ClassificationModulesExamples/GMMExample/GMMExample.cpp

This class implements the Gaussian Mixture Model Classifier algorithm. The Gaussian Mixture Model Classifier (GMM) is basic but useful classification algorithm that can be used to classify an N-dimensional signal.

Remarks

This implementation is based on Duda, Richard O., and Peter E. Hart. Pattern classification and scene analysis. Vol. 3. New York: Wiley, 1973.

Note

The GMM algorithm can fail to train on some occasions, if this happens just try and run the training algorithm again and it should eventially converge.

/*
 GRT MIT License
 Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>
 
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/*
 GRT GMM Example
 This examples demonstrates how to initialize, train, and use the GMM algorithm for classification.
 
 The Gaussian Mixture Model Classifier (GMM) is basic but useful classification algorithm that can be used to
 classify an N-dimensional signal.
 
 In this example we create an instance of a GMM classifier and then train the algorithm using some pre-recorded training data.
 The trained GMM algorithm is then used to predict the class label of some test data.
 
 This example shows you how to:
 - Create an initialize the GMM algorithm
 - Load some ClassificationData from a file and partition the training data into a training dataset and a test dataset
 - Train the GMM algorithm using the training dataset
 - Test the GMM algorithm using the test dataset
 - Manually compute the accuracy of the classifier

You should run this example with one argument pointing to the data you want to load. A good dataset to run this example is acc-orientation.grt, which can be found in the GRT data folder.
*/

//You might need to set the specific path of the GRT header relative to your project
#include <GRT/GRT.h>
using namespace GRT;
using namespace std;

int main (int argc, const char * argv[])
{
 //Parse the data filename from the argument list
 if( argc != 2 ){
 cout << "Error: failed to parse data filename from command line. You should run this example with one argument pointing to the data filename!\n";
 return EXIT_FAILURE;
 }
 const string filename = argv[1];
 
 //Create a new GMM instance and set the number of mixture models to 2
 GMM gmm;
 gmm.setNumMixtureModels(2);
 
 //Load some training data to train the classifier
 ClassificationData trainingData;
 
 if( !trainingData.load( filename ) ){
 cout << "Failed to load training data!\n";
 return EXIT_FAILURE;
 }
 
 //Use 20% of the training dataset to create a test dataset
 ClassificationData testData = trainingData.split( 80 );
 
 //Train the classifier
 if( !gmm.train( trainingData ) ){
 cout << "Failed to train classifier!\n";
 return EXIT_FAILURE;
 }
 
 //Save the GMM model to a file
 if( !gmm.save("GMMModel.grt") ){
 cout << "Failed to save the classifier model!\n";
 return EXIT_FAILURE;
 }
 
 //Load the GMM model from a file
 if( !gmm.load("GMMModel.grt") ){
 cout << "Failed to load the classifier model!\n";
 return EXIT_FAILURE;
 }
 
 //Use the test dataset to test the GMM model
 double accuracy = 0;
 for(UINT i=0; i<testData.getNumSamples(); i++){
 //Get the i'th test sample
 UINT classLabel = testData[i].getClassLabel();
 VectorFloat inputVector = testData[i].getSample();
 
 //Perform a prediction using the classifier
 bool predictSuccess = gmm.predict( inputVector );
 
 if( !predictSuccess ){
 cout << "Failed to perform prediction for test sampel: " << i <<"\n";
 return EXIT_FAILURE;
 }
 
 //Get the predicted class label
 UINT predictedClassLabel = gmm.getPredictedClassLabel();
 VectorFloat classLikelihoods = gmm.getClassLikelihoods();
 VectorFloat classDistances = gmm.getClassDistances();
 
 //Update the accuracy
 if( classLabel == predictedClassLabel ) accuracy++;
 
 cout << "TestSample: " << i << " ClassLabel: " << classLabel << " PredictedClassLabel: " << predictedClassLabel << endl;
 }
 
 cout << "Test Accuracy: " << accuracy/double(testData.getNumSamples())*100.0 << "%" << endl;
 
 return EXIT_SUCCESS;
}