本文整理汇总了Scala中org.apache.spark.mllib.fpm.FPGrowth类的典型用法代码示例。如果您正苦于以下问题：Scala FPGrowth类的具体用法？Scala FPGrowth怎么用？Scala FPGrowth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FPGrowth类的5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: SampleFPGrowthApp
//设置package包名称以及导入依赖的类
package com.sparksample
import org.apache.spark.mllib.fpm.FPGrowth
object SampleFPGrowthApp {
def main(args: Array[String]) {
val transactions = Seq(
"r z h k p",
"z y x w v u t s",
"s x o n r",
"x z y m t s q e",
"z",
"x z y r q t p")
.map(_.split(" "))
val sc = Util.sc
val rdd = sc.parallelize(transactions, 2).cache()
val fpg = new FPGrowth()
val model6 = fpg
.setMinSupport(0.2)
.setNumPartitions(1)
.run(rdd)
model6.freqItemsets.collect().foreach { itemset =>
println(itemset.items.mkString("[", ",", "]") + ", " + itemset.freq)
}
}
} 开发者ID:PacktPublishing，项目名称:Machine-Learning-with-Spark-Second-Edition，代码行数:31，代码来源:SampleFPGrowthApp.scala
示例2: SampleFPGrowthApp
//设置package包名称以及导入依赖的类
import org.apache.spark.SparkContext
import org.apache.spark.mllib.fpm.FPGrowth
object SampleFPGrowthApp {
def main(args: Array[String]) {
val transactions = Seq(
"r z h k p",
"z y x w v u t s",
"s x o n r",
"x z y m t s q e",
"z",
"x z y r q t p")
.map(_.split(" "))
val sc = new SparkContext("local[2]", "Chapter 5 App")
val rdd = sc.parallelize(transactions, 2).cache()
val fpg = new FPGrowth()
val model = fpg
.setMinSupport(0.2)
.setNumPartitions(1)
.run(rdd)
model.freqItemsets.collect().foreach { itemset =>
println(itemset.items.mkString("[", ",", "]") + ", " + itemset.freq)
}
}
} 开发者ID:PacktPublishing，项目名称:Machine-Learning-with-Spark-Second-Edition，代码行数:30，代码来源:SampleFPGrowthApp.scala
示例3:
//设置package包名称以及导入依赖的类
import org.apache.spark.SparkContext
import org.apache.spark.mllib.fpm.FPGrowth
import org.apache.spark.mllib.recommendation.Rating
import scala.collection.mutable.ListBuffer
val rawRatings = rawData.map(_.split("\t").take(3))
rawRatings.first()
// 14/03/30 13:22:44 INFO SparkContext: Job finished: first at <console>:21, took 0.003703 s
// res25: Array[String] = Array(196, 242, 3)
val ratings = rawRatings.map { case Array(user, movie, rating) => Rating(user.toInt, movie.toInt, rating.toDouble) }
val ratingsFirst = ratings.first()
println(ratingsFirst)
val userId = 789
val K = 10
val movies = sc.textFile(PATH + "/ml-100k/u.item")
val titles = movies.map(line => line.split("\\|").take(2)).map(array => (array(0).toInt, array(1))).collectAsMap()
titles(123)
var eRDD = sc.emptyRDD
var z = Seq[String]()
val l = ListBuffer()
val aj = new Array[String](100)
var i = 0
for( a <- 801 to 900) {
val moviesForUserX = ratings.keyBy(_.user).lookup(a)
val moviesForUserX_10 = moviesForUserX.sortBy(-_.rating).take(10)
val moviesForUserX_10_1 = moviesForUserX_10.map(r => r.product)
var temp = ""
for( x <- moviesForUserX_10_1){
temp = temp + " " + x
println(temp)
}
aj(i) = temp
i += 1
}
z = aj
val transaction2 = z.map(_.split(" "))
val rddx = sc.parallelize(transaction2, 2).cache()
val fpg = new FPGrowth()
val model6 = fpg
.setMinSupport(0.1)
.setNumPartitions(1)
.run(rddx)
model6.freqItemsets.collect().foreach { itemset =>
println(itemset.items.mkString("[", ",", "]") + ", " + itemset.freq)
}
sc.stop()
}
} 开发者ID:PacktPublishing，项目名称:Machine-Learning-with-Spark-Second-Edition，代码行数:62，代码来源:MovieLensFPGrowthApp.scala
示例4:
//设置package包名称以及导入依赖的类
package com.sparksample
import org.apache.spark.mllib.fpm.FPGrowth
import org.apache.spark.mllib.recommendation.Rating
import scala.collection.mutable.ListBuffer
val rawRatings = rawData.map(_.split("\t").take(3))
rawRatings.first()
val ratings = rawRatings.map { case Array(user, movie, rating) => Rating(user.toInt, movie.toInt, rating.toDouble) }
val ratingsFirst = ratings.first()
println(ratingsFirst)
val movies = Util.getMovieData()
val titles = movies.map(line => line.split("\\|").take(2)).map(array => (array(0).toInt, array(1))).collectAsMap()
titles(123)
var eRDD = sc.emptyRDD
var z = Seq[String]()
val l = ListBuffer()
val aj = new Array[String](400)
var i = 0
for( a <- 501 to 900) {
val moviesForUserX = ratings.keyBy(_.user).lookup(a)
val moviesForUserX_10 = moviesForUserX.sortBy(-_.rating).take(10)
val moviesForUserX_10_1 = moviesForUserX_10.map(r => r.product)
var temp = ""
for( x <- moviesForUserX_10_1){
if(temp.equals(""))
temp = x.toString
else {
temp = temp + " " + x
}
}
aj(i) = temp
i += 1
}
z = aj
val transaction = z.map(_.split(" "))
val rddx = sc.parallelize(transaction, 2).cache()
val fpg = new FPGrowth()
val model = fpg
.setMinSupport(0.1)
.setNumPartitions(1)
.run(rddx)
model.freqItemsets.collect().foreach { itemset =>
println(itemset.items.mkString("[", ",", "]") + ", " + itemset.freq)
}
sc.stop()
}
} 开发者ID:PacktPublishing，项目名称:Machine-Learning-with-Spark-Second-Edition，代码行数:59，代码来源:MovieLensFPGrowthApp.scala
示例5: FPMiningApp
//设置package包名称以及导入依赖的类
package org.apress.prospark
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.mllib.fpm.FPGrowth
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
object FPMiningApp {
def main(args: Array[String]) {
if (args.length != 3) {
System.err.println(
"Usage: FPMiningApp <appname> <batchInterval> <iPath>")
System.exit(1)
}
val Seq(appName, batchInterval, iPath) = args.toSeq
val conf = new SparkConf()
.setAppName(appName)
.setJars(SparkContext.jarOfClass(this.getClass).toSeq)
val ssc = new StreamingContext(conf, Seconds(batchInterval.toInt))
val minSupport = 0.4
ssc.textFileStream(iPath)
.map(r => r.split(" "))
.foreachRDD(transactionRDD => {
val fpg = new FPGrowth()
.setMinSupport(minSupport)
val model = fpg.run(transactionRDD)
model.freqItemsets
.collect()
.foreach(itemset => println("Items: %s, Frequency: %s".format(itemset.items.mkString(" "), itemset.freq)))
})
ssc.start()
ssc.awaitTermination()
}
}