本文整理汇总了Scala中org.apache.spark.ml.feature.LabeledPoint类的典型用法代码示例。如果您正苦于以下问题：Scala LabeledPoint类的具体用法？Scala LabeledPoint怎么用？Scala LabeledPoint使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了LabeledPoint类的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Scala代码示例。
示例1: mltest
//设置package包名称以及导入依赖的类
package spark.mltest
import org.apache.spark.ml.feature.LabeledPoint
import org.apache.spark.sql.SQLContext
import org.apache.spark.{SparkConf, SparkContext}
/**
* Created by I311352 on 3/29/2017.
*/
class mltest {
}
object mltest extends App {
val conf = new SparkConf().setAppName("mltest").setMaster("local[2]")
val sc = new SparkContext(conf)
val sQLContext = new SQLContext(sc)
println("OK")
val training = sc.parallelize(Seq(
LabeledPoint
))
} 示例2: NeuralNetworkSpec
//设置package包名称以及导入依赖的类
package io.spinor.sparkdemo.mllib
import io.spinor.sparkdemo.data.MNISTData
import io.spinor.sparkdemo.util.DemoUtil
import org.apache.spark.ml.classification.MultilayerPerceptronClassifier
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
import org.apache.spark.ml.feature.LabeledPoint
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.sql.SparkSession
import org.apache.spark.{SparkConf, SparkContext}
import org.scalatest.{FlatSpec, Matchers}
import org.slf4j.LoggerFactory
class NeuralNetworkSpec extends FlatSpec with DemoUtil with Matchers {
val logger = LoggerFactory.getLogger(classOf[NeuralNetworkSpec])
"Training on MNIST data" should " run" in {
val sparkConf = new SparkConf()
sparkConf.setAppName("NeuralNetworkDemo")
sparkConf.setMaster("local[2]")
val sparkContext = new SparkContext(sparkConf)
val sparkSession = SparkSession.builder().config(sparkConf).getOrCreate()
val sqlContext = sparkSession.sqlContext
import sqlContext.implicits._
val mNISTData = new MNISTData()
val trainingData = mNISTData.getTrainingData()
val trainingPoints = sparkContext.parallelize(trainingData.map(entry => LabeledPoint(entry._2, Vectors.dense(entry._1)))).toDF()
val classifier = new MultilayerPerceptronClassifier()
classifier
.setLayers(Array(784, 100))
.setBlockSize(125)
.setSeed(1234L)
.setMaxIter(10)
val model = classifier.fit(trainingPoints)
val testData = mNISTData.getTestData()
val testPoints = sparkContext.parallelize(testData.map(entry => {
LabeledPoint(entry._2, Vectors.dense(entry._1))})).toDF()
val result = model.transform(testPoints)
val predictionAndLabels = result.select("prediction", "label")
val evaluator = new MulticlassClassificationEvaluator().setMetricName("accuracy")
logger.info("accuracy:" + evaluator.evaluate(predictionAndLabels))
}
} 示例3: WatherScript
//设置package包名称以及导入依赖的类
package naivebayes
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.ml.classification.NaiveBayes
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
import org.apache.spark.ml.feature.LabeledPoint
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}
object WatherScript extends App {
val conf = new SparkConf().setAppName("Simple Application")
val sc = new SparkContext(conf)
val spark = SparkSession
.builder()
.appName("Spark SQL basic example")
.config("spark.some.config.option", "some-value")
.getOrCreate()
// For implicit conversions like converting RDDs to DataFrames
import spark.implicits._
val watherRaw: RDD[String] = sc.textFile("/Users/mateusz/Workspace/mllib/spark-naive-bayes/src/main/resources/wather-nums.csv")
val dataRaw = watherRaw.map(_.split(";")).map { csv =>
val label = csv.last.toDouble
val point = csv.init.map(_.toDouble)
(label, point)
}
val data: Dataset[LabeledPoint] = dataRaw
.map { case (label, point) =>
LabeledPoint(label, Vectors.dense(point))
}.toDS()
val Array(training: Dataset[LabeledPoint], test: Dataset[LabeledPoint]) = data.randomSplit(Array(0.7, 0.3), seed = 1234L)
val model = new NaiveBayes()
.setModelType("multinomial")
.fit(training)
val predictions = model.transform(test)
predictions.show()
val evaluator = new MulticlassClassificationEvaluator()
.setLabelCol("label")
.setPredictionCol("prediction")
.setMetricName("accuracy")
val accuracy = evaluator.evaluate(predictions)
println("Test set accuracy = " + accuracy)
}