本文整理了Java中org.apache.spark.mllib.clustering.KMeans.K_MEANS_PARALLEL()方法的一些代码示例，展示了KMeans.K_MEANS_PARALLEL()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.K_MEANS_PARALLEL()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：K_MEANS_PARALLEL

KMeans.K_MEANS_PARALLEL介绍

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代码示例

代码示例来源：origin: OryxProject/oryx

public KMeansUpdate(Config config) {
 super(config);
 initializationStrategy = config.getString("oryx.kmeans.initialization-strategy");
 evaluationStrategy = Enum.valueOf(KMeansEvalStrategy.class, config.getString("oryx.kmeans.evaluation-strategy"));
 numberOfRuns = config.getInt("oryx.kmeans.runs");
 maxIterations = config.getInt("oryx.kmeans.iterations");
 hyperParamValues = new ArrayList<>();
 hyperParamValues.add(HyperParams.fromConfig(config, "oryx.kmeans.hyperparams.k"));
 inputSchema = new InputSchema(config);
 Preconditions.checkArgument(maxIterations > 0);
 Preconditions.checkArgument(numberOfRuns > 0);
 Preconditions.checkArgument(
   initializationStrategy.equals(KMeans.K_MEANS_PARALLEL()) ||
     initializationStrategy.equals(KMeans.RANDOM()));
 // Should be an unsupervised problem. This impl only supports numeric features.
 Preconditions.checkArgument(!inputSchema.hasTarget());
 for (int i = 0; i < inputSchema.getNumFeatures(); i++) {
  Preconditions.checkArgument(!inputSchema.isCategorical(i));
 }
}

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

@Test
public void runKMeansUsingStaticMethods() {
 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 = KMeans.train(data.rdd(), 1, 1, 1, KMeans.K_MEANS_PARALLEL());
 assertEquals(1, model.clusterCenters().length);
 assertEquals(expectedCenter, model.clusterCenters()[0]);
 model = KMeans.train(data.rdd(), 1, 1, 1, KMeans.RANDOM());
 assertEquals(expectedCenter, model.clusterCenters()[0]);
}

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

@Test
public void runKMeansUsingStaticMethods() {
 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 = KMeans.train(data.rdd(), 1, 1, 1, KMeans.K_MEANS_PARALLEL());
 assertEquals(1, model.clusterCenters().length);
 assertEquals(expectedCenter, model.clusterCenters()[0]);
 model = KMeans.train(data.rdd(), 1, 1, 1, KMeans.RANDOM());
 assertEquals(expectedCenter, model.clusterCenters()[0]);
}

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

@Test
public void runKMeansUsingStaticMethods() {
 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 = KMeans.train(data.rdd(), 1, 1, 1, KMeans.K_MEANS_PARALLEL());
 assertEquals(1, model.clusterCenters().length);
 assertEquals(expectedCenter, model.clusterCenters()[0]);
 model = KMeans.train(data.rdd(), 1, 1, 1, KMeans.RANDOM());
 assertEquals(expectedCenter, model.clusterCenters()[0]);
}