first draft for first deadline.

toni/octave/training.m

@@ -1,9 +1,12 @@
 #train features using svm
 display("Train Features")
+page_screen_output(0);
+page_output_immediately(1);
+more off;
 
 #load all features
 # features = [sampleLabel, classLabel, binMeans, rms, psd, windowMean, windowSTD, windowVariance, windowKurtosis, windowIQR];
-load "features.txt"; #matrix is also called features
+load "eval/512/features_512_xyz_nomag_psd18.txt"; #matrix is also called features
 
 # split features into training and test features using leave-one-out method
 # class idx:
@@ -13,37 +16,79 @@ load "features.txt"; #matrix is also called features
 # idx 4 -> situps
 # idx 5 -> jumpingjack
 
-# define which sampleSet is used as testset and not for training.
-leaveOut = find(features(:,1) == 3 & features(:,2) == 2); #sampleset 3 class 2
-testFeatures = features(leaveOut, :); #set testSignal
-features(leaveOut,:) = []; #remove the testFeatures
-features(:,1) = []; #remove the sampleLabel
+#remove features for evaluation
+#features(:, 3:7) = []; #remove binMeans
+#features(:, 8:25) = []; #remove psd
+#features(:, 26) = []; #remove rms
+#features(:, 27:31) = []; #remove statistical stuff
 
-# bring the feature matrix into libsvm format. 
-# 1. the label vector: 
-trainLabel = features(:,1);
+size(features, 2)
 
-# 2. sparse matrix with every feature in one column:
-features(:,1) = []; #remove the classLabel
-trainFeatures = sparse(features);
+#samples_class = features(:,1:2);
+#for numClass = 1 : max(samples_class(:,2)) 
+#  for numSample = 1 : max(samples_class(find(samples_class(:,2)==1),1))
+    
+    t=cputime;
 
-# before training we need to scale the feature values
-minimums = min(trainFeatures);
-ranges = max(trainFeatures) - minimums;
+    # define which sampleSet is used as testset and not for training.    
+    leaveOut = randperm(length(features), 1000);
+    
+    numSample = 2;
+    numClass = 5;
+    #leaveOut = find(features(:,1) == numSample & features(:,2) == numClass); #sampleset x class y
+    testFeatures = features(leaveOut, :); #set testSignal
+    features(leaveOut,:) = []; #remove the testFeatures
+    features(:,1) = []; #remove the sampleLabel 
+    
 
-trainFeatures = (trainFeatures - repmat(minimums, size(trainFeatures, 1), 1)) ./ repmat(ranges, size(trainFeatures, 1), 1); 
+    # bring the feature matrix into libsvm format. 
+    # 1. the label vector: 
+    trainLabel = features(:,1);
 
-# training: svm with default settings
-model = svmtrain(trainLabel, trainFeatures);
+    # 2. sparse matrix with every feature in one column:
+    features(:,1) = []; #remove the classLabel
+    trainFeatures = sparse(features);
 
-display("Classify Features")
-# for testing we need to scale again
-testLabel = testFeatures(:,2);
-testFeatures(:,1:2) = []; #remove the labels
-testFeatures = (testFeatures - repmat(minimums, size(testFeatures, 1), 1)) ./ repmat(ranges, size(testFeatures, 1), 1);
-
-# classification
-[predict_label, accuracy, dec_values] = svmpredict(testLabel, testFeatures, model);
+    #write out libsvm file
+    #libsvmwrite(strcat("trainFeatures_512_", num2str(numClass), "_", num2str(numSample), ".txt"), trainLabel, trainFeatures);
 
+    # before training we need to scale the feature values
+    minimums = min(trainFeatures);
+    ranges = max(trainFeatures) - minimums;
+
+    trainFeatures = (trainFeatures - repmat(minimums, size(trainFeatures, 1), 1)) ./ repmat(ranges, size(trainFeatures, 1), 1); 
+
+    # training: svm with default settings
+    model = svmtrain(trainLabel, trainFeatures, '-h 0 -c 32768 -g 8 -q');
+
+    disp("Classify Features");
+    # for testing we need to scale again
+    testLabel = testFeatures(:,2);
+    testFeatures(:,1:2) = []; #remove the labels
+    testFeatures = sparse(testFeatures);
+
+    #write out libsvm file
+    #libsvmwrite(strcat("testFeatures_512_", num2str(numClass), "_", num2str(numSample), ".txt"), testLabel, testFeatures);
+
+    testFeatures = (testFeatures - repmat(minimums, size(testFeatures, 1), 1)) ./ repmat(ranges, size(testFeatures, 1), 1);
+
+    # classification
+    [predict_label, accuracy, dec_values] = svmpredict(testLabel, testFeatures, model);
+    
+       
+    # get evaluation matrix
+    evaluation = [];
+    for i = 1:5
+      for j = 1:5          
+        evaluation(i,j) = sum(testLabel == i & predict_label == j);
+      end
+    end
+    disp(evaluation);
+    
+    save evaluation_512.txt evaluation accuracy;
+       
+    printf('Total cpu time: %f seconds\n', cputime-t);
+#  end
+#end