function plotEnsError( allModels,ist,X,Y,figureName)

%PLOTENSERROR Plots the ensemble error for each semantic rating in a single

%figure.

%

%   param allModels

%       a cell array containing a model for each semantic characteristic.

%   param ist

%       a cell matrix indicating which values are testing for each semantic 

%       characteristic. Sidenote: I think training error can be plotted the

%       same way by passing the allTraining cell matrix instead of the 

%       allTesting cell matrix.

%   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.

%

%   param figureName

%      A string indicating the name of the figure. Example: 'Testing Error'

%% Setup

    %Semantic labels

    label = {'Subtlety', 'Sphericity', 'Margin', 'Lobulation',... 

              'Spiculation', 'Texture', 'Malignancy'};

    n = size(allModels,2);

    subplotX = 4;

    subplotY = 2;

    figure('name',figureName);    

%% Plot error rate of all models

    for i=1:n

        %build testing sets

        testingY = Y(:,i);

        testingX = X(ist{i},:);

        testingY = testingY(ist{i});

        %plot the error

        subplot(subplotY,subplotX,i);

        %This is for if you do kfold cross-validation

        %L = kfoldLoss(allModels{i},'mode','average');

        L = loss(allModels{i},testingX,testingY,'mode','cumulative');

        L = 1-L;

        plot(L);

        title(label{i});

        grid on;

        xlabel('Number of iterations');

        ylabel('Test accuracy');

    end

end