testCBRs.m

function [perFoldF1s] = testCBRs(metric, k)


  % Load the data in for testing
  [x,y] = loaddata('noisydata_students.txt');

  stats  = cell(0);
  confusionMatrix = zeros(6,6);
  perFoldF1s = zeros(10, 6);

  foldIndices = cell(1,10);
  totalNumber = size(x,1);

  for i = 1:10
    foldIndices{i} = [round(((i-1)*totalNumber/10)+1):round(i*totalNumber/10)];
  end


  for i = 1:10
    trainExamples = x([foldIndices{1:(i-1)}, foldIndices{i+1:end}], :);
    trainTargets = y([foldIndices{1:(i-1)}, foldIndices{i+1:end}]);
    testExamples = x([foldIndices{i}], :);
    testTargets = y([foldIndices{i}]);

    % Set up the CBR
    [cbr] = CBRinit(trainExamples, trainTargets);

    % Simulate using the test data
    [pred] = testCBR(cbr, testExamples, k, metric);

    % Build the confusion matrix
    thisCM = buildCM(pred, testTargets);

    % Calculate the recall and precision
    [thisRecall, thisPrecision] = recall_precision(thisCM);
    thisF1 = f1measure(thisRecall, thisPrecision);

    % Add the recall and precision for averaging later
    % recall = recall + thisRecall;
    % precision = precision + thisPrecision;
    confusionMatrix = confusionMatrix + thisCM;
    perFoldF1s(i, :) = thisF1;
  end

  confusionMatrix

  % Averages recall and precision, and calculates the f1 measure
  %recall = recall/10
  %precision = precision/10
  [recall, precision] = recall_precision(confusionMatrix);
  recall
  precision
  f1 = f1measure(recall, precision)
  % Store the statistics to the stats variable for saving to file
  stats{1} = confusionMatrix;
  stats{2} = recall;
  stats{3} = precision;
  stats{4} = f1;

  save(strcat('tests/testCBRsMetric', num2str(metric), 'k', num2str(k)), 'stats');

end

function [CM] = buildCM(predictions, testTargets)

  CM = zeros(6,6);

  for i = 1:length(predictions)
    CM(predictions(i), testTargets(i)) = CM(predictions(i), testTargets(i)) + 1;
  end

end