//设置package包名称以及导入依赖的类
import org.apache.spark.SparkContext
import org.apache.spark.SparkConf

import org.apache.spark.ml.clustering.LDA
import org.apache.spark.mllib.linalg.{VectorUDT, Vectors}
import org.apache.spark.sql.{Row, SQLContext}
import org.apache.spark.sql.types.{StructField, StructType}


object SimpleApp {
  def main(args: Array[String]) {
    val conf = new SparkConf().setAppName("Simple Application").set("spark.ui.enabled", "false")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)

    // Loads data
    val rowRDD = sc.textFile("/tmp/lda_data.txt").filter(_.nonEmpty)
      .map(_.split(" ").map(_.toDouble)).map(Vectors.dense).map(Row(_))
    val schema = StructType(Array(StructField("name", new VectorUDT, false)))
    val dataset = sqlContext.createDataFrame(rowRDD, schema)
    dataset.show()

    val lda = new LDA()
      .setK(10)
      .setMaxIter(10)
      .setFeaturesCol("name")
    val model = lda.fit(dataset)
    val transformed = model.transform(dataset)

    val ll = model.logLikelihood(dataset)
    val lp = model.logPerplexity(dataset)

    // describeTopics
    val topics = model.describeTopics(3)

    // Shows the result
    topics.show(false)
    transformed.show(false)
  }
} 

//设置package包名称以及导入依赖的类
package com.study.spark.datasource

import org.apache.spark.{Partition, TaskContext}
import org.apache.spark.annotation.DeveloperApi
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.{Row, SQLContext}


class StudyRDD(sqlContext: SQLContext, schema: StructType) extends RDD[Row](sqlContext.sparkContext, deps=Nil) {
  @DeveloperApi
  override def compute(split: Partition, context: TaskContext): Iterator[Row] = new StudyReader(context, schema, split)

  // ??? ?? ????? 2?? ???? ??? ????.
  // ? Executor? ???? ??? ????. ???? ???? 2? ??? ???, ??? ??? ? ?? Executor? ?? 2???.
  override protected def getPartitions: Array[Partition] = {
    val arr: Array[Partition] = new Array[Partition](2)
    arr.update(0, new Partition() {
      override def index: Int = 0
    })
    arr.update(1, new Partition() {
      override def index: Int = 1
    })
    arr
  }
} 

//设置package包名称以及导入依赖的类
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.types.IntegerType
import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.Row
import com.gxq.learn.recontool.utils.SparkContextFactory

object SchemaTest {
  def main(args: Array[String]): Unit = {
    val (sc, ss) = SparkContextFactory.getSparkContext("local")
    val data = sc.parallelize(Seq("Bern;10;12")) // mock for real data

    val schema = new StructType()
    .add("city", StringType, true)
    .add("female", IntegerType, true)
    .add("male", IntegerType, true)

    val cities = data.map(line => {
      val Array(city, female, male) = line.split(";")
      Row(
        city,
        female.toInt,
        male.toInt)
    })

    val citiesDF = ss.createDataFrame(cities, schema)
    citiesDF.show
  }
} 

//设置package包名称以及导入依赖的类
package unus.stage

import unus.helpers.Conf
import org.apache.spark.sql.catalyst.ScalaReflection
import org.apache.spark.sql.types.StructType
import scala.reflect.runtime.universe._
import org.apache.spark.sql.SaveMode

class DatabaseBackup[T: TypeTag](table: String) {
  private lazy val schema = ScalaReflection.schemaFor[T].dataType.asInstanceOf[StructType]

  def save(): Unit = {
    Conf.spark.read
      .format("jdbc")
      .option("url", Conf.dbUrl)
      .option("dbtable", s""""$table"""")
      .option("user", Conf.dbUsername)
      .option("password", Conf.dbPassword)
      .option("jdbcdriver","org.postgresql.Driver")
      .load()
      .write
      .format("csv")
      .option("header", "true")
      .save(Conf.dataDir + "/" + table + ".csv")
  }

  def load(): Unit = {
    Conf.spark.read
      .format("csv")
      .option("header", "true")
      .schema(schema)
      .load(Conf.dataDir + "/" + table + ".csv.gz")
      .write
      .format("jdbc")
      .option("url", Conf.dbUrl)
      .option("dbtable", s""""$table"""")
      .option("user", Conf.dbUsername)
      .option("password", Conf.dbPassword)
      .option("jdbcdriver","org.postgresql.Driver")
      .mode(SaveMode.Append)
      .save()
  }
} 

//设置package包名称以及导入依赖的类
package services.users

import java.io.File
import java.nio.charset.Charset
import java.sql.Timestamp

import org.apache.commons.io.FileUtils
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.{StringType, StructField, StructType}
import org.apache.spark.streaming.StreamingContext
import services.events.EventStream
import services.Util


case class User(userId: String, testFinishTime: Timestamp, nickname: String, gender: String)

object User {
  val DELIMITER = ','
  val USER_FEED = "/Users/mahesh/data/affinitas/feeds/users/"
  val USER_DATA = "/Users/mahesh/data/affinitas/tables/users/"

  var ssc: StreamingContext = null
  var sql: SparkSession = null


  lazy val usersFeedDF = sql.read
    .format("com.databricks.spark.csv")
    .option("header", false)
    .schema(StructType(Array(
      StructField("userId", StringType, true),
      StructField("nickname", StringType, true),
      StructField("gender", StringType, true)
    )
    )).load(User.USER_FEED)

  //EventStream.testFinishStream.print()
  lazy val usersMap = usersFeedDF.rdd.map(record => (record.getString(0), (record.getString(1), record.getString(2))))


  def initialize(sscO: StreamingContext, sqlO: SparkSession) = {
    ssc = sscO
    sql = sqlO

    new File(USER_FEED).mkdirs()
    new File(USER_DATA).mkdirs()

    EventStream.testFinishStream.foreachRDD( {
      rdd => {
        val testFinishMap = rdd.map(record => (record.userId, record.timestamp))
        val userData = testFinishMap.join(usersMap)
          .map(record => Array(record._1, record._2._1, record._2._2._1, record._2._2._2))
          .collect()
        Util.writeCsvToDir(userData, DELIMITER.toString, USER_DATA)
      }
    })
  }
} 

//设置package包名称以及导入依赖的类
package com.jasonfeist.spark.tika

import org.apache.spark.input.PortableDataStream
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{Row, SQLContext}
import org.apache.spark.sql.sources.{BaseRelation, TableScan}
import org.apache.spark.sql.types.{StructType}
import org.slf4j.LoggerFactory


class TikaMetadataRelation protected[tika] (path: String,
                                            userSchema: StructType,
                                            metadataExtractor: MetadataExtractor,
                                            fieldDataExtractor: FieldDataExtractor)
                          (@transient val sqlContext: SQLContext)
  extends BaseRelation with TableScan with Serializable {

  val logger = LoggerFactory.getLogger(classOf[TikaMetadataRelation])

  override def schema: StructType = this.userSchema

  override def buildScan(): RDD[Row] = {

    val rdd = sqlContext
      .sparkContext.binaryFiles(path)
    rdd.map(extractFunc(_))
  }

  def extractFunc(
                    file: (String, PortableDataStream)
                  ) : Row  =
  {
    val extractedData = metadataExtractor.extract(file)
    val rowArray = new Array[Any](schema.fields.length)
    var index = 0
    while (index < schema.fields.length) {
      val field = schema(index)
      val fieldData = fieldDataExtractor.matchedField(field.name,
        field.dataType, extractedData._1, file._1, extractedData._2,
        extractedData._3)
      rowArray(index) = fieldData
      index = index + 1
    }
    Row.fromSeq(rowArray)
  }
} 

//设置package包名称以及导入依赖的类
package com.study.spark.datasource

import org.apache.spark.rdd.RDD
import org.apache.spark.sql.sources.{BaseRelation, TableScan}
import org.apache.spark.sql.types.{StringType, StructField, StructType}
import org.apache.spark.sql.{Row, SQLContext}


class StudyRelation(parameters: Map[String, String])(@transient val sqlContext: SQLContext)
  extends BaseRelation with TableScan {

  override def schema: StructType = {
    // ??? ?? ?????, ?? ??? ???? ????. ???? ?????? ???? ??????, ???? ?? ??? ????
    val fields: Array[StructField] = new Array[StructField](3)
    fields.update(0, new StructField("field1", StringType))
    fields.update(1, new StructField("field2", StringType))
    fields.update(2, new StructField("field2", StringType))
    new StructType(fields.asInstanceOf[Array[StructField]])
  }

  // RDD[Row]? ???? StudyRDD? ???.
  override def buildScan(): RDD[Row] = new StudyRDD(sqlContext, schema)
} 

//设置package包名称以及导入依赖的类
package com.study.spark.datasource

import org.apache.spark.{Partition, TaskContext}
import org.apache.spark.sql.Row
import org.apache.spark.sql.types.StructType


class StudyReader(context: TaskContext, schema: StructType, split: Partition) extends Iterator[Row] {
  private[this] var counter: Int = 0

  // Task? ???? ???? close? ????? ??.
  if(context != null) {
    context.addTaskCompletionListener(context => close())
  }

  // 100?? Row? ??? ??
  override def hasNext: Boolean = {
    if(counter < 100) {
      true
    } else {
      false
    }
  }

  // 1?? Row? ????.
  override def next(): Row = {
    if(!hasNext) {
      throw new NoSuchElementException("End of stream")
    }
    counter += 1
    Row(split.index + " field1 " + counter, "field2 " + counter, "field3: " + counter)
  }

  // close?? ? ??? ??? ??? close??.
  def close() = println("closed")
} 

//设置package包名称以及导入依赖的类
package unus.stage

import unus.helpers.Conf
import unus.db.{Patient, Repository}
import unus.helpers.Cacher
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.ScalaReflection
import org.apache.spark.sql.types.StructType

class PatientCacher extends Cacher[Patient] {
  override protected val name: String = "patient"

  override protected def build(): RDD[Patient] = {
    val schema = ScalaReflection.schemaFor[Patient].dataType.asInstanceOf[StructType]

    import Conf.spark.implicits._

    Conf.spark.read.format("csv")
      .schema(schema)
      .option("header", "true")
      .load(Conf.dataDir + "/FInalDataset.csv")
      .as[Patient]
      .rdd
  }
} 

//设置package包名称以及导入依赖的类
package com.rishabh.spark.datasource.s3


import org.apache.spark.rdd.RDD
import org.apache.spark.sql.sources.{BaseRelation, TableScan}
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.{Row, SQLContext}


case class sample(id: Integer)

class S3Relation(accesskey: String, secretkey: String, fileType: String, bucket: String, path:
String, write: Boolean)
                (@transient
                 val sqlContext: SQLContext) extends BaseRelation with TableScan {

  import sqlContext.implicits._

  val dummyData = Seq(sample(1))
  var df = sqlContext.sparkContext.parallelize(dummyData, 4).toDF()
  val s3Path = "s3a://" + bucket + path

  val hadoopConf = sqlContext.sparkContext.hadoopConfiguration
  hadoopConf.set("fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem")
  hadoopConf.set("fs.s3a.access.key", accesskey)
  hadoopConf.set("fs.s3a.secret.key", secretkey)

  override def schema: StructType = {
    fileType match {
      case "json" =>
        df = sqlContext.read.json(s3Path)
      case "csv" =>
        df = sqlContext.read.format("com.databricks.spark.csv").load(s3Path)
      case "parquet" =>
        df = sqlContext.read.parquet(s3Path)
    }
    df.schema
  }

  override def buildScan(): RDD[Row] = {
    df.rdd
  }
} 

//设置package包名称以及导入依赖的类
package org.wikimedia.research.recommendation.job.translation

import java.io.File

import org.apache.log4j.{LogManager, Logger}
import org.apache.spark.ml.regression.RandomForestRegressionModel
import org.apache.spark.sql.types.{DoubleType, StringType, StructField, StructType}
import org.apache.spark.sql.{DataFrame, Row, SaveMode, SparkSession}

import scala.collection.parallel.mutable.ParArray

object ScorePredictor {
  val log: Logger = LogManager.getLogger(ScorePredictor.getClass)

  def predictScores(spark: SparkSession,
                    modelsInputDir: File,
                    predictionsOutputDir: Option[File],
                    sites: ParArray[String],
                    featureData: DataFrame): Unit = {
    log.info("Scoring items")

    val predictions: Array[DataFrame] = sites.map(target => {
      try {
        log.info("Scoring for " + target)
        log.info("Getting work data for " + target)
        val workData: DataFrame = Utils.getWorkData(spark, featureData, target, exists = false)
        log.info("Loading model for " + target)
        val model = RandomForestRegressionModel.load(
          new File(modelsInputDir, target).getAbsolutePath)
        log.info("Scoring data for " + target)
        val predictions = model
          .setPredictionCol(target)
          .transform(workData)
          .select("id", target)

        predictions
      } catch {
        case unknown: Throwable =>
          log.error("Score for " + target + " failed", unknown)
          val schema = StructType(Seq(
            StructField("id", StringType, nullable = false),
            StructField(target, DoubleType, nullable = true)))
          spark.createDataFrame(spark.sparkContext.emptyRDD[Row], schema)
      }
    }).toArray

    val predictedScores = predictions.reduce((left, right) => left.join(right, Seq("id"), "outer"))

    log.info("Saving predictions")
    predictionsOutputDir.foreach(f = o =>
      predictedScores.coalesce(1)
        .write
        .mode(SaveMode.ErrorIfExists)
        .option("header", value = true)
        .option("compression", "bzip2")
        .csv(new File(o, "allPredictions").getAbsolutePath))
  }
} 

//设置package包名称以及导入依赖的类
package com.github.uosdmlab.nkp

import org.apache.spark.sql.{SparkSession, Row}
import org.apache.spark.sql.types.{StringType, StructField, StructType}



object Dictionary {

  import org.bitbucket.eunjeon.seunjeon.{Analyzer => EunjeonAnalyzer}

  private var words = Seq.empty[String]

  private def chain(fn: => Any): this.type = {
    fn
    this
  }

  private def syncDictionary(): Unit = EunjeonAnalyzer.setUserDict(words.toIterator)

  def addWords(word: String, words: String*): this.type = addWords(word +: words)

  def addWords(words: Traversable[String]): this.type = chain {
    this.words = this.words ++ words
    syncDictionary()
  }

  def reset(): this.type = chain {
    EunjeonAnalyzer.resetUserDict()
    words = Seq.empty[String]
  }

  def addWordsFromCSV(path: String, paths: String*): this.type =
    addWordsFromCSV(path +: paths)

  def addWordsFromCSV(paths: Traversable[String]): this.type = chain {
    val spark = SparkSession.builder().getOrCreate()

    import spark.implicits._

    val schema = StructType(Array(
      StructField("word", StringType, nullable = false),
      StructField("cost", StringType, nullable = true)))

    val df = spark.read
      .option("sep", ",")
      .option("inferSchema", value = false)
      .option("header", value = false)
      .schema(schema)
      .csv(paths.toSeq: _*)

    val words = df.map {
      case Row(word: String, cost: String) =>
        s"$word,$cost"
      case Row(word: String, null) =>
        word
    }.collect()

    addWords(words)
  }
} 

//设置package包名称以及导入依赖的类
package net.sansa_stack.ml.spark.mining.amieSpark

import org.apache.spark.sql.types.{StringType, StructField, StructType}
import org.apache.spark.sql.{DataFrame, Row, SQLContext}

/**
  * @author Lorenz Buehmann
  */
object EmptyRDFGraphDataFrame {

  def get(sqlContext: SQLContext): DataFrame = {
    // convert RDD to DataFrame
    val schemaString = "subject predicate object"

    // generate the schema based on the string of schema
    val schema = StructType(schemaString.split(" ").map(fieldName => StructField(fieldName, StringType, true)))

    // convert triples RDD to rows
    val rowRDD = sqlContext.sparkContext.emptyRDD[Row]

    // apply the schema to the RDD
    val triplesDataFrame = sqlContext.createDataFrame(rowRDD, schema)

    // register the DataFrame as a table
    triplesDataFrame.createOrReplaceTempView("TRIPLES")

    triplesDataFrame
  }
} 

//设置package包名称以及导入依赖的类
package com.springml.spark.zuora

import com.springml.spark.zuora.model.ZuoraInput
import org.apache.log4j.Logger
import org.apache.spark.sql.{DataFrame, SQLContext, SaveMode}
import org.apache.spark.sql.sources.{BaseRelation, CreatableRelationProvider, RelationProvider, SchemaRelationProvider}
import org.apache.spark.sql.types.StructType

import scala.collection.mutable


class DefaultSource extends RelationProvider with SchemaRelationProvider with CreatableRelationProvider {
  @transient val logger = Logger.getLogger(classOf[DefaultSource])

  override def createRelation(sqlContext: SQLContext,
                              parameters: Map[String, String]): BaseRelation = {
    createRelation(sqlContext, parameters, null)
  }

  override def createRelation(sqlContext: SQLContext,
                              parameters: Map[String, String],
                              schema: StructType): BaseRelation = {
    val email = param(parameters, "email")
    val password = param(parameters, "password")
    val zoql = param(parameters, "zoql")
    val instanceUrl = parameters.getOrElse("instanceURL", "https://rest.zuora.com")
    val apiVersion = parameters.getOrElse("apiVersion", "38.0")

    // TODO
    val pageSizeParam = parameters.getOrElse("pageSize", "1000")
    val pageSize = pageSizeParam.toInt

    val zuoraInput = new ZuoraInput(email, password, zoql, instanceUrl, apiVersion, pageSize)

    val records = new ZuoraReader(zuoraInput) read()
    new DatasetRelation(records, sqlContext, schema)
  }

  override def createRelation(sqlContext: SQLContext,
                              mode: SaveMode,
                              parameters: Map[String, String],
                              data: DataFrame): BaseRelation = {
    logger.error("Save not supported by Zuora connector")
    throw new UnsupportedOperationException
  }

  private def param(parameters: Map[String, String],
                    paramName: String) : String = {
    val paramValue = parameters.getOrElse(paramName,
      sys.error(s"""'$paramName' must be specified for Spark Zuora package"""))

    if ("password".equals(paramName)) {
      logger.debug("Param " + paramName + " value " + paramValue)
    }

    paramValue
  }

} 

//设置package包名称以及导入依赖的类
package sparknlp

import org.apache.spark.sql.Row
import org.apache.spark.sql.types.{MapType, StringType, StructField, StructType}

case class Document(
  id: String,
  text: String,
  metadata: scala.collection.Map[String, String] = Map()
)

object Document {
  def apply(row: Row): Document = {
    Document(row.getString(0), row.getString(1), row.getMap[String, String](2))
  }

  val DocumentDataType: StructType = StructType(Array(
    StructField("id",StringType,nullable = true),
    StructField("text",StringType,nullable = true),
    StructField("metadata",MapType(StringType,StringType,valueContainsNull = true),nullable = true)
  ))
} 

//设置package包名称以及导入依赖的类
package test.yumi.pipeline

import com.typesafe.config.Config
import org.apache.spark.SparkContext
import org.apache.spark.sql.{DataFrameReader, SparkSession}
import org.apache.spark.sql.types.StructType
import org.mockito.invocation.InvocationOnMock
import org.mockito.stubbing.Answer
import yumi.Job
import yumi.metastore.Metastore
import yumi.pipeline._

trait MockSessionSpec extends BaseSpec {

  trait MockSessionScope extends BaseMockScope {

    implicit val yumiContext = mock[YumiContext]
    val pipelineFactory = mock[PipelineFactory]
    val activityLoader = mock[ActivityLoader]
    val contextFactory = mock[YumiContextFactory]
    val sparkSession = mock[SparkSession]
    val sparkContext = mock[SparkContext]
    val dataFrameReader = mock[DataFrameReader]
    val dataFrameWriter = mock[DataFrameWriter]
    val emptyParameters = new Parameters

    when(sparkSession.sparkContext).thenReturn(sparkContext)

    when(yumiContext.sparkSession).thenReturn(sparkSession)
    when(yumiContext.sparkContext).thenReturn(sparkContext)
    when(yumiContext.dataFrameWriter).thenReturn(dataFrameWriter)

    when(sparkSession.read).thenReturn(dataFrameReader)

    when(dataFrameReader.format(any[String])).thenReturn(dataFrameReader)
    when(dataFrameReader.schema(any[StructType])).thenReturn(dataFrameReader)
    when(dataFrameReader.option(any[String], any[String])).thenReturn(dataFrameReader)
    when(dataFrameReader.options(any[Map[String, String]])).thenReturn(dataFrameReader)
  }
} 

//设置package包名称以及导入依赖的类
package org.sparksamples

import java.awt.Font

import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
import org.jfree.chart.axis.CategoryLabelPositions

import scalax.chart.module.ChartFactories



    val customSchema = StructType(Array(
      StructField("user_id", IntegerType, true),
      StructField("movie_id", IntegerType, true),
      StructField("rating", IntegerType, true),
      StructField("timestamp", IntegerType, true)))

    val spConfig = (new SparkConf).setMaster("local").setAppName("SparkApp")
    val spark = SparkSession
      .builder()
      .appName("SparkRatingData").config(spConfig)
      .getOrCreate()

    val rating_df = spark.read.format("com.databricks.spark.csv")
      .option("delimiter", "\t").schema(customSchema)
      .load("../../data/ml-100k/u.data")

    val rating_df_count = rating_df.groupBy("rating").count().sort("rating")
    //val rating_df_count_sorted = rating_df_count.sort("count")
    rating_df_count.show()
    val rating_df_count_collection = rating_df_count.collect()

    val ds = new org.jfree.data.category.DefaultCategoryDataset
    val mx = scala.collection.immutable.ListMap()

    for( x <- 0 until rating_df_count_collection.length) {
      val occ = rating_df_count_collection(x)(0)
      val count = Integer.parseInt(rating_df_count_collection(x)(1).toString)
      ds.addValue(count,"UserAges", occ.toString)
    }


    //val sorted =  ListMap(ratings_count.toSeq.sortBy(_._1):_*)
    //val ds = new org.jfree.data.category.DefaultCategoryDataset
    //sorted.foreach{ case (k,v) => ds.addValue(v,"Rating Values", k)}

    val chart = ChartFactories.BarChart(ds)
    val font = new Font("Dialog", Font.PLAIN,5);

    chart.peer.getCategoryPlot.getDomainAxis().
      setCategoryLabelPositions(CategoryLabelPositions.UP_90);
    chart.peer.getCategoryPlot.getDomainAxis.setLabelFont(font)
    chart.show()
    Util.sc.stop()
  }
} 

//设置package包名称以及导入依赖的类
package org.sparksamples

import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.{IntegerType, StructField, StructType}

import scalax.chart.module.ChartFactories


object UserRatingsChart {

  def main(args: Array[String]) {

    val customSchema = StructType(Array(
      StructField("user_id", IntegerType, true),
      StructField("movie_id", IntegerType, true),
      StructField("rating", IntegerType, true),
      StructField("timestamp", IntegerType, true)))

    val spConfig = (new SparkConf).setMaster("local").setAppName("SparkApp")
    val spark = SparkSession
      .builder()
      .appName("SparkRatingData").config(spConfig)
      .getOrCreate()

    val rating_df = spark.read.format("com.databricks.spark.csv")
      .option("delimiter", "\t").schema(customSchema)
      .load("../../data/ml-100k/u.data")

    val rating_nos_by_user = rating_df.groupBy("user_id").count().sort("count")
    val ds = new org.jfree.data.category.DefaultCategoryDataset
    rating_nos_by_user.show(rating_nos_by_user.collect().length)
    val rating_nos_by_user_collect =rating_nos_by_user.collect()

    var mx = Map(0 -> 0)
    val min = 1
    val max = 1000
    val bins = 100

    val step = (max/bins).toInt
    for (i <- step until (max + step) by step) {
      mx += (i -> 0);
    }
    for( x <- 0 until rating_nos_by_user_collect.length) {
      val user_id = Integer.parseInt(rating_nos_by_user_collect(x)(0).toString)
      val count = Integer.parseInt(rating_nos_by_user_collect(x)(1).toString)
      ds.addValue(count,"Ratings", user_id)
    }
   // ------------------------------------------------------------------

    val chart = ChartFactories.BarChart(ds)
    chart.peer.getCategoryPlot.getDomainAxis().setVisible(false)

    chart.show()
    Util.sc.stop()
  }
} 

//设置package包名称以及导入依赖的类
package org.sparksamples

import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.sql._
import org.apache.spark.sql.types.{StringType, StructField, StructType}


object Util {
  val PATH = "/home/ubuntu/work/spark-2.0.0-bin-hadoop2.7/"
  val DATA_PATH= "../../../data/ml-100k"
  val PATH_MOVIES = DATA_PATH + "/u.item"

  def reduceDimension2(x: Vector) : String= {
    var i = 0
    var l = x.toArray.size
    var l_2 = l/2.toInt
    var x_ = 0.0
    var y_ = 0.0

    for(i <- 0 until l_2) {
      x_ += x(i).toDouble
    }
    for(i <- (l_2 + 1) until l) {
      y_ += x(i).toDouble
    }
    var t = x_ + "," + y_
    return t
  }

  def getMovieDataDF(spark : SparkSession) : DataFrame = {

    //1|Toy Story (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Toy%20Story%20(1995)
    // |0|0|0|1|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0
    val customSchema = StructType(Array(
      StructField("id", StringType, true),
      StructField("name", StringType, true),
      StructField("date", StringType, true),
      StructField("url", StringType, true)));
    val movieDf = spark.read.format("com.databricks.spark.csv")
      .option("delimiter", "|").schema(customSchema)
      .load(PATH_MOVIES)
    return movieDf
  }

} 

//设置package包名称以及导入依赖的类
package net.sansa_stack.inference.spark.data.loader.sql

import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{Row, SQLContext}
import org.apache.spark.sql.sources.{BaseRelation, TableScan}
import org.apache.spark.sql.types.{StringType, StructField, StructType}

import net.sansa_stack.inference.utils.NTriplesStringToRDFTriple

class NTriplesRelation(location: String, userSchema: StructType)
                      (@transient val sqlContext: SQLContext)
    extends BaseRelation
      with TableScan
      with Serializable {
    override def schema: StructType = {
      if (this.userSchema != null) {
        this.userSchema
      }
      else {
        StructType(
          Seq(
            StructField("s", StringType, true),
            StructField("p", StringType, true),
            StructField("o", StringType, true)
        ))
      }
    }
    override def buildScan(): RDD[Row] = {
      val rdd = sqlContext
        .sparkContext
        .textFile(location)

      val converter = new NTriplesStringToRDFTriple()

      val rows = rdd.flatMap(x => converter.apply(x)).map(t => Row.fromSeq(Seq(t.s, t.p, t.o)))

      rows
    }
  }