fixed some of the graphs

1 files changed, 130 insertions, 0 deletions

diff --git a/data/combined/scriptSkeletonFixedSplit.m b/data/combined/scriptSkeletonFixedSplit.m
new file mode 100644
index 0000000..9430dc8
--- /dev/null
+++ b/data/combined/scriptSkeletonFixedSplit.m
@@ -0,0 +1,130 @@
+

+mostFreqClasses = 100;

+numEx = 1;

+scenario = 'off';

+solver = argv(){3};

+

+% load data

+trainD = dlmread(argv(){1}, ',', 1, 0);

+testD = dlmread(argv(){2}, ',', 1, 0);

+D = [trainD; testD];

+X = D(:, 6 : end);

+y = D(:, 2);

+session = D(:, 2);

+z = D(:, 5);

+

+% remove missing values and outliers

+active = all(X ~= -1, 2);

+active = active & (z > 2) & (z < 3);

+

+% remove least frequent classes

+tab = tabulate(y);

+[nil, delc] = sort(tab(:, 2), 'descend');

+delc = delc(mostFreqClasses + 1 : end);

+for c = delc'

+  active(y == c) = false;

+end

+

+% update data

+X = X(active, :);

+y = y(active);

+session = session(active);

+z = z(active);

+numClasses = max(y);

+N = size(X, 1);

+K = size(X, 2);

+

+% normalization

+X = zscore(X);

+

+train = 1 : sum(active(1 : size(trainD, 1)));

+test = train(end) + 1 : N;

+

+prec = zeros(numEx, 0);

+recall = zeros(numEx, 0);

+for ex = 1 : numEx

+  % NB classifier with multivariate Gaussians

+  Py = zeros(numClasses, 1);

+  Pxy = zeros(numClasses, K);

+  Sigma = zeros(K, K);

+  for c = 1 : numClasses

+    sub = train(y(train) == c);

+    if (~isempty(sub))

+      Py(c) = length(sub) / length(train);

+      Pxy(c, :) = mean(X(sub, :), 1);

+      Sigma = Sigma + Py(c) * cov(X(sub, :));

+    end

+  end

+  

+  switch (solver)

+    case 'NB'

+      % NB inference

+      logp = repmat(log(Py)', N, 1);

+      for c = 1 : numClasses

+        if (Py(c) > 0)

+          logp(:, c) = log(Py(c)) + log(mvnpdf(X, Pxy(c, :), Sigma));

+        end

+      end

+

+    case 'SHT'

+      % sequential hypothesis testing

+      logp = zeros(N, numClasses);

+      for c = 1 : numClasses

+        if (Py(c) > 0)

+          logp(:, c) = log(mvnpdf(X, Pxy(c, :), Sigma));

+        end

+      end

+

+      nhyp = zeros(N, 1);

+      for i = 1 : N

+        if ((i == 1) || (session(i - 1) ~= session(i)))

+          logp(i, :) = logp(i, :) + log(Py');

+          nhyp(i) = 2;

+        else

+          logp(i, :) = logp(i, :) + logp(i - 1, :);

+          nhyp(i) = nhyp(i - 1) + 1;

+        end

+      end

+  end

+  

+  % prediction

+  [conf, yp] = max(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 : 1000

+    th = 3 - i;

+    sub = test(norm1(test) < th);

+    prec(ex, i) = mean(y(sub) == yp(sub));

+    recall(ex, i) = length(sub) / length(test);

+  end

+end

+prec(isnan(prec)) = 1;

+

+hold on;

+plot(100 * recall, 100 * prec, '-', ...

+  'LineWidth', 1, 'MarkerSize', 4, 'MarkerFaceColor', 'w');

+xlabel('Recall [%]');

+ylabel('Precision [%]');

+hold off;

+pause

+pr = [recall',prec'];

+save pr.mat pr;

+

+% figure;

+% A = X - Pxy(y, :);

+% for k = 1 : 9

+%   subplot(2, 5, k);

+%   hist(A(:, k), -5 : 0.1 : 5);

+%   h = findobj(gca, 'Type', 'patch');

+%   set(h, 'FaceColor', [0.5, 1, 0.5], 'LineStyle', 'none')

+%   axis([-3, 3, 0, Inf]);

+%   xlabel(sprintf('x_%i - E[x_%i | y]', k, k));

+%   set(gca, 'XTick', []);

+%   ylabel(sprintf('P(x_%i - E[x_%i | y])', k, k));

+%   set(gca, 'YTick', []);

+% end