org.jpmml.converter.clustering.ClusteringModelUtil Java Examples

The following examples show how to use org.jpmml.converter.clustering.ClusteringModelUtil. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.

Example #1

Source File: KMeansModelConverter.java From jpmml-sparkml with GNU Affero General Public License v3.0

6 votes

@Override
public ClusteringModel encodeModel(Schema schema){
	KMeansModel model = getTransformer();

	List<Cluster> clusters = new ArrayList<>();

	Vector[] clusterCenters = model.clusterCenters();
	for(int i = 0; i < clusterCenters.length; i++){
		Cluster cluster = new Cluster(PMMLUtil.createRealArray(VectorUtil.toList(clusterCenters[i])))
			.setId(String.valueOf(i));

		clusters.add(cluster);
	}

	ComparisonMeasure comparisonMeasure = new ComparisonMeasure(ComparisonMeasure.Kind.DISTANCE, new SquaredEuclidean())
		.setCompareFunction(CompareFunction.ABS_DIFF);

	return new ClusteringModel(MiningFunction.CLUSTERING, ClusteringModel.ModelClass.CENTER_BASED, clusters.size(), ModelUtil.createMiningSchema(schema.getLabel()), comparisonMeasure, ClusteringModelUtil.createClusteringFields(schema.getFeatures()), clusters);
}

Example #2

Source File: KMeans.java From jpmml-sklearn with GNU Affero General Public License v3.0

5 votes

@Override
public ClusteringModel encodeModel(Schema schema){
	int[] shape = getClusterCentersShape();

	int numberOfClusters = shape[0];
	int numberOfFeatures = shape[1];

	List<? extends Number> clusterCenters = getClusterCenters();
	List<Integer> labels = getLabels();

	Multiset<Integer> labelCounts = HashMultiset.create();

	if(labels != null){
		labelCounts.addAll(labels);
	}

	List<Cluster> clusters = new ArrayList<>();

	for(int i = 0; i < numberOfClusters; i++){
		Cluster cluster = new Cluster(PMMLUtil.createRealArray(CMatrixUtil.getRow(clusterCenters, numberOfClusters, numberOfFeatures, i)))
			.setId(String.valueOf(i))
			.setSize((labelCounts.size () > 0 ? labelCounts.count(i) : null));

		clusters.add(cluster);
	}

	ComparisonMeasure comparisonMeasure = new ComparisonMeasure(ComparisonMeasure.Kind.DISTANCE, new SquaredEuclidean())
		.setCompareFunction(CompareFunction.ABS_DIFF);

	ClusteringModel clusteringModel = new ClusteringModel(MiningFunction.CLUSTERING, ClusteringModel.ModelClass.CENTER_BASED, numberOfClusters, ModelUtil.createMiningSchema(schema.getLabel()), comparisonMeasure, ClusteringModelUtil.createClusteringFields(schema.getFeatures()), clusters)
		.setOutput(ClusteringModelUtil.createOutput(FieldName.create("Cluster"), DataType.DOUBLE, clusters));

	return clusteringModel;
}

Example #3

Source File: KMeansConverter.java From jpmml-r with GNU Affero General Public License v3.0

5 votes

@Override
public Model encodeModel(Schema schema){
	RGenericVector kmeans = getObject();

	RDoubleVector centers = kmeans.getDoubleElement("centers");
	RIntegerVector size = kmeans.getIntegerElement("size");

	RIntegerVector centersDim = centers.dim();

	int rows = centersDim.getValue(0);
	int columns = centersDim.getValue(1);

	List<Cluster> clusters = new ArrayList<>();

	RStringVector rowNames = centers.dimnames(0);
	for(int i = 0; i < rowNames.size(); i++){
		Cluster cluster = new Cluster(PMMLUtil.createRealArray(FortranMatrixUtil.getRow(centers.getValues(), rows, columns, i)))
			.setId(String.valueOf(i + 1))
			.setName(rowNames.getValue(i))
			.setSize(size.getValue(i));

		clusters.add(cluster);
	}

	ComparisonMeasure comparisonMeasure = new ComparisonMeasure(ComparisonMeasure.Kind.DISTANCE, new SquaredEuclidean())
		.setCompareFunction(CompareFunction.ABS_DIFF);

	ClusteringModel clusteringModel = new ClusteringModel(MiningFunction.CLUSTERING, ClusteringModel.ModelClass.CENTER_BASED, rows, ModelUtil.createMiningSchema(schema.getLabel()), comparisonMeasure, ClusteringModelUtil.createClusteringFields(schema.getFeatures()), clusters)
		.setOutput(ClusteringModelUtil.createOutput(FieldName.create("cluster"), DataType.DOUBLE, clusters));

	return clusteringModel;
}