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diff --git a/FEAST/MIToolbox/demonstration_algorithms/CMIM.m b/FEAST/MIToolbox/demonstration_algorithms/CMIM.m
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+function selectedFeatures = CMIM(k, featureMatrix, classColumn)
+%function selectedFeatures = CMIM(k, featureMatrix, classColumn)
+%Computes conditional mutual information maximisation algorithm from
+%"Fast Binary Feature Selection with Conditional Mutual Information"
+%by F. Fleuret (2004)
+
+%Computes the top k features from
+%a dataset featureMatrix with n training examples and m features
+%with the classes held in classColumn.
+
+noOfTraining = size(classColumn,1);
+noOfFeatures = size(featureMatrix,2);
+
+partialScore = zeros(noOfFeatures,1);
+m = zeros(noOfFeatures,1);
+score = 0;
+answerFeatures = zeros(k,1);
+highestMI = 0;
+highestMICounter = 0;
+
+for n = 1 : noOfFeatures
+  partialScore(n) = mi(featureMatrix(:,n),classColumn);
+  if partialScore(n) > highestMI
+    highestMI = partialScore(n);
+    highestMICounter = n;
+  end
+end
+
+answerFeatures(1) = highestMICounter;
+
+for i = 2 : k
+  score = 0;
+  limitI = i - 1;
+  for n = 1 : noOfFeatures
+    while ((partialScore(n) >= score) && (m(n) < limitI))
+      m(n) = m(n) + 1;
+      conditionalInfo = cmi(featureMatrix(:,n),classColumn,featureMatrix(:,answerFeatures(m(n))));
+      if partialScore(n) > conditionalInfo
+        partialScore(n) = conditionalInfo;
+      end
+    end
+    if partialScore(n) >= score
+      score = partialScore(n);
+      answerFeatures(i) = n;
+    end
+  end
+end
+
+selectedFeatures = answerFeatures;