38 files changed, 885 insertions, 104 deletions

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
index cf871ead..1745bc35 100644
--- 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
@@ -35,7 +35,7 @@ class Main {
                 
                 //human input has different package declaration
 //              reader = new GraphReader(Yakindumm2PackageImpl.eINSTANCE);
-                val human = new RWInformation("inputs/Random/", "outputs/", 1);
+                val human = new RWInformation("inputs/Fake_Random_Random/", "outputs/", 1);
                 calculateAllModels(human.inputFolder, human.outputFolder,human.numRuns, reader);
                 
                 
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
index cdd06027..71fa5fed 100644
--- 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
@@ -1,5 +1,6 @@
 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
@@ -17,13 +18,14 @@ import org.eclipse.emf.ecore.util.EcoreUtil
 import org.eclipse.viatra.dse.api.Solution
 
 class PartialInterpretationMetric {
-        var static state = 0;
         var static KSDistance ks;
+        var static JSDistance js;
         
         def static void initPaths(){
                 new File("debug/metric/").mkdir();
                 new File("debug/metric/trajectories/").mkdir();
                 ks = new KSDistance(Domain.Yakinduum);
+                js = new JSDistance(Domain.Yakinduum);
         }
         
         def static MetricDistanceGroup calculateMetricDistance(PartialInterpretation partial){
@@ -35,6 +37,22 @@ class PartialInterpretationMetric {
                 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 static MetricDistanceGroup calculateMetricDistanceKS(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 = ks.mpcDistance(metricSamples.mpcSamples);
                 var na = ks.naDistance(metricSamples.naSamples);
                 var outDegree = ks.outDegreeDistance(metricSamples.outDegreeSamples);
@@ -52,7 +70,6 @@ class PartialInterpretationMetric {
                 //make dir since the folder can be none existing
                 new File(path).mkdir();
                 val filename = path + "/state_"+currentStateId+"-"+counter+".csv";
-                state++;
                 val metricCalculator = new PartialInterpretationGraph(partial, metrics, currentStateId);
                 
                 CsvFileWriter.write(metricCalculator.evaluateAllMetrics(), filename);
@@ -60,20 +77,22 @@ class PartialInterpretationMetric {
         
         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.modelWithEditingDomain = emptySolutionCopy
+                        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)
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..ced9eadb
--- /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 +1,95 @@
+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 com.google.common.collect.Sets
+import java.text.DecimalFormat
+import java.util.HashMap
+import java.util.List
+
+class JSDistance {
+        var HashMap<String, Double> mpcPMF;
+        var HashMap<String, Double> naPMF;
+        var HashMap<String, Double> outDegreePMF;
+        var DecimalFormat formatter;
+        
+        new(Domain d){
+                var metrics = RepMetricsReader.read(d);
+                var mpcSamples = metrics.mpcSamples;
+                var naSamples = metrics.naSamples.stream.mapToDouble([it]).toArray();
+                var outDegreeSamples = metrics.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);     
+        }
+        
+        def private 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;
+        }
+        
+        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;
+        }
+        
+        def 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);
+        }
+        
+        def 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);
+        }
+        
+        def 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);
+        }
+}
\ 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
index 1fb21529..58e0a8a3 100644
--- 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
@@ -1,9 +1,9 @@
 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 java.util.List
 import org.apache.commons.math3.stat.inference.KolmogorovSmirnovTest
-import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io.RepMetricsReader
 
 class KSDistance {
         var static ksTester = new KolmogorovSmirnovTest();
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
index bed356e9..01e3940b 100644
--- 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
@@ -21,7 +21,7 @@ class CsvFileWriter {
                                         output.append(datarow.get(i) + ',');
                                 }
                                 
-                                if(datarow.size > 1){
+                                if(datarow.size >= 1){
                                         output.append(datarow.get(datarow.size() - 1));
                                         output.append('\n');
                                 }
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_all/Node Activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_all/Node Activity.jpg
new file mode 100644
index 00000000..6b987b3e
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_all/Node Activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_all/mpc.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_all/mpc.jpg
new file mode 100644
index 00000000..ee8d5755
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_all/mpc.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_all/out_degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_all/out_degree.jpg
new file mode 100644
index 00000000..25e850fe
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_all/out_degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/Node Activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/Node Activity.jpg
index d2dbd970..8b3421b1 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/Node Activity.jpg
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/Node Activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/mpc.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/mpc.jpg
index 3ca2e0bc..d459f5c8 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/mpc.jpg
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/mpc.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/out_degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/out_degree.jpg
index 5b623374..fdaa28e6 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/out_degree.jpg
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/out_degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_node_activity/Node Activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_node_activity/Node Activity.jpg
new file mode 100644
index 00000000..08445457
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_node_activity/Node Activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_node_activity/mpc.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_node_activity/mpc.jpg
new file mode 100644
index 00000000..74ab2449
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_node_activity/mpc.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_node_activity/out_degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_node_activity/out_degree.jpg
new file mode 100644
index 00000000..61c4489e
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_node_activity/out_degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_out_degree/Node Activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_out_degree/Node Activity.jpg
new file mode 100644
index 00000000..ccbca653
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_out_degree/Node Activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_out_degree/mpc.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_out_degree/mpc.jpg
new file mode 100644
index 00000000..31965557
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_out_degree/mpc.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_out_degree/out_degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_out_degree/out_degree.jpg
new file mode 100644
index 00000000..d4de5301
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_out_degree/out_degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/mpc.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/mpc.jpg
index 7a744ea6..ee1d84dc 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/mpc.jpg
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/mpc.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/node activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/node activity.jpg
index dfcc973e..1a7e8bed 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/node activity.jpg
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/node activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/out degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/out degree.jpg
index b3f4f912..81d9b709 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/out degree.jpg
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/out degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/Node Activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/Node Activity.jpg
index f0d09211..e26d6cf6 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/Node Activity.jpg
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/Node Activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/mpc.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/mpc.jpg
index 94b070d4..e1b9a1cf 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/mpc.jpg
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/mpc.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/out_degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/out_degree.jpg
index 8d0151fa..713ed4f4 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/out_degree.jpg
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/out_degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_normal/mpc.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_normal/mpc.jpg
new file mode 100644
index 00000000..9c26be81
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_normal/mpc.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_normal/node activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_normal/node activity.jpg
new file mode 100644
index 00000000..3cb75479
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_normal/node activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_normal/out degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_normal/out degree.jpg
new file mode 100644
index 00000000..0a869df6
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_normal/out degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/MPC.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/MPC.jpg
index b10d97e6..b6e86773 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/MPC.jpg
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/MPC.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/node activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/node activity.jpg
index e41c998b..486577a6 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/node activity.jpg
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/node activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/out degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/out degree.jpg
index d2776bc8..4f338049 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/out degree.jpg
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/out degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_control_all_with_consistency/Node Activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_control_all_with_consistency/Node Activity.jpg
new file mode 100644
index 00000000..c1dba220
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_control_all_with_consistency/Node Activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_control_all_with_consistency/mpc.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_control_all_with_consistency/mpc.jpg
new file mode 100644
index 00000000..f51449d0
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_control_all_with_consistency/mpc.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_control_all_with_consistency/out_degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_control_all_with_consistency/out_degree.jpg
new file mode 100644
index 00000000..dff91312
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_control_all_with_consistency/out_degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/MPC.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/MPC.jpg
index 19feb5e8..2dc66b35 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/MPC.jpg
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/MPC.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/node activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/node activity.jpg
index bc7a83fc..847bad62 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/node activity.jpg
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/node activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/out degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/out degree.jpg
index 13094840..90ff8006 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/out degree.jpg
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/out degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/metrics_distance_with_selector.ipynb b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/metrics_distance_with_selector.ipynb
index 4c7fecb3..000822bf 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/metrics_distance_with_selector.ipynb
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/metrics_distance_with_selector.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -81,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -111,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -144,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -168,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -198,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -214,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -248,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -272,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -283,7 +283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -297,18 +297,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9519be563fbc41c28921c77ef6481b17",
+       "model_id": "a8471e4dd66a47ecb6abb2371be43321",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={'../input/viatra_nocon_output/traject…"
       ]
      },
      "metadata": {},
@@ -320,7 +320,7 @@
        "<function __main__.plot_out_degree(lines)>"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -333,18 +333,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c896725e542c4bf8a1bc76ba66819b20",
+       "model_id": "ad6e466cc3fe44d393d2c82d48244d83",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={'../input/viatra_nocon_output/traject…"
       ]
      },
      "metadata": {},
@@ -356,7 +356,7 @@
        "<function __main__.plot_out_na(lines)>"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -369,18 +369,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "880410d675624545ab73977a463bb5c9",
+       "model_id": "d88ebc8e4062473a96ac35fe800028ef",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={'../input/viatra_nocon_output/traject…"
       ]
      },
      "metadata": {},
@@ -392,7 +392,7 @@
        "<function __main__.plot_out_mpc(lines)>"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -412,7 +412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 17,
    "metadata": {},
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    "source": [
@@ -427,13 +427,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0d04d6db770a49f4a160ff55cc7131f6",
+       "model_id": "1eb2ba5848a048389bca8d804fc8340a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -450,7 +450,7 @@
        "<function __main__.plot_out_degree(lines)>"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -463,13 +463,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "96eebad1f6274d79ad377c8c54b44615",
+       "model_id": "6e5840f7a5ad4515bce9080088b644f2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -486,7 +486,7 @@
        "<function __main__.plot_na(lines)>"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -499,13 +499,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4fc2714a3cd3440daf5014bb4b942b9a",
+       "model_id": "9e30f267b092491ba1ffe8f83c5f68ce",
        "version_major": 2,
        "version_minor": 0
       },
@@ -522,7 +522,7 @@
        "<function __main__.plot_mpc(lines)>"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -542,7 +542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 21,
    "metadata": {},
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@@ -557,13 +557,13 @@
   },
   {
    "cell_type": "code",
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+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4401931533b5497f864f146d7b4dcd3c",
+       "model_id": "cc1f64c92e814c32a81cd5ec5d4e50dc",
        "version_major": 2,
        "version_minor": 0
       },
@@ -580,7 +580,7 @@
        "<function __main__.plot_out_degree(lines)>"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -593,13 +593,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fb7bdedff841420bb8f817013f565020",
+       "model_id": "75021f4f68db4a809ce7c86c0d25ef1b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -616,7 +616,7 @@
        "<function __main__.plot_node_activity(lines)>"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -629,13 +629,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
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      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6b0c349c4a3b4813825513f739ea30da",
+       "model_id": "86f5c376905a4759a7b44ad52804424d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -652,7 +652,7 @@
        "<function __main__.plot_mpc(lines)>"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -672,7 +672,7 @@
   },
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-   "execution_count": 67,
+   "execution_count": 25,
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@@ -687,13 +687,13 @@
   },
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+   "execution_count": 26,
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      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b76901ba9d44433984032e0dc5679fa9",
+       "model_id": "57ba4d8443c145ad845fb862e3ef7519",
        "version_major": 2,
        "version_minor": 0
       },
@@ -710,7 +710,7 @@
        "<function __main__.plot_out_degree(lines)>"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -723,13 +723,13 @@
   },
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+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9e0d61e29b02467cb52618860a1bde7f",
+       "model_id": "c020ecb466c14f3ca1bfc0fd2fe03b7b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -746,7 +746,7 @@
        "<function __main__.plot_na(lines)>"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 27,
      "metadata": {},
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     }
@@ -759,13 +759,13 @@
   },
   {
    "cell_type": "code",
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+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "70074805fee44a1aa5b9ccb3770b5c0c",
+       "model_id": "2165668057fd47ad92459e749ec68bad",
        "version_major": 2,
        "version_minor": 0
       },
@@ -782,7 +782,7 @@
        "<function __main__.plot_mpc(lines)>"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -802,7 +802,7 @@
   },
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@@ -817,13 +817,13 @@
   },
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      "data": {
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-       "model_id": "912ba2fdfd7c46848065f174aa6177e0",
+       "model_id": "907d7824033b4dfe980c391db0da63eb",
        "version_major": 2,
        "version_minor": 0
       },
@@ -840,7 +840,7 @@
        "<function __main__.plot_out_degree(lines)>"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -853,13 +853,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0ba621dd0e7d4957aaff2cf209bba165",
+       "model_id": "08a32c21d0b64217a556715caa8db7b5",
        "version_major": 2,
        "version_minor": 0
       },
@@ -876,7 +876,7 @@
        "<function __main__.plot_na(lines)>"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -889,13 +889,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d432bbae1c6f48c3acd1767f2e2b13c7",
+       "model_id": "9dad041ff05d46ce969cfacb07c2ba98",
        "version_major": 2,
        "version_minor": 0
       },
@@ -912,7 +912,7 @@
        "<function __main__.plot_mpc(lines)>"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -924,6 +924,651 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Controlled Viatra with Out Degree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "con_viatra_stats = readStats('../input/controlled_viatra_out_degree/',10000)\n",
+    "con_viatra_dic = calDistanceDic(con_viatra_stats, human_rep)\n",
+    "\n",
+    "# trajectories and colors\n",
+    "trajectories = {}\n",
+    "w = createSelectionWidge(trajectories)\n",
+    "colors = createRandomColors(len(trajectories))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd77560284d9419daec57192a64b75ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_degree(lines)>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_out_degree(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out_degree', '../output/controled_viatra_with_out_degree/')\n",
+    "interact(plot_out_degree, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab11afebf7674cebae8d7318c661cf3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_na(lines)>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_na(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.na_distance, colors, 'Node Activity', '../output/controled_viatra_with_out_degree/')\n",
+    "interact(plot_na, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c20b42abcba646c18d7caa6eeb54c403",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_mpc(lines)>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_mpc(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc', '../output/controled_viatra_with_out_degree/')\n",
+    "interact(plot_mpc, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Controlled Viatra with Node Activity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "con_viatra_stats = readStats('../input/controlled_viatra_out_degree_node_activity/',20000)\n",
+    "con_viatra_dic = calDistanceDic(con_viatra_stats, human_rep)\n",
+    "\n",
+    "# trajectories and colors\n",
+    "trajectories = {}\n",
+    "w = createSelectionWidge(trajectories)\n",
+    "colors = createRandomColors(len(trajectories))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "902b580a11fa4c8db9d03508ad629067",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_degree(lines)>"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_out_degree(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out_degree', '../output/controled_viatra_with_node_activity/')\n",
+    "interact(plot_out_degree, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "851b567e745940288b577d9bd27e6f08",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_na(lines)>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_na(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.na_distance, colors, 'Node Activity', '../output/controled_viatra_with_node_activity/')\n",
+    "interact(plot_na, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7de173291f394b10b5113e3312b7b2e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_mpc(lines)>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_mpc(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc', '../output/controled_viatra_with_node_activity/')\n",
+    "interact(plot_mpc, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Random EMF With Normal(2,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "random_emf_stats = readStats('../input/random_emf_normal/',6000)\n",
+    "random_emf_dic = calDistanceDic(random_emf_stats, human_rep)\n",
+    "\n",
+    "# trajectories and colors\n",
+    "trajectories = {}\n",
+    "w = createSelectionWidge(trajectories)\n",
+    "colors = createRandomColors(len(trajectories))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b9ee873d9ca41649cf05f3b713d9142",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_degree(lines)>"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_out_degree(lines):\n",
+    "    plot(random_emf_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out degree', '../output/random_emf_normal/')\n",
+    "interact(plot_out_degree, lines=[[]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88f258a0b0ac4417aba320beca7508cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_node_activity(lines)>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_node_activity(lines):\n",
+    "    plot(random_emf_dic, lines, 0, lambda a: a.na_distance, colors, 'node activity', '../output/random_emf_normal/')\n",
+    "interact(plot_node_activity, lines=[[]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d71cf26018184ee6953c50b74908f52d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_mpc(lines)>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_mpc(lines):\n",
+    "    plot(random_emf_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc', '../output/random_emf_normal/')\n",
+    "interact(plot_mpc, lines=[[]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "con_viatra_stats = readStats('../input/controlled_viatra_all/',20000)\n",
+    "con_viatra_dic = calDistanceDic(con_viatra_stats, human_rep)\n",
+    "\n",
+    "# trajectories and colors\n",
+    "trajectories = {}\n",
+    "w = createSelectionWidge(trajectories)\n",
+    "colors = createRandomColors(len(trajectories))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db15ac26aad84683b9da99fc54749850",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_degree(lines)>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_out_degree(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out_degree', '../output/controled_viatra_all/')\n",
+    "interact(plot_out_degree, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "30bfaf8dd45d4b21b0b43afe5e9fdb8a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_na(lines)>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_na(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.na_distance, colors, 'Node Activity', '../output/controled_viatra_all/')\n",
+    "interact(plot_na, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5636d37b4416474db5441fe47e7a8a30",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_mpc(lines)>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_mpc(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc', '../output/controled_viatra_all/')\n",
+    "interact(plot_mpc, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Viatra With Both metric and consistency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "con_viatra_stats = readStats('../input/viatra_control_all_with_consistency_1/',20000)\n",
+    "con_viatra_dic = calDistanceDic(con_viatra_stats, human_rep)\n",
+    "\n",
+    "# trajectories and colors\n",
+    "trajectories = {}\n",
+    "w = createSelectionWidge(trajectories)\n",
+    "colors = createRandomColors(len(trajectories))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5c7231686544d959527cff36c1f1a5e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_degree(lines)>"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_out_degree(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out_degree', '../output/viatra_control_all_with_consistency_1/')\n",
+    "interact(plot_out_degree, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e043705333bb474e89582ea9358c57c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_na(lines)>"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_na(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.na_distance, colors, 'Node Activity', '../output/viatra_control_all_with_consistency_1/')\n",
+    "interact(plot_na, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee4723b62293402e87e6a3f798019b36",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_mpc(lines)>"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_mpc(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc', '../output/viatra_control_all_with_consistency_1/')\n",
+    "interact(plot_mpc, lines=w)"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb
index 9653b2a0..78f408fc 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb
@@ -63,16 +63,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1253"
+       "304"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -90,7 +90,7 @@
     ")\n",
     "\n",
     "\n",
-    "humanFiles = reader.readmultiplefiles('../input/humanOutput/', 1300, False)\n",
+    "humanFiles = reader.readmultiplefiles('../input/human_output_100/', 1300, False)\n",
     "modelToFileName = {}\n",
     "for name in humanFiles:\n",
     "    modelToFileName[GraphStat(name)] = name\n",
@@ -115,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -144,21 +144,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "#### For all human models\n",
     "* the rep found is ../input/humanOutput\\R_20158_run_1.csv\n",
-    "* the average distance between it and others is 0.05515988287586802"
+    "* the average distance between it and others is 0.05515988287586802\n",
+    "\n",
+    "#### For human models with $100 \\pm 10$ nodes\n",
+    "* the rep found is ../input/human_output_100\\R_2015225_run_1.csv\n",
+    "* the average distance between it and others is $0.046150929558524685$"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "../input/humanOutput\\R_20158_run_1.csv\n",
-      "../input/humanOutput\\R_20158_run_1.csv\n"
+      "../input/human_output_100\\R_2015225_run_1.csv\n",
+      "../input/human_output_100\\R_2015225_run_1.csv\n"
      ]
     }
    ],
@@ -171,14 +176,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.05515988287586802\n"
+      "0.046150929558524685\n"
      ]
     }
    ],
@@ -201,41 +206,46 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "#### For all human models\n",
     "* the rep found is ../input/humanOutput\\R_2016176_run_1.csv\n",
-    "* the average distance between it and others is 0.05275267434589047"
+    "* the average distance between it and others is 0.05275267434589047\n",
+    "\n",
+    "#### For human models with $100 \\pm 10$ nodes\n",
+    "* the rep found is ../input/human_output_100\\R_2017419_run_1.csv\n",
+    "* the average distance between it and others is $0.04679429311806747$"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "../input/humanOutput\\R_2016176_run_1.csv\n",
-      "../input/humanOutput\\R_2016176_run_1.csv\n"
+      "../input/human_output_100\\R_2017419_run_1.csv\n",
+      "../input/human_output_100\\R_2017419_run_1.csv\n"
      ]
     }
    ],
    "source": [
-    "total_distance = 0\n",
-    "for model in models:\n",
-    "    total_distance += ks_value(od_rep_model.mpc, model.mpc)\n",
-    "print(total_distance / len(models))"
+    "na_rep_index = findRep(models, lambda m: m.na)\n",
+    "print(list(modelToFileName.values())[na_rep_index])\n",
+    "na_rep_model = models[na_rep_index]\n",
+    "print(modelToFileName[na_rep_model])\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.05275267434589047\n"
+      "0.04679429311806747\n"
      ]
     }
    ],
@@ -243,7 +253,7 @@
     "total_distance = 0\n",
     "count = 0\n",
     "for model in models:\n",
-    "    total_distance += ks_value(od_rep_model.na, model.na)\n",
+    "    total_distance += ks_value(na_rep_model.na, model.na)\n",
     "print(total_distance / len(models))"
    ]
   },
@@ -258,21 +268,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "#### For all human models\n",
     "* the rep found is ../input/humanOutput\\R_2015246_run_1.csv\n",
-    "* the average distance between it and others is 0.08556632702185384"
+    "* the average distance between it and others is 0.08556632702185384\n",
+    "\n",
+    "#### For human models with $100 \\pm 10$ nodes\n",
+    "* the rep found is ../input/human_output_100\\R_2016324_run_1.csv\n",
+    "* the average distance between it and others is $0.07028909225833631$"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "../input/humanOutput\\R_2015246_run_1.csv\n",
-      "../input/humanOutput\\R_2015246_run_1.csv\n"
+      "../input/human_output_100\\R_2016324_run_1.csv\n",
+      "../input/human_output_100\\R_2016324_run_1.csv\n"
      ]
     }
    ],
@@ -285,14 +300,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.08556632702185384\n"
+      "0.07028909225833631\n"
      ]
     }
    ],
@@ -300,7 +315,7 @@
     "total_distance = 0\n",
     "count = 0\n",
     "for model in models:\n",
-    "    total_distance += ks_value(od_rep_model.mpc, model.mpc)\n",
+    "    total_distance += ks_value(mpc_rep_model.mpc, model.mpc)\n",
     "print(total_distance / len(models))"
    ]
   },
@@ -310,6 +325,13 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/Metrics/Metrics-Calculation/metrics_plot/utils/constants.py b/Metrics/Metrics-Calculation/metrics_plot/utils/constants.py
index ce9d4255..aba81b13 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/utils/constants.py
+++ b/Metrics/Metrics-Calculation/metrics_plot/utils/constants.py
@@ -18,8 +18,8 @@ METAMODEL = 'Meta Mode'
 
 STATE_ID = 'State Id'
 
-HUMAN_OUT_D_REP = '../input/humanOutput/R_20158_run_1.csv'
+HUMAN_OUT_D_REP = '../input/humanOutput/R_2015225_run_1.csv'
 
-HUMAN_MPC_REP = '../input/humanOutput/R_2015246_run_1.csv'
+HUMAN_MPC_REP = '../input/humanOutput/R_2016324_run_1.csv'
 
-HUMAN_NA_REP = '../input/humanOutput/R_2016176_run_1.csv'
+HUMAN_NA_REP = '../input/humanOutput/R_2017419_run_1.csv'