From f34c02f5f99583f96a5289b7f7c46c55b11a97f4 Mon Sep 17 00:00:00 2001
From: Gabor Szarnyas <szarnyasg@gmail.com>
Date: Fri, 16 Aug 2019 19:52:20 +0200
Subject: Initial implementation of TCC1 metric

---
 .../metrics/TypedClusteringCoefficientMetric.xtend | 90 ++++++++++++++++++++++
 1 file changed, 90 insertions(+)
 create mode 100644 Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/TypedClusteringCoefficientMetric.xtend

(limited to 'Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner')

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..93f5ccd4
--- /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 +1,90 @@
+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 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);
+			
+			//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 type1EdgeTargetNodes = g.outgoingEdges.get(type1).values;
+			
+			// number of wedges
+			val d = type1EdgeTargetNodes.size
+			wedges += d * (d-1)
+
+			// pairs of neighbors
+			for (n1: type1EdgeTargetNodes) {
+				for (n2: type1EdgeTargetNodes) {
+					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/wedges
+		}
+	}
+}
\ No newline at end of file
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