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@ -1,9 +1,9 @@
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package org.wlld.imageRecognition.segmentation;
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import org.wlld.MatrixTools.Matrix;
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import org.wlld.i.PsoFunction;
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import org.wlld.imageRecognition.RGBNorm;
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import org.wlld.MatrixTools.MatrixOperation;
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import org.wlld.imageRecognition.TempleConfig;
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import org.wlld.imageRecognition.border.Knn;
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import org.wlld.pso.PSO;
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import java.util.*;
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@ -16,6 +16,11 @@ import java.util.*;
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* @Description 维度映射
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*/
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public class DimensionMappingStudy {
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private Knn knn = new Knn(1);//一个knn分类器，决定进这一层哪个类别
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private int id;//类别id
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private Map<Integer, DimensionMappingStudy> deepMappingMap = new HashMap<>();
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private double[] mappingSigma;//映射层
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private double getDist(Matrix data1, Matrix data2) throws Exception {
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int size = data1.getY();
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double sigma = 0;
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@ -25,93 +30,137 @@ public class DimensionMappingStudy {
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return Math.sqrt(sigma);
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}
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private double compareSame(List<Matrix> testFeatures) throws Exception {//比较同类找最小值
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int size = testFeatures.size();
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double max = 0;
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for (int i = 0; i < size; i++) {
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Matrix feature = testFeatures.get(i);
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double min = -1;
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for (int j = 0; j < size; j++) {
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if (i != j) {
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double dist = getDist(feature, testFeatures.get(j));
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if (min == -1 || dist < min) {
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min = dist;
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class MinTypeSort implements Comparator<MinType> {
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@Override
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public int compare(MinType o1, MinType o2) {
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if (o1.minDist < o2.minDist) {
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return -1;
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} else if (o1.minDist > o2.minDist) {
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return 1;
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}
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return 0;
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}
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}
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if (min > max) {
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max = min;
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class MinType {
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private double minDist;
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private int type;
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}
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private double getSub(List<Matrix> featureSame, List<Matrix> featureDifferent) throws Exception {
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double minSub = 0;
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for (int i = 0; i < featureSame.size(); i++) {
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Matrix sameFeature = featureSame.get(i);
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double differentMin = -1;//与异类相比的最小值
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double sameMin = -1;//与同类相比的最小值
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for (int j = 0; j < featureDifferent.size(); j++) {
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Matrix differentFeature = featureDifferent.get(j);
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double dist = getDist(sameFeature, differentFeature);
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if (differentMin < 0 || dist < differentMin) {
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differentMin = dist;
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}
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return max;
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}
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private double compareDifferent(List<Matrix> featureSame, List<Matrix> featureDifferent) throws Exception {
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double min = -1;//比较异类取最小值
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for (Matrix myFeature : featureSame) {
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for (Matrix feature : featureDifferent) {
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double dist = getDist(myFeature, feature);
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if (min < 0 || dist < min) {
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min = dist;
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for (int k = 0; k < featureSame.size(); k++) {
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if (k != i) {
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Matrix feature = featureSame.get(k);
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double dist = getDist(sameFeature, feature);
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if (sameMin < 0 || dist < sameMin) {
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sameMin = dist;
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}
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}
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}
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return min;
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double sub = differentMin - sameMin;//异类距离与同类距离的差距,选出其中最小的
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if (i == 0) {
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minSub = sub;
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} else if (sub < minSub) {
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minSub = sub;
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}
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class MinType {
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private double minDist;
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private int type;
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}
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return minSub;
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}
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public void selfTest(TempleConfig templeConfig) throws Exception {//对模型数据进行自检测
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Map<Integer, List<Matrix>> featureMap = templeConfig.getKnn().getFeatureMap();
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Map<Integer, Double> maxMap = new HashMap<>();//保存与该类别最大间距
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private void nextDeep(TempleConfig templeConfig) throws Exception {//对模型数据进行自检测
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Map<Integer, List<Matrix>> featureMap = knn.getFeatureMap();
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int nub = featureMap.size() / 2;
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if (nub > 1) {
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MinTypeSort minTypeSort = new MinTypeSort();
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Map<Integer, List<MinType>> minMap = new HashMap<>();//保存与该类别最相似的类别,及距离值
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for (Map.Entry<Integer, List<Matrix>> entry : featureMap.entrySet()) {
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int key = entry.getKey();
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List<MinType> minTypes = new ArrayList<>();
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List<Matrix> myFeatures = entry.getValue();
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double sameMax = compareSame(myFeatures);//同类之间的最大值
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maxMap.put(key, sameMax);
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for (Map.Entry<Integer, List<Matrix>> entry2 : featureMap.entrySet()) {
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int testKey = entry2.getKey();
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if (testKey != key) {//找出最小距离
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List<Matrix> features = entry2.getValue();
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MinType minType = new MinType();
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double minDist = compareDifferent(myFeatures, features);//该类别的最小值
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if (minDist < sameMax) {
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double minDist = getSub(myFeatures, features);
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minType.minDist = minDist;
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minType.type = testKey;
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minTypes.add(minType);
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}
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}
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Collections.sort(minTypes, minTypeSort);
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int len = 0;
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for (int i = 0; i < minTypes.size(); i++) {
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if (minTypes.get(i).minDist < 0) {
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len++;
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} else {
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break;
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}
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}
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if (len < nub) {
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minTypes = minTypes.subList(0, nub);
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} else {
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minTypes = minTypes.subList(0, len);
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}
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minMap.put(key, minTypes);
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}
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for (Map.Entry<Integer, Double> entry : maxMap.entrySet()) {
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int key = entry.getKey();
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double maxValue = entry.getValue();
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System.out.println("=============================================");
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System.out.println("类别：" + key + ",最大类间距为：" + maxValue);
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List<MinType> minTypes = minMap.get(key);
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for (int i = 0; i < minTypes.size(); i++) {
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MinType minType = minTypes.get(i);
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System.out.println("相近类:" + minType.type + ",最小类间距为：" + minType.minDist);
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for (Map.Entry<Integer, List<MinType>> entry : minMap.entrySet()) {
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if (nub == 2 || nub == 3) {
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System.out.println("类别===============================" + entry.getKey());
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}
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List<MinType> minTypeList = entry.getValue();
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DimensionMappingStudy dimensionMappingStudy = new DimensionMappingStudy();
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Knn myKnn = dimensionMappingStudy.getKnn();
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for (MinType minType : minTypeList) {
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if (nub == 2 || nub == 3) {
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System.out.println("type==" + minType.type + ",dist==" + minType.minDist);
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}
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List<Matrix> featureList = featureMap.get(minType.type);
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for (Matrix matrix : featureList) {
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myKnn.insertMatrix(matrix, minType.type);
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}
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}
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deepMappingMap.put(entry.getKey(), dimensionMappingStudy);
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}
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for (Map.Entry<Integer, DimensionMappingStudy> entry : deepMappingMap.entrySet()) {
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entry.getValue().start(templeConfig);
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}
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}
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}
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public int toClassification(Matrix feature) throws Exception {//进行识别 先映射
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feature = mapping(feature, mappingSigma);
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int type = knn.getType(feature);
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if (deepMappingMap.size() > 0 && deepMappingMap.containsKey(type)) {//继续下一层
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return deepMappingMap.get(type).toClassification(feature);
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} else {//返回最后结果
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return type;
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}
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}
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public void start(TempleConfig templeConfig) throws Exception {
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Map<Integer, List<Matrix>> featureMap = templeConfig.getKnn().getFeatureMap();
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Map<Integer, List<Matrix>> featureMap = knn.getFeatureMap();
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FeatureMapping featureMapping = new FeatureMapping(featureMap);
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int dimensionNub = templeConfig.getFeatureNub() * 3 * 2;//PSO维度
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//创建粒子群
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PSO pso = new PSO(dimensionNub, null, null, 200, 100,
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featureMapping, 0.4, 2, 2, true, 0.2, 0.01);
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PSO pso = new PSO(dimensionNub, null, null, 500, 100,
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featureMapping, 0.8, 2, 2, true, 0.2, 0.01);
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List<double[]> mappings = pso.start();
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int size = mappings.size();
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double[] mappingSigma = new double[dimensionNub];
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mappingSigma = new double[dimensionNub];
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for (int i = 0; i < size; i++) {
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double[] mapping = mappings.get(i);
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for (int j = 0; j < mapping.length; j++) {
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@ -121,9 +170,10 @@ public class DimensionMappingStudy {
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for (int i = 0; i < mappingSigma.length; i++) {
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mappingSigma[i] = mappingSigma[i] / size;
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}
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templeConfig.getFood().setMappingParameter(mappingSigma);
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//还要把所有已存在的knn特征完成映射
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featureMapping(featureMap, mappingSigma, templeConfig);
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//进行下一次映射选择
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nextDeep(templeConfig);
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}
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private void featureMapping(Map<Integer, List<Matrix>> featureMap, double[] mapping
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@ -150,4 +200,8 @@ public class DimensionMappingStudy {
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}
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return matrix;
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}
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public Knn getKnn() {
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return knn;
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}
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}
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lidapeng
4 years ago