2. Example Usage of parallised ART 2 A clustering in single machine precision.

Example Usage of ART 2-A clustering for a paralellised clustering in single machine precision.

To illustrate the library, an example of the application of paralellised clustering with simple machine precision is presented step by step

1. The first step is to import a float data matrix. The data matrix contains the inputs that need to be clustered.
   
2. Initialisation of an ExecutorService that can use up to 9 threads concurrently.
   
3. This is followed by the initialisation of 9 cluster tasks with different vigilance parameters.
   
4. All 9 tasks are received and executed in the thread pool.
   
5. The result of the invokeAll() call is stored in the variable tmpFuturesList. This is a list of futures objects representing the status and result of each task.
   
6. Finally, the result of each Future object can be accessed.
   
   float[][] tmpTestDataMatrix = FileUtil.importFloatDataMatrixFromTextFile(
        "src/test/resources/de/unijena/cheminf/clustering/art2a/Bit_Fingerprints.txt", ','); ExecutorService tmpExecutorService = Executors.newFixedThreadPool(9); // number of tasks List tmpClusteringTask = new LinkedList<>(); for (float tmpVigilanceParameter = 0.1f; tmpVigilanceParameter < 1.0f; tmpVigilanceParameter += 0.1f) {     Art2aClusteringTask tmpART2aFloatClusteringTask = new Art2aClusteringTask(             tmpVigilanceParameter, tmpTestDataMatrix, 100, true);     tmpClusteringTask.add(tmpART2aFloatClusteringTask); } List<Future> tmpFuturesList; tmpFuturesList = tmpExecutorService.invokeAll(tmpClusteringTask); for (Future tmpFuture : tmpFuturesList) {     IArt2aClusteringResult tmpClusteringResult = tmpFuture.get();     System.out.println("Vigilance parameter: " + tmpClusteringResult.getVigilanceParameter());     System.out.println("Number of detected clusters: " + tmpClusteringResult.getNumberOfDetectedClusters());     System.out.println("Get cluster indices in cluster 0: " + tmpClusteringResult.getClusterIndices(2));     System.out.println("Get cluster representatives for cluster 0: "         + tmpClusteringResult.getClusterRepresentatives(2)); }

Output:
Vigilance parameter: 0.1
Number of detected clusters: 6
Get cluster indices in cluster 0: {4, 6, 7, 9}
Get cluster representatives for cluster 0: 9

Vigilance parameter: 0.2
Number of detected clusters: 6
Get cluster indices in cluster 0: {4, 6, 7, 9}
Get cluster representatives for cluster 0: 9

Vigilance parameter: 0.3
Number of detected clusters: 6
Get cluster indices in cluster 0: {4, 6, 7, 9}
Get cluster representatives for cluster 0: 9

Vigilance parameter: 0.4
Number of detected clusters: 8
Get cluster indices in cluster 0: {6, 9}
Get cluster representatives for cluster 0: 9

Vigilance parameter: 0.5
Number of detected clusters: 9
Get cluster indices in cluster 0: {9}
Get cluster representatives for cluster 0: 9

Vigilance parameter: 0.6
Number of detected clusters: 10
Get cluster indices in cluster 0: {9}
Get cluster representatives for cluster 0: 9

Vigilance parameter: 0.7
Number of detected clusters: 10
Get cluster indices in cluster 0: {9}
Get cluster representatives for cluster 0: 9

Vigilance parameter: 0.8
Number of detected clusters: 10
Get cluster indices in cluster 0: {9}
Get cluster representatives for cluster 0: 9

Vigilance parameter: 0.9
Number of detected clusters: 10
Get cluster indices in cluster 0: {9}
Get cluster representatives for cluster 0: 9

INFORMATION: Paralellised clustering is also possible with double machine precision.