本文整理了Java中org.apache.spark.mllib.clustering.KMeans.setInitializationMode()方法的一些代码示例，展示了KMeans.setInitializationMode()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.setInitializationMode()方法的具体详情如下：
包路径：org.apache.spark.mllib.clustering.KMeans
类名称：KMeans
方法名：setInitializationMode

KMeans.setInitializationMode介绍

暂无

代码示例

代码示例来源：origin: locationtech/geowave

kmeans.setInitializationMode("kmeans||");
kmeans.setK(numClusters);
kmeans.setMaxIterations(numIterations);

代码示例来源：origin: org.apache.spark/spark-mllib

@Test
public void runKMeansUsingConstructor() {
 List<Vector> points = Arrays.asList(
  Vectors.dense(1.0, 2.0, 6.0),
  Vectors.dense(1.0, 3.0, 0.0),
  Vectors.dense(1.0, 4.0, 6.0)
 );
 Vector expectedCenter = Vectors.dense(1.0, 3.0, 4.0);
 JavaRDD<Vector> data = jsc.parallelize(points, 2);
 KMeansModel model = new KMeans().setK(1).setMaxIterations(5).run(data.rdd());
 assertEquals(1, model.clusterCenters().length);
 assertEquals(expectedCenter, model.clusterCenters()[0]);
 model = new KMeans()
  .setK(1)
  .setMaxIterations(1)
  .setInitializationMode(KMeans.RANDOM())
  .run(data.rdd());
 assertEquals(expectedCenter, model.clusterCenters()[0]);
}

代码示例来源：origin: org.apache.spark/spark-mllib_2.11

@Test
public void runKMeansUsingConstructor() {
 List<Vector> points = Arrays.asList(
  Vectors.dense(1.0, 2.0, 6.0),
  Vectors.dense(1.0, 3.0, 0.0),
  Vectors.dense(1.0, 4.0, 6.0)
 );
 Vector expectedCenter = Vectors.dense(1.0, 3.0, 4.0);
 JavaRDD<Vector> data = jsc.parallelize(points, 2);
 KMeansModel model = new KMeans().setK(1).setMaxIterations(5).run(data.rdd());
 assertEquals(1, model.clusterCenters().length);
 assertEquals(expectedCenter, model.clusterCenters()[0]);
 model = new KMeans()
  .setK(1)
  .setMaxIterations(1)
  .setInitializationMode(KMeans.RANDOM())
  .run(data.rdd());
 assertEquals(expectedCenter, model.clusterCenters()[0]);
}

代码示例来源：origin: org.apache.spark/spark-mllib_2.10

@Test
public void runKMeansUsingConstructor() {
 List<Vector> points = Arrays.asList(
  Vectors.dense(1.0, 2.0, 6.0),
  Vectors.dense(1.0, 3.0, 0.0),
  Vectors.dense(1.0, 4.0, 6.0)
 );
 Vector expectedCenter = Vectors.dense(1.0, 3.0, 4.0);
 JavaRDD<Vector> data = jsc.parallelize(points, 2);
 KMeansModel model = new KMeans().setK(1).setMaxIterations(5).run(data.rdd());
 assertEquals(1, model.clusterCenters().length);
 assertEquals(expectedCenter, model.clusterCenters()[0]);
 model = new KMeans()
  .setK(1)
  .setMaxIterations(1)
  .setInitializationMode(KMeans.RANDOM())
  .run(data.rdd());
 assertEquals(expectedCenter, model.clusterCenters()[0]);
}