clusterer.setNumExecutionSlots(numSlots); clusterer.setNumClusters(numProductCentroids); clusterer.setMaxIterations(maxIterations);
clusterer.setSeed(seed); clusterer.setNumClusters(numClusters); clusterer.setMaxIterations(maxIterations); clusterer.setNumExecutionSlots(numSlots); clusterer.setFastDistanceCalc(true);
setMaxIterations(Integer.parseInt(optionString));
setMaxIterations(Integer.parseInt(optionString));
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(); }