reducing noise by half

1 files changed, 128 insertions, 0 deletions

diff --git a/data/combined/scriptSkeleton.m b/data/combined/scriptSkeleton.m
new file mode 100755
index 0000000..9752c48
--- /dev/null
+++ b/data/combined/scriptSkeleton.m
@@ -0,0 +1,128 @@
+

+% 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);

+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);

+

+mostFreqClasses = 100;

+tab = tabulate(y);

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

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

+for c = delc'

+  active(y == c) = false;

+end

+

+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);

+

+numEx = 10;

+splits = [1; find(session(1 : end - 1) ~= session(2 : end)) + 1; N + 1];

+ns = length(splits) - 1;

+

+prec = [];

+recall = [];

+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;

+  

+  % 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

+  % Sigma = diag(diag(Sigma));

+

+  solver = 'SHT';

+  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

+      logp = logp ./ repmat(nhyp, 1, numClasses);

+  end

+  

+  % prediction

+  [conf, yp] = max(logp, [], 2);

+  conf = exp(conf) ./ sum(exp(logp), 2);

+  

+  % evaluation

+  for i = 1 : 50

+    th = 1 - 1 / (1.33 ^ i);

+    sub = test(conf(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 * mean(recall), 100 * mean(prec), '-ro', ...

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

+xlabel('Recall [%]');

+ylabel('Precision [%]');

+hold off;

+pause

+

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

+% for k = 1 : 9

+%   subplot(3, 3, k);

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

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

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

+%   axis([-5, 5, 0, Inf]);

+%   title(sprintf('X_%i', k));

+% end