--- a
+++ b/Semantic Features/NotUsed/dtLearning.m
@@ -0,0 +1,69 @@
+function [ classValue, errorRate ] = dtLearning(X, Y, category)
+%dtLearning Builds decion trees on a subset, tets on the rest and
+%returns the perfect errror. Repeats to get a value for all samples
+%Category Selection:
+%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
+    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},:) = [];
+    
+    tree = classregtree(Xtrain, Ytrain);
+    classValue = vertcat(classValue, eval(tree,Xtest));
+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},:) = [];
+    
+tree = classregtree(Xtrain, Ytrain);
+classValue = vertcat(classValue(2:end,:), eval(tree,Xtest)); %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
+