1 files changed, 102 insertions, 0 deletions

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
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+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/KSDistance.xtend
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+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;
+        }
+}
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