distance map

src/algorithms/Classifier1NNNareshKumarMS.java

 package algorithms;
 
-import Jama.EigenvalueDecomposition;
-import Jama.Matrix;
-import objects.Template;
-
 public class Classifier1NNNareshKumarMS extends Classifier {
 
     public Classifier1NNNareshKumarMS() {
@@ -11,39 +7,4 @@ public class Classifier1NNNareshKumarMS extends Classifier {
         decision.setDistanceTemplates(new DistanceTemplatesNareshKumarMS());
         setDecision(decision);
     }
-
-    public final class DistanceTemplatesNareshKumarMS extends DistanceTemplates {
-
-        public DistanceTemplatesNareshKumarMS() {
-        }
-
-        @Override
-        public String getDescription() {
-            return "NareshKumarMS";
-        }
-
-        @Override
-        public double getDistance(Template template1, Template template2) {
-            double sum = 0.0;
-            double[] generalizedEigenvalues = new EigenvalueDecomposition(getCovarianceMatrix(template2).inverse().times(getCovarianceMatrix(template1))).getRealEigenvalues();
-            for (int l = 0; l < generalizedEigenvalues.length; l++) {
-                sum += Math.pow(Math.log(Math.abs(generalizedEigenvalues[l])), 2);
-            }
-            return Math.sqrt(sum);
-        }
-
-        private Matrix getCovarianceMatrix(Template template) {
-            Matrix matrix = template.getMatrix();
-            int rows = matrix.getRowDimension();
-            int columns = matrix.getColumnDimension();
-            Matrix covarianceMatrix = new Matrix(columns, columns);
-            for (int r = 0; r < rows; r++) {
-                Matrix d = matrix.getMatrix(r, r, 0, columns - 1);
-                Matrix u = getDistanceTemplatesMatrix().getMatrix(r, r, 0, columns - 1);
-                Matrix diff = d.minus(u);
-                covarianceMatrix.plusEquals((diff.transpose().times(diff)).times((double) 1 / (rows - 1)));
-            }
-            return covarianceMatrix;
-        }
-    }
 }

src/algorithms/ClassifierTransform1NNMMCMahalanobis.java

@@ -39,8 +39,15 @@ public class ClassifierTransform1NNMMCMahalanobis extends ClassifierTransform {
             double[] column = (getMeanMatrix(samplesOfClass).minus(meanSampleMatrix)).times(Math.sqrt((double) samplesOfClass.size() / numberOfSamples)).getColumnPackedCopy();
             Upsilon.setMatrix(0, dimension - 1, c, c, new Matrix(column, column.length));
         }
-        SingularValueDecomposition svdChi = new SingularValueDecomposition(Chi);
-        Matrix Omega = svdChi.getU();
+        SingularValueDecomposition svdChi;
+        Matrix Omega;
+        if (dimension < numberOfSamples) {
+            svdChi = new SingularValueDecomposition(Chi.transpose());
+            Omega = svdChi.getV().transpose();
+        } else {
+            svdChi = new SingularValueDecomposition(Chi);
+            Omega = svdChi.getU();
+        }
         Matrix ThetaInverseSquareRoot = svdChi.getS();
         int dim = Math.min(ThetaInverseSquareRoot.getRowDimension(), ThetaInverseSquareRoot.getColumnDimension());
         for (int d = 0; d < dim; d++) {

src/algorithms/Clustering.java → src/algorithms/Clusterer.java

@@ -3,9 +3,9 @@ package algorithms;
 import java.util.List;
 import objects.Template;
 
-public abstract class Clustering extends Retriever {
+public abstract class Clusterer extends Retriever {
 
-    public Clustering() {
+    public Clusterer() {
     }
 
     public abstract List<List<Template>> cluster(List<Template> templatesGallery);

src/algorithms/ClusteringAgglomerativeHierarchical.java → src/algorithms/ClustererAgglomerativeHierarchical.java

@@ -4,17 +4,17 @@ import java.util.ArrayList;
 import java.util.List;
 import objects.Template;
 
-public class ClusteringAgglomerativeHierarchical extends Clustering {
+public class ClustererAgglomerativeHierarchical extends Clusterer {
 
     private final int k;
     private final double d;
 
-    public ClusteringAgglomerativeHierarchical(int k) {
+    public ClustererAgglomerativeHierarchical(int k) {
         this.k = k;
         this.d = Double.MAX_VALUE;
     }
 
-    public ClusteringAgglomerativeHierarchical(double d) {
+    public ClustererAgglomerativeHierarchical(double d) {
         this.k = Integer.MAX_VALUE;
         this.d = d;
     }

src/algorithms/ClusteringKmeans.java → src/algorithms/ClustererKmeans.java

@@ -5,11 +5,11 @@ import java.util.List;
 import java.util.Random;
 import objects.Template;
 
-public class ClusteringKmeans extends Clustering {
+public class ClustererKmeans extends Clusterer {
 
     private final int k;
 
-    public ClusteringKmeans(int k) {
+    public ClustererKmeans(int k) {
         this.k = k;
     }
 

src/algorithms/Decision.java

@@ -7,9 +7,6 @@ public abstract class Decision extends Retriever {
 
     private List gallery;
 
-    public Decision() {
-    }
-
     public List getGallery() {
         return gallery;
     }

src/algorithms/DecisionRandom.java

 package algorithms;
 
-import java.util.ArrayList;
 import java.util.List;
 import java.util.Random;
 import objects.Template;
@@ -14,16 +13,12 @@ public class DecisionRandom extends Decision {
 
     @Override
     public void importGallery(List<Template> templatesGallery) {
-        List<String> gallery = new ArrayList();
-        for (Template template : templatesGallery) {
-            gallery.add(template.getSubject());
-        }
-        setGallery(gallery);
+        setGallery(templatesGallery);
     }
 
     @Override
     public String decide(Template templateProbe) {
-        List<String> gallery = getGallery();
-        return gallery.get(new Random().nextInt(gallery.size()));
+        List<Template> gallery = getGallery();
+        return gallery.get(new Random().nextInt(gallery.size())).getSubject();
     }
 }

src/algorithms/DecisionSVM.java

-package algorithms;
-
-import java.util.List;
-import libsvm.svm;
-import libsvm.svm_model;
-import libsvm.svm_node;
-import libsvm.svm_parameter;
-import libsvm.svm_problem;
-import objects.Template;
-
-public class DecisionSVM extends Decision {
-    
-    @Override
-    public String getDescription() {
-        return "SVM";
-    }
-    
-    @Override
-    public void importGallery(List<Template> templatesGallery) {
-        setGallery(templatesGallery);
-    }
-
-    @Override
-    public String decide(Template templateProbe) {
-        svm svm = new svm();
-        svm_model svm_model = new svm_model();
-        svm_problem svm_problem = new svm_problem();
-        
-        return "";
-    }
-    
-    double[][] train = new double[1000][]; 
-    double[][] test = new double[10][];
-
-    private svm_model svmTrain() {
-        svm_problem prob = new svm_problem();
-        int dataCount = train.length;
-        prob.y = new double[dataCount];
-        prob.l = dataCount;
-        prob.x = new svm_node[dataCount][];
-
-        for (int i = 0; i < dataCount; i++) {
-            double[] features = train[i];
-            prob.x[i] = new svm_node[features.length - 1];
-            for (int j = 1; j < features.length; j++) {
-                svm_node node = new svm_node();
-                node.index = j;
-                node.value = features[j];
-                prob.x[i][j - 1] = node;
-            }
-            prob.y[i] = features[0];
-        }
-
-        svm_parameter param = new svm_parameter();
-        param.probability = 1;
-        param.gamma = 0.5;
-        param.nu = 0.5;
-        param.C = 1;
-        param.svm_type = svm_parameter.C_SVC;
-        param.kernel_type = svm_parameter.LINEAR;
-        param.cache_size = 20000;
-        param.eps = 0.001;
-
-        svm_model model = svm.svm_train(prob, param);
-
-        return model;
-    }
-}

src/algorithms/DistanceTemplates.java

 package algorithms;
 
-import Jama.Matrix;
 import java.io.Serializable;
 import objects.Template;
 
 public abstract class DistanceTemplates implements Serializable {
     
-    private Matrix distanceTemplatesMatrix;
-
-    public DistanceTemplates() {
-    }
-
-    public Matrix getDistanceTemplatesMatrix() {
-        return distanceTemplatesMatrix;
-    }
-    
-    public void setDistanceTemplatesMatrix(Matrix distanceTemplatesMatrix) {
-        this.distanceTemplatesMatrix = distanceTemplatesMatrix;
-    }
-    
     public abstract String getDescription();
 
     public abstract double getDistance(Template template1, Template template2);

src/algorithms/DistanceTemplatesMahalanobis.java

@@ -5,9 +5,15 @@ import objects.Template;
 
 public final class DistanceTemplatesMahalanobis extends DistanceTemplates {
 
+    private Matrix totalScatterMatrix;
+
     public DistanceTemplatesMahalanobis() {
     }
 
+    public void setTotalScatterMatrix(Matrix totalScatterMatrix) {
+        this.totalScatterMatrix = totalScatterMatrix;
+    }
+
     @Override
     public String getDescription() {
         return "Mahalanobis";
@@ -16,6 +22,6 @@ public final class DistanceTemplatesMahalanobis extends DistanceTemplates {
     @Override
     public double getDistance(Template template1, Template template2) {
         Matrix diff = template1.getMatrix().minus(template2.getMatrix());
-        return Math.sqrt(diff.transpose().times(getDistanceTemplatesMatrix()).times(diff).get(0, 0));
+        return Math.sqrt(diff.transpose().times(totalScatterMatrix).times(diff).get(0, 0));
     }
 }

src/algorithms/DistanceTemplatesNareshKumarMS.java

+package algorithms;
+
+import Jama.EigenvalueDecomposition;
+import Jama.Matrix;
+import objects.Template;
+
+public final class DistanceTemplatesNareshKumarMS extends DistanceTemplates {
+    
+    private Matrix meanMatrix;
+
+    public DistanceTemplatesNareshKumarMS() {
+    }
+
+    public void setMeanMatrix(Matrix meanMatrix) {
+        this.meanMatrix = meanMatrix;
+    }
+
+    @Override
+    public String getDescription() {
+        return "NareshKumarMS";
+    }
+
+    @Override
+    public double getDistance(Template template1, Template template2) {
+        double sum = 0.0;
+        double[] generalizedEigenvalues = new EigenvalueDecomposition(getCovarianceMatrix(template2).inverse().times(getCovarianceMatrix(template1))).getRealEigenvalues();
+        for (int l = 0; l < generalizedEigenvalues.length; l++) {
+            sum += Math.pow(Math.log(Math.abs(generalizedEigenvalues[l])), 2);
+        }
+        return Math.sqrt(sum);
+    }
+
+    private Matrix getCovarianceMatrix(Template template) {
+        Matrix matrix = template.getMatrix();
+        int rows = matrix.getRowDimension();
+        int columns = matrix.getColumnDimension();
+        Matrix covarianceMatrix = new Matrix(columns, columns);
+        for (int r = 0; r < rows; r++) {
+            Matrix d = matrix.getMatrix(r, r, 0, columns - 1);
+            Matrix u = meanMatrix.getMatrix(r, r, 0, columns - 1);
+            Matrix diff = d.minus(u);
+            covarianceMatrix.plusEquals((diff.transpose().times(diff)).times((double) 1 / (rows - 1)));
+        }
+        return covarianceMatrix;
+    }
+}

src/methods/Method.java → src/algorithms/Method.java

-package methods;
+package algorithms;
 
 import Jama.Matrix;
-import algorithms.Classifier;
-import algorithms.MotionLoader;
+import executor.MotionLoader;
 import java.io.File;
 import java.io.FileOutputStream;
 import java.io.FileWriter;

src/methods/MethodAhmedF.java → src/algorithms/MethodAhmedF.java

-package methods;
+package algorithms;
 
-import algorithms.Classifier;
-import algorithms.MotionLoaderJointCoordinates;
+import executor.MotionLoaderJointCoordinates;
 import java.util.ArrayList;
 import java.util.List;
 import objects.Feature;

src/methods/MethodAhmedM.java → src/algorithms/MethodAhmedM.java

-package methods;
+package algorithms;
 
-import algorithms.Classifier;
-import algorithms.MotionLoaderJointCoordinates;
+import executor.MotionLoaderJointCoordinates;
 import java.util.ArrayList;
 import java.util.List;
 import objects.Axis;

src/methods/MethodAliS.java → src/algorithms/MethodAliS.java

-package methods;
+package algorithms;
 
-import algorithms.Classifier;
-import algorithms.MotionLoaderJointCoordinates;
+import executor.MotionLoaderJointCoordinates;
 import java.util.ArrayList;
 import java.util.List;
 import objects.Feature;

src/methods/MethodAnderssonVO.java → src/algorithms/MethodAnderssonVO.java

-package methods;
+package algorithms;
 
-import algorithms.Classifier;
-import algorithms.MotionLoaderJointCoordinates;
+import executor.MotionLoaderJointCoordinates;
 import java.util.ArrayList;
 import java.util.List;
 import objects.Axis;

src/methods/MethodBallA.java → src/algorithms/MethodBallA.java

-package methods;
+package algorithms;
 
-import algorithms.Classifier;
-import algorithms.MotionLoaderJointCoordinates;
+import executor.MotionLoaderJointCoordinates;
 import java.util.ArrayList;
 import java.util.List;
 import objects.Axis;

src/methods/MethodDikovskiB.java → src/algorithms/MethodDikovskiB.java

-package methods;
+package algorithms;
 
-import algorithms.Classifier;
-import algorithms.MotionLoaderJointCoordinates;
+import executor.MotionLoaderJointCoordinates;
 import java.util.ArrayList;
 import java.util.List;
 import objects.Axis;

src/methods/MethodJiangS.java → src/algorithms/MethodJiangS.java

-package methods;
+package algorithms;
 
-import algorithms.Classifier;
-import algorithms.MotionLoaderJointCoordinates;
+import executor.MotionLoaderJointCoordinates;
 import java.util.ArrayList;
 import java.util.List;
 import objects.Axis;
 import objects.Feature;
 import objects.Motion;
 import objects.MotionJointCoordinates;
+import objects.Sample;
 
 public class MethodJiangS extends Method {