lidapeng
4 years ago
parent
f399fac619
commit
4c01112ba0
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package org.wlld.imageRecognition;
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import org.wlld.imageRecognition.modelEntity.MappingBody;
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import java.util.Comparator;
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/**
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* @param
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* @DATA
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* @Author LiDaPeng
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* @Description
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*/
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public class MappingSort implements Comparator<MappingBody> {
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@Override
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public int compare(MappingBody o1, MappingBody o2) {
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if (o1.getMappingNub() > o2.getMappingNub()) {
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return -1;
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} else if (o1.getMappingNub() < o2.getMappingNub()) {
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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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@ -1,41 +0,0 @@
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package org.wlld.imageRecognition.segmentation;
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import org.wlld.i.PsoFunction;
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import org.wlld.tools.Frequency;
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import java.util.List;
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/**
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* @param
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* @DATA
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* @Author LiDaPeng
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* @Description 维度映射
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*/
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public class DimensionMapping extends Frequency implements PsoFunction {
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private List<double[]> features;
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private int size;
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public DimensionMapping(List<double[]> features) {
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this.features = features;
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size = features.size();
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}
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@Override
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public double getResult(double[] parameter, int id) throws Exception {
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double sigma = 0;
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for (int i = 0; i < parameter.length; i++) {
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sigma = sigma + Math.pow(parameter[i], 2);
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}
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sigma = Math.sqrt(sigma);
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double[] mapping = new double[size];//在新维度的映射集合
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for (int i = 0; i < size; i++) {
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double[] feature = features.get(i);
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double s = 0;
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for (int j = 0; j < feature.length; j++) {
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s = s + feature[j] * parameter[j];
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}
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mapping[i] = s / sigma;
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}
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return variance(mapping);
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}
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}
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@ -0,0 +1,153 @@
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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.imageRecognition.TempleConfig;
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import org.wlld.pso.PSO;
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import java.util.*;
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/**
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* @param
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* @DATA
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* @Author LiDaPeng
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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 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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for (int i = 0; i < size; i++) {
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sigma = sigma + Math.pow(data1.getNumber(0, i) - data2.getNumber(0, i), 2);
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}
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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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}
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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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}
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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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}
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}
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}
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return min;
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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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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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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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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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}
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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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}
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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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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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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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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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mappingSigma[j] = mappingSigma[j] + mapping[j];
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}
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}
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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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}
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private void featureMapping(Map<Integer, List<Matrix>> featureMap, double[] mapping
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, TempleConfig templeConfig) throws Exception {
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Map<Integer, List<Matrix>> featureMap2 = new HashMap<>();
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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<Matrix> features = entry.getValue();
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List<Matrix> mappingFeatures = new ArrayList<>();
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for (Matrix matrix : features) {
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mappingFeatures.add(mapping(matrix, mapping));
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}
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featureMap2.put(key, mappingFeatures);
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}
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templeConfig.getKnn().setFeatureMap(featureMap2);
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}
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private Matrix mapping(Matrix feature, double[] mapping) throws Exception {
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int size = feature.getY();
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Matrix matrix = new Matrix(1, size);
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for (int i = 0; i < size; i++) {
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double nub = feature.getNumber(0, i) * mapping[i] + mapping[size + i];
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matrix.setNub(0, i, nub);
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}
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return matrix;
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}
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}
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@ -0,0 +1,115 @@
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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.tools.Frequency;
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import java.util.ArrayList;
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import java.util.HashMap;
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import java.util.List;
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import java.util.Map;
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/**
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* @param
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* @DATA
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* @Author LiDaPeng
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* @Description 特征映射
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*/
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public class FeatureMapping extends Frequency implements PsoFunction {
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private Map<Integer, List<Matrix>> featureMap;//特征
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public FeatureMapping(Map<Integer, List<Matrix>> featureMap) {
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this.featureMap = featureMap;
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}
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@Override
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public double getResult(double[] parameter, int id) throws Exception {
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return featuresMapping(parameter);
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}
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private double[] mapping(Matrix matrix, double[] parameter) throws Exception {
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int size = matrix.getY();
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double[] mapping = new double[size];
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for (int i = 0; i < size; i++) {
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mapping[i] = matrix.getNumber(0, i) * parameter[i] + parameter[size + i];
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}
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return mapping;
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}
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private double getDist(double[] data1, double[] data2) {
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int size = data1.length;
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double sigma = 0;
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for (int i = 0; i < size; i++) {
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sigma = sigma + Math.pow(data1[i] - data2[i], 2);
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}
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return Math.sqrt(sigma);
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}
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private double compareSame(List<double[]> testFeatures) {//比较同类找最大值
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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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double min = -1;
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double[] feature = testFeatures.get(i);
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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 < 0 || dist < min) {
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min = dist;
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}
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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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}
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}
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return max;
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}
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private double compareDifferent(List<double[]> featureSame, List<double[]> featureDifferent) {
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double min = -1;//比较异类取最小值
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for (double[] myFeature : featureSame) {
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for (double[] 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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}
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}
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}
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return min;
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}
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private double compareDist(Map<Integer, List<double[]>> mapping) {//比较距离
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double sigma = 0;
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for (Map.Entry<Integer, List<double[]>> entry : mapping.entrySet()) {
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int key = entry.getKey();
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List<double[]> myFeature = entry.getValue();
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List<double[]> featureDifferent = new ArrayList<>();
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for (Map.Entry<Integer, List<double[]>> entry2 : mapping.entrySet()) {
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if (entry2.getKey() != key) {
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featureDifferent.addAll(entry2.getValue());
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}
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}
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double sameMax = compareSame(myFeature);//同类最大值
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double differentMin = compareDifferent(myFeature, featureDifferent);//异类最小值
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double sub = differentMin - sameMax;
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sigma = sigma + sub;
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}
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return sigma;
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}
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private double featuresMapping(double[] parameter) throws Exception {//对当前所有数据进行映射
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Map<Integer, List<double[]>> mapping = new HashMap<>();
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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<Matrix> features = entry.getValue();//当前类别的所有特征
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List<double[]> featureList = new ArrayList<>();
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for (Matrix matrix : features) {
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featureList.add(mapping(matrix, parameter));
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}
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mapping.put(key, featureList);
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}
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return compareDist(mapping);
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}
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}
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