From 199c8fdee50243b59083bdd15ef3e411f158a17d Mon Sep 17 00:00:00 2001
From: Jon Whiteaker <jbw@berkeley.edu>
Date: Wed, 29 Feb 2012 19:00:04 -0800
Subject: reducing noise by half

---
 data/combined/scriptSkeleton.m | 128 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 128 insertions(+)
 create mode 100755 data/combined/scriptSkeleton.m

(limited to 'data/combined/scriptSkeleton.m')

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
-- 
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