ClusteringModulesExamples/KMeansExample/KMeansExample.cpp

/*
 GRT MIT License
 Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>
 
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/*
 GRT KMeans Example
 This examples demonstrates how to use the KMeans module for unsupervised clustering.

 The ClusterData.csv data contains 1000 training samples, randomly generated from 3 clusters.
 The 3 clusters are centered at:
 1: 2 6
 2: 6 4
 3: 7 8
*/

//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[])
{
 //Create a new KMeans instance
 KMeans kmeans;
 kmeans.setComputeTheta( true );
 kmeans.setMinChange( 1.0e-10 );
 kmeans.setMinNumEpochs( 10 );
 kmeans.setMaxNumEpochs( 10000 );

 //There are a number of ways of training the KMeans algorithm, depending on what you need the KMeans for
 //These are:
 //- with labelled training data (in the ClassificationData format)
 //- with unlablled training data (in the UnlabelledData format)
 //- with unlabelled training data (in a simple MatrixDouble format)
 
 //This example shows you how to train the algorithm with ClassificationData
 
 //Load some training data to train the KMeans algorithm
 ClassificationData trainingData;
 
 if( !trainingData.load("LabelledClusterData.csv") ){
 cout << "Failed to load training data!\n";
 return EXIT_FAILURE;
 }
 
 //Train the KMeans algorithm - K will automatically be set to the number of classes in the training dataset
 if( !kmeans.train( trainingData ) ){
 cout << "Failed to train model!\n";
 return EXIT_FAILURE;
 }
 
 //Get the K clusters from the KMeans instance and print them
 cout << "\nClusters:\n";
 MatrixFloat clusters = kmeans.getClusters();
 for(unsigned int k=0; k<clusters.getNumRows(); k++){
 for(unsigned int n=0; n<clusters.getNumCols(); n++){
 cout << clusters[k][n] << "\t";
 }cout << endl;
 }
 
 return EXIT_SUCCESS;
}