1 files changed, 50 insertions, 26 deletions
diff --git a/data/combined/scriptSkeleton.m b/data/combined/scriptSkeleton.m
index 9752c48..378f158 100755..100644
--- a/data/combined/scriptSkeleton.m
+++ b/data/combined/scriptSkeleton.m
@@ -1,12 +1,17 @@
 
+mostFreqClasses = str2double(argv(){4});
+numEx = 10;
+% scenario = 'off';
+% solver = 'SHT';
+scenario = argv(){3};
+solver = argv(){2};
+
+% load data
 % trainD = dlmread('train_limbs.csv', ',', 1, 0);
 % testD = dlmread('test_limbs.csv', ',', 1, 0);
-% trainD = dlmread(argv(){1}, ',', 1, 0);
-% testD = dlmread(argv(){2}, ',', 1, 0);
 % D = [trainD; testD];
-% D = dlmread(argv(){1}, ',', 1, 0);
-D = dlmread('mean.csv', ',', 1, 0);
-X = D(:, 6 : end);
+D = dlmread(argv(){1}, ',', 1, 0);
+X = D(:, 7 : end);
 y = D(:, 2);
 session = D(:, 2);
 z = D(:, 5);
@@ -15,7 +20,7 @@ z = D(:, 5);
 active = all(X ~= -1, 2);
 active = active & (z > 2) & (z < 3);
 
-mostFreqClasses = 100;
+% remove least frequent classes
 tab = tabulate(y);
 [nil, delc] = sort(tab(:, 2), 'descend');
 delc = delc(mostFreqClasses + 1 : end);
@@ -23,6 +28,7 @@ for c = delc'
   active(y == c) = false;
 end
 
+% update data
 X = X(active, :);
 y = y(active);
 session = session(active);
@@ -34,19 +40,35 @@ K = size(X, 2);
 % normalization
 X = zscore(X);
 
-numEx = 10;
+% training and testing splits
 splits = [1; find(session(1 : end - 1) ~= session(2 : end)) + 1; N + 1];
 ns = length(splits) - 1;
 
-prec = [];
-recall = [];
+trains = cell(1, numEx);
+tests = cell(1, numEx);
 for ex = 1 : numEx
-  test = splits(round(ns * (ex - 1) / numEx) + 1) : ...
-    splits(round(ns * ex / numEx) + 1) - 1;
-  train = setdiff(1 : N, test);
-%   train = 1 : splits(round(ns * ex / numEx) + 1) - 1;
-%   test = splits(round(ns * ex / numEx) + 1) : ...
-%     splits(round(ns * (ex + 1) / numEx) + 1) - 1;
+  switch (scenario)
+    case 'off'
+      % offline learning (cross-validation)
+      test = splits(round(ns * (ex - 1) / numEx) + 1) : ...
+        splits(round(ns * ex / numEx) + 1) - 1;
+      train = setdiff(1 : N, test);
+      
+    case 'on'
+      % online learning
+      train = 1 : splits(round(ns * ex / (numEx + 1)) + 1) - 1;
+      test = splits(round(ns * ex / (numEx + 1)) + 1) : ...
+        splits(round(ns * (ex + 1) / (numEx + 1)) + 1) - 1;
+  end
+  trains{ex} = train;
+  tests{ex} = test;
+end
+
+prec = zeros(numEx, 0);
+recall = zeros(numEx, 0);
+for ex = 1 : numEx
+  train = trains{ex};
+  test = tests{ex};
   
   % NB classifier with multivariate Gaussians
   Py = zeros(numClasses, 1);
@@ -60,9 +82,7 @@ for ex = 1 : numEx
       Sigma = Sigma + Py(c) * cov(X(sub, :));
     end
   end
-  % Sigma = diag(diag(Sigma));
-
-  solver = 'SHT';
+  
   switch (solver)
     case 'NB'
       % NB inference
@@ -92,17 +112,20 @@ for ex = 1 : numEx
           nhyp(i) = nhyp(i - 1) + 1;
         end
       end
-      logp = logp ./ repmat(nhyp, 1, numClasses);
   end
   
   % prediction
   [conf, yp] = max(logp, [], 2);
-  conf = exp(conf) ./ sum(exp(logp), 2);
+  
+  % sum up all but the highest probability
+  norm1 = logp - repmat(conf, 1, numClasses);
+  norm1((1 : N) + (yp' - 1) * N) = -Inf;
+  norm1 = log(sum(exp(norm1), 2));
   
   % evaluation
-  for i = 1 : 50
-    th = 1 - 1 / (1.33 ^ i);
-    sub = test(conf(test) > th);
+  for i = 1 : 1000
+    th = 5 - i/10;
+    sub = test(norm1(test) < th);
     prec(ex, i) = mean(y(sub) == yp(sub));
     recall(ex, i) = length(sub) / length(test);
   end
@@ -110,13 +133,14 @@ end
 prec(isnan(prec)) = 1;
 
 hold on;
-plot(100 * mean(recall), 100 * mean(prec), '-ro', ...
-  'LineWidth', 1, 'MarkerSize', 4, 'MarkerFaceColor', 'w')
+plot(100 * mean(recall), 100 * mean(prec), '-', ...
+  'LineWidth', 1, 'MarkerSize', 4, 'MarkerFaceColor', 'w');
 xlabel('Recall [%]');
 ylabel('Precision [%]');
 hold off;
 pause
-
+pr = [mean(recall)',mean(prec)'];
+save pr.mat pr;
 % A = X - Pxy(y, :);
 % for k = 1 : 9
 %   subplot(3, 3, k);