Class can be "static" (improves encapsulation).
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@ -119,7 +119,8 @@ public class MiniBatchKMeansClusterer<T extends Clusterable>
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final List<T> pointList = new ArrayList<>(points);
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List<CentroidCluster<T>> clusters = initialCenters(pointList);
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final MiniBatchImprovementEvaluator evaluator = new MiniBatchImprovementEvaluator();
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final ImprovementEvaluator evaluator = new ImprovementEvaluator(batchSize,
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maxNoImprovementTimes);
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for (int i = 0; i < max; i++) {
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clearClustersPoints(clusters);
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final List<T> batchPoints = ListSampler.sample(getRandomGenerator(), pointList, batchSize);
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@ -238,7 +239,11 @@ public class MiniBatchKMeansClusterer<T extends Clusterable>
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* The evaluator checks whether improvement occurred during the
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* {@link #maxNoImprovementTimes allowed number of successive iterations}.
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*/
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private class MiniBatchImprovementEvaluator {
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private static class ImprovementEvaluator {
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/** Batch size. */
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private final int batchSize;
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/** Maximum number of iterations during which no improvement is occuring. */
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private final int maxNoImprovementTimes;
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/** Missing doc. */
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private double ewaInertia = Double.NaN;
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/** Missing doc. */
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@ -246,6 +251,17 @@ public class MiniBatchKMeansClusterer<T extends Clusterable>
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/** Missing doc. */
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private int noImprovementTimes = 0;
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/**
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* @param batchSize Number of elements for each batch iteration.
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* @param maxNoImprovementTimes Maximum number of iterations during
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* which no improvement is occuring.
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*/
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private ImprovementEvaluator(int batchSize,
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int maxNoImprovementTimes) {
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this.batchSize = batchSize;
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this.maxNoImprovementTimes = maxNoImprovementTimes;
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}
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/**
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* Stopping criterion.
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*
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