diff --git a/src/fr/inrialpes/exmo/align/impl/InstanceBasedMatrixMeasure.java b/src/fr/inrialpes/exmo/align/impl/InstanceBasedMatrixMeasure.java
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+/*
+ * $Id$
+ *
+ * Copyright (C) INRIA, 2010
+ *
+ * Modifications to the initial code base are copyright of their
+ * respective authors, or their employers as appropriate. Authorship
+ * of the modifications may be determined from the ChangeLog placed at
+ * the end of this file.
+ *
+ * This program is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU Lesser General Public License
+ * as published by the Free Software Foundation; either version 2.1 of
+ * the License, or (at your option) any later version.
+ *
+ * This program is distributed in the hope that it will be useful, but
+ * WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+ * Lesser General Public License for more details.
+ *
+ * You should have received a copy of the GNU Lesser General Public
+ * License along with this program; if not, write to the Free Software
+ * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307
+ * USA.
+ */
+
+package fr.inrialpes.exmo.align.impl;
+
+// Alignment API classes
+import org.semanticweb.owl.align.Alignment;
+import org.semanticweb.owl.align.AlignmentException;
+
+// Alignment API implementation classes
+import fr.inrialpes.exmo.align.impl.MatrixMeasure;
+
+import fr.inrialpes.exmo.ontowrap.OntologyFactory;
+import fr.inrialpes.exmo.ontowrap.HeavyLoadedOntology;
+import fr.inrialpes.exmo.ontowrap.LoadedOntology;
+import fr.inrialpes.exmo.ontowrap.OntowrapException;
+
+// Java standard classes
+import java.util.Set;
+import java.util.Properties;
+import java.util.Vector;
+
+/**
+ * InstanceBasedMatrixMeasure
+ *
+ * This is a generic distance store in which
+ * - a distance between instances is computed and stored in the table
+ * - the classical distance between classes is computed from the distance between
+ * instances through classical measure (linkage, etc.).
+ * - the rest is as usual
+ *
+ * The only method to implement is computeInstanceDistance( params ).
+ * For additional flexibility, initialize( LoadedOntology onto1, LoadedOntology onto2, Alignment align ) can be refined.
+ * Note that it uses HeavyLoadedOntology.
+ *
+ */
+
+public abstract class InstanceBasedMatrixMeasure extends MatrixMeasure {
+
+ Set<Object>[] classinst1 = null;
+ Set<Object>[] classinst2 = null;
+
+ public InstanceBasedMatrixMeasure() {
+ similarity = false; // This is a distance
+ };
+
+ public void initialize( LoadedOntology onto1, LoadedOntology onto2, Alignment align ) {
+ // create the matrices and all structures
+ super.initialize( onto1, onto2, align );
+ try {
+ if ( !(onto1 instanceof HeavyLoadedOntology)
+ || !(onto2 instanceof HeavyLoadedOntology) )
+ throw new AlignmentException( "InstanceBasedMatrixMeasure requires HeavyLoadedOntology");
+ HeavyLoadedOntology ontology1 = (HeavyLoadedOntology)onto1;
+ HeavyLoadedOntology ontology2 = (HeavyLoadedOntology)onto2;
+
+ // Normalise class comparators (which instance belongs to which class)
+ classinst1 = new Set[nbclass1];
+ for( Object cl1 : ontology1.getClasses() ) {
+ classinst1[ classlist1.get( cl1 ).intValue() ] = ontology1.getInstances( cl1, OntologyFactory.LOCAL, OntologyFactory.FULL, OntologyFactory.NAMED );
+ }
+ classinst2 = new Set[nbclass2];
+ for( Object cl2 : ontology2.getClasses() ) {
+ classinst2[ classlist2.get( cl2 ).intValue() ] = ontology2.getInstances( cl2, OntologyFactory.LOCAL, OntologyFactory.FULL, OntologyFactory.NAMED );
+ }
+ } catch (OntowrapException owex) {
+ owex.printStackTrace();
+ } catch (AlignmentException alex) {
+ alex.printStackTrace();
+ }
+
+ }
+
+ public void compute( Properties params ) {
+ // First compute the distance on instances
+ computeInstanceDistance( params );
+ // Then compute class distances wrt parameters
+ computeClassDistance( params );
+ }
+
+ /**
+ * This is the empty method of this abstract class
+ * it must compute the instance distance
+ * and fill the adequate indmatrix with these distances.
+ */
+ public abstract void computeInstanceDistance( Properties params );
+
+ public void computeClassDistance( Properties params ) {
+ String cmeasure = params.getProperty( "cmeasure" );
+ if ( cmeasure != null && !cmeasure.equals( "singlel" ) ) {
+ if ( cmeasure.equals( "fulll" ) ) {
+ computeFullLinkage();
+ } else if ( cmeasure.equals( "averagel" ) ) {
+ computeAverageLinkage();
+ } else if ( cmeasure.equals( "hausdorff" ) ) {
+ computeHausdorffDistance();
+ }
+ } else {
+ computeSingleLinkage();
+ }
+ }
+
+ private void computeSingleLinkage() {
+ for ( int i=0; i < nbclass1; i++ ) {
+ for ( int j=0; j < nbclass2; j++ ) {
+ double min = 1.;
+ for ( Object in1 : classinst1[i] ) {
+ for ( Object in2 : classinst2[j] ) {
+ double val = indmatrix[indlist1.get( in1 ).intValue()][indlist2.get( in2 ).intValue()];
+ if ( val < min ) { min = val; }
+ }
+ }
+ clmatrix[i][j] = min;
+ }
+ }
+ }
+
+ private void computeFullLinkage() {
+ for ( int i=0; i < nbclass1; i++ ) {
+ for ( int j=0; j < nbclass2; j++ ) {
+ double max = 0.;
+ for ( Object in1 : classinst1[i] ) {
+ for ( Object in2 : classinst2[j] ) {
+ double val = indmatrix[indlist1.get( in1 ).intValue()][indlist2.get( in2 ).intValue()];
+ if ( val > max ) { max = val; }
+ }
+ }
+ clmatrix[i][j] = max;
+ }
+ }
+ }
+ private void computeAverageLinkage() {
+ for ( int i=0; i < nbclass1; i++ ) {
+ for ( int j=0; j < nbclass2; j++ ) {
+ int nbval = 0;
+ double dist = 0.;
+ for ( Object in1 : classinst1[i] ) {
+ for ( Object in2 : classinst2[j] ) {
+ nbval++;
+ dist += indmatrix[indlist1.get( in1 ).intValue()][indlist2.get( in2 ).intValue()];
+ }
+ }
+ if ( nbval == 0 ) clmatrix[i][j] = 1.0;
+ else clmatrix[i][j] = dist/(double)nbval;
+ }
+ }
+ }
+
+ private void computeHausdorffDistance() {
+ for ( int i=0; i < nbclass1; i++ ) {
+ for ( int j=0; j < nbclass2; j++ ) {
+ if ( classinst1[i].size() == 0 && classinst2[j].size() == 0 ) clmatrix[i][j] = 1.;
+ double max = 0.;
+ for ( Object in1 : classinst1[i] ) {
+ double min = 1.;
+ for ( Object in2 : classinst2[j] ) {
+ double val = indmatrix[indlist1.get( in1 ).intValue()][indlist2.get( in2 ).intValue()];
+ if ( val < min ) min = val;
+ }
+ if ( min > max ) max = min;
+ }
+ for ( Object in2 : classinst2[j] ) {
+ double min = 1.;
+ for ( Object in1 : classinst1[i] ) {
+ double val = indmatrix[indlist1.get( in1 ).intValue()][indlist2.get( in2 ).intValue()];
+ if ( val < min ) min = val;
+ }
+ if ( min > max ) max = min;
+ }
+ clmatrix[i][j] = max;
+ }
+ }
+ }
+
+ // These are useless because "compute" is overridden
+ public double measure( Object cl1, Object cl2 ) throws Exception {
+ return 0.;
+ }
+ public double classMeasure( Object cl1, Object cl2 ) throws Exception {
+ return 0.;
+ }
+ public double propertyMeasure( Object pr1, Object pr2 ) throws Exception {
+ return 0.;
+ }
+ public double individualMeasure( Object id1, Object id2 ) throws Exception {
+ //if ( debug > 4 ) System.err.println( "ID:"+id1+" -- "+id2);
+ // compute edit distance between both norms
+ //norm1[indlist1.get(ob1).intValue()], norm2[indlist2.get(ob2).intValue()]
+ return 0.;
+ }
+
+}
+