classifyRuleCoef(m_model, m_dap, dummyData)

function classifyRuleCoefList = classifyRuleCoef(m_model, m_dap, dummyData)

% Computes the Classification Rule Coefficients

noCV = struct('D', 1, 'detD', 1); % No Cross-Validation for Quadratic Rules

if strcmp(m_model, 'cpc')
	for j = 1:m_dap.constants.numGrp
        [dummy, constCoef, linCoef, quadCoef] = quadraticRule(m_dap.meanList{j}, m_dap.modeledInvCovList.cpc{j}, dummyData, ...
            m_dap.constants.aprioriProb(j), noCV);
        classifyRuleCoefList{j} = {constCoef, linCoef, quadCoef};
	end
elseif strcmp(m_model, 'proportional')
    for j = 1:m_dap.constants.numGrp
        [dummy, constCoef, linCoef, quadCoef] = quadraticRule(m_dap.meanList{j}, m_dap.modeledInvCovList.proportional{j}, dummyData, ...
            m_dap.constants.aprioriProb(j), noCV);
        classifyRuleCoefList{j} = {constCoef, linCoef, quadCoef};
	end
elseif strcmp(m_model, 'quadratic')
    for j = 1:m_dap.constants.numGrp
        [dummy, constCoef, linCoef, quadCoef] = quadraticRule(m_dap.meanList{j}, m_dap.invCovList{j}, dummyData, ...
            m_dap.constants.aprioriProb(j), noCV);
        classifyRuleCoefList{j} = {constCoef, linCoef, quadCoef};
	end
else
    noCV = 1; % No Cross-Validation for Linear Rule
    for j = 1:m_dap.constants.numGrp
        [dummy, constCoef, linCoef] = linearRule(m_dap.meanList{j}, m_dap.modeledInvCovList.linear{1}, dummyData, m_dap.constants.aprioriProb(j), noCV);
        classifyRuleCoefList{j} = {constCoef, linCoef};
    end
end