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;
}
