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Java JavaRDD.randomSplit方法代码示例

本文整理汇总了Java中org.apache.spark.api.java.JavaRDD.randomSplit方法的典型用法代码示例。如果您正苦于以下问题:Java JavaRDD.randomSplit方法的具体用法?Java JavaRDD.randomSplit怎么用?Java JavaRDD.randomSplit使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在org.apache.spark.api.java.JavaRDD的用法示例。


在下文中一共展示了JavaRDD.randomSplit方法的3个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Java代码示例。

示例1: findBestModel

import org.apache.spark.api.java.JavaRDD; //导入方法依赖的package包/类
public TrainedModel findBestModel(JavaRDD<Rating> ratings) {
    double weights[] = {6, 2, 2};
    JavaRDD<Rating>[] randomRatings = ratings.randomSplit(weights, 0L);
    JavaRDD<Rating> trainingRdd = randomRatings[0];
    JavaRDD<Rating> validationRdd = randomRatings[1];
    JavaRDD<Rating> testRdd = randomRatings[2];
    TrainConfig trainConfig = findBestTrainingParameters(trainingRdd, validationRdd);

    TrainedModel model = ModelFactory.create(trainingRdd, testRdd, trainConfig.getRankNr(),
            trainConfig.getIterationsNr());
    logger.info("best model have RMSE = " + model.getError());

    return model;
}
 
开发者ID:cosminseceleanu,项目名称:movie-recommender,代码行数:15,代码来源:ModelFinder.java

示例2: createAlsModel

import org.apache.spark.api.java.JavaRDD; //导入方法依赖的package包/类
private TrainedModel createAlsModel(JavaRDD<Rating> ratings, TrainConfig trainConfig) {
    double[] weights = {8, 2};
    JavaRDD<Rating>[] randomRatings = ratings.randomSplit(weights, 0L);

    return ModelFactory.create(randomRatings[0],
            randomRatings[1],
            trainConfig.getRankNr(),
            trainConfig.getIterationsNr()
    );
}
 
开发者ID:cosminseceleanu,项目名称:movie-recommender,代码行数:11,代码来源:RecommendationEngine.java

示例3: main

import org.apache.spark.api.java.JavaRDD; //导入方法依赖的package包/类
public static void main(String args[]){

		SparkConf configuration = new SparkConf().setMaster("local[4]").setAppName("Any");
		JavaSparkContext sc = new JavaSparkContext(configuration);

		// Load and parse the data file.
		String input = "data/rf-data.txt";
		JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc.sc(), input).toJavaRDD();
		// Split the data into training and test sets (30% held out for testing)
		JavaRDD<LabeledPoint>[] dataSplits = data.randomSplit(new double[]{0.7, 0.3});
		JavaRDD<LabeledPoint> trainingData = dataSplits[0];
		JavaRDD<LabeledPoint> testData = dataSplits[1];

		// Train a RandomForest model.
		Integer numClasses = 2;
		HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();//  Empty categoricalFeaturesInfo indicates all features are continuous.
		Integer numTrees = 3; // Use more in practice.
		String featureSubsetStrategy = "auto"; // Let the algorithm choose.
		String impurity = "gini";
		Integer maxDepth = 5;
		Integer maxBins = 32;
		Integer seed = 12345;

		final RandomForestModel rfModel = RandomForest.trainClassifier(trainingData, numClasses,
				categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins,
				seed);

		// Evaluate model on test instances and compute test error
		JavaPairRDD<Double, Double> label =
				testData.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
					public Tuple2<Double, Double> call(LabeledPoint p) {
						return new Tuple2<Double, Double>(rfModel.predict(p.features()), p.label());
					}
				});

		Double testError =
				1.0 * label.filter(new Function<Tuple2<Double, Double>, Boolean>() {
					public Boolean call(Tuple2<Double, Double> pl) {
						return !pl._1().equals(pl._2());
					}
				}).count() / testData.count();

		System.out.println("Test Error: " + testError);
		System.out.println("Learned classification forest model:\n" + rfModel.toDebugString());
	}
 
开发者ID:PacktPublishing,项目名称:Java-Data-Science-Cookbook,代码行数:46,代码来源:RandomForestMlib.java


注:本文中的org.apache.spark.api.java.JavaRDD.randomSplit方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。