plotClassification(m_model, classificationList, classifyRuleCoefList, m_dap, m_method)

function plotClassification(m_model, classificationList, classifyRuleCoefList, m_dap, m_method)

% Plots the classified data along with discrimination regions

figureTitle = sprintf('Data Classification of Classify Sample: %s Model/%s Method', upper(m_model), m_method);
figure('Name', figureTitle, 'NumberTitle','off')

rowCarry = 0;
for i = 1:m_dap.constants.numGrp
    for j = 1:size(classificationList{i},2)
        group{j + rowCarry,1} = ['Group ', num2str(i)];
        VAR1(j + rowCarry) = m_dap.datasets.classifySample(classificationList{i}(j),1);
        VAR2(j + rowCarry) = m_dap.datasets.classifySample(classificationList{i}(j),2);
    end
    rowCarry = rowCarry + size(classificationList{i},2);
    groupLegendNames{i} = ['Group ', num2str(i)];
end

gscatter(VAR1, VAR2, group, 'rgymcwkb', 'vo^sdph<>.+*',[],'off');
legend(groupLegendNames, -1)

xyLim = [min(VAR1) max(VAR1) min(VAR2) max(VAR2)];
xyInc = [0.5 0.5];

plotClassificationBoundaries(m_model, m_dap, classifyRuleCoefList, xyLim, xyInc);

xlabel('VAR1')
ylabel('VAR2')
title('{\bf Data Classification with Modeled Group Concentration Boundaries}')