new script

3 files changed, 16001 insertions, 15974 deletions

diff --git a/data/combined/mean.csv b/data/combined/mean.csv
index 756d0fa..d7b5cee 100644
--- a/data/combined/mean.csv
+++ b/data/combined/mean.csv
@@ -1,15946 +1,15946 @@
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+2,9,121,670168,2.66785195,0.0707360653194,0.423279,0.201365356353,0.215652330321,0.278225818127,0.261280468962,0.330906397896,0.0668985928117,0.118141549805,0.579020890644,0.424862563406
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+2,9,121,670173,2.40151885,0.0562453429756,0.410738,0.197643016139,0.2176325084,0.282157769622,0.25023012598,0.32398971942,0.0653280661204,0.11884804547,0.530802407524,0.421488345747
+2,9,121,670174,2.3470267,0.0451533591926,0.412348,0.197744957536,0.216649492756,0.297833506742,0.249859945235,0.324730093915,0.0654854523795,0.117799171252,0.506095601223,0.428291868749
+2,9,121,670175,2.29598605,0.0385237213587,0.394467,0.216815531875,0.208306899151,0.30841511432,0.249731583391,0.30844803968,0.0622507917745,0.117099856234,0.507374410289,0.428335706588
+2,9,121,670176,2.24385075,0.033464329295,0.372267,0.23297567489,0.203834913976,0.315813223443,0.251274694329,0.299901609091,0.060560785955,0.116519982764,0.507229212131,0.428931034673
+2,9,121,670177,2.1908655,0.0308513682139,0.344625,0.23747981434,0.204901398095,0.315353888724,0.255445251813,0.296787045296,0.0599450656955,0.117740554407,0.512679822407,0.430756122844
+2,9,121,670178,2.1344349,0.0309747167931,0.30487,0.215288012442,0.209818862759,0.3107650051,0.261630713355,0.315039751558,0.0628430271261,0.119422697536,0.522991771603,0.433943849069
+2,9,121,670179,2.07974295,0.0345964882113,0.305352,0.203050184154,0.213582338321,0.303271988268,0.261967449447,0.319538035656,0.0644196350627,0.120679929435,0.523285931979,0.444299856264
+2,9,121,670180,1.972009,0.0218749921702,0.332695,0.194520664641,0.216885182359,0.294833042516,0.261882638337,0.320403681208,0.064568814624,0.121500134625,0.522344088324,-1
+2,9,121,670181,1.87413673333,0.0112703171737,0.140892,0.189865197699,0.217087080823,0.289185800534,0.260432737246,0.31748108894,0.0639821700764,0.121585781471,0.471246016767,-1
+2,9,121,670182,1.86877035294,0.0310139212579,0.320966,0.189619781025,0.216875743631,0.279196039961,0.258822388543,0.31311330333,0.063115916253,0.121502121239,0.494738461631,0.401177815102
+2,9,121,670183,1.800953,0.0311533796681,0.144139,0.192351792891,0.216253190593,0.271406270941,0.258555298195,0.307865543579,0.0620710305792,0.122188944751,0.455341087055,-1
+2,9,121,670184,1.752675375,0.0335818910192,0.146436,0.194367067271,0.215028856436,0.269019062099,0.253491271542,0.304437794073,0.0613832173841,0.122274461746,0.456127070477,-1
+2,9,121,670185,1.706276625,0.0330217739749,0.12977,0.196568260641,0.214571199941,0.266736823787,0.248582535471,0.300079907574,0.0604960347696,0.122849078075,0.458019816385,-1
+2,9,121,670186,1.66452713333,0.0335378488089,0.120328,0.197895389186,0.212609723779,0.266843444643,0.249054651821,0.29565190721,0.0595743219626,0.122686893124,0.45983450018,-1
+2,9,121,670187,1.57001058333,0.0108562594209,0.108627,0.198317495944,0.212854324607,0.267031396252,0.249605317416,0.291447531315,0.05868932584,0.123062720791,-1,-1
+2,9,121,670188,1.508237,0.0115899746522,0.237435,0.198500783451,0.204646402551,0.292480185592,0.248650052484,0.296773557924,0.0597316192472,0.118127684148,0.452837624174,-1
+2,9,121,670189,1.43968263636,0.0069704402935,0.084216,0.198812632498,0.189484613115,0.298097841602,0.248577527952,0.309496171229,0.0622464260493,0.109393690615,-1,-1
+2,9,121,670190,1.36894141667,0.00533821481858,0.076414,0.1968778431,0.174617335514,0.308583214426,0.249307102629,0.331828145651,0.0670365115422,0.0970471078839,-1,-1
+2,9,121,670191,1.31621291667,0.00323225113791,0.061231,0.189346129778,0.169247784516,0.312130646662,0.251045567812,0.350444018598,0.0729292608055,0.0827710849385,-1,-1
+2,9,121,670192,1.2952656,0.00116411417524,0.047158,0.218691577725,0.138255485243,0.32015242026,0.252014534279,0.339256937243,0.0629864555683,0.083628012602,-1,-1
+2,9,121,670193,1.25789122222,0.00247132258417,0.053392,0.220414086062,0.117640093108,0.323765194532,0.252058222824,0.322309178688,0.0621942893312,0.0834553494302,-1,-1
+2,9,121,670194,1.233943125,0.00309261322811,0.053865,0.19537204804,0.11940604899,0.318048803205,-1,0.348945992864,0.0686447003077,0.0691103749697,-1,-1
+2,9,121,670195,1.19589285714,0.00372361567955,0.063084,-1,0.180763990838,0.286272370351,-1,0.393625291241,0.0774278188618,0.0634772119582,-1,-1
+2,9,121,670196,1.169533,0.001872156368,0.054541,-1,0.197750656074,-1,-1,0.388528559332,0.0763193085135,0.065155789537,-1,-1
+2,9,121,670197,1.22066316667,0.00763147515747,0.147038,0.178742441607,-1,-1,-1,0.364282207973,0.072577797798,0.0710724405753,-1,-1
diff --git a/data/combined/mean.py b/data/combined/mean.py
index 2499beb..dd1f56a 100755
--- a/data/combined/mean.py
+++ b/data/combined/mean.py
@@ -23,15 +23,18 @@ for line in sk_file:
     if "-1" not in line and 2 < float(line[labels.index('z-value')]) < 3:
         id = line[labels.index('name')]
         for x in labels[beg:]: data[id][x] += [float(line[labels.index(x)])]
-
+var = []
 for i in data:
     out = [i]
     for l in labels[beg:]:
         if len(data[i][l]) > 1:
+            var += [np.var(data[i][l])]
             data[i][l] = np.average(data[i][l])
             out += [str(np.average(data[i][l]))]
     #if len(out) > 1:
     #    print ','.join(out)
+sys.stderr.write(str(div**0.5)+'\n')
+sys.stderr.write(str(np.average(var))+'\n')
 
 sk_file.seek(0)
 print sk_file.readline().strip()
@@ -42,6 +45,6 @@ for line in sk_file:
         obs = float(line[labels.index(l)])
         if obs > 0:
             diff = obs-data[id][l]
-            line[labels.index(l)] = str(obs - diff/div)
+            line[labels.index(l)] = str(data[id][l] + diff/(div**0.5))
     print ','.join(line)
 
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);