SimpleKMeans kmeans = ... // your code ... Instances instances = kmeans.getClusterCentroids();
System.out.println("Saving best sub-quantizer in file.."); Instances clusterCentroids = bestClusterer.getClusterCentroids(); for (int j = 0; j < clusterCentroids.numInstances(); j++) { Instance centroid = clusterCentroids.instance(j);
Instances clusterCentroids = clusterer.getClusterCentroids(); for (int j = 0; j < clusterCentroids.numInstances(); j++) { Instance centroid = clusterCentroids.instance(j);
private void trainModel(Map<Long, Double> metricData) throws Exception { //Model has a single metric_value attribute Attribute value = new Attribute("metric_value"); FastVector attributes = new FastVector(); attributes.addElement(value); trainingData = new Instances("metric_value_data", attributes, 0); for (Double val : metricData.values()) { double[] valArray = new double[] { val }; Instance instance = new Instance(1.0, valArray); trainingData.add(instance); } //Create and train the model model = new SimpleKMeans(); model.setNumClusters(k); model.setMaxIterations(20); model.setPreserveInstancesOrder(true); model.buildClusterer(trainingData); clusterCentroids = model.getClusterCentroids(); centroidAssignments = model.getAssignments(); setMeanDistancesToCentroids(); }
private void trainModel(Map<Long, Double> metricData) throws Exception { //Model has a single metric_value attribute Attribute value = new Attribute("metric_value"); FastVector attributes = new FastVector(); attributes.addElement(value); trainingData = new Instances("metric_value_data", attributes, 0); for (Double val : metricData.values()) { double[] valArray = new double[] { val }; Instance instance = new Instance(1.0, valArray); trainingData.add(instance); } //Create and train the model model = new SimpleKMeans(); model.setNumClusters(k); model.setMaxIterations(20); model.setPreserveInstancesOrder(true); model.buildClusterer(trainingData); clusterCentroids = model.getClusterCentroids(); centroidAssignments = model.getAssignments(); setMeanDistancesToCentroids(); }
SimpleKMeans.KMEANS_PLUS_PLUS, SimpleKMeans.TAGS_SELECTION)); localKMeans.buildClusterer(sketchForRun); finalStartPointsForRuns.add(localKMeans.getClusterCentroids()); } catch (Exception ex) { throw new DistributedWekaException(ex);
m_priorsPrev = new double[m_num_clusters]; Instances centers = bestK.getClusterCentroids(); Instances stdD = bestK.getClusterStandardDevs(); double[][][] nominalCounts = bestK.getClusterNominalCounts();
m_priorsPrev = new double[m_num_clusters]; Instances centers = bestK.getClusterCentroids(); Instances stdD = bestK.getClusterStandardDevs(); double[][][] nominalCounts = bestK.getClusterNominalCounts();