function [estClass] = calcCentroidalParametricClass(testMat, db, classPriors)

% calcParametricClass
% assumes similarity measure is Hamming similarity
% = counting common features.
% output is a vector of class Labels corresponding to the test points
% which are rows of testMat
%
% models the similarity s(x, mu1) by an exponential distribution 
% (which is max ent/max likelihood distribution)
% then compares log posterior probability of each class.

% test = test feature matrix where each row is a test feature vector
% db = database of all training vectors
[numTrain, prenumFeatures] = size(db);
[numTest, junk] = size(testMat);
numFeatures = prenumFeatures-1;
trainLabel = db(:, prenumFeatures);
numClasses = length(classPriors);

%%%%%%% GET PARAMETRIC MODEL FOR EACH CLASS %%%%%%%%%%%

% first we need a representative guy for each class
for g = 1:numClasses
    index = getMostRepElement(db(find(db(:,prenumFeatures) ==g), 1:numFeatures));
    muVect(g, :) = db(index,1:numFeatures);
end
 



% now we need to know what the descriptive statistic is.
% for each class
% here we assume the descriptive statistic is 
% mean sim(c1, mu1) for all points c1 in class c1. 
classMean = zeros(numClasses,1); % initialize to zero
countClass = zeros(numClasses, 1); % initialize count to zero
for j= 1:numTrain    
% get the similarity between the jth object in the database and
% its class centroid
classMean(trainLabel(j)) = classMean(trainLabel(j)) + getSimCounting(db(j,1:numFeatures), muVect(trainLabel(j), :));
countClass(trainLabel(j)) = countClass(trainLabel(j)) +1;

end
    
% final class means are:
classMean = classMean./countClass;

% since this is counting measure, the domain of possible
% similarities is 0,1,2, ... numFeatures
domainVector = 0:numFeatures;

% Now numerically optimize the lambda parameter in the 
% exponential model
for g = 1:numClasses
lambda(g) = findLambda(classMean(g), domainVector);
end

% compute the normalization constants normC as well
for g = 1:numClasses
normC(g) = 1/(sum( exp(lambda(g)*domainVector)));
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%% ITERATE THROUGH TEST POINTS %%%%%%%%%%%%
for i = 1:numTest
    
    x = testMat(i, :); % pull off an x. 
        
    % get simTest = s(x, classmean) for each class
    % and figure out how probable that similarity is
    for g = 1:numClasses
     simTest(g) = getSimCounting(x, muVect(g,:));
    end
   
    % Now compute the log posterior for each class:
    logPosteriors =  lambda.*simTest + log(normC) + log(classPriors);

    % classify as whoever has the largest logPosterior
    % this assumes 0-1 costs
    [who, estClass(i)] = max(logPosteriors);
end %iteration over test points
    
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%