1 files changed, 73 insertions, 0 deletions
diff --git a/FEAST/MIToolbox/demonstration_algorithms/DISR.m b/FEAST/MIToolbox/demonstration_algorithms/DISR.m
new file mode 100644
index 0000000..c8f5669
--- /dev/null
+++ b/FEAST/MIToolbox/demonstration_algorithms/DISR.m
@@ -0,0 +1,73 @@
+function selectedFeatures = DISR(k, featureMatrix, classColumn)
+%function selectedFeatures = DISR(k, featureMatrix, classColumn)
+%
+%Computers optimal features according to DISR algorithm from 
+%On the Use of variable "complementarity for feature selection" 
+%by P Meyer, G Bontempi (2006)
+%
+%Computes the top k features from
+%a dataset featureMatrix with n training examples and m features
+%with the classes held in classColumn.
+%
+%DISR - arg(Xi) max(sum(Xj mem XS)(SimRel(Xij,Y)))
+%where SimRel = MI(Xij,Y) / H(Xij,Y)
+
+totalFeatures = size(featureMatrix,2);
+classMI = zeros(totalFeatures,1);
+unselectedFeatures = ones(totalFeatures,1);
+score = 0;
+currentScore = 0;
+innerScore = 0;
+iMinus = 0;
+answerFeatures = zeros(k,1);
+highestMI = 0;
+highestMICounter = 0;
+currentHighestFeature = 0;
+
+%create a matrix to hold the SRs of a feature pair. 
+%initialised to -1 as you can't get a negative SR.
+featureSRMatrix = -(ones(k,totalFeatures));
+
+for n = 1 : totalFeatures
+	classMI(n) = mi(featureMatrix(:,n),classColumn);
+	if classMI(n) > highestMI
+		highestMI = classMI(n);
+		highestMICounter = n;
+	end
+end
+
+answerFeatures(1) = highestMICounter;
+unselectedFeatures(highestMICounter) = 0;
+
+for i = 2 : k
+	score = 0;
+	currentHighestFeature = 0;
+	iMinus = i-1;
+  for j = 1 : totalFeatures
+		if unselectedFeatures(j) == 1
+			%DISR - arg(Xi) max(sum(Xj mem XS)(SimRel(Xij,Y)))
+			%where SimRel = MI(Xij,Y) / H(Xij,Y)
+			currentScore = 0;
+			for m = 1 : iMinus
+        if featureSRMatrix(m,j) == -1
+          unionedFeatures = joint([featureMatrix(:,answerFeatures(m)),featureMatrix(:,j)]);
+				  tempUnionMI = mi(unionedFeatures,classColumn);
+				  tempTripEntropy = h([unionedFeatures,classColumn]);
+				  featureSRMatrix(m,j) = tempUnionMI/tempTripEntropy;
+        end
+        
+				currentScore =  currentScore + featureSRMatrix(m,j);
+			end
+			if (currentScore > score)
+				score = currentScore;
+				currentHighestFeature = j;
+			end
+		end
+	end
+	%now highest feature is selected in currentHighestFeature
+	%store it
+	unselectedFeatures(currentHighestFeature) = 0;
+	answerFeatures(i) = currentHighestFeature;
+end
+
+selectedFeatures = answerFeatures;