Softwareprojektmanagement/src/main/java/de/hsel/spm/baudas/analysis/Cluster.java

package de.hsel.spm.baudas.analysis;

import org.jetbrains.annotations.NotNull;
import weka.clusterers.SimpleKMeans;
import weka.core.Instances;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Remove;

import java.io.File;
import java.util.HashMap;
import java.util.Map;

/**
 * Cluster Analysis.
 *
 * @author Johannes Theiner
 * @version 0.1
 * @since 1.0
 */
public class Cluster implements Analysis<Map<Integer, Map<String, String>>> {

    private Instances instances;
    private Map<Integer, Map<String, String>> result;

    public Cluster(File file) {
        instances = load(file);
    }

    /**
     * get result of cluster analysis, cluster 0 is the average.
     *
     * @return Result
     */
    @Override
    public Map<Integer, Map<String, String>> getResult() {
        if (result != null) {
            return result;
        }
        result = new HashMap<>();
        int[] keepIndexes = new int[]{Attribute.SEX, Attribute.AGE, Attribute.MARITAL_STATUS, Attribute.SHOPPING_DAY, Attribute.SHOPPING_HOUR, Attribute.RESIDENCE};
        Remove remove = new Remove();

        try {
            remove.setAttributeIndicesArray(keepIndexes);
            remove.setInvertSelection(true);
            remove.setInputFormat(instances);
            instances = Filter.useFilter(instances, remove);
        } catch (Exception e) {
            e.printStackTrace();
        }

        //creating a single cluster to get average, weka has no way to get that from a bigger one.
        SimpleKMeans averageMeans = new SimpleKMeans();
        Instances averageCentroids = null;
        try {
            averageMeans.setNumClusters(1);
            averageMeans.setPreserveInstancesOrder(true);
            averageMeans.buildClusterer(instances);
            averageCentroids = averageMeans.getClusterCentroids();

        } catch (Exception ex) {
            ex.printStackTrace();
        }
        assert averageCentroids != null;

        //creating real cluster
        SimpleKMeans fullMeans = new SimpleKMeans();
        try {
            fullMeans.setNumClusters(5);
            fullMeans.setPreserveInstancesOrder(true);
            fullMeans.buildClusterer(instances);

            int count = 0;
            count = putIntoMap(averageCentroids, count);

            Instances centroids = fullMeans.getClusterCentroids();
            putIntoMap(centroids, count);

        } catch (Exception ex) {
            ex.printStackTrace();
        }
        return result;
    }

    /**
     * puts data into map.
     *
     * @param centroids cluster analysis result
     * @param count     current insert count
     * @return count increment
     */
    private int putIntoMap(@NotNull Instances centroids, int count) {
        for (int i = 0; i < centroids.numInstances(); i++) {
            Map<String, String> map = new HashMap<>();
            for (int j = 0; j < centroids.numAttributes(); j++) {
                map.put(centroids.attribute(j).name(), centroids.instance(i).stringValue(j));
            }
            result.put(count, map);
            count++;
        }
        return count;
    }
}