function [ classValue, errorRate, errorHeaders, confusionMatrix, sens, spec  ] = MLSuite(X, Y, settings)

%MLSuite Runs all the base level machine learning methods on the X and Y matrices

%given using the set of options given

categories = settings.categories;

numCategories = length(categories);

clError         = zeros(1, numCategories); %To hold classification success/error rates

clHeader        = cell(1,1);

classValue   = 0;

errorRate    = 0;

errorHeaders = 0;

confusionMatrix = 0; %Code exists, but not used since changing from classes to numbers for the labels

sens = 0; %Code exists, but not used since changing from classes to numbers for the labels

spec = 0; %Code exists, but not used since changing from classes to numbers for the labels

%Note: each algo, each run will test on different rows

classValue = zeros(size(X,1), numCategories, 1);

%-------------------------------------------------------------------

% Standard Learning

%-------------------------------------------------------------------

if settings.doLearning == 1

    if settings.doTrees == 1

        %-------------------------------------------------------------------

        % Decion tree learning

        %-------------------------------------------------------------------      

        fprintf('\nDecision tree learning from mean of each group vs mean of features \n');

        clHeader = vertcat(clHeader, {'DT Mean to Mean'});

        errorVector = zeros(1, numCategories);

        for i = 1:numCategories

            fprintf('Learning and evaluating success on category: %s\t\t', str2mat(categories(i)));

            [dtClassValue(:,i), errorVector(i), ~] = CrossValLearn(X, Y(:,i), @classregtree, @(X, trainedStruct) eval(trainedStruct, X));

            %[dtClassValue(:,i), errorVector(i)] = dtLearning(X, Y, i);

            fprintf('Error: %f class Success %f\n', errorVector(i), GetClassSuccessRate(dtClassValue(:,i), Y(:,i)) );

        end

        clError = vertcat(clError, errorVector);

        classValue = cat(3, classValue, dtClassValue);

    end

    %-------------------------------------------------------------------

    % Neural Network Learning

    %-------------------------------------------------------------------

    if settings.doNN == 1

        fprintf('\nNeural Network training.\n');

        clHeader = vertcat(clHeader, {'NN'});

        errorVector = zeros(1, numCategories);

        for i = 1:numCategories

            fprintf('NN on category: %s ', str2mat(categories(i)));

            [nnClassValue(:,i), errorVector(i)] = nnLearning(X, Y, settings.hiddenLayer, i);

            fprintf('\t\tAverage Error: %.4f Class Success %f\n', errorVector(i), GetClassSuccessRate(nnClassValue(:,i), Y(:,i)) );

            %fprintf('Average Error: %.4f Pstdev: %.4f\n', nnEvaluation(i).Eout, nnEvaluation(i).sigmaOut);

        end

        clError = vertcat(clError, errorVector);

        classValue = cat(3, classValue, nnClassValue); 

    end

    %-------------------------------------------------------------------

    % SVM Learning

    %-------------------------------------------------------------------

    %Multiclass/Class & Probability/Continuous numerical predictions by SVM

    %never got to work reliably and was removed. I hear scikit for python

    %works good

    %-------------------------------------------------------------------

    % Bayesian Learning

    %-------------------------------------------------------------------

    %Does not work! Works 99%, but <1% of the samples will result in a NaN

    %prediction thus destroying all the math steps that come afterwords.

    %You can try to figure out how to fix it, or just convert NaN into 0s

    %and be fine with failing those guys. 

    if settings.doBayes == 1   

        fprintf('\nNaieve Bayes Learning.\n');

        clHeader = vertcat(clHeader, {'Bayes'});        

        errorVector = zeros(1, numCategories);

        for i = 1:numCategories

            fprintf('Bayes on category: %s ', str2mat(categories(i)));

            [bayesClassValue(:,i), errorVector(i), ~] = CrossValLearn(X, Y(:,i), ...

                @(X, Y) NaiveBayes.fit(X, Y, 'Distribution', 'kernel'),...

                @(X, trainedStruct) posterior(trainedStruct, X, 'HandleMissing', 'On') ...

                );

            fprintf('\t\tAverage Error: %.4f Class Success %f\n', errorVector(i), GetClassSuccessRate(bayesClassValue(:,i), Y(:,i)) );

        end

        clError = vertcat(clError, errorVector);

        classValue = cat(3, classValue, nnClassValue); 

    end

    %-------------------------------------------------------------------

    % Ensemble Learning (Still a first layer classifier)

    %-------------------------------------------------------------------

    if settings.doBagging == 1

        %-------------------------------------------------------------------

        % Bagging

        %-------------------------------------------------------------------

        fprintf('\nPerforming Bagging\n');

        clHeader = vertcat(clHeader, {'Bagging Trees'});

        for i = 1:numCategories

            fprintf('Learning and evaluating success on category: %s\n', str2mat(categories(i)));

            tBag = TreeBagger(settings.numTrees, X, Y(:,i), 'method', 'regression', 'OOBPred', 'on', 'NPrint', 25);% , 'minleaf', 15);

            %errorArray = oobError(tBag);

            %errorVector(i) = errorArray(end);

            %bagClassValue(:,i) = predict(tBag, X); %tBag consists of

            %trees that don't apply to all data. Some trees were

            %trained with certain points! Therefore not all trees can

            %be applied to each observation. 

            bagClassValue(:,i) = oobPredict(tBag);

            errorVector(i) = RMSE(bagClassValue(:,i), Y(:,i));

            fprintf('Error: %f classSuccess %f\n', errorVector(i), GetClassSuccessRate(bagClassValue(:,i), Y(:,i)) );

            %For regression bagClassProb is just the standard

                %deviation across all the trees, so its kind of an

                %indication

            if settings.doPlot == 1

                %plot the change over time. Real slow so might as well look at something

                figure;

                plot(oobError(tBag));

                xlabel('number of grown trees');

                ylabel('out-of-bag regression error');

                title(str2mat(categories(i)));

            end

        end

    clError = vertcat(clError, errorVector);

    classValue = cat(3, classValue, bagClassValue);

    end

end

%-------------------------------------------------------------------

% Compile results

%-------------------------------------------------------------------

%clError = horzcat(clError, mean(clError, 2));

%bagError = horzcat(bagError, mean(bagError, 2));

%nnError = horzcat(nnError, mean(nnError, 2));

%allErrors = vertcat(clError, bagError);

%allErrors = vertcat(allErrors, nnError);

%averageError = [mean(clError, 2);mean(bagError, 2);mean(nnError, 2)];

%Remove blank lines at the beginning. This was the easiest way I found to

%1. Guarantee variable initialized to keep matlab from complaining and 2.

%Have an unspecified length, so you don't have to rework the code if you

%add more classifiers to the mix, or turn some of them off in the settings.

errorRate = clError(2:end,:);

errorHeaders = clHeader(2:end);

classValue = classValue(:,:,2:end);