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Java Cluster类代码示例

原作者: [db:作者] 来自: [db:来源] 收藏 邀请

本文整理汇总了Java中org.dmg.pmml.clustering.Cluster类的典型用法代码示例。如果您正苦于以下问题：Java Cluster类的具体用法？Java Cluster怎么用？Java Cluster使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。



Cluster类属于org.dmg.pmml.clustering包，在下文中一共展示了Cluster类的13个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Java代码示例。

示例1: encodeModel

import org.dmg.pmml.clustering.Cluster; //导入依赖的package包/类
@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()
			.setId(String.valueOf(i))
			.setArray(PMMLUtil.createRealArray(VectorUtil.toList(clusterCenters[i])));

		clusters.add(cluster);
	}

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

	return new ClusteringModel(MiningFunction.CLUSTERING, ClusteringModel.ModelClass.CENTER_BASED, clusters.size(), ModelUtil.createMiningSchema(schema.getLabel()), comparisonMeasure, ClusteringModelUtil.createClusteringFields(schema.getFeatures()), clusters);
}
开发者ID:jpmml，项目名称:jpmml-sparkml，代码行数:22，代码来源:KMeansModelConverter.java


示例2: pmmlClusteringModel

import org.dmg.pmml.clustering.Cluster; //导入依赖的package包/类
private ClusteringModel pmmlClusteringModel(KMeansModel model,
                                            Map<Integer,Long> clusterSizesMap) {
  Vector[] clusterCenters = model.clusterCenters();

  List<ClusteringField> clusteringFields = new ArrayList<>();
  for (int i = 0; i < inputSchema.getNumFeatures(); i++) {
    if (inputSchema.isActive(i)) {
      FieldName fieldName = FieldName.create(inputSchema.getFeatureNames().get(i));
      ClusteringField clusteringField =
          new ClusteringField(fieldName).setCenterField(ClusteringField.CenterField.TRUE);
      clusteringFields.add(clusteringField);
    }
  }

  List<Cluster> clusters = new ArrayList<>(clusterCenters.length);
  for (int i = 0; i < clusterCenters.length; i++) {
    clusters.add(new Cluster().setId(Integer.toString(i))
                     .setSize(clusterSizesMap.get(i).intValue())
                     .setArray(AppPMMLUtils.toArray(clusterCenters[i].toArray())));
  }

  return new ClusteringModel(
      MiningFunction.CLUSTERING,
      ClusteringModel.ModelClass.CENTER_BASED,
      clusters.size(),
      AppPMMLUtils.buildMiningSchema(inputSchema),
      new ComparisonMeasure(ComparisonMeasure.Kind.DISTANCE).setMeasure(new SquaredEuclidean()),
      clusteringFields,
      clusters);
}
开发者ID:oncewang，项目名称:oryx2，代码行数:31，代码来源:KMeansUpdate.java


示例3: createOutput

import org.dmg.pmml.clustering.Cluster; //导入依赖的package包/类
static
public Output createOutput(FieldName name, DataType dataType, List<Cluster> clusters){
	List<OutputField> outputFields = new ArrayList<>();
	outputFields.add(ModelUtil.createPredictedField(name, DataType.STRING, OpType.CATEGORICAL));
	outputFields.addAll(ModelUtil.createAffinityFields(dataType, clusters));

	Output output = new Output(outputFields);

	return output;
}
开发者ID:jpmml，项目名称:jpmml-converter，代码行数:11，代码来源:ClusteringModelUtil.java


示例4: encodeModel

import org.dmg.pmml.clustering.Cluster; //导入依赖的package包/类
@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()
			.setId(String.valueOf(i))
			.setSize((labelCounts.size () > 0 ? labelCounts.count(i) : null))
			.setArray(PMMLUtil.createRealArray(CMatrixUtil.getRow(clusterCenters, numberOfClusters, numberOfFeatures, i)));

		clusters.add(cluster);
	}

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

	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;
}
开发者ID:jpmml，项目名称:jpmml-sklearn，代码行数:37，代码来源:KMeans.java


示例5: encodeModel

import org.dmg.pmml.clustering.Cluster; //导入依赖的package包/类
@Override
public Model encodeModel(Schema schema){
	RGenericVector kmeans = getObject();

	RDoubleVector centers = (RDoubleVector)kmeans.getValue("centers");
	RIntegerVector size = (RIntegerVector)kmeans.getValue("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()
			.setId(String.valueOf(i + 1))
			.setName(rowNames.getValue(i))
			.setSize(size.getValue(i))
			.setArray(PMMLUtil.createRealArray(FortranMatrixUtil.getRow(centers.getValues(), rows, columns, i)));

		clusters.add(cluster);
	}

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

	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;
}
开发者ID:jpmml，项目名称:jpmml-r，代码行数:35，代码来源:KMeansConverter.java


示例6: getEntityRegistry

import org.dmg.pmml.clustering.Cluster; //导入依赖的package包/类
@Override
public BiMap<String, Cluster> getEntityRegistry(){

	if(this.entityRegistry == null){
		this.entityRegistry = getValue(ClusteringModelEvaluator.entityCache);
	}

	return this.entityRegistry;
}
开发者ID:jpmml，项目名称:jpmml-evaluator，代码行数:10，代码来源:ClusteringModelEvaluator.java


示例7: evaluateDistance

import org.dmg.pmml.clustering.Cluster; //导入依赖的package包/类
private <V extends Number> ClusterAffinityDistribution<V> evaluateDistance(ValueFactory<V> valueFactory, ComparisonMeasure comparisonMeasure, List<ClusteringField> clusteringFields, List<FieldValue> values){
	ClusteringModel clusteringModel = getModel();

	List<Cluster> clusters = clusteringModel.getClusters();

	Value<V> adjustment;

	MissingValueWeights missingValueWeights = clusteringModel.getMissingValueWeights();
	if(missingValueWeights != null){
		Array array = missingValueWeights.getArray();

		List<? extends Number> adjustmentValues = ArrayUtil.asNumberList(array);
		if(values.size() != adjustmentValues.size()){
			throw new InvalidElementException(missingValueWeights);
		}

		adjustment = MeasureUtil.calculateAdjustment(valueFactory, values, adjustmentValues);
	} else

	{
		adjustment = MeasureUtil.calculateAdjustment(valueFactory, values);
	}

	ClusterAffinityDistribution<V> result = createClusterAffinityDistribution(Classification.Type.DISTANCE, clusters);

	for(Cluster cluster : clusters){
		List<FieldValue> clusterValues = CacheUtil.getValue(cluster, ClusteringModelEvaluator.clusterValueCache);

		if(values.size() != clusterValues.size()){
			throw new InvalidElementException(cluster);
		}

		Value<V> distance = MeasureUtil.evaluateDistance(valueFactory, comparisonMeasure, clusteringFields, values, clusterValues, adjustment);

		result.put(cluster, distance);
	}

	return result;
}
开发者ID:jpmml，项目名称:jpmml-evaluator，代码行数:40，代码来源:ClusteringModelEvaluator.java


示例8: createClusterAffinityDistribution

import org.dmg.pmml.clustering.Cluster; //导入依赖的package包/类
private <V extends Number> ClusterAffinityDistribution<V> createClusterAffinityDistribution(Classification.Type type, List<Cluster> clusters){
	ClusterAffinityDistribution<V> result = new ClusterAffinityDistribution<V>(type, new ValueMap<String, V>(2 * clusters.size())){

		@Override
		public BiMap<String, Cluster> getEntityRegistry(){
			return ClusteringModelEvaluator.this.getEntityRegistry();
		}
	};

	return result;
}
开发者ID:jpmml，项目名称:jpmml-evaluator，代码行数:12，代码来源:ClusteringModelEvaluator.java


示例9: load

import org.dmg.pmml.clustering.Cluster; //导入依赖的package包/类
@Override
public List<FieldValue> load(Cluster cluster){
	Array array = cluster.getArray();

	List<? extends Number> values = ArrayUtil.asNumberList(array);

	return ImmutableList.copyOf(FieldValueUtil.createAll(null, OpType.CONTINUOUS, values));
}
开发者ID:jpmml，项目名称:jpmml-evaluator，代码行数:9，代码来源:ClusteringModelEvaluator.java


示例10: buildDummyClusteringModel

import org.dmg.pmml.clustering.Cluster; //导入依赖的package包/类
public static PMML buildDummyClusteringModel() {
  PMML pmml = PMMLUtils.buildSkeletonPMML();

  List<DataField> dataFields = new ArrayList<>();
  dataFields.add(new DataField(FieldName.create("x"), OpType.CONTINUOUS, DataType.DOUBLE));
  dataFields.add(new DataField(FieldName.create("y"), OpType.CONTINUOUS, DataType.DOUBLE));
  DataDictionary dataDictionary =
      new DataDictionary(dataFields).setNumberOfFields(dataFields.size());
  pmml.setDataDictionary(dataDictionary);

  List<MiningField> miningFields = new ArrayList<>();
  MiningField xMF = new MiningField(FieldName.create("x"))
      .setOpType(OpType.CONTINUOUS).setUsageType(MiningField.UsageType.ACTIVE);
  miningFields.add(xMF);
  MiningField yMF = new MiningField(FieldName.create("y"))
      .setOpType(OpType.CONTINUOUS).setUsageType(MiningField.UsageType.ACTIVE);
  miningFields.add(yMF);
  MiningSchema miningSchema = new MiningSchema(miningFields);

  List<ClusteringField> clusteringFields = new ArrayList<>();
  clusteringFields.add(new ClusteringField(
      FieldName.create("x")).setCenterField(ClusteringField.CenterField.TRUE));
  clusteringFields.add(new ClusteringField(
      FieldName.create("y")).setCenterField(ClusteringField.CenterField.TRUE));

  List<Cluster> clusters = new ArrayList<>();
  clusters.add(new Cluster().setId("0").setSize(1).setArray(AppPMMLUtils.toArray(1.0, 0.0)));
  clusters.add(new Cluster().setId("1").setSize(2).setArray(AppPMMLUtils.toArray(2.0, -1.0)));
  clusters.add(new Cluster().setId("2").setSize(3).setArray(AppPMMLUtils.toArray(-1.0, 0.0)));

  pmml.addModels(new ClusteringModel(
      MiningFunction.CLUSTERING,
      ClusteringModel.ModelClass.CENTER_BASED,
      clusters.size(),
      miningSchema,
      new ComparisonMeasure(ComparisonMeasure.Kind.DISTANCE).setMeasure(new SquaredEuclidean()),
      clusteringFields,
      clusters));

  return pmml;
}
开发者ID:oncewang，项目名称:oryx2，代码行数:42，代码来源:KMeansPMMLUtilsTest.java


示例11: testKMeans

import org.dmg.pmml.clustering.Cluster; //导入依赖的package包/类
@Test
public void testKMeans() throws Exception {
  Path tempDir = getTempDir();
  Path dataDir = tempDir.resolve("data");
  Path modelDir = tempDir.resolve("model");

  Map<String,Object> overlayConfig = new HashMap<>();
  overlayConfig.put("oryx.batch.update-class", KMeansUpdate.class.getName());
  ConfigUtils.set(overlayConfig, "oryx.batch.storage.data-dir", dataDir);
  ConfigUtils.set(overlayConfig, "oryx.batch.storage.model-dir", modelDir);
  overlayConfig.put("oryx.batch.streaming.generation-interval-sec", GEN_INTERVAL_SEC);
  overlayConfig.put("oryx.kmeans.hyperparams.k", NUM_CLUSTERS);
  overlayConfig.put("oryx.kmeans.iterations", 5);
  overlayConfig.put("oryx.input-schema.num-features", NUM_FEATURES);
  overlayConfig.put("oryx.input-schema.categorical-features", "[]");
  overlayConfig.put("oryx.kmeans.evaluation-strategy", EVALUATION_STRATEGY);

  Config config = ConfigUtils.overlayOn(overlayConfig, getConfig());

  startMessaging();

  List<KeyMessage<String, String>> updates = startServerProduceConsumeTopics(
      config,
      new RandomKMeansDataGenerator(NUM_FEATURES),
      DATA_TO_WRITE,
      WRITE_INTERVAL_MSEC);

  List<Path> modelInstanceDirs = IOUtils.listFiles(modelDir, "*");

  int generations = modelInstanceDirs.size();
  checkIntervals(generations, DATA_TO_WRITE, WRITE_INTERVAL_MSEC, GEN_INTERVAL_SEC);

  for (Path modelInstanceDir : modelInstanceDirs) {
    Path modelFile = modelInstanceDir.resolve(MLUpdate.MODEL_FILE_NAME);
    assertNonEmpty(modelFile);
    PMMLUtils.read(modelFile); // Shouldn't throw exception
  }

  InputSchema schema = new InputSchema(config);

  for (KeyMessage<String,String> km : updates) {

    String type = km.getKey();
    String value = km.getMessage();

    assertContains(Arrays.asList("MODEL", "MODEL-REF"), type);
    PMML pmml = AppPMMLUtils.readPMMLFromUpdateKeyMessage(type, value, null);

    checkHeader(pmml.getHeader());

    checkDataDictionary(schema, pmml.getDataDictionary());

    Model rootModel = pmml.getModels().get(0);

    ClusteringModel clusteringModel = (ClusteringModel) rootModel;

    // Check if Basic hyperparameters match
    assertEquals(NUM_CLUSTERS, clusteringModel.getNumberOfClusters());
    assertEquals(NUM_CLUSTERS, clusteringModel.getClusters().size());
    assertEquals(NUM_FEATURES, clusteringModel.getClusteringFields().size());
    assertEquals(ComparisonMeasure.Kind.DISTANCE,
                 clusteringModel.getComparisonMeasure().getKind());
    assertEquals(NUM_FEATURES, clusteringModel.getClusters().get(0).getArray().getN().intValue());
    for (Cluster cluster : clusteringModel.getClusters()) {
      assertGreater(cluster.getSize(), 0);
    }
  }
}
开发者ID:oncewang，项目名称:oryx2，代码行数:69，代码来源:KMeansUpdateIT.java


示例12: getDisplayValue

import org.dmg.pmml.clustering.Cluster; //导入依赖的package包/类
@Override
public String getDisplayValue(){
	Cluster cluster = getEntity();

	return cluster.getName();
}
开发者ID:jpmml，项目名称:jpmml-evaluator，代码行数:7，代码来源:ClusterAffinityDistribution.java


示例13: evaluateSimilarity

import org.dmg.pmml.clustering.Cluster; //导入依赖的package包/类
private <V extends Number> ClusterAffinityDistribution<V> evaluateSimilarity(ValueFactory<V> valueFactory, ComparisonMeasure comparisonMeasure, List<ClusteringField> clusteringFields, List<FieldValue> values){
	ClusteringModel clusteringModel = getModel();

	List<Cluster> clusters = clusteringModel.getClusters();

	ClusterAffinityDistribution<V> result = createClusterAffinityDistribution(Classification.Type.SIMILARITY, clusters);

	BitSet flags = MeasureUtil.toBitSet(values);

	for(Cluster cluster : clusters){
		BitSet clusterFlags = CacheUtil.getValue(cluster, ClusteringModelEvaluator.clusterFlagCache);

		if(flags.size() != clusterFlags.size()){
			throw new InvalidElementException(cluster);
		}

		Value<V> similarity = MeasureUtil.evaluateSimilarity(valueFactory, comparisonMeasure, clusteringFields, flags, clusterFlags);

		result.put(cluster, similarity);
	}

	return result;
}
开发者ID:jpmml，项目名称:jpmml-evaluator，代码行数:24，代码来源:ClusteringModelEvaluator.java



注：本文中的org.dmg.pmml.clustering.Cluster类示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。

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