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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
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+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetricDistance.xtend
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+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);
+        }
+}
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