Commit c878fa79 authored by Alexander David Hellwig's avatar Alexander David Hellwig
Browse files

Add distance matrix creation

parent 2ad264ad
......@@ -26,7 +26,6 @@ import java.util.stream.Collectors;
public class AutomaticClusteringHelper {
// public static double[][] createAdjacencyMatrix(List<ExpandedComponentInstanceSymbol> subcomps, Collection<ConnectorSymbol> connectors, Map<String, Integer> subcompLabels) {
public static double[][] createAdjacencyMatrix(List<EMAComponentInstanceSymbol> subcomps, Collection<EMAConnectorInstanceSymbol> connectors, Map<String, Integer> subcompLabels) {
// Nodes = subcomponents
// Verts = connectors between subcomponents
......@@ -37,18 +36,18 @@ public class AutomaticClusteringHelper {
EMAComponentInstanceSymbol sourceCompOpt = con.getSourcePort().getComponentInstance();
EMAComponentInstanceSymbol targetCompOpt = con.getTargetPort().getComponentInstance();
int index1 = subcompLabels.get(sourceCompOpt.getFullName());
int index2 = subcompLabels.get(targetCompOpt.getFullName());
int index1 = subcompLabels.get(sourceCompOpt.getFullName());
int index2 = subcompLabels.get(targetCompOpt.getFullName());
res[index1][index2] += getTypeCostHeuristic(con.getSourcePort());
res[index2][index1] += getTypeCostHeuristic(con.getSourcePort());
res[index1][index2] += getTypeCostHeuristic(con.getSourcePort());
res[index2][index1] += getTypeCostHeuristic(con.getSourcePort());
});
return res;
}
public static double[][] guaranteedConnectedAdjacencyMatrix(List<EMAComponentInstanceSymbol> subcomps, Collection<EMAConnectorInstanceSymbol> connectors, Map<String, Integer> subcompLabels){
public static double[][] guaranteedConnectedAdjacencyMatrix(List<EMAComponentInstanceSymbol> subcomps, Collection<EMAConnectorInstanceSymbol> connectors, Map<String, Integer> subcompLabels) {
double[][] res = createAdjacencyMatrix(subcomps, connectors, subcompLabels);
......@@ -57,7 +56,7 @@ public class AutomaticClusteringHelper {
double max = 0;
for (double[] doubles : res) {
for (double adj : doubles) {
if(adj > max){
if (adj > max) {
max = adj;
}
}
......@@ -71,7 +70,7 @@ public class AutomaticClusteringHelper {
for (Integer a : representativeLabels) {
for (Integer b : representativeLabels) {
if(!a.equals(b)){
if (!a.equals(b)) {
res[a][b] = unconnectedCost;
}
}
......@@ -79,32 +78,60 @@ public class AutomaticClusteringHelper {
return res;
}
public static List<Set<EMAComponentInstanceSymbol>> getConnectedSubcomponentSets(List<EMAComponentInstanceSymbol> subcomps, Collection<EMAConnectorInstanceSymbol> connectors){
public static double[][] getDistanceMatrix(double[][] adjacencyMatrix) {
//Uses Floyd–Warshall
double[][] res = new double[adjacencyMatrix.length][adjacencyMatrix[0].length];
for (int i = 0; i < adjacencyMatrix.length; i++) {
for (int j = 0; j < adjacencyMatrix[0].length; j++) {
if (i != j) {
double curVal = adjacencyMatrix[i][j];
res[i][j] = Math.abs(curVal) <= 0.00000001d ? Double.MAX_VALUE : curVal;
} else {
res[i][i] = 0d;
}
}
}
for (int k = 0; k < adjacencyMatrix.length; k++) {
for (int i = 0; i < adjacencyMatrix.length; i++) {
for (int j = 0; j < adjacencyMatrix.length; j++) {
if (res[i][j] > res[i][k] + res[k][j]) {
res[i][j] = res[i][k] + res[k][j];
}
}
}
}
return res;
}
public static List<Set<EMAComponentInstanceSymbol>> getConnectedSubcomponentSets(List<EMAComponentInstanceSymbol> subcomps, Collection<EMAConnectorInstanceSymbol> connectors) {
Graph<EMAComponentInstanceSymbol, DefaultEdge> graph = new SimpleGraph<>(DefaultEdge.class);
subcomps.forEach(graph::addVertex);
connectors.stream()
.filter(c -> subcomps.contains(c.getSourcePort().getComponentInstance()))
.filter(c -> subcomps.contains(c.getTargetPort().getComponentInstance()))
.forEach(c -> graph.addEdge(c.getSourcePort().getComponentInstance(), c.getTargetPort().getComponentInstance()));
.filter(c -> subcomps.contains(c.getSourcePort().getComponentInstance()))
.filter(c -> subcomps.contains(c.getTargetPort().getComponentInstance()))
.forEach(c -> graph.addEdge(c.getSourcePort().getComponentInstance(), c.getTargetPort().getComponentInstance()));
ConnectivityInspector<EMAComponentInstanceSymbol, DefaultEdge> connectivityInspector = new ConnectivityInspector<>(graph);
return connectivityInspector.connectedSets();
}
public static double[][] adjacencyMatrix2transitionMatrix(double[][] adjacencyMatrix) {
double[][] transitionMatrix= adjacencyMatrix;
double[][] transitionMatrix = adjacencyMatrix;
int degree;
for(int i = 0; i < adjacencyMatrix[0].length; i++) {
degree= 0;
for(int j = 0; j < adjacencyMatrix[0].length; j++) {
for (int i = 0; i < adjacencyMatrix[0].length; i++) {
degree = 0;
for (int j = 0; j < adjacencyMatrix[0].length; j++) {
if (adjacencyMatrix[i][j] == 1) degree++;
}
for(int j = 0; j < adjacencyMatrix[0].length; j++) {
if (adjacencyMatrix[i][j] == 1) transitionMatrix[i][j] = 1.0/degree;
for (int j = 0; j < adjacencyMatrix[0].length; j++) {
if (adjacencyMatrix[i][j] == 1) transitionMatrix[i][j] = 1.0 / degree;
}
}
......@@ -114,18 +141,18 @@ public class AutomaticClusteringHelper {
// generic matrix normalizer
public static double[][] normalizeMatrix(double[][] matrix) {
double[][] normalizedMatrix= matrix;
double[][] normalizedMatrix = matrix;
double normalizer;
double sum;
for(int i = 0; i < matrix[0].length; i++) {
normalizer= 0;
sum= 0;
for(int j = 0; j < matrix[0].length; j++) {
sum+= normalizedMatrix[i][j];
for (int i = 0; i < matrix[0].length; i++) {
normalizer = 0;
sum = 0;
for (int j = 0; j < matrix[0].length; j++) {
sum += normalizedMatrix[i][j];
}
if (sum>0) normalizer= 1.0/sum;
for(int j = 0; j < matrix[0].length; j++) {
if (sum > 0) normalizer = 1.0 / sum;
for (int j = 0; j < matrix[0].length; j++) {
normalizedMatrix[i][j] = matrix[i][j] * normalizer;
}
}
......@@ -136,11 +163,11 @@ public class AutomaticClusteringHelper {
// calculate the inverse probabilities of a transition matrix
// (regard zero as immutable zero probability)
public static double[][] inverseProbabilitiesMatrix(double[][] matrix) {
double[][] inverseProbabilityMatrix= matrix;
double[][] inverseProbabilityMatrix = matrix;
for(int i = 0; i < matrix[0].length; i++) {
for (int i = 0; i < matrix[0].length; i++) {
for (int j = 0; j < matrix[0].length; j++) {
if (matrix[i][j] > 0) inverseProbabilityMatrix[i][j] = 1.0/matrix[i][j];
if (matrix[i][j] > 0) inverseProbabilityMatrix[i][j] = 1.0 / matrix[i][j];
}
}
......@@ -164,17 +191,17 @@ public class AutomaticClusteringHelper {
EMAComponentInstanceSymbol sourceComp = con.getSourcePort().getComponentInstance();
EMAComponentInstanceSymbol targetComp = con.getTargetPort().getComponentInstance();
for(int i = 0; i < clusters.size(); i++){
if(clusters.get(i).contains(sourceComp)){
for (int i = 0; i < clusters.size(); i++) {
if (clusters.get(i).contains(sourceComp)) {
sourceClusterLabel = i;
}
if(clusters.get(i).contains(targetComp)){
if (clusters.get(i).contains(targetComp)) {
targetClusterLabel = i;
}
}
if(sourceClusterLabel != targetClusterLabel){
if (sourceClusterLabel != targetClusterLabel) {
con.getSourcePort().setMiddlewareSymbol(new RosConnectionSymbol());
con.getTargetPort().setMiddlewareSymbol(new RosConnectionSymbol());
}
......@@ -183,7 +210,7 @@ public class AutomaticClusteringHelper {
}
public static double getTypeCostHeuristic(EMAComponentInstanceSymbol componentInstanceSymbol, List<Set<EMAComponentInstanceSymbol>> clustering){
public static double getTypeCostHeuristic(EMAComponentInstanceSymbol componentInstanceSymbol, List<Set<EMAComponentInstanceSymbol>> clustering) {
List<EMAConnectorInstanceSymbol> interClusterConnectors = getInterClusterConnectors(componentInstanceSymbol, clustering);
return interClusterConnectors.stream()
......@@ -222,18 +249,18 @@ public class AutomaticClusteringHelper {
.collect(Collectors.toList());
}
public static double getTypeCostHeuristic(EMAPortSymbol port){
public static double getTypeCostHeuristic(EMAPortSymbol port) {
return getTypeCostHeuristic(port.getTypeReference());
}
public static double getTypeCostHeuristic(MCTypeReference<? extends MCTypeSymbol> typeReference) {
if (typeReference.getName().equals("CommonMatrixType")){
if (typeReference.getName().equals("CommonMatrixType")) {
double value = getTypeCostHeuristicHelper(
((ASTCommonMatrixType)((MCASTTypeSymbolReference)typeReference).getAstType()).getElementType().getName());
((ASTCommonMatrixType) ((MCASTTypeSymbolReference) typeReference).getAstType()).getElementType().getName());
double res = 0;
List<ASTExpression> vectors = ((ASTCommonMatrixType) ((MCASTTypeSymbolReference) typeReference).
getAstType()).getDimension().getDimensionList();
for (ASTExpression expression : vectors){
for (ASTExpression expression : vectors) {
if (((ASTNumberExpression) expression).getNumberWithUnit().getNumber().isPresent()) {
res += value * ((ASTNumberExpression) expression).getNumberWithUnit().getNumber().get();
}
......@@ -267,11 +294,11 @@ public class AutomaticClusteringHelper {
public static ClusteringResultList executeClusteringFromParams(EMAComponentInstanceSymbol emaComponentInstance, List<AlgorithmCliParameters> algoParams) {
ClusteringResultList res = new ClusteringResultList();
for (int i = 0; i < algoParams.size(); i++) {
System.out.println("Clustering with algorithm " + (i+1) + "/" + algoParams.size() + ": " +algoParams.get(i).toString());
System.out.println("Clustering with algorithm " + (i + 1) + "/" + algoParams.size() + ": " + algoParams.get(i).toString());
ClusteringResult result = ClusteringResult.fromParameters(emaComponentInstance, algoParams.get(i));
if(result.isValid()){
if (result.isValid()) {
res.add(result);
}else{
} else {
Log.warn("Ignoring the result! It is invalid!");
}
}
......
......@@ -115,6 +115,35 @@ public class AutomaticClusteringTest extends AbstractSymtabTest{
}
@Test
public void testDistanceMatrixCreation(){
// a -(10)-> b -(20)-> c | d
double[][] adj = {
{0, 10, 0, 0},
{10, 0, 20, 0},
{0, 20, 0, 0},
{0, 0, 0, 0}};
double[][] dist = AutomaticClusteringHelper.getDistanceMatrix(adj);
double m = Double.MAX_VALUE;
double[][] expDist = {
{0, 10, 30, m},
{10, 0, 20, m},
{30, 20, 0, m},
{m, m, m, 0}};
for(int i = 0; i< expDist.length; i++){
for(int j = 0; j < expDist[i].length;j++){
assertTrue(expDist[i][j] == dist[i][j]);
}
}
}
@Test
public void testSpectralClustering(){
......
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