1 files changed, 7 insertions, 6 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
index c486a328..eec3a880 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
@@ -60,17 +60,18 @@ class KSDistance extends CostDistance {
                 return value;
         }
         
+        // actually calculates Manhattan Distance due to KS-Distance does not make sense for discrete distributions
         def nodeTypeDistance(HashMap<String, Double> samples){
                 var typesDistMap = g.nodeTypeSamples;
-                var sourceDist = newArrayList();
-                var instanceDist = newArrayList();
+                var keys = new HashSet<String>(typesDistMap.keySet());
+                keys.addAll(samples.keySet());
+                var distance = 0.0;
                 
-                for(key : typesDistMap.keySet()){
-                        sourceDist.add(typesDistMap.get(key));
-                        instanceDist.add(samples.getOrDefault(key, 0.0));
+                for(key : keys){
+                        distance += Math.abs(typesDistMap.getOrDefault(key, 0.0) - samples.getOrDefault(key, 0.0));
                 }
                 
-                return ks_distance_two_dist(sourceDist, instanceDist);
+                return distance;
         }
         
         def edgeTypeDistance(HashMap<String, Double> samples){