@Test public void testMap() { GmmPartitionData data = new GmmPartitionData( Arrays.asList( new LabeledVector<>(VectorUtils.of(1, 0), 0.), new LabeledVector<>(VectorUtils.of(0, 1), 0.), new LabeledVector<>(VectorUtils.of(1, 1), 0.) ), new double[][] { new double[] {0.5, 0.1}, new double[] {1.0, 0.4}, new double[] {0.3, 0.2} } ); List<MeanWithClusterProbAggregator> res = MeanWithClusterProbAggregator.map(data, 2); assertEquals(2, res.size()); MeanWithClusterProbAggregator agg1 = res.get(0); assertEquals(0.6, agg1.clusterProb(), 1e-2); assertArrayEquals(new double[] {0.44, 0.72}, agg1.mean().asArray(), 1e-2); MeanWithClusterProbAggregator agg2 = res.get(1); assertEquals(0.23, agg2.clusterProb(), 1e-2); assertArrayEquals(new double[] {0.42, 0.85}, agg2.mean().asArray(), 1e-2); }
static List<MeanWithClusterProbAggregator> map(GmmPartitionData data, int countOfComponents) { List<MeanWithClusterProbAggregator> aggregators = new ArrayList<>(); for (int i = 0; i < countOfComponents; i++) aggregators.add(new MeanWithClusterProbAggregator()); for (int i = 0; i < data.size(); i++) { for (int c = 0; c < countOfComponents; c++) aggregators.get(c).add(data.getX(i), data.pcxi(c, i)); } return aggregators; }
MeanWithClusterProbAggregator implements Serializable { static List<MeanWithClusterProbAggregator> map(GmmPartitionData data, int countOfComponents) { List<MeanWithClusterProbAggregator> aggregators = new ArrayList<>(); for (int i = 0; i < countOfComponents; i++) aggregators.add(new MeanWithClusterProbAggregator()); for (int i = 0; i < data.size(); i++) { for (int c = 0; c < countOfComponents; c++) aggregators.get(c).add(data.getX(i), data.pcxi(c, i)); } return aggregators; } }
MeanWithClusterProbAggregator implements Serializable { static List<MeanWithClusterProbAggregator> map(GmmPartitionData data, int countOfComponents) { List<MeanWithClusterProbAggregator> aggregators = new ArrayList<>(); for (int i = 0; i < countOfComponents; i++) aggregators.add(new MeanWithClusterProbAggregator()); for (int i = 0; i < data.size(); i++) { for (int c = 0; c < countOfComponents; c++) aggregators.get(c).add(data.getX(i), data.pcxi(c, i)); } return aggregators; } MeanWithClusterProbAggregator();   MeanWithClusterProbAggregator(Vector weightedXsSum, double pcxiSum, int rowCount); }
MeanWithClusterProbAggregator implements Serializable { static List<MeanWithClusterProbAggregator> map(GmmPartitionData data, int countOfComponents) { List<MeanWithClusterProbAggregator> aggregators = new ArrayList<>(); for (int i = 0; i < countOfComponents; i++) aggregators.add(new MeanWithClusterProbAggregator()); for (int i = 0; i < data.size(); i++) { for (int c = 0; c < countOfComponents; c++) aggregators.get(c).add(data.getX(i), data.pcxi(c, i)); } return aggregators; } MeanWithClusterProbAggregator();   MeanWithClusterProbAggregator(Vector weightedXsSum, double pcxiSum, int rowCount); Vector mean(); double clusterProb(); static AggregatedStats aggreateStats(Dataset<EmptyContext, GmmPartitionData> dataset, int countOfComponents); }
MeanWithClusterProbAggregator implements Serializable { static List<MeanWithClusterProbAggregator> map(GmmPartitionData data, int countOfComponents) { List<MeanWithClusterProbAggregator> aggregators = new ArrayList<>(); for (int i = 0; i < countOfComponents; i++) aggregators.add(new MeanWithClusterProbAggregator()); for (int i = 0; i < data.size(); i++) { for (int c = 0; c < countOfComponents; c++) aggregators.get(c).add(data.getX(i), data.pcxi(c, i)); } return aggregators; } MeanWithClusterProbAggregator();   MeanWithClusterProbAggregator(Vector weightedXsSum, double pcxiSum, int rowCount); Vector mean(); double clusterProb(); static AggregatedStats aggreateStats(Dataset<EmptyContext, GmmPartitionData> dataset, int countOfComponents);  }