Java DBIDs.isEmpty方法代码示例

import de.lmu.ifi.dbs.elki.database.ids.DBIDs; //导入方法依赖的package包/类
/**
 * Inserts the specified objects into this index. If a bulk load mode is
 * implemented, the objects are inserted in one bulk.
 * 
 * @param ids the objects to be inserted
 */
@Override
public final void insertAll(DBIDs ids) {
  if(ids.isEmpty() || (ids.size() == 1)) {
    return;
  }

  // Make an example leaf
  if(canBulkLoad()) {
    List<SpatialEntry> leafs = new ArrayList<>(ids.size());
    for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
      leafs.add(createNewLeafEntry(DBIDUtil.deref(iter)));
    }
    bulkLoad(leafs);
  }
  else {
    for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
      insert(iter);
    }
  }

  doExtraIntegrityChecks();
}
 

import de.lmu.ifi.dbs.elki.database.ids.DBIDs; //导入方法依赖的package包/类
/**
 * Inserts the specified objects into this index. If a bulk load mode is
 * implemented, the objects are inserted in one bulk.
 *
 * @param ids the objects to be inserted
 */
@Override
public final void insertAll(DBIDs ids) {
  if(ids.isEmpty() || (ids.size() == 1)) {
    return;
  }

  // Make an example leaf
  if(canBulkLoad()) {
    List<RdKNNEntry> leafs = new ArrayList<>(ids.size());
    for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
      leafs.add(createNewLeafEntry(DBIDUtil.deref(iter)));
    }
    bulkLoad(leafs);
  }
  else {
    for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
      insert(iter);
    }
  }

  doExtraIntegrityChecks();
}
 

import de.lmu.ifi.dbs.elki.database.ids.DBIDs; //导入方法依赖的package包/类
/**
 * Inserts the specified objects into this index. If a bulk load mode is
 * implemented, the objects are inserted in one bulk.
 * 
 * @param ids the objects to be inserted
 */
@Override
public void insertAll(DBIDs ids) {
  if(ids.isEmpty() || (ids.size() == 1)) {
    return;
  }

  // Make an example leaf
  if(canBulkLoad()) {
    List<SpatialEntry> leafs = new ArrayList<>(ids.size());
    for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
      leafs.add(createNewLeafEntry(iter));
    }
    bulkLoad(leafs);
  }
  else {
    for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
      insert(DBIDUtil.deref(iter));
    }
  }

  doExtraIntegrityChecks();
}
 

import de.lmu.ifi.dbs.elki.database.ids.DBIDs; //导入方法依赖的package包/类
/**
 * Inserts the specified objects into this index. If a bulk load mode is
 * implemented, the objects are inserted in one bulk.
 * 
 * @param ids the objects to be inserted
 */
@Override
public final void insertAll(DBIDs ids) {
  if(ids.isEmpty() || (ids.size() == 1)) {
    return;
  }

  // Make an example leaf
  if(canBulkLoad()) {
    List<DeLiCluEntry> leafs = new ArrayList<>(ids.size());
    for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
      leafs.add(createNewLeafEntry(DBIDUtil.deref(iter)));
    }
    bulkLoad(leafs);
  }
  else {
    for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
      insert(iter);
    }
  }

  doExtraIntegrityChecks();
}
 

import de.lmu.ifi.dbs.elki.database.ids.DBIDs; //导入方法依赖的package包/类
/**
 * Make the cluster for the given object
 *
 * @param lead Leading object
 * @param depth Linkage depth
 * @param members Member objects
 * @return Cluster
 */
protected Cluster<DendrogramModel> makeCluster(DBIDRef lead, double depth, DBIDs members) {
  final String name;
  if(members == null || (members.size() == 1 && members.contains(lead))) {
    name = "obj_" + DBIDUtil.toString(lead);
  }
  else if(members.size() == 0) {
    name = "mrg_" + DBIDUtil.toString(lead) + "_" + depth;
  }
  else if(depth < Double.POSITIVE_INFINITY) {
    name = "clu_" + DBIDUtil.toString(lead) + "_" + depth;
  }
  else {
    // Complete data set only?
    name = "top_" + DBIDUtil.toString(lead);
  }

  DendrogramModel model;
  if(members != null && !members.isEmpty() && pointerresult instanceof PointerPrototypeHierarchyRepresentationResult) {
    model = new PrototypeDendrogramModel(depth, ((PointerPrototypeHierarchyRepresentationResult) pointerresult).findPrototype(members));
  }
  else {
    model = new DendrogramModel(depth);
  }
  return new Cluster<>(name, members, model);
}
 

import de.lmu.ifi.dbs.elki.database.ids.DBIDs; //导入方法依赖的package包/类
/**
 * Inserts the specified objects into this index. If a bulk load mode is
 * implemented, the objects are inserted in one bulk.
 * 
 * @param objects the objects to be inserted
 */
@Override
public final void insertAll(DBIDs ids) {
  if(ids.isEmpty() || (ids.size() == 1)) {
    return;
  }

  // Make an example leaf
  if(canBulkLoad()) {
    List<SpatialEntry> leafs = new ArrayList<>(ids.size());
    for(DBIDIter id = ids.iter(); id.valid(); id.advance()) {
      leafs.add(createNewLeafEntry(id));
    }
    bulkLoad(leafs);
  }
  else {
    for(DBIDIter id = ids.iter(); id.valid(); id.advance()) {
      insert(id);
    }
  }

  doExtraIntegrityChecks();
}
 

import de.lmu.ifi.dbs.elki.database.ids.DBIDs; //导入方法依赖的package包/类
/**
 * Computes the loglikelihood of all normal objects. Gaussian model
 *
 * @param objids Object IDs for 'normal' objects.
 * @param relation Database
 * @return loglikelihood for normal objects
 */
private double loglikelihoodNormal(DBIDs objids, Relation<V> relation) {
  if(objids.isEmpty()) {
    return 0;
  }
  CovarianceMatrix builder = CovarianceMatrix.make(relation, objids);
  double[] mean = builder.getMeanVector();
  double[][] covarianceMatrix = builder.destroyToSampleMatrix();

  // test singulaere matrix
  double[][] covInv = inverse(covarianceMatrix);

  double covarianceDet = new LUDecomposition(covarianceMatrix).det();
  double fakt = 1.0 / FastMath.sqrt(MathUtil.powi(MathUtil.TWOPI, RelationUtil.dimensionality(relation)) * covarianceDet);
  // for each object compute probability and sum
  double prob = 0;
  for(DBIDIter iter = objids.iter(); iter.valid(); iter.advance()) {
    double[] x = minusEquals(relation.get(iter).toArray(), mean);
    double mDist = transposeTimesTimes(x, covInv, x);
    prob += FastMath.log(fakt * FastMath.exp(-mDist * .5));
  }
  return prob;
}
 

import de.lmu.ifi.dbs.elki.database.ids.DBIDs; //导入方法依赖的package包/类
/**
 * Make the cluster for the given object
 *
 * @param lead Leading object
 * @param depth Linkage depth
 * @param members Member objects
 * @return Cluster
 */
protected Cluster<DendrogramModel> makeCluster(DBIDRef lead, double depth, DBIDs members) {
  final String name;
  if(members == null || members.size() == 1 && members.contains(lead)) {
    name = "obj_" + DBIDUtil.toString(lead);
    if (members == null) {
      ArrayModifiableDBIDs m = DBIDUtil.newArray(1);
      m.add(lead);
      members = m;
    }
  }
  else if(members.size() == 0) {
    name = "mrg_" + DBIDUtil.toString(lead) + "_" + depth;
  }
  else if(depth < Double.POSITIVE_INFINITY) {
    name = "clu_" + DBIDUtil.toString(lead) + "_" + depth;
  }
  else {
    // Complete data set only?
    name = "top_" + DBIDUtil.toString(lead);
  }

  DendrogramModel model;
  if(members != null && !members.isEmpty() && pointerresult instanceof PointerPrototypeHierarchyRepresentationResult) {
    model = new PrototypeDendrogramModel(depth, ((PointerPrototypeHierarchyRepresentationResult) pointerresult).findPrototype(members));
  }
  else {
    model = new DendrogramModel(depth);
  }
  return new Cluster<>(name, members, model);
}
 

import de.lmu.ifi.dbs.elki.database.ids.DBIDs; //导入方法依赖的package包/类
/**
 * Run the clustering.
 *
 * @param database the Database
 * @param relation the Relation
 * @return Clustering result
 */
public Clustering<?> run(final Database database, final Relation<DiscreteUncertainObject> relation) {
  if(relation.size() <= 0) {
    return new Clustering<>("Uk-Means Clustering", "ukmeans-clustering");
  }
  // Choose initial means randomly
  DBIDs sampleids = DBIDUtil.randomSample(relation.getDBIDs(), k, rnd);
  List<double[]> means = new ArrayList<>(k);
  for(DBIDIter iter = sampleids.iter(); iter.valid(); iter.advance()) {
    means.add(ArrayLikeUtil.toPrimitiveDoubleArray(relation.get(iter).getCenterOfMass()));
  }

  // Setup cluster assignment store
  List<ModifiableDBIDs> clusters = new ArrayList<>();
  for(int i = 0; i < k; i++) {
    clusters.add(DBIDUtil.newHashSet((int) (relation.size() * 2. / k)));
  }
  WritableIntegerDataStore assignment = DataStoreUtil.makeIntegerStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT, -1);
  double[] varsum = new double[k];

  IndefiniteProgress prog = LOG.isVerbose() ? new IndefiniteProgress("UK-Means iteration", LOG) : null;
  DoubleStatistic varstat = LOG.isStatistics() ? new DoubleStatistic(this.getClass().getName() + ".variance-sum") : null;
  int iteration = 0;
  for(; maxiter <= 0 || iteration < maxiter; iteration++) {
    LOG.incrementProcessed(prog);
    boolean changed = assignToNearestCluster(relation, means, clusters, assignment, varsum);
    logVarstat(varstat, varsum);
    // Stop if no cluster assignment changed.
    if(!changed) {
      break;
    }
    // Recompute means.
    means = means(clusters, means, relation);
  }
  LOG.setCompleted(prog);
  if(LOG.isStatistics()) {
    LOG.statistics(new LongStatistic(KEY + ".iterations", iteration));
  }

  // Wrap result
  Clustering<KMeansModel> result = new Clustering<>("Uk-Means Clustering", "ukmeans-clustering");
  for(int i = 0; i < clusters.size(); i++) {
    DBIDs ids = clusters.get(i);
    if(ids.isEmpty()) {
      continue;
    }
    result.addToplevelCluster(new Cluster<>(ids, new KMeansModel(means.get(i), varsum[i])));
  }
  return result;
}