From 945f487a08b643392a5d5918c631640b9a0e4605 Mon Sep 17 00:00:00 2001
From: 20001LastOrder <boqi.chen@mail.mcgill.ca>
Date: Tue, 3 Nov 2020 22:52:26 -0500
Subject: add realistic solver

---
 .../metrics/calculator/distance/JSDistance.xtend   | 88 ++++++++++++++++++++++
 1 file changed, 88 insertions(+)
 create mode 100644 Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/JSDistance.xtend

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

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..4a0a0dc3
--- /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 +1,88 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.MetricSampleGroup
+import com.google.common.collect.Sets
+import java.text.DecimalFormat
+import java.util.HashMap
+import java.util.List
+
+class JSDistance extends CostDistance {
+	var HashMap<String, Double> mpcPMF;
+	var HashMap<String, Double> naPMF;
+	var HashMap<String, Double> outDegreePMF;
+	var HashMap<String, Double> nodeTypesPMF;
+	var DecimalFormat formatter;
+	
+	new(MetricSampleGroup g){
+		var mpcSamples = g.mpcSamples;
+		var naSamples = g.naSamples.stream.mapToDouble([it]).toArray();
+		var outDegreeSamples = g.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);	
+   		nodeTypesPMF = g.nodeTypeSamples; 
+	}
+	
+	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;
+	}
+	
+	override 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);
+	}
+	
+	override 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);
+	}
+	
+	override 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);
+	}
+	
+	def nodeTypeDistance(HashMap<String, Double> samples){
+		return klDivergence(samples, nodeTypesPMF);
+	}
+}
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