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- % brief: Demo-program showing how to use the GPHIK Interface (without a class wrapper)
- % author: Alexander Freytag
- % date: 07-01-2014 (dd-mm-yyyy)
- myData = [ 0.2 0.3 0.5;
- 0.3 0.2 0.5;
- 0.9 0.0 0.1;
- 0.8 0.1 0.1;
- 0.1 0.1 0.8;
- 0.1 0.0 0.9
- ];
- myLabels = [1,1,2,2,3,3];
- % init new GPHIKClassifier object
- myGPHIKClassifier = GPHIKClassifierMex ( 'new', 'verbose', 'false', ...
- 'optimization_method', 'none', 'varianceApproximation', 'approximate_rough',...
- 'nrOfEigenvaluesToConsiderForVarApprox',4,...
- 'uncertaintyPredictionForClassification', false ...
- );
- % run train method
- GPHIKClassifierMex ( 'train', myGPHIKClassifier, myData, myLabels);
- myDataTest = [ 0.3 0.4 0.3
- ];
- myLabelsTest = [1];
- % run single classification call
- [ classNoEst, score, uncertainty] = GPHIKClassifierMex ( 'classify', myGPHIKClassifier, myDataTest )
- % compute predictive variance
- uncertainty = GPHIKClassifierMex ( 'uncertainty', myGPHIKClassifier, myDataTest )
- % run test method evaluating arr potentially using multiple examples
- [ arr, confMat, scores] = GPHIKClassifierMex ( 'test', myGPHIKClassifier, myDataTest, myLabelsTest )
- % add a single new example
- newExample = [ 0.5 0.5 0.0
- ];
- newLabel = [4];
- GPHIKClassifierMex ( 'addExample', myGPHIKClassifier, newExample, newLabel);
- % add mutiple new examples
- newExamples = [ 0.3 0.3 0.4;
- 0.1, 0.2, 0.7
- ];
- newLabels = [1,3];
- GPHIKClassifierMex ( 'addMultipleExamples', myGPHIKClassifier, newExamples, newLabels );
- % perform evaluation again
- % run single classification call
- [ classNoEst, score, uncertainty] = GPHIKClassifierMex ( 'classify', myGPHIKClassifier, myDataTest )
- % compute predictive variance
- uncertainty = GPHIKClassifierMex ( 'uncertainty', myGPHIKClassifier, myDataTest )
- % run test method evaluating arr potentially using multiple examples
- [ arr, confMat, scores] = GPHIKClassifierMex ( 'test', myGPHIKClassifier, myDataTest, myLabelsTest )
- % clean up and delete object
- GPHIKClassifierMex ( 'delete',myGPHIKClassifier);
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