RS012-data-mining-toolbox/KMeansNode.cpp at master · MATF-RS20/RS012-data-mining-toolbox · GitHub

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#include "KMeansNode.hpp"

//Constructors
KMeansNode::KMeansNode(std::string name) : ClusteringNode(name), numberOfClusters(2),
                                           distance(distances::EuclideanDistance), maxNumberOfIterations(1000) {}
KMeansNode::KMeansNode(std::string name, size_t numCLus) : KMeansNode(name) { numberOfClusters = numCLus; }

KMeansNode::KMeansNode(std::string name, distances d) : KMeansNode(name) { distance = d; }

KMeansNode::KMeansNode(std::string name, size_t numCLus, size_t maxNumIter) : KMeansNode(name) { numberOfClusters = numCLus;
                                                                                                 maxNumberOfIterations = maxNumIter; }
KMeansNode::KMeansNode(std::string name, distances d, size_t maxNumIter) : KMeansNode(name) { distance = d;
                                                                                              maxNumberOfIterations = maxNumIter;}
KMeansNode::KMeansNode(std::string name, size_t numCLus, distances d, size_t maxNumIter) : ClusteringNode(name), numberOfClusters(numCLus),
                                                                                           distance(d), maxNumberOfIterations(maxNumIter) {}
//Getters
size_t KMeansNode::NumberOfClusters() {

    return  numberOfClusters;
}

KMeansNode::distances KMeansNode::Distance() {

    return distance;
}

size_t KMeansNode::MaxNumberOfIterations() {

    return  maxNumberOfIterations;
}

//Setters
void KMeansNode::SetNumberOfClusters(size_t number) {

    numberOfClusters = number;
}

void KMeansNode::SetDistance(distances dist) {

    distance = dist;
}

void KMeansNode::SetMaxNumberOfIterations(size_t number) {

    maxNumberOfIterations = number;
}


void KMeansNode::run() {

    //Make a model for adequate distance and cluster the data
    if(distances::EuclideanDistance == distance) {

        mlpack::kmeans::KMeans<mlpack::metric::EuclideanDistance> model(maxNumberOfIterations);
        model.Cluster(trans(inputDataTable->DataMatrix()), numberOfClusters, labels, centroids, false, false);
    } else if(distances::ManhattanDistance == distance) {

        mlpack::kmeans::KMeans<mlpack::metric::ManhattanDistance> model(maxNumberOfIterations);
        model.Cluster(trans(inputDataTable->DataMatrix()), numberOfClusters, labels, centroids, false, false);
    } else if(distances::SquaredEuclideanDistance == distance) {

        mlpack::kmeans::KMeans<mlpack::metric::SquaredEuclideanDistance> model(maxNumberOfIterations);
        model.Cluster(trans(inputDataTable->DataMatrix()), numberOfClusters, labels, centroids, false, false);
    } else if(distances::ChebyshevDistance == distance) {

        mlpack::kmeans::KMeans<mlpack::metric::ChebyshevDistance> model(maxNumberOfIterations);
        model.Cluster(trans(inputDataTable->DataMatrix()), numberOfClusters, labels, centroids, false, false);
    }

    centroids = trans(centroids);

    //Estimate results
    silhouette_shadow();

    //Set output
    std::string result = "Centroids:\n";
    for (unsigned long i = 0; i < centroids.n_rows; i++){
        result += std::to_string(i+1);
        result += ". ";
        for (unsigned long j = 0; j < centroids.n_cols; j++){
            result += std::to_string(centroids(i, j));
            result += " ";
        }
        result += "\n";
    }
    result += "Silhouette score: ";
    result += std::to_string(SilhouetteScore());
    result += "\n";
    setOutputMessage(result);

    DataTable dt(*InputDataTable());
    setOutDataTable(dt);

}