本文整理了Java中de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeans.setK()方法的一些代码示例，展示了KMeans.setK()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.setK()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：setK

KMeans.setK介绍

[英]Set the value of k. Needed for some types of nested k-means.
[中]设置某些类型的嵌套k-均值所需的k值。

代码示例

代码示例来源：origin: elki-project/elki

@Override
public void setK(int k) {
 innerkMeans.setK(k);
}

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

@Override
public void setK(int k) {
 innerkMeans.setK(k);
}

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

@Override
public void setK(int k) {
 innerkMeans.setK(k);
}

代码示例来源：origin: elki-project/elki

innerKMeans.setK(k_min);
LOG.statistics(new StringStatistic(KEY + ".initialization", initializer.toString()));
splitInitializer.setInitialMeans(initializer.chooseInitialMeans(database, relation, k_min, getDistanceFunction()));
 innerKMeans.setK(nextClusters.size());
 clustering = innerKMeans.run(database, relation);
 clusters.clear();

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

innerKMeans.setK(k_min);
LOG.statistics(new StringStatistic(KEY + ".initialization", initializer.toString()));
splitInitializer.setInitialMeans(initializer.chooseInitialMeans(database, relation, k_min, getDistanceFunction()));
 innerKMeans.setK(nextClusters.size());
 clustering = innerKMeans.run(database, relation);
 clusters.clear();

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

innerKMeans.setK(k_min);
if(LOG.isStatistics()) {
 LOG.statistics(new StringStatistic(KEY + ".initialization", initializer.toString()));
 innerKMeans.setK(nextClusters.size());
 clustering = innerKMeans.run(database, relation);
 clusters.clear();

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 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-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

innerKMeans.setK(2);
Clustering<M> childClustering = innerKMeans.run(proxyDB);

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

innerKMeans.setK(2);
Clustering<M> childClustering = innerKMeans.run(proxyDB);

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

innerKMeans.setK(2);
Clustering<M> childClustering = innerKMeans.run(proxyDB);