--- a
+++ b/classification/RUSboost/runRUSBoosting.m
@@ -0,0 +1,67 @@
+function [allModels, allTraining, allTesting] = runRUSBoosting(X,Y)
+%RUNRUSBOOSTING runs RUSBoost on a given LIDC dataset broken into two
+%parameters, X and Y.
+%   param X
+%      This is the data that is input matrix that contains image features
+%      calculated from nodules in the LIDC.
+%   param Y
+%      This is a cell matrix that contains semantic ratings from the LIDC.
+%   return allModels
+%      a cell array containing a model for each semantic characteristic.
+%   return allTraining
+%      a cell matrix indicating which values are testing for each semantic 
+%      characteristic.
+%   return allTesting
+%      a cell matrix indicating which values are training for each 
+%      semantic characteristic.
+                 
+
+
+%% Setup the data and the labels
+    %number of semantic classes in the array 'Y'
+    n = size(Y,2);
+    
+    %Semantic labels
+    labels = {'Subtlety', 'Sphericity', 'Margin', 'Lobulation',... 
+              'Spiculation', 'Texture', 'Malignancy'};
+  
+    %create a cell array containing the models
+    allModels = cell(1,1);
+    allTraining = cell(1,1);
+    allTesting = cell(1,1);
+
+    %Show distribution for each semantic label
+    for i = 1:n
+        fprintf('%s%s\n','Tabulation for: ', labels{i});
+        tabulate(Y(:,i));
+    end
+      
+    %create weak learner template for RUSBoosting to build an ensemble on
+    % TODO: CHANGE THESE PARAMS TO SOMETHING ELSE
+    t = ClassificationTree.template('MinParent',60,'minleaf',30);
+    
+%% Build testing and training sets
+    
+    %build training and testing sets
+    for i=1:n
+        part = cvpartition(Y(:,i),'holdout',0.25);
+        allTraining{i} = training(part);
+        allTesting{i} = test(part);
+    end  
+    
+%% Run RUSBoosting
+
+    %iterate through each semantic class label and run boosting
+    for i=1:n
+        fprintf('Growing ensemble for: %s\n',labels{i});
+        tic
+        tempY = Y(:,i);
+        tempY = tempY(allTraining{i});
+        tempX = X(allTraining{i},:);
+        %run the RUSboost on a semantic label
+        allModels{i} = fitensemble(tempX,tempY,'RUSBoost',1500,t,...
+            'LearnRate',0.1,'nprint',100);
+        toc
+        
+    end
+