本文整理汇总了Scala中org.apache.spark.mllib.classification.SVMModel类的典型用法代码示例。如果您正苦于以下问题：Scala SVMModel类的具体用法？Scala SVMModel怎么用？Scala SVMModel使用的例子？那么, 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SVMModel类的1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: Consumer
//设置package包名称以及导入依赖的类
import org.apache.spark.streaming.kafka010.KafkaUtils
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark.SparkConf
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent
import org.apache.spark.streaming.kafka010.ConsumerStrategies.Subscribe
import org.apache.spark.mllib.classification.SVMModel
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.sql.SparkSession
object Consumer {
def main(args: Array[String]): Unit = {
val kafkaParams = Map[String, Object](
"bootstrap.servers" -> "localhost:9092",
"key.deserializer" -> classOf[StringDeserializer],
"value.deserializer" -> classOf[StringDeserializer],
"group.id" -> "use_a_separate_group_id_for_each_stream",
"auto.offset.reset" -> "latest",
"enable.auto.commit" -> (false: java.lang.Boolean)
)
val topics = Array("streaming")
val sparkConf = new SparkConf().setMaster("local[8]").setAppName("KafkaTest")
val streamingContext = new StreamingContext(sparkConf, Seconds(1))
// Create a input direct stream
val kafkaStream = KafkaUtils.createDirectStream[String, String](
streamingContext,
PreferConsistent,
Subscribe[String, String](topics, kafkaParams)
)
val sc = SparkSession.builder().master("local[8]").appName("KafkaTest").getOrCreate()
val model = SVMModel.load(sc.sparkContext, "/home/xiaoyu/model")
val result = kafkaStream.map(record => (record.key, record.value))
result.foreachRDD(
patient => {
patient.collect().toBuffer.foreach(
(x: (Any, String)) => {
val features = x._2.split(',').map(x => x.toDouble).tail
println(model.predict(Vectors.dense(features)))
}
)
}
)
streamingContext.start()
streamingContext.awaitTermination()
}
}