--- a
+++ b/Semantic Features/NotUsed/bayesLearning.m
@@ -0,0 +1,76 @@
+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
+
+