27 files changed, 2169 insertions, 0 deletions

diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Domain.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Domain.xtend
new file mode 100644
index 00000000..c8fd435b
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Domain.xtend
@@ -0,0 +1,7 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app
+
+enum Domain{
+        Yakindumm,
+        Ecore,
+        Github
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Main.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Main.xtend
new file mode 100644
index 00000000..dfde6593
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Main.xtend
@@ -0,0 +1,96 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.EMFGraph
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io.CsvFileWriter
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io.GraphReader
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.validation.ViolationCheck
+import hu.bme.mit.inf.dslreasoner.domains.yakindu.sgraph.yakindumm.impl.YakindummPackageImpl
+import java.io.File
+import java.util.ArrayList
+import org.eclipse.emf.ecore.EPackage
+import org.eclipse.emf.ecore.EcorePackage
+import org.eclipse.viatra.query.runtime.rete.matcher.ReteEngine
+import org.eclipse.emf.ecore.impl.EcorePackageImpl
+import org.eclipse.emf.ecore.EReference
+
+//import yakindumm2.impl.Yakindumm2PackageImpl
+
+class Main {
+        var static Domain d = Domain.Yakindumm;
+        val static String suffix = '.xmi'
+        val static String OUTPUT_FOLDER = "Inputs/measurement2/yakindu/Alloy/";
+        val static String INPUT_FOLDER = "outputs/measurement2/yakindu/Alloy/";
+        val static int NUM_RUNS = 1;
+        
+        static class RWInformation{
+                public var String inputFolder;
+                public var String outputFolder;
+                public var int numRuns;
+                
+                new(String inputFolder, String outputFolder, int numRuns){
+                        this.inputFolder = inputFolder;
+                        this.outputFolder = outputFolder;
+                        this.numRuns = numRuns;
+                }
+        }
+        
+        def static void main(String[] args){
+                //init model
+                var EPackage metamodel;
+                //init viatra engine for the violation checker
+                ReteEngine.getClass();
+                
+                if(d == Domain.Yakindumm){
+                        YakindummPackageImpl.eINSTANCE.eClass;
+                        metamodel = YakindummPackageImpl.eINSTANCE;
+                }else if (d == Domain.Ecore){
+                        EcorePackage.eINSTANCE.eClass;
+                        metamodel = EcorePackageImpl.eINSTANCE;
+                }else if (d == Domain.Github){
+                        //TODO: Initialize Github Package
+                }
+                
+                                                                                
+                println("Start Reading Models...");
+                var reader = new GraphReader(metamodel, suffix);
+
+                val models = new RWInformation(OUTPUT_FOLDER, INPUT_FOLDER, NUM_RUNS);
+                calculateAllModels(models.inputFolder, models.outputFolder,models.numRuns, reader);     
+                println("finished");
+        }
+        
+        static def calculateAllModels(String inputFolder, String outputFolder, int numRuns, GraphReader reader){
+                (new File(outputFolder)).mkdir();
+                for(var i = 1; i <= numRuns; i++){
+                        val models = new ArrayList<EMFGraph>();
+                        models.addAll(reader.readModels(inputFolder + "run" + i));
+                        for(model : models){
+                                calculateAndOutputMetrics(model, YakindummPackageImpl.eNAME, outputFolder+model.name+"_run_"+i+".csv");
+                        }
+                }
+                println("output results Ended for: " + outputFolder);
+                
+                
+        }
+        
+        static def calculateAndOutputMetrics(EMFGraph model, String metaModel, String fileName){
+                //println("evaluating for " + model.name);              
+                model.metaModel = metaModel;
+                
+                //remove eGenericType for Ecore domain
+                if(d == Domain.Ecore){
+                        var refsToRemove = EcorePackageImpl.eINSTANCE.eAllContents.filter(EReference).filter[
+                                it.name.equals('eGenericType') || it.name.equals('eGenericSuperTypes') || it.name.equals('eFactoryInstance')||
+                                it.name.equals('eGenericExceptions') || it.name.equals('references') || it.name.equals('contents');
+                        ];
+                        refsToRemove.forEach[model.removeReference(it)];
+                }
+                
+                var outputs = model.evaluateAllMetrics();
+                var violations = ViolationCheck.calculateViolationCounts(model.root, d);
+                println(violations);
+                var violationsOutput = newArrayList('violations', violations+'');
+                outputs.add(violationsOutput);
+                CsvFileWriter.write(outputs, fileName);
+        }
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetric.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetric.xtend
new file mode 100644
index 00000000..5e62b586
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetric.xtend
@@ -0,0 +1,85 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance.JSDistance
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance.KSDistance
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.PartialInterpretationGraph
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io.CsvFileWriter
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.Metric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.MultiplexParticipationCoefficientMetric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.NodeActivityMetric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.OutDegreeMetric
+import hu.bme.mit.inf.dslreasoner.viatrasolver.partialinterpretationlanguage.partialinterpretation.PartialInterpretation
+import java.io.File
+import java.io.FileNotFoundException
+import java.io.PrintWriter
+import java.util.ArrayList
+import java.util.List
+import org.eclipse.emf.ecore.util.EcoreUtil
+import org.eclipse.viatra.dse.api.Solution
+
+class PartialInterpretationMetric {
+        var static KSDistance ks;
+        var static JSDistance js;
+        
+        def static void initPaths(){
+                new File("debug/metric/").mkdir();
+                new File("debug/metric/trajectories/").mkdir();
+        }
+        
+        // calculate the metrics for a state
+        def static void calculateMetric(PartialInterpretation partial, String path, String currentStateId, Integer counter){
+                val metrics = new ArrayList<Metric>();
+                metrics.add(new OutDegreeMetric());
+                metrics.add(new NodeActivityMetric());
+                metrics.add(new MultiplexParticipationCoefficientMetric());
+                
+                //make dir since the folder can be none existing
+                new File(path).mkdir();
+                val filename = path + "/state_"+currentStateId+"-"+counter+".csv";
+                val metricCalculator = new PartialInterpretationGraph(partial, metrics, currentStateId);
+                
+                CsvFileWriter.write(metricCalculator.evaluateAllMetrics(), filename);
+        }
+        
+        def static void outputTrajectories(PartialInterpretation empty, List<Solution>  solutions){     
+                for(solution : solutions){
+                        
+                        //need to copy the empty solution because the transition directly worked on the graph
+                        val emptySolutionCopy = EcoreUtil.copy(empty)
+                        val trajectory = solution.shortestTrajectory;
+                        trajectory.model = emptySolutionCopy
+                        
+                        // state codes that will record the trajectory
+                        val stateCodes = newArrayList()
+                        var counter = 0
+
+                        //transform and record the state codes for each state
+                        while(trajectory.doNextTransformation){
+                                //println(trajectory.stateCoder.createStateCode)
+                                val stateId = trajectory.stateCoder.createStateCode.toString
+                                val interpretation = trajectory.getModel();
+                                println(stateId)
+                                //calculate metrics of current state
+                                calculateMetric(interpretation as PartialInterpretation, "debug/metric/output", stateId, counter)
+                                stateCodes.add(stateId)
+                                counter++
+                        }
+                        
+                        
+                        //output the trajectory
+                        try{
+                                new File("debug/metric/trajectories/").mkdir();
+                                val path = "debug/metric/trajectories/trajectory"+trajectory.stateCoder.createStateCode.toString+".csv"
+                                val PrintWriter writer = new PrintWriter(new File(path))
+                                val output = new StringBuilder
+                                for(stateCode : stateCodes){
+                                        output.append(stateCode+'\n')
+                                }
+                                writer.write(output.toString())
+                                writer.close()
+                        }catch(FileNotFoundException e) {
+                                e.printStackTrace()
+                        }
+                }                       
+        }
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetricDistance.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetricDistance.xtend
new file mode 100644
index 00000000..697b2639
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetricDistance.xtend
@@ -0,0 +1,216 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance.EuclideanDistance
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance.JSDistance
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance.KSDistance
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance.StateData
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.PartialInterpretationGraph
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io.RepMetricsReader
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.Metric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.MetricSampleGroup
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.MultiplexParticipationCoefficientMetric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.NodeActivityMetric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.NodeTypeMetric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.OutDegreeMetric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.predictor.LinearModel
+import hu.bme.mit.inf.dslreasoner.viatrasolver.partialinterpretationlanguage.partialinterpretation.PartialInterpretation
+import java.util.ArrayList
+import java.util.HashMap
+import java.util.List
+import java.util.Map
+import org.apache.commons.math3.stat.regression.OLSMultipleLinearRegression
+import org.eclipse.xtend.lib.annotations.Accessors
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.EdgeTypeMetric
+
+class PartialInterpretationMetricDistance {
+        
+        var KSDistance ks;
+        var JSDistance js;
+        var EuclideanDistance ed;
+        var Map<Object, StateData> stateAndHistory;
+        var OLSMultipleLinearRegression regression;
+        List<StateData> samples;
+        var MetricSampleGroup g;
+        @Accessors(PUBLIC_GETTER)
+        var LinearModel linearModel;
+        
+        
+        new(Domain d){
+                var metrics = RepMetricsReader.read(d);
+                this.g = metrics;
+                ks = new KSDistance(g);
+                js = new JSDistance(g);
+                ed = new EuclideanDistance(g);
+                regression = new OLSMultipleLinearRegression();
+                regression.noIntercept = false;
+                stateAndHistory = new HashMap<Object, StateData>();
+                samples = new ArrayList<StateData>();
+                linearModel = new LinearModel(0.01);
+        }
+        
+        def MetricDistanceGroup calculateMetricDistanceKS(PartialInterpretation partial){
+                val metrics = new ArrayList<Metric>();
+                metrics.add(new OutDegreeMetric());
+                metrics.add(new NodeActivityMetric());
+                metrics.add(new MultiplexParticipationCoefficientMetric());
+                metrics.add(new NodeTypeMetric());
+                val metricCalculator = new PartialInterpretationGraph(partial, metrics, null);          
+                var metricSamples = metricCalculator.evaluateAllMetricsToSamples();
+                
+                var mpc = ks.mpcDistance(metricSamples.mpcSamples);
+                var na = ks.naDistance(metricSamples.naSamples);
+                var outDegree = ks.outDegreeDistance(metricSamples.outDegreeSamples);
+                var nodeType = ks.nodeTypeDistance(metricSamples.nodeTypeSamples);
+                //var typedOutDegree = ks.typedOutDegreeDistance(metricSamples.typedOutDegreeSamples);
+                var distance = new MetricDistanceGroup(mpc, na, outDegree, nodeType);
+                distance.nodeTypeInfo = metricSamples.nodeTypeSamples;
+                return distance;
+        }
+        
+        def MetricDistanceGroup calculateMetricEuclidean(PartialInterpretation partial){
+                val metrics = new ArrayList<Metric>();
+                metrics.add(new OutDegreeMetric());
+                metrics.add(new NodeActivityMetric());
+                metrics.add(new MultiplexParticipationCoefficientMetric());
+                
+                val metricCalculator = new PartialInterpretationGraph(partial, metrics, null);          
+                var metricSamples = metricCalculator.evaluateAllMetricsToSamples();
+                
+                var mpc = ed.mpcDistance(metricSamples.mpcSamples);
+                var na = ed.naDistance(metricSamples.naSamples);
+                var outDegree = ed.outDegreeDistance(metricSamples.outDegreeSamples);
+                
+                return new MetricDistanceGroup(mpc, na, outDegree);
+        }
+        
+        def MetricDistanceGroup calculateMetricDistance(PartialInterpretation partial){
+                val metrics = new ArrayList<Metric>();
+                metrics.add(new OutDegreeMetric());
+                metrics.add(new NodeActivityMetric());
+                metrics.add(new MultiplexParticipationCoefficientMetric());
+                
+                val metricCalculator = new PartialInterpretationGraph(partial, metrics, null);          
+                var metricSamples = metricCalculator.evaluateAllMetricsToSamples();
+                
+                var mpc = js.mpcDistance(metricSamples.mpcSamples);
+                var na = js.naDistance(metricSamples.naSamples);
+                var outDegree = js.outDegreeDistance(metricSamples.outDegreeSamples);
+                
+                return new MetricDistanceGroup(mpc, na, outDegree);
+        }
+        
+        def resetRegression(Object state){
+                samples.clear();
+                
+                if(stateAndHistory.containsKey(state)){
+                        var data = stateAndHistory.get(state);
+                        
+                        var curState = state;
+                        
+                        samples.add(data);
+                        
+                        while(stateAndHistory.containsKey(data.lastState) && data.lastState != curState){
+                                curState = data.lastState;
+                                data = stateAndHistory.get(data.lastState);
+                                samples.add(data);
+                        }
+                                        
+                        if(samples.size == 0){
+                                println('state: ' + state);
+                                println('last state: ' + data.lastState);
+                        }
+                }
+                println("trajectory sample size:" + samples.size)
+        }
+        
+        def feedData(Object state, double[] features, double value, Object lastState){
+                var data = new StateData(features, value, lastState);
+                stateAndHistory.put(state, data);
+                samples.add(data);
+        }
+        
+        def getPredictionForNextDataSample(double[] features, double value, double[] featuresToPredict){        
+                if(samples.size <= 4){
+                        println('OK');
+                }       
+                var data = new StateData(features, value, null);
+                samples.add(data);
+                
+                // create training set from current data                
+                var double[][] xSamples = samples.map[it.features];
+                var double[] ySamples = samples.map[it.value];
+                
+                
+                regression.newSampleData(ySamples, xSamples);
+                var prediction = predict(featuresToPredict);    
+                
+                //remove the last element just added
+                samples.remove(samples.size - 1);
+                return prediction;
+        }
+        
+        def private predict(double[] featuresToPredict){
+                var parameters = regression.estimateRegressionParameters();
+                // the regression will add an initial column for 1's, the first parameter is constant term
+                var result = parameters.get(0);
+                for(var i = 0; i < featuresToPredict.length; i++){
+                        result += parameters.get(i+1) * featuresToPredict.get(i);
+                }
+                return result;
+        }
+        
+        def double[] calculateFeature(int step, int violations){
+                var features = newDoubleArrayOfSize(2);
+                //constant term
+                features.set(0, 1); //a
+                features.set(0, Math.sqrt(step) + 30) // b
+                features.set(1, 1.0 / (step + 30) );// c
+                
+                
+//              features.set(2, violations);
+//              features.set(3, Math.pow(violations, 2));
+                
+                return features;
+        }
+}
+
+class MetricDistanceGroup{
+        var double mpcDistance;
+        var double naDistance;
+        var double outDegreeDistance;
+        var double nodeTypeDistance;
+        protected var HashMap<String, Double> nodeTypeInfo;
+                
+        new(double mpcDistance, double naDistance, double outDegreeDistance, double nodeTypeDistance){
+                this.mpcDistance = mpcDistance;
+                this.naDistance = naDistance;
+                this.outDegreeDistance = outDegreeDistance;
+                this.nodeTypeDistance = nodeTypeDistance;
+        }
+        
+        new(double mpcDistance, double naDistance, double outDegreeDistance){
+                this.mpcDistance = mpcDistance;
+                this.naDistance = naDistance;
+                this.outDegreeDistance = outDegreeDistance;
+        }
+        
+        def double getNodeTypeDistance(){
+                return this.nodeTypeDistance;
+        }
+        
+        def double getMPCDistance(){
+                return this.mpcDistance
+        }
+        
+        def double getNADistance(){
+                return this.naDistance
+        }
+        
+        def double getOutDegreeDistance(){
+                return this.outDegreeDistance
+        }
+        
+        def double getNodeTypePercentage(String typeName){
+                return nodeTypeInfo.getOrDefault(typeName, 0.0);
+        }
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/CostDistance.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/CostDistance.xtend
new file mode 100644
index 00000000..613f0f43
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/CostDistance.xtend
@@ -0,0 +1,38 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance
+
+import java.text.DecimalFormat
+import java.util.HashMap
+import java.util.List
+import org.eclipse.xtend.lib.annotations.Accessors
+
+abstract class CostDistance {
+        def abstract double naDistance(List<Double> samples);   
+        def abstract double mpcDistance(List<Double> samples);
+        def abstract double outDegreeDistance(List<Double> samples);
+        
+        def protected pmfFromSamples(double[] samples, DecimalFormat formatter){
+                var length = samples.length;
+                var pmfMap = new HashMap<String, Double>();
+                
+                for(sample : samples){
+                        pmfMap.put(formatter.format(sample), pmfMap.getOrDefault(formatter.format(sample), 0.0) + 1.0 / length);
+                }
+                
+                return pmfMap;
+        }
+}
+
+class StateData{
+        @Accessors(PUBLIC_GETTER)
+        var double[] features;
+        @Accessors(PUBLIC_GETTER)
+        var double value;
+        @Accessors(PUBLIC_GETTER)
+        var Object lastState;
+        
+        new(double[] features, double value, Object lastState){
+                this.features = features;
+                this.value = value
+                this.lastState = lastState;
+        }
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/EuclideanDistance.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/EuclideanDistance.xtend
new file mode 100644
index 00000000..d6adcc9a
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/EuclideanDistance.xtend
@@ -0,0 +1,72 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app.Domain
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io.RepMetricsReader
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.MetricSampleGroup
+import com.google.common.collect.Sets
+import java.text.DecimalFormat
+import java.util.ArrayList
+import java.util.HashMap
+import java.util.List
+import java.util.Map
+import java.util.Set
+
+class EuclideanDistance extends CostDistance{
+        var MetricSampleGroup g;
+        var HashMap<String, Double> mpcPMF;
+        var HashMap<String, Double> naPMF;
+        var HashMap<String, Double> outDegreePMF;
+        var DecimalFormat formatter;
+        
+        new(MetricSampleGroup g){
+                this.g = g;
+                
+                var mpcSamples = g.mpcSamples;
+                var naSamples = g.naSamples.stream.mapToDouble([it]).toArray();
+                var outDegreeSamples = g.outDegreeSamples.stream.mapToDouble([it]).toArray();
+                
+                //needs to format the number to string avoid precision issue
+                formatter = new DecimalFormat("#0.00000");  
+                
+                mpcPMF = pmfFromSamples(mpcSamples, formatter);
+                naPMF = pmfFromSamples(naSamples, formatter);
+                outDegreePMF = pmfFromSamples(outDegreeSamples, formatter);     
+        }
+        
+        override naDistance(List<Double> samples) {
+                var pmfMap = pmfFromSamples(samples, formatter);
+                return euclideanDistance(pmfMap, naPMF);
+        }
+        
+        override mpcDistance(List<Double> samples) {
+                var pmfMap = pmfFromSamples(samples, formatter);
+                return euclideanDistance(pmfMap, mpcPMF);
+        }
+        
+        override outDegreeDistance(List<Double> samples) {
+                var pmfMap = pmfFromSamples(samples, formatter);
+                return euclideanDistance(pmfMap, outDegreePMF);
+        }
+        
+        
+        def private euclideanDistance(HashMap<String, Double> pmf1, HashMap<String, Double> pmf2){
+                var keys = Sets.union(pmf1.keySet(), pmf2.keySet());
+                var pmfList1 = pmfMapToList(pmf1, keys);
+                var pmfList2 = pmfMapToList(pmf2, keys);
+                var distance = 0.0;
+                for(var i = 0; i < pmfList1.size(); i++){
+                        distance += Math.pow(pmfList1.get(i) + pmfList2.get(i), 2);
+                }
+                
+                return Math.sqrt(distance);
+        }       
+        
+        def private pmfMapToList(Map<String, Double> map, Set<String> keys){
+                var list = new ArrayList<Double>();
+                for(key : keys){
+                        var value = map.getOrDefault(key, 0.0);
+                        list.add(value);
+                }
+                return list;
+        }
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/JSDistance.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/JSDistance.xtend
new file mode 100644
index 00000000..4a0a0dc3
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/JSDistance.xtend
@@ -0,0 +1,88 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.MetricSampleGroup
+import com.google.common.collect.Sets
+import java.text.DecimalFormat
+import java.util.HashMap
+import java.util.List
+
+class JSDistance extends CostDistance {
+        var HashMap<String, Double> mpcPMF;
+        var HashMap<String, Double> naPMF;
+        var HashMap<String, Double> outDegreePMF;
+        var HashMap<String, Double> nodeTypesPMF;
+        var DecimalFormat formatter;
+        
+        new(MetricSampleGroup g){
+                var mpcSamples = g.mpcSamples;
+                var naSamples = g.naSamples.stream.mapToDouble([it]).toArray();
+                var outDegreeSamples = g.outDegreeSamples.stream.mapToDouble([it]).toArray();
+                
+                //needs to format the number to string avoid precision issue
+                formatter = new DecimalFormat("#0.00000");  
+                
+                mpcPMF = pmfFromSamples(mpcSamples, formatter);
+                naPMF = pmfFromSamples(naSamples, formatter);
+                outDegreePMF = pmfFromSamples(outDegreeSamples, formatter);     
+                nodeTypesPMF = g.nodeTypeSamples; 
+        }
+        
+        def private combinePMF(HashMap<String, Double> pmf1, HashMap<String, Double> pmf2){
+                var pmfMap = new HashMap<String, Double>();
+                
+                var union = Sets.union(pmf1.keySet(), pmf2.keySet());
+                
+                for(key : union){
+                        // corresponding to M in JS distance
+                        var value = 1.0/2 * (pmf1.getOrDefault(key, 0.0) + pmf2.getOrDefault(key, 0.0));
+                        pmfMap.put(key, value);
+                }
+                return pmfMap;
+        }
+        
+        def private jsDivergence(HashMap<String, Double> p, HashMap<String, Double> q){
+                val m = combinePMF(q, p);
+                var distance = 1.0/2 * klDivergence(p, m) + 1.0/2 * klDivergence(q, m);
+                return distance;
+        }
+        
+        def klDivergence(HashMap<String, Double> p, HashMap<String, Double> q){
+                var distance = 0.0;
+                for(key : q.keySet()){
+                        //need to convert log e to log 2
+                        if(p.containsKey(key)){
+                                distance -= p.get(key) * Math.log(q.get(key) / p.get(key)) / Math.log(2);
+                        }
+                }
+                return distance;
+        }
+        
+        override double mpcDistance(List<Double> samples){
+                // map list to array
+                var map = pmfFromSamples(samples.stream().mapToDouble([it]).toArray(), formatter);
+                //if the size of array is smaller than 2, ks distance cannot be performed, simply return 1
+                if(map.size < 2) return 1;
+                return jsDivergence(map, mpcPMF);
+        }
+        
+        override double naDistance(List<Double> samples){
+                // map list to array
+                var map = pmfFromSamples(samples.stream().mapToDouble([it]).toArray(), formatter);
+                
+                //if the size of array is smaller than 2, ks distance cannot be performed, simply return 1
+                if(map.size < 2) return 1;
+                return jsDivergence(map, naPMF);
+        }
+        
+        override double outDegreeDistance(List<Double> samples){
+                // map list to array
+                var map = pmfFromSamples(samples.stream().mapToDouble([it]).toArray(), formatter);
+                //if the size of array is smaller than 2, ks distance cannot be performed, simply return 1
+                if(map.size < 2) return 1;
+                return jsDivergence(map, outDegreePMF);
+        }
+        
+        def nodeTypeDistance(HashMap<String, Double> samples){
+                return klDivergence(samples, nodeTypesPMF);
+        }
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/KSDistance.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/KSDistance.xtend
new file mode 100644
index 00000000..c486a328
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/KSDistance.xtend
@@ -0,0 +1,102 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.MetricSampleGroup
+import java.util.HashMap
+import java.util.HashSet
+import java.util.List
+import org.apache.commons.math3.stat.inference.KolmogorovSmirnovTest
+
+class KSDistance extends CostDistance {
+        var static ksTester = new KolmogorovSmirnovTest();
+        var MetricSampleGroup g;
+        
+        new(MetricSampleGroup g){
+                this.g = g;     
+        }
+        override double mpcDistance(List<Double> samples){
+                //if the size of array is smaller than 2, ks distance cannot be performed, simply return 1
+                if(samples.size < 2) return 1;
+                return ksTester.kolmogorovSmirnovStatistic(g.mpcSamples, samples);
+        }
+        
+        override double naDistance(List<Double> samples){
+                //if the size of array is smaller than 2, ks distance cannot be performed, simply return 1
+                if(samples.size < 2) return 1;
+                return ksTester.kolmogorovSmirnovStatistic(g.naSamples as double[], samples);
+        }
+        
+        override double outDegreeDistance(List<Double> samples){
+                //if the size of array is smaller than 2, ks distance cannot be performed, simply return 1
+                if(samples.size < 2) return 1;
+                return ksTester.kolmogorovSmirnovStatistic(g.outDegreeSamples, samples);
+        }
+        
+        def double typedOutDegreeDistance(HashMap<String, List<Integer>> map){
+                var value = 0.0;
+                // map list to array
+                val keySet = new HashSet<String>(map.keySet);
+                keySet.addAll(g.typedOutDegreeSamples.keySet);
+                for(key : keySet){
+                        if(!map.containsKey(key) ){
+                                value += 1;
+                        }else if(!g.typedOutDegreeSamples.containsKey(key)){
+                                value += map.get(key).size * 100;       
+                        }else{
+                                var double[] rep = g.typedOutDegreeSamples.get(key).stream().mapToDouble([it|it]).toArray();
+                                var double[] ins = map.get(key).stream().mapToDouble([it|it]).toArray();
+                                if((rep.size < 2 || ins.size < 2) ){
+                                        if(rep.size < 2 && rep.containsAll(ins)){
+                                                value += 0;
+                                        }else{
+                                                value += 1;
+                                        }
+                                }else if(rep.size >= 2 && ins.size >= 2){
+                                        value += ksTester.kolmogorovSmirnovStatistic(rep, ins);
+                                }
+                        }
+                } 
+                
+                
+                return value;
+        }
+        
+        def nodeTypeDistance(HashMap<String, Double> samples){
+                var typesDistMap = g.nodeTypeSamples;
+                var sourceDist = newArrayList();
+                var instanceDist = newArrayList();
+                
+                for(key : typesDistMap.keySet()){
+                        sourceDist.add(typesDistMap.get(key));
+                        instanceDist.add(samples.getOrDefault(key, 0.0));
+                }
+                
+                return ks_distance_two_dist(sourceDist, instanceDist);
+        }
+        
+        def edgeTypeDistance(HashMap<String, Double> samples){
+                var typesDistMap = g.edgeTypeSamples;
+                var sourceDist = newArrayList();
+                var instanceDist = newArrayList();
+                
+                for(key : typesDistMap.keySet()){
+                        sourceDist.add(typesDistMap.get(key));
+                        instanceDist.add(samples.getOrDefault(key, 0.0));
+                }
+                
+                return ks_distance_two_dist(sourceDist, instanceDist);
+        }
+        
+        def double ks_distance_two_dist(List<Double> dist1, List<Double> dist2){
+                // Since we already know the pdf, we compute the ks-test manully
+                var ksStatistics = 0.0;
+                var sum1 = 0.0;
+                var sum2 = 0.0;
+                for(var i = 0; i < dist1.size(); i++){
+                        sum1 += dist1.get(i);
+                        sum2 += dist2.get(i);
+                        
+                        ksStatistics = Math.max(ksStatistics, Math.abs(sum1 - sum2));
+                } 
+                return ksStatistics;
+        }
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/graph/EMFGraph.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/graph/EMFGraph.xtend
new file mode 100644
index 00000000..8fa29fe6
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/graph/EMFGraph.xtend
@@ -0,0 +1,124 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.Metric
+import java.util.ArrayList
+import java.util.HashSet
+import java.util.List
+import org.eclipse.emf.common.util.EList
+import org.eclipse.emf.ecore.EObject
+import org.eclipse.emf.ecore.EReference
+import org.eclipse.xtend.lib.annotations.Accessors
+
+class EMFGraph extends Graph{   
+        @Accessors(PUBLIC_GETTER)
+        var EObject root;
+        
+        def void init (EObject root, List<Metric> metrics, String name, List<EReference> referenceTypes){
+                val otherContents = root.eAllContents.toList();
+                this.root = root;
+                otherContents.add(root);
+                init(otherContents, metrics, name, referenceTypes);
+        }
+        
+        /**
+         * init the graph with all nodes and reference types in the meta model
+         * @param objects: objects in the instance model (exclude root)
+         * @param metrics: metrics to be evaluated
+         * @param name: name of the instance model
+         * @param ReferenceTypes: reference types defined in the meta model
+         */
+        def void init(List<EObject> objects, List<Metric> metrics, String name, List<EReference> referenceTypes){
+                objects.forEach[it|
+                        // TODO: Maybe want to consider all the super types as well
+                        var types = new HashSet();
+                        types.add(it.eClass.name);
+                        statistic.addNodeWithAllTypes(it, types);
+                ]
+                
+                referenceTypes.forEach[it|              
+                        // Only consider the edges that are not derived to preserve the statistical property
+                        if(!it.derived){
+                                statistic.addEdgeType(it.name);
+                        }
+                ];
+                
+                objects.forEach[source|
+                        source.eClass.EAllReferences.forEach[r|
+                                //many references
+                                if(r.isMany){
+                                        source.getNeighbours(r).forEach[target|
+                                                addEdge(source, target, r);
+                                        ]
+                                }else{
+                                        //single references
+                                        val target = source.eGet(r) as EObject;
+                                        addEdge(source, target, r);
+                                }
+                        ]
+                ]
+                
+                this.metrics = metrics;
+                this.name = name;
+        }
+        
+        def void removeReference(EReference r){
+                if (statistic.containsEdgeType(r.name)){
+                        statistic.removeReference(r.name, r.containment);
+                }
+        }
+        
+        /**
+         * Set basic information for the output
+         */
+   override setBasicInformation(ArrayList<ArrayList<String>> output){
+                val metaInfo = new ArrayList<String>();
+                metaInfo.add(META_MODEL_HEADER);
+                metaInfo.add(this.metaModel);
+                
+                val edgeInfo = new ArrayList<String>();
+                edgeInfo.add(NUM_EDGE_TYPE_HEADER);
+                edgeInfo.add(this.statistic.allTypes.size()+"");
+                
+                val nodeInfo = new ArrayList<String>();
+                nodeInfo.add(NUM_NODE_HEADER);
+                nodeInfo.add(this.statistic.allNodes.size()+"");
+                
+                val stateInfo = new ArrayList<String>();
+                stateInfo.add(STATE_ID_HEADER);
+                stateInfo.add(this.name);
+                
+                
+                output.add(metaInfo);
+                output.add(edgeInfo);
+                output.add(nodeInfo);
+                output.add(stateInfo);
+        }
+        
+        def EList<EObject> getNeighbours(EObject o, EReference r){
+                return (o.eGet(r, true) as EList<EObject>);
+        }
+        
+        def addEdge(EObject source, EObject target, EReference r){
+                //Only add the edge if the reference is not derived to preserve the statistical property
+                if(target !== null && r !== null && !r.derived){
+                        statistic.addEdge(source, target, r.name);
+                }
+        }
+        
+        override GraphStatistic getStatistic(){
+                return this.statistic;
+        }
+        
+        override String getName(){
+                return this.name;
+        }
+        
+        def void setMetaModel(String model){
+                this.metaModel = model;
+        }
+        
+        def String getMetaModel(){
+                return this.metaModel;
+        }
+        
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/graph/Graph.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/graph/Graph.xtend
new file mode 100644
index 00000000..6b400b0d
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/graph/Graph.xtend
@@ -0,0 +1,71 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.Metric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.MetricSampleGroup
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.MultiplexParticipationCoefficientMetric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.NodeActivityMetric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.OutDegreeMetric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.TypedOutDegree
+import java.util.ArrayList
+import java.util.HashMap
+import java.util.List
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.NodeTypeMetric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.EdgeTypeMetric
+
+abstract class Graph {
+        
+        protected static val String META_MODEL_HEADER = "Meta Mode"
+        protected static val String NUM_NODE_HEADER = "Number Of Nodes";
+        protected static val String NUM_EDGE_TYPE_HEADER = "Number of Edge types"; 
+        protected static val String STATE_ID_HEADER = "State Id";
+        
+        protected val statistic = new GraphStatistic();
+        protected var List<Metric> metrics;
+        protected var String name = "";
+        protected var String metaModel = "";
+        
+        /**
+         * evaluate all metrics for this model
+         * return the result as a two dimentional list
+         */
+        def ArrayList<ArrayList<String>> evaluateAllMetrics(){
+                val result = new ArrayList<ArrayList<String>>();
+                setBasicInformation(result);
+                
+                for(metric : this.metrics){
+                        val datas = metric.evaluate(this.statistic);
+                        for(row : datas){
+                                result.add(new ArrayList<String>(row));
+                        }
+                }
+                return result;
+        }       
+        
+        def MetricSampleGroup evaluateAllMetricsToSamples(){
+                var sample = new MetricSampleGroup();
+                
+                for(metric : this.metrics){
+                        if(metric instanceof MultiplexParticipationCoefficientMetric){
+                                sample.mpcSamples = metric.evaluateSamples(this.statistic) as ArrayList<Double>;                                
+                        }else if(metric instanceof NodeActivityMetric){
+                                sample.naSamples = metric.evaluateSamples(this.statistic) as ArrayList<Double>;
+                        }else if(metric instanceof OutDegreeMetric){
+                                sample.outDegreeSamples = metric.evaluateSamples(this.statistic) as ArrayList<Double>;
+                        }else if(metric instanceof TypedOutDegree){
+                                sample.typedOutDegreeSamples = metric.evaluateSamples(this.statistic) as HashMap<String, List<Integer>>;
+                        }else if(metric instanceof NodeTypeMetric){
+                                sample.nodeTypeSamples = metric.evaluateSamples(this.statistic) as HashMap<String, Double>;
+                        }else if (metric instanceof EdgeTypeMetric){
+                                sample.edgeTypeSamples = metric.evaluateSamples(this.statistic) as HashMap<String, Double>;
+                        }
+                }
+                
+                return sample;
+        }
+        
+        def void setBasicInformation(ArrayList<ArrayList<String>> result);
+        
+        def GraphStatistic getStatistic();
+        
+        def String getName();
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/graph/GraphStatistic.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/graph/GraphStatistic.xtend
new file mode 100644
index 00000000..31788bb2
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/graph/GraphStatistic.xtend
@@ -0,0 +1,194 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph
+
+import com.google.common.collect.ArrayListMultimap
+import com.google.common.collect.Multimap
+import java.util.HashMap
+import java.util.HashSet
+import java.util.List
+import java.util.Map
+import java.util.Set
+import org.eclipse.emf.ecore.EObject
+import org.eclipse.emf.ecore.EReference
+
+class GraphStatistic {
+        val incomingEdges = new HashMap<String, Multimap<EObject, EObject>>;
+        val outgoingEdges = new HashMap<String, Multimap<EObject, EObject>>;
+
+        val edgeTypes = new HashSet<String>();
+        val nodeToType = new HashMap<EObject, Set<String>>();   
+
+        /**
+         * Add an edge type to to the graph
+         * @param type: type to add
+         */
+        def void addEdgeType(String type){
+                if(edgeTypes.contains(type)){
+                        return;
+                }
+                
+                edgeTypes.add(type);
+                incomingEdges.put(type, ArrayListMultimap.create());
+                outgoingEdges.put(type, ArrayListMultimap.create());
+        }
+        
+        /**
+         * Add a node to the graph with one type in its type hierarchy
+         * @param node: node to add
+         */
+         def void addNodeWithType(EObject n, String Type){
+                var types = nodeToType.getOrDefault(n, new HashSet<String>());
+                types.add(Type);
+                nodeToType.put(n, types);
+         }
+         
+         def boolean containsNode(EObject o){
+                return nodeToType.containsKey(o);
+         }
+         
+         def Set<String> getTypesForNode(EObject o){
+                return nodeToType.getOrDefault(o, new HashSet<String>());
+         }
+         
+         def void overwriteCurrentType(EObject o, String type){
+                var typeSet = nodeToType.getOrDefault(o, new HashSet<String>());
+                
+                // clear current types
+                typeSet.clear();
+                typeSet.add(type);
+                nodeToType.put(o, typeSet);
+         }
+         
+         /**
+          * Add a node to the graph with all types in its type hierarchy
+          */
+         def void addNodeWithAllTypes(EObject n, Set<String> types){
+                nodeToType.put(n, types);
+         }
+         
+         /**
+          * Add an edge to the graph
+          * @param source: source node
+          * @param target: target node
+          * @param type: type of the reference
+          */
+          def void addEdge(EObject source, EObject target, String type){
+                outgoingEdges.get(type).put(source, target);
+                incomingEdges.get(type).put(target, source);
+          }
+          
+          /**
+           * check if this graph contains a specific edge type
+           */
+           def boolean containsEdgeType(String typeName){
+                 if(outgoingEdges.containsKey(typeName) && incomingEdges.containsKey(typeName)){
+                        return true;
+                 }
+                 return false;
+           }
+           
+           /**
+            * remove references from the statistics, potentially remove the nodes associated with it
+            * @Param name: name of the reference
+            * @Param isContainment: if true then the corresponding nodes on the incoming side will also be removed
+            */
+            def removeReference(String name, boolean isContainment){
+                if(!edgeTypes.contains(name)){
+                        return;
+                }
+                
+                edgeTypes.remove(name);
+                var incomingSet = incomingEdges.remove(name);
+                outgoingEdges.remove(name);
+                
+                // if the reference is not a containment, then removing the reference is enough
+                if(!isContainment){
+                        return;
+                }
+                
+                // else remove all corresponding nodes
+                val nodesToRemove = incomingSet.keySet();
+                
+                //remove nodes from node sets
+                nodesToRemove.forEach[nodeToType.remove(it)];
+                
+                val removeForMultimap = [Multimap<EObject, EObject> refMap| 
+                        nodesToRemove.forEach[refMap.removeAll(it)];
+                        var values = refMap.values()
+                        //remove the values from the list is equavalent to remove it in the multimap
+                        values.removeAll(nodesToRemove); 
+                        return;
+                ];
+                
+                //remove nodes from all other references on incomingEdges
+                incomingEdges.values.forEach(removeForMultimap);
+                outgoingEdges.values.forEach(removeForMultimap);
+            }
+          
+          /**
+           * calculate the out degree for an object
+           */
+          def int outDegree(EObject o){
+                var count = 0;
+                
+                for (String type : edgeTypes){
+                        count += outgoingEdges.get(type).get(o).size();
+                }
+                return count;
+          }
+          
+          /**
+           * calculate the in degree of an object
+           */
+          def int inDegree(EObject o){
+                var count = 0;
+                
+                for (String type : edgeTypes){
+                        count += incomingEdges.get(type).get(o).size();
+                }
+                return count;
+          }
+          
+          /**
+           * calculate the dimentional degree of a node
+           */
+          def int dimentionalDegree(EObject o, String type){
+                return incomingEdges.get(type).get(o).size() + outgoingEdges.get(type).get(o).size();
+          }
+          
+          /**
+           * calculate the number of edge types for a given node.
+           */
+          def int numOfEdgeTypes(EObject o){
+                var count = 0;
+                
+                for(String type : edgeTypes){
+                        if(dimentionalDegree(o, type) > 0){
+                                count++;
+                        }
+                }
+                
+                return count;
+          }
+          
+          def List<String> getAllTypes(){
+                return edgeTypes.toList();
+          }
+          
+          def Map<EObject, Set<String>> getNodeToTypesMap(){
+                return nodeToType;
+          }
+          
+          def List<EObject> getAllNodes(){
+                return nodeToType.keySet().toList();
+          }
+          
+          def HashMap<String, Multimap<EObject, EObject>> getOutgoingEdges(){
+                return outgoingEdges; 
+          }
+          
+          def HashMap<String, Multimap<EObject, EObject>> incomingEdges(){
+                return incomingEdges; 
+          }
+          
+}
+
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/graph/PartialInterpretationGraph.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/graph/PartialInterpretationGraph.xtend
new file mode 100644
index 00000000..a2934eb9
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/graph/PartialInterpretationGraph.xtend
@@ -0,0 +1,134 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.Metric
+import hu.bme.mit.inf.dslreasoner.logic.model.logiclanguage.RelationDeclaration
+import hu.bme.mit.inf.dslreasoner.logic.model.logiclanguage.Type
+import hu.bme.mit.inf.dslreasoner.logic.model.logiclanguage.TypeDefinition
+import hu.bme.mit.inf.dslreasoner.viatrasolver.partialinterpretationlanguage.partialinterpretation.BinaryElementRelationLink
+import hu.bme.mit.inf.dslreasoner.viatrasolver.partialinterpretationlanguage.partialinterpretation.PartialInterpretation
+import hu.bme.mit.inf.dslreasoner.viatrasolver.partialinterpretationlanguage.partialinterpretation.impl.PartialComplexTypeInterpretationImpl
+import java.util.ArrayList
+import java.util.List
+
+class PartialInterpretationGraph extends Graph{
+        val typeToExclude = "undefinedpart";
+        val classSuffix = " class";
+        
+        /**
+         * Define a new PartialInterpretationGraph by parse every element from a PartialInterpretation
+         */
+        new(PartialInterpretation partial, List<Metric> metrics, String name){          
+                //the edge types are defined in terms of RelationDeclaration
+                partial.problem.relations.filter(RelationDeclaration).forEach[
+                        //only need the name of the reference type (remove everything with and after "reference")
+                        var n = it.name.split(" ").get(0);
+                                this.statistic.addEdgeType(n);
+                ]
+                // add all elements
+                val typeInterpretations = getTypes(partial);
+                for(type : typeInterpretations){
+                        //Only consider the most concrete class
+                        if(isConcreteType(type.interpretationOf)){
+                                var typeName = type.interpretationOf.name.replace(classSuffix, '');
+                                for(node : type.elements){
+                                        if(!this.statistic.containsNode(node)){
+                                                this.statistic.addNodeWithType(node, typeName);
+                                        }else{
+                                                // if the current type of the node is a super type of the type to check, 
+                                                // substitute the current type with the new type
+                                                var currentType = statistic.getTypesForNode(node).get(0);
+                                                if(isSuperType(currentType, type.interpretationOf)){
+                                                        statistic.overwriteCurrentType(node, typeName);
+                                                }
+                                        }
+                                }
+                        }
+                }
+                
+                for(relationInterpretation : partial.partialrelationinterpretation) {
+                        //only need the name of the reference type (remove everything with and after "reference")
+                        val type = relationInterpretation.interpretationOf.name.split(" ").get(0);
+                        for(edge : relationInterpretation.relationlinks.filter(BinaryElementRelationLink)){
+                                statistic.addEdge(edge.param1, edge.param2, type);
+                        }       
+                }       
+                
+                this.name = name;
+                this.metrics = metrics; 
+        }
+        
+        /**
+         * recursively check if a type is the super type of another
+         */
+         def boolean isSuperType(String typeName, Type subtypeToCheck){
+                var superTypes = subtypeToCheck.supertypes;
+                if(superTypes.size == 0){
+                        return false;
+                }else if(subtypeToCheck.supertypes.map[it.name.replace(classSuffix, '')].contains(typeName)){
+                        return true;
+                }else{
+                        for(superType : superTypes){
+                                if(isSuperType(typeName, superType)){
+                                        return true;
+                                }
+                        }
+                        return false;
+                }
+         }
+         
+         /**
+          * Check if a Type object is the class that we want to consider
+          *     A type object is to be considered if it satisfy one of the following:
+          *     1. if it is not abstract
+          *     2. if it is abstract but has a subclass of type TypeDefinition (This means the generation is
+          *        started with nodes in this type) 
+          */
+        def boolean isConcreteType(Type t){
+                if(!t.isAbstract || t.subtypes.findFirst[it instanceof TypeDefinition] !== null){
+                        return true;
+                }
+                return false;
+        }
+        
+        /**
+         * Set basic information for the output
+         */
+        override setBasicInformation(ArrayList<ArrayList<String>> output){
+                val metaInfo = new ArrayList<String>();
+                metaInfo.add(META_MODEL_HEADER);
+                metaInfo.add(this.metaModel);
+                
+                val edgeInfo = new ArrayList<String>();
+                edgeInfo.add(NUM_EDGE_TYPE_HEADER);
+                edgeInfo.add(this.statistic.allTypes.size()+"");
+                
+                val nodeInfo = new ArrayList<String>();
+                nodeInfo.add(NUM_NODE_HEADER);
+                nodeInfo.add(this.statistic.allNodes.size()+"");
+                
+                val stateInfo = new ArrayList<String>();
+                stateInfo.add(STATE_ID_HEADER);
+                stateInfo.add(this.name);
+                
+                output.add(metaInfo);
+                output.add(edgeInfo);
+                output.add(nodeInfo);
+                output.add(stateInfo);
+        }
+        
+        private def getTypes(PartialInterpretation partial){
+                //only the complex type interpretations are the ones defined in meta model
+                //do not care about undefined types as it will be included in the class type
+                return partial.partialtypeinterpratation.filter(PartialComplexTypeInterpretationImpl)
+                                                                                                .filter[!it.interpretationOf.name.toLowerCase.contains(typeToExclude)];
+        }
+        
+        override getStatistic() {
+                throw new UnsupportedOperationException("TODO: auto-generated method stub")
+        }
+        
+        override getName() {
+                return name;
+        }
+        
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/CsvFileWriter.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/CsvFileWriter.xtend
new file mode 100644
index 00000000..00b38d90
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/CsvFileWriter.xtend
@@ -0,0 +1,52 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io;
+
+import java.io.File
+import java.io.FileNotFoundException
+import java.io.FileOutputStream
+import java.io.PrintWriter
+import java.util.ArrayList
+import java.util.List
+
+class CsvFileWriter {
+        
+        def static void write(ArrayList<ArrayList<String>> datas, String uri) {
+                if(datas.size() <= 0) {
+                        return;
+                }
+                val PrintWriter writer = new PrintWriter(new File(uri));
+                output(writer, datas, uri);
+        }
+        
+        def static void append(ArrayList<ArrayList<String>> datas, String uri) {
+                if(datas.size() <= 0) {
+                        return;
+                }
+                val PrintWriter writer = new PrintWriter(new FileOutputStream(new File(uri), true));
+                output(writer, datas, uri);
+        }
+        
+        
+        def private static void output(PrintWriter writer, ArrayList<ArrayList<String>> datas, String uri) {
+                //println("Output csv for " + uri);
+                try  {
+                        
+                        val output = new StringBuilder;
+                        for(List<String> datarow : datas){
+                                for(var i = 0; i < datarow.size() - 1; i++){
+                                        output.append(datarow.get(i) + ',');
+                                }
+                                
+                                if(datarow.size >= 1){
+                                        output.append(datarow.get(datarow.size() - 1));
+                                        output.append('\n');
+                                }
+                        }
+                        
+                        writer.write(output.toString());
+                        writer.close();
+                        //println("Output csv finished");
+                }catch(FileNotFoundException e) {
+                        e.printStackTrace();
+                }
+        }
+}
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/GraphReader.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/GraphReader.xtend
new file mode 100644
index 00000000..053e0da3
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/GraphReader.xtend
@@ -0,0 +1,112 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io;
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.EMFGraph
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.EdgeTypeMetric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.Metric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.MultiplexParticipationCoefficientMetric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.NodeActivityMetric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.NodeTypeMetric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.OutDegreeMetric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.TypedOutDegree
+import java.io.File
+import java.io.FileNotFoundException
+import java.util.ArrayList
+import java.util.List
+import org.eclipse.emf.common.util.URI
+import org.eclipse.emf.ecore.EObject
+import org.eclipse.emf.ecore.EPackage
+import org.eclipse.emf.ecore.EReference
+import org.eclipse.emf.ecore.resource.Resource
+import org.eclipse.emf.ecore.resource.ResourceSet
+import org.eclipse.emf.ecore.resource.impl.ResourceSetImpl
+import org.eclipse.emf.ecore.xmi.impl.XMIResourceFactoryImpl
+import org.eclipse.emf.ecore.EGenericType
+import org.eclipse.emf.ecore.EStructuralFeature
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.TypedClusteringCoefficientMetric
+
+class GraphReader{
+        val ResourceSet resSet = new ResourceSetImpl();
+        val referenceTypes = new ArrayList<EReference>();
+        var String suffix;
+        
+        new(EPackage metaModel, String suffix) {
+                Resource.Factory.Registry.INSTANCE.extensionToFactoryMap.put("*",new XMIResourceFactoryImpl)
+                this.suffix = suffix;
+                
+                //find all reference types in the meta model
+                metaModel.eAllContents.forEach[
+                        if(it instanceof EReference){
+                                referenceTypes.add(it);
+                        }
+                ]
+        }
+        
+        def List<EMFGraph> readModels(String path){
+                val dir = new File(path);
+                if(!dir.isDirectory){
+                        throw new Exception("expecting a directory");
+                }
+                
+                val graphs = new ArrayList<EMFGraph>();
+                
+                val metrics = new ArrayList<Metric>();
+                metrics.add(new OutDegreeMetric());
+                metrics.add(new NodeActivityMetric());
+                metrics.add(new MultiplexParticipationCoefficientMetric());
+                metrics.add(new TypedOutDegree());              
+                metrics.add(new NodeTypeMetric());
+                metrics.add(new EdgeTypeMetric());
+                var count = 1
+                //check all files in the directory with suffix
+                for(String name : dir.list.filter[it| it.endsWith(suffix)]){
+                        val file = new File(name);
+                        val roots = readModel(EObject, path,  file.name);                       
+                        //add a list of metrics
+                        val g = new EMFGraph();
+                        for(root : roots){
+                                g.init(root, metrics, name.replaceFirst(suffix, ""), referenceTypes);
+                        }
+
+                        count ++;
+                        graphs.add(g);
+                }
+                
+                return graphs;
+        }
+        
+        def EMFGraph readModel(String path, String filename){
+                val metrics = new ArrayList<Metric>();
+                metrics.add(new OutDegreeMetric());
+                metrics.add(new NodeActivityMetric());
+                metrics.add(new MultiplexParticipationCoefficientMetric());
+                metrics.add(new TypedOutDegree());              
+                metrics.add(new NodeTypeMetric());
+                metrics.add(new EdgeTypeMetric());
+                
+                val file = new File(filename);
+                val roots = readModel(EObject, path,  file.name);                       
+                //add a list of metrics
+                val g = new EMFGraph();
+                for(root : roots){
+                        g.init(root, metrics, filename.replaceFirst(suffix, ""), referenceTypes);
+                }
+                return g
+        }
+        
+        def <RootType extends EObject> List<RootType> readModel(Class<RootType> type, String path, String name) {
+                try {
+                        val resource = resSet.getResource(getURI(path, name),true);
+                        if(resource === null) throw new FileNotFoundException(getURI(path, name).toString)
+                        else {          
+                                return resource.contents as List<RootType>
+                        }
+                } catch(Exception e) {
+                        e.printStackTrace();
+                        throw new Exception(getURI(path, name).toString());
+                }
+        }
+        
+        def static getURI(String path, String name) {
+                URI.createFileURI(path + "/"  + name)
+        }
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/RepMetricsReader.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/RepMetricsReader.xtend
new file mode 100644
index 00000000..2e7be586
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/RepMetricsReader.xtend
@@ -0,0 +1,100 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app.Domain
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.MetricSampleGroup
+import github.impl.GithubPackageImpl
+import hu.bme.mit.inf.dslreasoner.domains.yakindu.sgraph.yakindumm.impl.YakindummPackageImpl
+import java.util.HashMap
+import org.eclipse.emf.ecore.EReference
+import org.eclipse.emf.ecore.impl.EcorePackageImpl
+
+/**
+ * Read the sample of the distribution of a metric provided the csv file of the metric
+ */
+class RepMetricsReader {
+        static var Domain domain;
+        static def read(Domain d){
+                var GraphReader reader;
+                if(d == Domain.Yakindumm){
+                        reader = new GraphReader(YakindummPackageImpl.eINSTANCE, '.xmi');
+                }else if (d == Domain.Ecore){
+                        reader = new GraphReader(EcorePackageImpl.eINSTANCE, '.ecore');
+                }else if (d == Domain.Github){
+                        reader = new GraphReader(GithubPackageImpl.eINSTANCE, '.githubmodel')
+                }
+                
+                domain = d;
+                
+                var domainRepPath = DataName.REP_PATH + d.name + '/';
+                var rep = new MetricSampleGroup()
+                var out_d = readMetrics(reader, domainRepPath + DataName.OUT_D_REP);
+                var mpc = readMetrics(reader, domainRepPath + DataName.MPC_REP);
+                rep.mpcSamples = mpc.mpcSamples;
+                rep.outDegreeSamples = out_d.outDegreeSamples;
+                rep.naSamples = readMetrics(reader, domainRepPath + DataName.NA_REP).naSamples;
+                rep.typedOutDegreeSamples = out_d.typedOutDegreeSamples;
+                rep.edgeTypeSamples = mpc.edgeTypeSamples;
+                
+                //TODO: Parameterize the prior node distribution
+                var nodeTypeSamples = new HashMap<String, Double>();
+                if(d == Domain.Yakindumm){
+                        nodeTypeSamples.put('Entry', 0.04257802080554814);
+                        nodeTypeSamples.put('Choice', 0.1267671379034409);
+                        nodeTypeSamples.put('State', 0.1596092291277674);
+                        nodeTypeSamples.put('Transition', 0.6138636969858629);
+                        nodeTypeSamples.put('Statechart', 0.010136036276340358);
+                        nodeTypeSamples.put('Region', 0.04467858095492131);
+                        nodeTypeSamples.put('Exit', 0.0018338223526273673);
+                        nodeTypeSamples.put('FinalState', 0.0005334755934915977);
+                }else if(d ==Domain.Ecore){
+                        nodeTypeSamples.put('EAttribute', 0.23539778449144008);
+                        nodeTypeSamples.put('EClass', 0.33081570996978854);
+                        nodeTypeSamples.put('EReference', 0.30996978851963747);
+                        nodeTypeSamples.put('EPackage', 0.012789526686807653);
+                        nodeTypeSamples.put('EAnnotation', 0.002517623363544813);
+                        nodeTypeSamples.put('EEnumLiteral', 0.07275931520644502);
+                        nodeTypeSamples.put('EEnum', 0.013645518630412891);
+                        nodeTypeSamples.put('EDataType', 0.004028197381671702);
+                        nodeTypeSamples.put('EParameter', 0.005941591137965764);
+                        nodeTypeSamples.put('EGenericType', 0.002014098690835851);
+                        nodeTypeSamples.put('EOperation', 0.009415911379657605);
+                        nodeTypeSamples.put('ETypeParameter', 0.0007049345417925478);   
+                }else if (d == Domain.Github){
+                        nodeTypeSamples.put('Project', 0.012636538873420432);
+                        nodeTypeSamples.put('Commit', 0.5525808524309276);
+                        nodeTypeSamples.put('User', 0.05847076461769116);
+                        nodeTypeSamples.put('Issue', 0.12743628185907047);
+                        nodeTypeSamples.put('PullRequest', 0.07560505461554937);
+                        nodeTypeSamples.put('IssueEvent', 0.17327050760334123);
+                }
+                
+                
+                
+                rep.nodeTypeSamples = nodeTypeSamples;
+                return rep;
+        }
+        
+        /**
+         * Read representative model
+         */
+        private static def readMetrics(GraphReader r, String path){
+                val model = r.readModels(path).head;
+                if(domain == Domain.Ecore){
+                        var refsToRemove = EcorePackageImpl.eINSTANCE.eAllContents.filter(EReference).filter[
+                                it.name.equals('eGenericType') || it.name.equals('eGenericSuperTypes') || it.name.equals('eFactoryInstance') ||
+                                it.name.equals('eGenericExceptions') || it.name.equals('references') || it.name.equals('contents');
+                        ];
+                        refsToRemove.forEach[model.removeReference(it)];
+                }
+                return model.evaluateAllMetricsToSamples();
+        }
+        
+}
+
+class DataName{
+        public static val REP_PATH = 'data/';
+        public static val MPC_REP = 'mpc_rep';
+        public static val NA_REP = 'na_rep';
+        public static val OUT_D_REP = 'out_d_rep';
+}
+
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/EdgeTypeMetric.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/EdgeTypeMetric.xtend
new file mode 100644
index 00000000..0c0fe3b8
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/EdgeTypeMetric.xtend
@@ -0,0 +1,41 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.GraphStatistic
+import java.util.ArrayList
+import java.util.HashMap
+
+class EdgeTypeMetric extends Metric {
+        
+        override evaluate(GraphStatistic g) {
+                var map = evaluateSamples(g) as HashMap<String, Double>;
+                var output = new ArrayList<String[]>();
+                output.add(newArrayList('Edge Type'));
+                
+                var keys = map.keySet;
+                var values = newArrayList();
+                for(key : keys){
+                        values.add(map.get(key)+'');
+                }
+                
+                output.add(keys);
+                output.add(values);
+                
+                return output;
+        }
+        
+        override evaluateSamples(GraphStatistic g) {
+                val map = new HashMap<String, Double>();
+                var outgoingEdges = g.outgoingEdges;
+                
+                //get the total number of edges
+                val edgeCount = outgoingEdges.values.fold(0, [r, t| r + t.asMap.values.fold(0, [r1, t1| r1 + t1.size])]) as double;
+                outgoingEdges.forEach[k, v|
+                        var value = v.asMap.values.fold(0, [r, t| r + t.size]) / edgeCount;
+                        map.put(k, value);
+                ]
+                
+                        
+                return map;
+        }
+        
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/Metric.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/Metric.xtend
new file mode 100644
index 00000000..cb242a5b
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/Metric.xtend
@@ -0,0 +1,8 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.GraphStatistic
+
+abstract class Metric {
+        abstract def String[][] evaluate(GraphStatistic g);
+        abstract def Object evaluateSamples(GraphStatistic g);
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/MetricSampleGroup.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/MetricSampleGroup.xtend
new file mode 100644
index 00000000..4e25bb86
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/MetricSampleGroup.xtend
@@ -0,0 +1,13 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics
+
+import java.util.HashMap
+import java.util.List
+
+class MetricSampleGroup{
+        public var List<Double> mpcSamples;
+        public var List<Double> naSamples;
+        public var List<Double> outDegreeSamples;
+        public var HashMap<String, List<Integer>> typedOutDegreeSamples;
+        public var HashMap<String, Double> nodeTypeSamples;
+        public var HashMap<String, Double> edgeTypeSamples;
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/MultiplexParticipationCoefficientMetric.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/MultiplexParticipationCoefficientMetric.xtend
new file mode 100644
index 00000000..eafa49b2
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/MultiplexParticipationCoefficientMetric.xtend
@@ -0,0 +1,80 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.GraphStatistic
+import java.text.DecimalFormat
+import java.util.ArrayList
+import java.util.HashMap
+import org.eclipse.emf.ecore.EObject
+
+class MultiplexParticipationCoefficientMetric extends Metric {
+        public static val countName = "MPCCount";
+        public static val valueName = "MPCValue";
+        val formatter = new DecimalFormat("#0.00000");    
+        
+        override evaluate(GraphStatistic g) {
+                //because the precision issue of double, we translate double values into String to be the key
+                 
+                
+                //get number of different types
+                val typeCounts = g.allTypes.size;
+                val map = new HashMap<String, Integer>();
+                //calculate the metric distribution
+                g.allNodes.forEach[n|
+                        var coef = calculateMPC(n, g, typeCounts);
+                        
+                        //format number to String
+                        val value = formatter.format(coef);
+                        if(!map.containsKey(value)){
+                                map.put(value, 1);
+                        }else{
+                                map.put(value, map.get(value) + 1);
+                        }               
+                        
+                ]
+                
+                //convert it into a 2 dimentional array
+                val String[][] datas = newArrayOfSize(2, map.size+1);
+                datas.get(0).set(0, valueName);
+                datas.get(1).set(0, countName)
+                var count = 1;
+                for(entry : map.entrySet.sortBy[it.key]){
+                        datas.get(0).set(count, entry.key+"");
+                        datas.get(1).set(count, entry.value+"");
+                        count++;
+                }
+                
+                return datas;
+        }
+        
+        override evaluateSamples(GraphStatistic g){
+                val samples = new ArrayList<Double>();
+                val typeCounts = g.allTypes.size;
+                //calculate the metric distribution
+                g.allNodes.forEach[
+                        samples.add(calculateMPC(it, g, typeCounts));
+                ]
+                
+                return samples;
+        }
+        
+        def double calculateMPC(EObject n, GraphStatistic g, int typeCounts){
+                val edgeCounts = g.outDegree(n) + g.inDegree(n);
+                        
+                var coef = 0.0;
+                
+                for(type : g.allTypes){
+                        val degree = g.dimentionalDegree(n, type) as double;
+                        coef += Math.pow(degree / edgeCounts, 2);
+                }               
+                coef = 1 - coef;                
+                coef = coef * typeCounts / (typeCounts-1);
+                
+                //Consider the case that either typeCounts-1 or the edgeCounts could be 0 in some situation
+                //in this case the metric should be evaluated to 0
+                if(Double.isNaN(coef)){
+                        coef = 0;
+                }
+                
+                return Double.parseDouble(formatter.format(coef));
+        }
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/NodeActivityMetric.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/NodeActivityMetric.xtend
new file mode 100644
index 00000000..fbf06c47
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/NodeActivityMetric.xtend
@@ -0,0 +1,49 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.GraphStatistic
+import java.util.ArrayList
+import java.util.HashMap
+
+class NodeActivityMetric extends Metric {
+        public static val countName = "NACount";
+        public static val valueName = "NAValue";
+        
+        override evaluate(GraphStatistic g) {
+                val map = new HashMap<Integer, Integer>();
+                
+                //calculate the metric distribution
+                g.allNodes.forEach[
+                        val value = g.numOfEdgeTypes(it);
+                        if(!map.containsKey(value)){
+                                map.put(value, 1);
+                        }else{
+                                map.put(value, map.get(value) + 1);
+                        }
+                ]
+                
+                //convert it into a 2 dimentional array
+                val String[][] datas = newArrayOfSize(2, map.size+1);
+                datas.get(0).set(0, valueName);
+                datas.get(1).set(0, countName)
+
+                var count = 1;
+                for(entry : map.entrySet.sortBy[it.key]){
+                        datas.get(0).set(count, entry.key+"");
+                        datas.get(1).set(count, entry.value+"");
+                        count++;
+                }
+                
+                return datas;
+        }
+        
+        override evaluateSamples(GraphStatistic g){
+                val samples = new ArrayList<Double>();
+                
+                //calculate the metric distribution
+                g.allNodes.forEach[
+                        samples.add(g.numOfEdgeTypes(it) as double);
+                ]
+                
+                return samples;
+        }
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/NodeTypeMetric.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/NodeTypeMetric.xtend
new file mode 100644
index 00000000..7cec2513
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/NodeTypeMetric.xtend
@@ -0,0 +1,42 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.GraphStatistic
+import java.util.ArrayList
+import java.util.HashMap
+
+class NodeTypeMetric extends Metric {
+        
+        
+        override evaluate(GraphStatistic g) {
+                var map = evaluateSamples(g) as HashMap<String, Double>;
+                var output = new ArrayList<String[]>();
+                output.add(newArrayList('Node Type'));
+                var keys = map.keySet;
+                var values = newArrayList();
+                
+                for(key : keys){
+                        values.add(map.get(key)+'');
+                }
+                
+                output.add(keys);
+                output.add(values);
+                
+                return output;
+        }
+        
+        override evaluateSamples(GraphStatistic g) {
+                var map = new HashMap<String, Double>();
+                var nodes = g.allNodes;
+                var single = 1.0 / nodes.size();
+                var nodeToType = g.nodeToTypesMap;
+                for(node : nodes){                      
+                        for(cl : nodeToType.get(node)){
+                                var value = map.getOrDefault(cl, 0.0);
+                                map.put(cl, value + single);
+                        }       
+                }
+                
+                return map;
+        }
+        
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/OutDegreeMetric.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/OutDegreeMetric.xtend
new file mode 100644
index 00000000..55046b14
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/OutDegreeMetric.xtend
@@ -0,0 +1,49 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.GraphStatistic
+import java.util.ArrayList
+import java.util.HashMap
+
+class OutDegreeMetric extends Metric {
+        public static val countName = "OutDegreeCount";
+        public static val valueName = "OutDegreeValue";
+        
+        override evaluate(GraphStatistic g) {
+                val map = new HashMap<Integer, Integer>();
+                
+                //calculate the metric distribution
+                g.allNodes.forEach[
+                        val value = g.outDegree(it);
+                        if(!map.containsKey(value)){
+                                map.put(value, 1);
+                        }else{
+                                map.put(value, map.get(value) + 1);
+                        }
+                ]
+                
+                //convert it into a 2 dimentional array
+                val String[][] datas = newArrayOfSize(2, map.size+1);
+                datas.get(0).set(0, valueName);
+                datas.get(1).set(0, countName)
+                var count = 1;
+                for(entry : map.entrySet.sortBy[it.key]){
+                        datas.get(0).set(count, entry.key+"");
+                        datas.get(1).set(count, entry.value+"");
+                        count++;
+                }
+                
+                return datas;
+        }
+        
+        override evaluateSamples(GraphStatistic g){
+                val samples = new ArrayList<Double>();
+                
+                //calculate the metric distribution
+                g.allNodes.forEach[
+                        samples.add(g.outDegree(it) as double);
+                ]
+                
+                return samples;
+        }
+        
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/TypedClusteringCoefficientMetric.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/TypedClusteringCoefficientMetric.xtend
new file mode 100644
index 00000000..0b6d61c6
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/TypedClusteringCoefficientMetric.xtend
@@ -0,0 +1,99 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.GraphStatistic
+import java.text.DecimalFormat
+import java.util.ArrayList
+import java.util.HashMap
+import java.util.HashSet
+import org.eclipse.emf.ecore.EObject
+
+class TypedClusteringCoefficientMetric extends Metric {
+        public static val countName = "TCCCount";
+        public static val valueName = "TCCValue";
+        val formatter = new DecimalFormat("#0.00000");    
+        
+        override evaluate(GraphStatistic g) {
+                //because the precision issue of double, we translate double values into String to be the key
+                
+                val map = new HashMap<String, Integer>();
+                //calculate the metric distribution
+                g.allNodes.forEach[n|
+                        var coef = calculateTCC1(n, g);
+                        if(coef > 0){
+                                println(n);
+                        }
+                        //format number to String
+                        val value = formatter.format(coef);
+                        if(!map.containsKey(value)){
+                                map.put(value, 1);
+                        }else{
+                                map.put(value, map.get(value) + 1);
+                        }               
+                        
+                ]
+                
+                //convert it into a 2 dimentional array
+                val String[][] datas = newArrayOfSize(2, map.size+1);
+                datas.get(0).set(0, valueName);
+                datas.get(1).set(0, countName)
+                var count = 1;
+                for(entry : map.entrySet.sortBy[it.key]){
+                        datas.get(0).set(count, entry.key+"");
+                        datas.get(1).set(count, entry.value+"");
+                        count++;
+                }
+                
+                return datas;
+        }
+        
+        override evaluateSamples(GraphStatistic g){
+                val samples = new ArrayList<Double>();
+                //calculate the metric distribution
+                g.allNodes.forEach[
+                        samples.add(calculateTCC1(it, g));
+                ]
+                
+                return samples;
+        }
+
+        /**
+         * Compute TCC1 metric for node n
+         */     
+        def double calculateTCC1(EObject n, GraphStatistic g){
+                var wedges = 0;
+                var triangles = 0;
+
+                for(type1 : g.allTypes){
+                        val typed1RelatedOfN = new HashSet<EObject>(g.outgoingEdges.get(type1).get(n));
+                        val type1EdgeSourceNodesOfN = new HashSet<EObject>(g.incomingEdges.get(type1).get(n));
+                        
+                        typed1RelatedOfN.addAll(type1EdgeSourceNodesOfN);
+                        
+                        
+                        
+                        // number of wedges
+                        val d = typed1RelatedOfN.size
+                        wedges += d * (d-1) // we will also count each closed triangle twice
+
+                        // pairs of neighbors
+                        for (n1: typed1RelatedOfN) {
+                                for (n2: typed1RelatedOfN) {
+                                        for(type2 : g.allTypes){
+                                                if ((type1 != type2) &&
+                                                        (g.outgoingEdges.get(type2).containsEntry(n1, n2) || 
+                                                         g.outgoingEdges.get(type2).containsEntry(n2, n1)
+                                                        )) {
+                                                        triangles++
+                                                }                                       
+                                        }
+                                }
+                        }
+                }               
+                
+                if (wedges == 0.0) {
+                        return 0.0
+                } else {
+                        return (triangles as double)/wedges
+                }
+        }
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/TypedOutDegree.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/TypedOutDegree.xtend
new file mode 100644
index 00000000..3b5dbcc5
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/TypedOutDegree.xtend
@@ -0,0 +1,60 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.GraphStatistic
+import java.util.ArrayList
+import java.util.HashMap
+import java.util.List
+
+class TypedOutDegree extends Metric{
+        
+        def private calculateMetric(GraphStatistic g){
+                var outgoingEdges = g.outgoingEdges;
+                //record metric as a list of samples for each node type / edge type pair
+                var metric = new HashMap<String, List<Integer>>();
+                var nodeToTypes = g.nodeToTypesMap;
+                
+                for(edgeType : outgoingEdges.keySet){
+                        for(node : outgoingEdges.get(edgeType).keySet){
+                                //find all classes the node belongs to
+                                var classes = nodeToTypes.get(node);
+                                for(cl : classes){
+                                        // get or create entry for node type / edge type pair
+                                        var key = cl + ' ' + edgeType;
+                                        var typeCount = metric.get(key);
+                                        if(typeCount === null){
+                                                typeCount = new ArrayList<Integer>();
+                                                metric.put(key, typeCount);
+                                        }
+                                        
+                                        // get or create sample list
+                                        typeCount.add(outgoingEdges.get(edgeType).get(node).size);
+                                }
+                        }
+                }
+                
+                return metric;
+        }
+        
+        override evaluate(GraphStatistic g) {
+                var metric = calculateMetric(g);
+                var output = new ArrayList<String[]>();
+                
+                output.add(newArrayList('Typed Out Degree'));
+                for(key : metric.keySet){
+                        var samples = metric.get(key);
+                        var String[] outputForOnePair = newArrayOfSize(samples.size+1);
+                        outputForOnePair.set(0, key);
+                        for(var i = 0; i < samples.size; i++){
+                                outputForOnePair.set(i+1, samples.get(i)+'');
+                        }
+                        output.add(outputForOnePair);
+                }
+                
+                return output;
+        }
+        
+        override evaluateSamples(GraphStatistic g) {
+                return calculateMetric(g);
+        }
+        
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/predictor/LinearModel.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/predictor/LinearModel.xtend
new file mode 100644
index 00000000..f0ded347
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/predictor/LinearModel.xtend
@@ -0,0 +1,91 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.predictor
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance.StateData
+import java.util.ArrayList
+import java.util.HashMap
+import java.util.List
+import java.util.Map
+import weka.core.matrix.LinearRegression
+import weka.core.matrix.Matrix
+
+class LinearModel {
+        var double ridge;
+        var Map<Object, StateData> stateAndHistory;
+        List<StateData> samples;
+        
+        new(double ridge){
+                this.ridge = ridge;
+                stateAndHistory = new HashMap<Object, StateData>();
+                samples = new ArrayList<StateData>();
+        }
+        
+        /**
+         * reset the current train data for regression to a new trajectory
+         * @param state: the last state of the trajectory
+         */
+        def resetRegression(Object state){
+                samples.clear();
+                
+                if(stateAndHistory.containsKey(state)){
+                        var data = stateAndHistory.get(state);
+                        var curState = state;
+                        
+                        samples.add(data);
+                        
+                        //loop through data until the oldest state in the record
+                        while(stateAndHistory.containsKey(data.lastState) && data.lastState != curState){
+                                curState = data.lastState;
+                                data = stateAndHistory.get(data.lastState);
+                                samples.add(data);
+                        }
+                }
+        }
+        
+        /**
+         * Add a new data point to the current training set
+         * @param state: the state on which the new data point is calculated
+         * @param features: the set of feature value(x)
+         * @param value: the value of the state (y)
+         * @param lastState: the state which transformed to current state, used to record the trajectory
+         */
+        def feedData(Object state, double[] features, double value, Object lastState){
+                var data = new StateData(features, value, lastState);
+                stateAndHistory.put(state, data);
+                samples.add(data);
+        }
+        
+        /**
+         * get prediction for next state, without storing the data point into the training set
+         * @param features: the feature values of current state
+         * @param value: the value of the current state
+         * @param: featuresToPredict: the features of the state wanted to be predected
+         * @return the value of the state to be predicted
+         */
+        def double getPredictionForNextDataSample(double[] features, double value, double[] featuresToPredict){         
+                var data = new StateData(features, value, null);
+                samples.add(data);
+                
+                // create training set from current data                
+                val double[][] xSamples = samples.map[it.features];
+                val double[] ySamples = samples.map[it.value];
+                
+                val x = new Matrix(xSamples);
+                val y = new Matrix(ySamples, ySamples.size());  
+                
+                val regression = new LinearRegression(x, y, ridge);
+                var prediction = predict(regression.coefficients, featuresToPredict);   
+                
+                //remove the last element just added
+                samples.remove(samples.size - 1);
+                return prediction;
+        }
+        
+        def private predict(double[] parameters, double[] featuresToPredict){
+                // the regression will add an initial column for 1's, the first parameter is constant term
+                var result = parameters.get(0);
+                for(var i = 0; i < featuresToPredict.length; i++){
+                        result += parameters.get(i) * featuresToPredict.get(i);
+                }
+                return result;
+        }
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/validation/ConstraintCollection.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/validation/ConstraintCollection.xtend
new file mode 100644
index 00000000..685c5836
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/validation/ConstraintCollection.xtend
@@ -0,0 +1,80 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.validation
+
+import java.util.ArrayList
+import java.util.HashMap
+import java.util.List
+import java.util.Map
+import org.eclipse.emf.common.notify.Notifier
+import org.eclipse.emf.common.util.URI
+import org.eclipse.emf.ecore.resource.impl.ResourceSetImpl
+import org.eclipse.viatra.addon.validation.core.api.IConstraintSpecification
+import org.eclipse.viatra.query.runtime.api.ViatraQueryEngine
+import org.eclipse.viatra.query.runtime.api.impl.BaseGeneratedPatternGroup
+import org.eclipse.viatra.query.runtime.emf.EMFScope
+
+class ConstraintCollection{
+        val constraints = new ArrayList<IConstraintSpecification>();
+        var BaseGeneratedPatternGroup patterns;
+        var List<Notifier> resources = new ArrayList<Notifier>(); 
+        
+        
+        new(List<IConstraintSpecification> constraints, List<String> uris, BaseGeneratedPatternGroup patterns){
+                this.constraints.addAll(constraints);
+                this.patterns = patterns;
+                setURIs(uris);
+        }
+        
+        new(List<IConstraintSpecification> constraints, BaseGeneratedPatternGroup patterns){
+                this.constraints.addAll(constraints);
+                this.patterns = patterns;
+        }
+        
+        def addModel(Notifier n ){
+                resources.add(n);
+        }
+        
+        def setURIs(List<String> uris){
+                val resSet = new ResourceSetImpl();
+                
+                for(uri : uris){
+                        var resource = resSet.getResource(URI.createURI(uri), true);
+                        resources.add(resource);
+                }
+        
+                println('reading model finished')
+        }
+        
+        def List<Integer> calculateViolations(){
+                var results = new ArrayList<Integer>();
+                
+                for(resource : resources){
+                        val engine = initEngine(resource);
+                        var matches = constraints.stream.mapToInt([ele| ele.querySpecification.getMatcher(engine).countMatches]).sum();
+                        results.add(matches);
+                }
+                
+                return results;
+        }
+        
+        def ArrayList<Map<String, Integer>> calculateViolationMaps(){
+                val result = new ArrayList<Map<String, Integer>>()
+                
+                for(resource : resources){
+                        val map = new HashMap<String, Integer>();
+                        val engine = initEngine(resource);
+                        constraints.forEach[
+                                var count = it.querySpecification.getMatcher(engine).countMatches;
+                                map.put(it.querySpecification.simpleName, count);
+                        ];
+                        result.add(map);
+                }
+                return result;
+        }
+        
+        private def initEngine(Notifier r){
+                var engine = ViatraQueryEngine.on(new EMFScope(r));
+                //init patterns with the new engine
+                patterns.prepare(engine);
+                return engine;
+        }
+}
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/validation/ViolationCheck.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/validation/ViolationCheck.xtend
new file mode 100644
index 00000000..4db5f940
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/validation/ViolationCheck.xtend
@@ -0,0 +1,66 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.validation
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app.Domain
+import com.google.common.reflect.ClassPath
+import ecore.Ecore
+import hu.bme.mit.inf.dslreasoner.partialsnapshot_mavo.yakindu.Patterns
+import java.util.ArrayList
+import org.eclipse.emf.ecore.EObject
+import org.eclipse.viatra.addon.validation.core.api.IConstraintSpecification
+
+class ViolationCheck {
+        /**
+         * Return the total number of violations
+         */
+        def static int calculateViolationCounts(EObject root, Domain d){
+                var packageName = '';
+                if(d == Domain.Yakindumm){
+                        packageName = 'constraints.yakindumm';
+                }else if (d == Domain.Ecore){
+                        packageName = 'constraints.ecore';
+                }else if (d == Domain.Github){
+                        return -1;
+                }
+                
+                var constriants = loadConstraints(packageName);
+                var collections = new ConstraintCollection(constriants, Ecore.instance);
+                collections.addModel(root);
+                var results = collections.calculateViolations();
+                if(results.size > 0){
+                        return results.get(0);
+                }else{
+                        throw new IllegalArgumentException("Calculate Violation Failed");
+                }
+        }
+        
+        /**
+         * return a map contain the count for each type of violation
+         */
+        def static violationMaps(EObject root){
+                var constriants = loadConstraints('hu.bme.mit.inf.dslreasoner.partialsnapshot_mavo.yakindu');
+                var collections = new ConstraintCollection(constriants, Patterns.instance);
+                collections.addModel(root);
+                var results = collections.calculateViolationMaps();
+                if(results.size > 0){
+                        return results.get(0);
+                }else{
+                        throw new IllegalArgumentException("Calculate Violation Failed");
+                }
+        }
+        
+        def static loadConstraints(String packageName){
+                val constraints = new ArrayList<IConstraintSpecification>();
+                
+                val classPath = ClassPath.from(ClassLoader.systemClassLoader);
+                val classInfos = classPath.getTopLevelClasses(packageName);
+                for(info : classInfos){
+                        if(info.load.interfaces.contains(IConstraintSpecification)){
+                                //IConstraintSpecification only has one constructor with empty argument list
+                                var constructor = info.load.constructors.get(0);
+                                var instance = constructor.newInstance();
+                                constraints.add(instance as IConstraintSpecification);
+                        }
+                }
+                return constraints
+        }
+}