function [ classValue, errorRate ] = bayesLearning(X, Y, category)

%bayesLearning

%   Detailed explanation goes here

%1 subtlety, 2 sphericity, 3 margin, 4 lobulation, 5 spiculation, 6 texture, 7 malignancy

Y = Y(:,category);

%Perform random sampling by just jumbling up the data then slicing the new

%set into 4ths or nths.

divisions = 10;

numSamples = size(X,1);

testSize = round(numSamples/divisions);

%get a random order of our rows

randomRows = randsample(numSamples, numSamples);

%get vector of row order to undo the scrambling of the rows

for i = 1:numSamples

    restoreRows(i) = find(i == randomRows);

end

Xmixed = X(randomRows,:);

Ymixed = Y(randomRows,:);

%perform process repeatedly with the test set different each time untill

%all are covered.

classValue = 0;

testrows = cell(divisions,1);

for i = 1:(divisions - 1) %perform all iterations guaranteeed to have a full share

    %start with testing at the beginning rows, then cycle down

    fprintf('Bayes learning %d0%%\n', i);

    testrows{i} = [(i-1)*testSize + 1:i*testSize];

    Xtest = Xmixed(testrows{i}, :);

    Ytest = Ymixed(testrows{i}, :);

    Xtrain = Xmixed;

    Xtrain(testrows{i},:) = [];

    Ytrain = Ymixed;

    Ytrain(testrows{i},:) = [];

    nb = NaiveBayes.fit(Xtrain, Ytrain, 'Distribution', 'kernel'); %'Distribution', 'kernel') or 'mvmn'

    [post,classValueTemp] = posterior(nb, Xtest, 'HandleMissing', 'On'); %returns classValue, an N-by-1 vector, containing the class to which each row of test has been assigned. cpre has the same type as nb.CLevels.

    classValue = vertcat(classValue, classValueTemp);

end

%collect all the remaining rows. Could be undersized, but eliminates

%problems of some rows getting lost

testrows{divisions} = [(divisions-1)*testSize + 1:numSamples];

Xtest = Xmixed(testrows{divisions}, :);

Ytest = Ymixed(testrows{divisions}, :);

Xtrain = Xmixed;

Xtrain(testrows{divisions},:) = [];

Ytrain = Ymixed;

Ytrain(testrows{divisions},:) = [];

nb = NaiveBayes.fit(Xtrain, Ytrain, 'Distribution', 'kernel');

%returns classValue, an N-by-1 vector, containing the class to which each row of test has been assigned. cpre has the same type as nb.CLevels.

%Also told to ignore collumns with NaN results and calculate on the rest of

%the columns

[post,classValueTemp] = posterior(nb, Xtest, 'HandleMissing', 'On'); 

classValue = vertcat(classValue(2:end,:), classValueTemp); %Chop off the zero we put at the beginning

%Resort everything to the original order so we can compare against other

%algorithms

classValue = classValue(restoreRows,:);

%perform RMSE on allll the samples

errorRate = RMSE(classValue, Y); %RMSE error. Maybe better as an array so we can combine in the future

end