playground/Tester.java at master · leaguilar/playground · GitHub

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package nervousNet;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.LinkedHashMap;
import java.util.concurrent.ThreadLocalRandom;


import nervousnet.challenge.Dumper;
import nervousnet.challenge.Loader;
import nervousnet.challenge.exceptions.IllegalHashMapArgumentException;
import nervousnet.challenge.exceptions.MissingDataException;
import nervousnet.challenge.exceptions.MissingFileException;
import nervousnet.challenge.exceptions.NullArgumentException;
import nervousnet.challenge.exceptions.UnsupportedRawFileFormatException;
import nervousnet.challenge.tags.Tags;
import weka.clusterers.SimpleKMeans;
import weka.core.Attribute;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;

@SuppressWarnings("unused")
public class Tester {

	private Loader loader = new Loader();
	private Dumper dumper = new Dumper();

	public int numOfClusters = 2;

	public Tester() {

	}

	private ArrayList<Double> kMeans(ArrayList<Double> raws, int numOfClusters) throws Exception {
		FastVector atts = new FastVector();
		atts.addElement(new Attribute("NN"));

		Instances kmeans = new Instances("kmeans", atts, 0);

		double epsilon = 2; // Epsilon>0

		Double[] keys = new Double[raws.size()];
		Double[] keys2 = new Double[raws.size()];
		double sum = 0.0;

		for (int i=0; i<raws.size(); i++) {
			keys[i] = ThreadLocalRandom.current().nextDouble(0, 1);
			sum += keys[i];
		}

		for (int i=0; i<raws.size(); i++) {
			keys2[i] = keys[i]/sum*epsilon;
		}

		int j=0;
		for(Double raw : raws) {

			double vals[] = new double[kmeans.numAttributes()];
			//double modifiedValue = ThreadLocalRandom.current().nextDouble(Math.max(0, raw-epsilon), raw+epsilon);

			vals[0] = raw + keys2[j];
			j++;

			//vals[0] = modifiedValue;

			kmeans.add(new Instance(1.0, vals));
		}

		SimpleKMeans kMeansAlgo = new SimpleKMeans();
		kMeansAlgo.setSeed(10);
		kMeansAlgo.setPreserveInstancesOrder(true);
		kMeansAlgo.setNumClusters(numOfClusters);
		kMeansAlgo.buildClusterer(kmeans);

		int[] assignments = kMeansAlgo.getAssignments();
		Instances centroids = kMeansAlgo.getClusterCentroids();

		ArrayList<Double> centroidList = new ArrayList<Double>();
		for(int i = 0; i < assignments.length; i++) {
			double rawValue = kmeans.instance(i).value(0);
			double centroidValue = centroids.instance(assignments[i]).value(0);
			centroidList.add(centroidValue);
		}

		return centroidList;
	}


	public void analyze() {
		try {
			HashMap<Integer, LinkedHashMap<Integer, LinkedHashMap<Integer, Double>>> rawMap = loader.exportRawValues();

			HashMap<Integer, LinkedHashMap<Integer, LinkedHashMap<Integer, Double>>> outputMap = Dumper.initOutputMap();
			System.out.println("Processing data..");
			for(Integer user : loader.getSortedUsers()) {
				for(Integer day : loader.getSortedDays(user)) {
					try {
						ArrayList<Double> rawValues = loader.getSortedRawValues(user, day);
						ArrayList<Double> summarizedValues = this.kMeans(rawValues, numOfClusters);

						for(int time = Tags.minTime, i = 0; time <= Tags.maxTime; time++, i++) {
							outputMap.get(user).get(day).put(time, summarizedValues.get(i));
						}

					} catch(Exception e) {
						e.printStackTrace();
					}
				}
			}

			dumper.dump(outputMap);

		} /*catch (UnsupportedRawFileFormatException e) {
			e.printStackTrace();
		} */catch (IllegalHashMapArgumentException e) {
			e.printStackTrace();
		} catch (NullArgumentException e) {
			e.printStackTrace();
		} catch (MissingDataException e) {
			e.printStackTrace();
		} catch (MissingFileException e) {
			e.printStackTrace();
		}
	}

	public static void main(String[] args) {
		Tester tester = new Tester();
		String numOfClusters = "16";
		tester.numOfClusters = Integer.parseInt(numOfClusters);
		tester.analyze();
	}

}