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

KMeans.train介绍

暂无

代码示例

代码示例来源：origin: OryxProject/oryx

/**
 * @param sparkContext    active Spark Context
 * @param trainData       training data on which to build a model
 * @param hyperParameters ordered list of hyper parameter values to use in building model
 * @param candidatePath   directory where additional model files can be written
 * @return a {@link PMML} representation of a model trained on the given data
 */
@Override
public PMML buildModel(JavaSparkContext sparkContext,
            JavaRDD<String> trainData,
            List<?> hyperParameters,
            Path candidatePath) {
 int numClusters = (Integer) hyperParameters.get(0);
 Preconditions.checkArgument(numClusters > 1);
 log.info("Building KMeans Model with {} clusters", numClusters);
 JavaRDD<Vector> trainingData = parsedToVectorRDD(trainData.map(MLFunctions.PARSE_FN));
 KMeansModel kMeansModel = KMeans.train(trainingData.rdd(), numClusters, maxIterations,
                     numberOfRuns, initializationStrategy);
 return kMeansModelToPMML(kMeansModel, fetchClusterCountsFromModel(trainingData, kMeansModel));
}

代码示例来源：origin: ypriverol/spark-java8

KMeansModel clusters = org.apache.spark.mllib.clustering.KMeans.train(parsedData.rdd(), numClusters, numIterations);

代码示例来源：origin: apache/lens

KMeansModel model = KMeans.train(trainableRDD.rdd(), k, maxIterations, runs, initializationMode);
 return new KMeansClusteringModel(modelId, model);
} catch (Exception e) {

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]);
}