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

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

% Plots the modeled concentration bounderies for the groups

xInc = xyInc(1);
yInc = xyInc(2);

if strcmp(m_model, 'linear') % The linear discriminant rule hasn't got the quadratic term nor the log generalized variance additive to the constant term so it has to be recomputed 
   noCV.D = 1;
   noCV.detD = 1;
   dummyData = m_dap.datasets.dataPerGroups{1}(1,:);
   for j = 1:m_dap.constants.numGrp
    [dummy, constCoef, linCoef, quadCoef] = quadraticRule(m_dap.meanList{j}, m_dap.modeledInvCovList.linear{1}, dummyData, ...
        m_dap.constants.aprioriProb(j), noCV);
    classifyRuleCoefList{j} = {constCoef, linCoef, quadCoef};
   end
end

for i = 1:m_dap.constants.numGrp
    hold on    
    C = classifyRuleCoefList{i}{1};
	L = classifyRuleCoefList{i}{2};
	Q = classifyRuleCoefList{i}{3};
    f = sprintf('%g + %g * x + %g * y + %g * x^2 + %g * x * y + %g * y^2', C, L(1), L(2), Q(1,1), Q(1,2) + Q(2,1), Q(2,2));
    ezplot(f, xyLim + [-xInc xInc -yInc yInc]);
end