ThreeChannelMatrix threeChannelMatrix15 = picture.getThreeMatrix(name + "o" + i + ".jpg");
+        ThreeChannelMatrix threeChannelMatrix16 = picture.getThreeMatrix(name + "p" + i + ".jpg");
+        ThreeChannelMatrix threeChannelMatrix17 = picture.getThreeMatrix(name + "q" + i + ".jpg");
+        ThreeChannelMatrix threeChannelMatrix18 = picture.getThreeMatrix(name + "r" + i + ".jpg");
+        ThreeChannelMatrix threeChannelMatrix19 = picture.getThreeMatrix(name + "s" + i + ".jpg");
+        ThreeChannelMatrix threeChannelMatrix20 = picture.getThreeMatrix(name + "t" + i + ".jpg");
+        ThreeChannelMatrix threeChannelMatrix21 = picture.getThreeMatrix(name + "u" + i + ".jpg");
+        ThreeChannelMatrix threeChannelMatrix22 = picture.getThreeMatrix(name + "v" + i + ".jpg");
+        ThreeChannelMatrix threeChannelMatrix23 = picture.getThreeMatrix(name + "w" + i + ".jpg");
+        ThreeChannelMatrix threeChannelMatrix24 = picture.getThreeMatrix(name + "x" + i + ".jpg");
+        operation.colorLook(threeChannelMatrix1, specificationsList);
+        operation.colorLook(threeChannelMatrix2, specificationsList);
+        operation.colorLook(threeChannelMatrix3, specificationsList);
+        operation.colorLook(threeChannelMatrix4, specificationsList);
+        operation.colorLook(threeChannelMatrix5, specificationsList);
+        operation.colorLook(threeChannelMatrix6, specificationsList);
+        operation.colorLook(threeChannelMatrix7, specificationsList);
+        operation.colorLook(threeChannelMatrix8, specificationsList);
+        operation.colorLook(threeChannelMatrix9, specificationsList);
+        operation.colorLook(threeChannelMatrix10, specificationsList);
         operation.colorLook(threeChannelMatrix11, specificationsList);
-//        operation.colorLook(threeChannelMatrix12, specificationsList);
+        operation.colorLook(threeChannelMatrix12, specificationsList);
         operation.colorLook(threeChannelMatrix13, specificationsList);
-//        operation.colorLook(threeChannelMatrix14, specificationsList);
-//        operation.colorLook(threeChannelMatrix15, specificationsList);
-//        operation.colorLook(threeChannelMatrix16, specificationsList);
-//        operation.colorLook(threeChannelMatrix17, specificationsList);
-//        operation.colorLook(threeChannelMatrix18, specificationsList);
-//        operation.colorLook(threeChannelMatrix19, specificationsList);
-//        operation.colorLook(threeChannelMatrix20, specificationsList);
-//        operation.colorLook(threeChannelMatrix21, specificationsList);
-//        operation.colorLook(threeChannelMatrix22, specificationsList);
-//        operation.colorLook(threeChannelMatrix23, specificationsList);
-//        operation.colorLook(threeChannelMatrix24, specificationsList);
-//
+        operation.colorLook(threeChannelMatrix14, specificationsList);
+        operation.colorLook(threeChannelMatrix15, specificationsList);
+        operation.colorLook(threeChannelMatrix16, specificationsList);
+        operation.colorLook(threeChannelMatrix17, specificationsList);
+        operation.colorLook(threeChannelMatrix18, specificationsList);
+        operation.colorLook(threeChannelMatrix19, specificationsList);
+        operation.colorLook(threeChannelMatrix20, specificationsList);
+        operation.colorLook(threeChannelMatrix21, specificationsList);
+        operation.colorLook(threeChannelMatrix22, specificationsList);
+        operation.colorLook(threeChannelMatrix23, specificationsList);
+        operation.colorLook(threeChannelMatrix24, specificationsList);
+
 //        test3(threeChannelMatrix1, operation, specificationsList, 1);
 //        test3(threeChannelMatrix2, operation, specificationsList, 2);
 //        test3(threeChannelMatrix3, operation, specificationsList, 3);
@@ -232,7 +235,7 @@ public class FoodTest {
 //        test3(threeChannelMatrix21, operation, specificationsList, 21);
 //        test3(threeChannelMatrix22, operation, specificationsList, 22);
 //        test3(threeChannelMatrix23, operation, specificationsList, 23);
-        //test3(threeChannelMatrix24, operation, specificationsList, 24);
+//        test3(threeChannelMatrix24, operation, specificationsList, 24);
 
     }
 
diff --git a/src/test/java/coverTest/ForestTest.java b/src/test/java/coverTest/ForestTest.java
index b306aaa..98f63db 100644
--- a/src/test/java/coverTest/ForestTest.java
+++ b/src/test/java/coverTest/ForestTest.java
@@ -84,7 +84,6 @@ public class ForestTest {
         templeConfig.setFeatureNub(3);//聚类特征数量
         //菜品识别实体类
         food.setShrink(5);//缩紧像素
-        food.setTimes(1);//聚类数据增强
         food.setRowMark(0.15);//0.12
         food.setColumnMark(0.15);//0.25
         food.setRegressionNub(20000);
diff --git a/src/test/java/coverTest/regionCut/RegionCut.java b/src/test/java/coverTest/regionCut/RegionCut.java
index 13c74e3..f2f9b27 100644
--- a/src/test/java/coverTest/regionCut/RegionCut.java
+++ b/src/test/java/coverTest/regionCut/RegionCut.java
@@ -36,7 +36,7 @@ public class RegionCut {
         long a = System.currentTimeMillis();
         PSO pso = new PSO(2, minBorder, maxBorder, 100, 100,
                 colorFunction, 0.2, 1, 0.5, true, 10, 1);
-        pso.start(fatherX, fatherY);
+        pso.start();
         long b = System.currentTimeMillis() - a;
         System.out.println("时间：" + b);
         //        Matrix matrixR = threeChannelMatrix.getMatrixR();