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