@FXML
public void compute() {
double[][] transformedTrajectory = getTransformedTrajectory();
if (transformedTrajectory == null) return;
// compute and display RP
BufferedImage rp =
DRQA.getRPImage(
transformedTrajectory, transformedTrajectory, recurrenceThresholdSlider.getValue());
rpImageView.setImage(SwingFXUtils.toFXImage(rp, null));
applyImageScale();
// compute and display CRT
DRQA.conditional_ww_limit = Integer.parseInt(crtLimit.getText());
DRQA.CRT_LOG_SCALE = logScaleCheckBox.isSelected();
drqa =
new DRQA(
transformedTrajectory, transformedTrajectory, recurrenceThresholdSlider.getValue());
BufferedImage crt = drqa.getCRTImage(DRQA.conditional_ww_limit, drqa.conditional_ww);
crtImageView.setImage(SwingFXUtils.toFXImage(crt, null));
String[] stats = drqa.crtStatistics().split("\t");
crtStats.setText(
String.format(
"mean row: %.2f\tmean col: %.2f\ncorrelation: %.2f\nmax row: %s\tmax col: %s\nlocal maxima: %s\nentropy: %.2f",
Double.parseDouble(stats[0]),
Double.parseDouble(stats[1]),
Double.parseDouble(stats[2]),
stats[3],
stats[4],
stats[5],
Double.parseDouble(stats[6])));
drqa.computeRQA(2, 2, 2);
rqaMeasures.setText(drqa.printableString(DRQA.STANDARD_RQA));
updateTimeSeriesChart();
updateDistanceDistributionChart();
updateLineLengthHistogram(
null, null, lineLengthTypeSelector.getSelectionModel().getSelectedIndex());
}
public void testClustering() {
final int numberOfTimeSeriesPerClass = 100;
final int timeSeriesLength = 1500;
final int sample_size = (9 * numberOfTimeSeriesPerClass) / 2;
final int numberOfResamples = 100;
final int maxIterations = 1000;
final int numberOfInitializations = 100;
// ------------------------------------
// compute DRQA measures
// ------------------------------------
TimeSeriesGenerator timeSeriesGenerator =
new TimeSeriesGenerator(numberOfTimeSeriesPerClass, timeSeriesLength, null);
ArrayList<DoublePoint> rqa_measures =
new ArrayList<DoublePoint>(9 * numberOfTimeSeriesPerClass);
System.out.println("Computing DRQA measures");
double[][][][] trajectories = timeSeriesGenerator.getAllTrajectories();
for (int system_idx = 0; system_idx < trajectories.length; system_idx++) {
System.out.println(String.format("System: %s", TimeSeriesGenerator.system_names[system_idx]));
double[][][] trajectory1 = trajectories[system_idx];
for (double[][] trajectory : trajectory1) {
DRQA drqa = new DRQA(trajectory, trajectory, 0.05);
drqa.computeRQA(2, 2, 2);
// rqa_measures[system_idx * numberOfTimeSeriesPerClass + i] = new
// DoublePoint(drqa.allRQAMeasures(DRQA.HistogramStatistic.values()));
rqa_measures.add(new DoublePoint(drqa.standardRQAMeasures()));
}
}
System.out.println(
String.format(
"Standard RQA of first point:\n%s", Arrays.toString(rqa_measures.get(0).getPoint())));
// ------------------------------------
// Cluster
// ------------------------------------
long before = System.currentTimeMillis();
RandomDataGenerator randomDataGenerator = new RandomDataGenerator();
final EuclideanDistance euclideanDistance = new EuclideanDistance();
ClusterEvaluator<DoublePoint> evaluator =
new SumOfClusterVariances<DoublePoint>(euclideanDistance);
ArrayList<DoublePoint> sample = new ArrayList<DoublePoint>(sample_size);
System.out.println(String.format("sample_size: %s", sample_size));
// generate random subsamples of the data set
for (int resample_idx = 0; resample_idx < numberOfResamples; resample_idx++) {
System.out.println(String.format("Resample: %s", resample_idx + 1));
// generate random sample of half size
Object[] randomSample = randomDataGenerator.nextSample(rqa_measures, sample_size);
sample.clear();
for (Object point : randomSample) sample.add((DoublePoint) point);
int bestK = 0;
double bestScore = Double.POSITIVE_INFINITY;
// cluster using different numbers of clusters
for (int k = 2; k < 17; k++) {
// seed the algorithm several times to have a better chance to escape local maxima
// this can also be done using
// org.apache.commons.math3.ml.clustering.MultiKMeansPlusPlusClusterer,
// but it doesn't return the actual scores, which is important in determining the number of
// clusters.
double bestInitScore = Double.POSITIVE_INFINITY;
for (int init_num = 0; init_num < numberOfInitializations; init_num++) {
KMeansPlusPlusClusterer<DoublePoint> clusterer =
new KMeansPlusPlusClusterer<DoublePoint>(
k, maxIterations, euclideanDistance, new ISAACRandom(init_num));
List<CentroidCluster<DoublePoint>> result = clusterer.cluster(sample);
final double score = evaluator.score(result);
bestInitScore = Math.min(bestInitScore, score);
} // for initializations
if (bestInitScore < bestScore) {
bestScore = bestInitScore;
bestK = k;
}
// for(CentroidCluster<DoublePoint> cc : result)
// System.out.println(Arrays.toString(cc.getCenter().getPoint()));
} // for number of clusters
System.out.println(String.format("Best k=%s, score=%s", bestK, bestScore));
} // for resampling
System.out.println(
String.format("Time for clustering: %s", System.currentTimeMillis() - before));
}
