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