de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeans.run()方法的使用及代码示例

x33g5p2x  于2022-01-24 转载在 其他  
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本文整理了Java中de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeans.run()方法的一些代码示例，展示了KMeans.run()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。KMeans.run()方法的具体详情如下：
包路径：de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeans
类名称：KMeans
方法名：run

KMeans.run介绍

[英]Run the clustering algorithm.
[中]运行聚类算法。

代码示例

代码示例来源：origin: elki-project/elki

LOG.statistics(new StringStatistic(KEY + ".initialization", initializer.toString()));
splitInitializer.setInitialMeans(initializer.chooseInitialMeans(database, relation, k_min, getDistanceFunction()));
Clustering<M> clustering = innerKMeans.run(database, relation);
 clustering = innerKMeans.run(database, relation);
 clusters.clear();
 clusters.addAll(clustering.getAllClusters());

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki-clustering

LOG.statistics(new StringStatistic(KEY + ".initialization", initializer.toString()));
splitInitializer.setInitialMeans(initializer.chooseInitialMeans(database, relation, k_min, getDistanceFunction()));
Clustering<M> clustering = innerKMeans.run(database, relation);
 clustering = innerKMeans.run(database, relation);
 clusters.clear();
 clusters.addAll(clustering.getAllClusters());

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki

Clustering<M> clustering = innerKMeans.run(database, relation);
 clustering = innerKMeans.run(database, relation);
 clusters.clear();
 clusters.addAll(clustering.getAllClusters());

代码示例来源：origin: elki-project/elki

Clustering<M> innerResult = innerkMeans.run(proxyDB);

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki-clustering

Clustering<M> innerResult = innerkMeans.run(proxyDB);

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki

@Override
public <T extends V, O extends NumberVector> List<O> chooseInitialMeans(Database database, Relation<T> relation, int k, NumberVectorDistanceFunction<? super T> distanceFunction, NumberVector.Factory<O> factory) {
 final DBIDs sample = DBIDUtil.randomSample(relation.getDBIDs(), rate, rnd);
 // Ugly cast, sorry
 @SuppressWarnings("unchecked")
 Relation<V> rel = (Relation<V>) relation;
 // FIXME: This does not necessarily hold. Check and fail!
 if(!distanceFunction.getInputTypeRestriction().isAssignableFromType(TypeUtil.NUMBER_VECTOR_FIELD)) {
  LoggingUtil.warning("Initializing k-means with k-means using specialized distance functions MAY fail, if the initialization method does require a distance defined on arbitrary number vectors.");
 }
 @SuppressWarnings("unchecked")
 NumberVectorDistanceFunction<? super V> pdf = (NumberVectorDistanceFunction<? super V>) distanceFunction;
 ProxyView<V> proxyv = new ProxyView<>(sample, rel);
 ProxyDatabase proxydb = new ProxyDatabase(sample, proxyv);
 innerkMeans.setK(k);
 innerkMeans.setDistanceFunction(pdf);
 Clustering<?> clusters = innerkMeans.run(proxydb, proxyv);
 List<O> means = new ArrayList<>();
 for(Cluster<?> cluster : clusters.getAllClusters()) {
  means.add(factory.newNumberVector(ModelUtil.getPrototype(cluster.getModel(), relation)));
 }
 return means;
}

代码示例来源：origin: elki-project/elki

@Override
public Clustering<M> run(Database database, Relation<V> relation) {
 if(!(innerkMeans.getDistanceFunction() instanceof PrimitiveDistanceFunction)) {
  throw new AbortException("K-Means results can only be evaluated for primitive distance functions, got: " + innerkMeans.getDistanceFunction().getClass());
 }
 @SuppressWarnings("unchecked")
 final NumberVectorDistanceFunction<? super NumberVector> df = (NumberVectorDistanceFunction<? super NumberVector>) innerkMeans.getDistanceFunction();
 Clustering<M> bestResult = null;
 double bestCost = Double.NaN;
 FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("K-means iterations", trials, LOG) : null;
 for(int i = 0; i < trials; i++) {
  Clustering<M> currentCandidate = innerkMeans.run(database, relation);
  double currentCost = qualityMeasure.quality(currentCandidate, df, relation);
  LOG.verbose("Cost of candidate " + i + ": " + currentCost);
  if(qualityMeasure.isBetter(currentCost, bestCost)) {
   bestResult = currentCandidate;
   bestCost = currentCost;
  }
  LOG.incrementProcessed(prog);
 }
 LOG.ensureCompleted(prog);
 return bestResult;
}

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki-clustering

@Override
public Clustering<M> run(Database database, Relation<V> relation) {
 if(!(innerkMeans.getDistanceFunction() instanceof PrimitiveDistanceFunction)) {
  throw new AbortException("K-Means results can only be evaluated for primitive distance functions, got: " + innerkMeans.getDistanceFunction().getClass());
 }
 @SuppressWarnings("unchecked")
 final NumberVectorDistanceFunction<? super NumberVector> df = (NumberVectorDistanceFunction<? super NumberVector>) innerkMeans.getDistanceFunction();
 Clustering<M> bestResult = null;
 double bestCost = Double.NaN;
 FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("K-means iterations", trials, LOG) : null;
 for(int i = 0; i < trials; i++) {
  Clustering<M> currentCandidate = innerkMeans.run(database, relation);
  double currentCost = qualityMeasure.quality(currentCandidate, df, relation);
  LOG.verbose("Cost of candidate " + i + ": " + currentCost);
  if(qualityMeasure.isBetter(currentCost, bestCost)) {
   bestResult = currentCandidate;
   bestCost = currentCost;
  }
  LOG.incrementProcessed(prog);
 }
 LOG.ensureCompleted(prog);
 return bestResult;
}

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki

Clustering<M> innerResult = innerkMeans.run(proxyDB);

代码示例来源：origin: elki-project/elki

@Override
public double[][] chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction) {
 if(relation.size() < k) {
  throw new IllegalArgumentException("Cannot choose k=" + k + " means from N=" + relation.size() + " < k objects.");
 }
 final DBIDs sample = DBIDUtil.randomSample(relation.getDBIDs(), rate, rnd);
 if(sample.size() < k) {
  throw new IllegalArgumentException("Sampling rate=" + rate + " from N=" + relation.size() + " yields only " + sample.size() + " < k objects.");
 }
 // Ugly cast, sorry
 @SuppressWarnings("unchecked")
 Relation<V> rel = (Relation<V>) relation;
 // FIXME: This does not necessarily hold. Check and fail!
 if(!distanceFunction.getInputTypeRestriction().isAssignableFromType(TypeUtil.NUMBER_VECTOR_FIELD)) {
  LoggingUtil.warning("Initializing k-means with k-means using specialized distance functions MAY fail, if the initialization method does require a distance defined on arbitrary number vectors.");
 }
 @SuppressWarnings("unchecked")
 NumberVectorDistanceFunction<? super V> pdf = (NumberVectorDistanceFunction<? super V>) distanceFunction;
 ProxyView<V> proxyv = new ProxyView<>(sample, rel);
 ProxyDatabase proxydb = new ProxyDatabase(sample, proxyv);
 innerkMeans.setK(k);
 innerkMeans.setDistanceFunction(pdf);
 Clustering<?> clusters = innerkMeans.run(proxydb, proxyv);
 double[][] means = new double[clusters.getAllClusters().size()][];
 int i = 0;
 for(Cluster<?> cluster : clusters.getAllClusters()) {
  means[i++] = ModelUtil.getPrototype(cluster.getModel(), relation).toArray();
 }
 return means;
}

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki

@Override
public Clustering<M> run(Database database, Relation<V> relation) {
 if(!(innerkMeans.getDistanceFunction() instanceof PrimitiveDistanceFunction)) {
  throw new AbortException("K-Means results can only be evaluated for primitive distance functions, got: " + innerkMeans.getDistanceFunction().getClass());
 }
 @SuppressWarnings("unchecked")
 final NumberVectorDistanceFunction<? super NumberVector> df = (NumberVectorDistanceFunction<? super NumberVector>) innerkMeans.getDistanceFunction();
 Clustering<M> bestResult = null;
 double bestCost = Double.NaN;
 FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("K-means iterations", trials, LOG) : null;
 for(int i = 0; i < trials; i++) {
  Clustering<M> currentCandidate = innerkMeans.run(database, relation);
  double currentCost = qualityMeasure.quality(currentCandidate, df, relation);
  if(LOG.isVerbose()) {
   LOG.verbose("Cost of candidate " + i + ": " + currentCost);
  }
  if(qualityMeasure.isBetter(currentCost, bestCost)) {
   bestResult = currentCandidate;
   bestCost = currentCost;
  }
  LOG.incrementProcessed(prog);
 }
 LOG.ensureCompleted(prog);
 return bestResult;
}

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki-clustering

@Override
public double[][] chooseInitialMeans(Database database, Relation<? extends NumberVector> relation, int k, NumberVectorDistanceFunction<?> distanceFunction) {
 if(relation.size() < k) {
  throw new IllegalArgumentException("Cannot choose k=" + k + " means from N=" + relation.size() + " < k objects.");
 }
 final DBIDs sample = DBIDUtil.randomSample(relation.getDBIDs(), rate, rnd);
 if(sample.size() < k) {
  throw new IllegalArgumentException("Sampling rate=" + rate + " from N=" + relation.size() + " yields only " + sample.size() + " < k objects.");
 }
 // Ugly cast, sorry
 @SuppressWarnings("unchecked")
 Relation<V> rel = (Relation<V>) relation;
 // FIXME: This does not necessarily hold. Check and fail!
 if(!distanceFunction.getInputTypeRestriction().isAssignableFromType(TypeUtil.NUMBER_VECTOR_FIELD)) {
  LoggingUtil.warning("Initializing k-means with k-means using specialized distance functions MAY fail, if the initialization method does require a distance defined on arbitrary number vectors.");
 }
 @SuppressWarnings("unchecked")
 NumberVectorDistanceFunction<? super V> pdf = (NumberVectorDistanceFunction<? super V>) distanceFunction;
 ProxyView<V> proxyv = new ProxyView<>(sample, rel);
 ProxyDatabase proxydb = new ProxyDatabase(sample, proxyv);
 innerkMeans.setK(k);
 innerkMeans.setDistanceFunction(pdf);
 Clustering<?> clusters = innerkMeans.run(proxydb, proxyv);
 double[][] means = new double[clusters.getAllClusters().size()][];
 int i = 0;
 for(Cluster<?> cluster : clusters.getAllClusters()) {
  means[i++] = ModelUtil.getPrototype(cluster.getModel(), relation).toArray();
 }
 return means;
}

代码示例来源：origin: elki-project/elki

Clustering<M> childClustering = innerKMeans.run(proxyDB);

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki-clustering

Clustering<M> childClustering = innerKMeans.run(proxyDB);

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki

Clustering<M> childClustering = innerKMeans.run(proxyDB);

代码示例来源：origin: elki-project/elki

Clustering<?> c = clusterer.run(database, relation);

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki

Clustering<?> c = clusterer.run(database, relation);

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