Spark LDA实战

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作者:代码空间

选取了10个文档，其中4个来自于一篇论文，3篇来自于一篇新闻，3篇来自于另一篇新闻。

首先在pom文件中加入mysql-connector-java：

        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.6</version>
        </dependency>

main函数：

  def main(args:Array[String]): Unit ={
    // 连接MySQL数据库
    case class Article(id:Int,article:String)
    case class Word(id:Int,word:String)
    case class Vocabulary(id:Int,word_id:Int,article_id:Int,count:Int)
//    Class.forName("com.mysql.jdbc.Driver")
    val sqlContext = new org.apache.spark.sql.SQLContext(sc)
    import sqlContext.implicits._
    val url = "jdbc:mysql://192.168.1.101:3306/machinelearning"
    val table_article = "article"
    val table_word = "word"
    val table_vocabulary = "vocabulary"
    val table_stopword = "stopword"
    val reader = sqlContext.read.format("jdbc")
    reader.option("url", url)
    reader.option("driver", "com.mysql.jdbc.Driver")
    reader.option("user", "hadoop")
    reader.option("password", "root")
    reader.option("dbtable", table_article)
    val df_article = reader.load()
    reader.option("dbtable", table_word)
    val df_word = reader.load()
    reader.option("dbtable", table_vocabulary)
    val df_vocabulary = reader.load()
    var vocuRow = df_vocabulary.collect()
    reader.option("dbtable", table_stopword)
    val df_stopword = reader.load()
    var stopRow = df_stopword.collect()

    // 记录属性
    val prop = new java.util.Properties
    prop.setProperty("user", "hadoop")
    prop.setProperty("password", "root")
    prop.setProperty("driver", "com.mysql.jdbc.Driver")

    // 读取文档的单词到vocabulary表
　　insertVocabulary("hdfs://master:9000/ml/data/Article_01.txt", vocuRow)
　　vocuRow = df_vocabulary.collect()
　　insertVocabulary("hdfs://master:9000/ml/data/Article_02.txt", vocuRow)
　　vocuRow = df_vocabulary.collect()
　　insertVocabulary("hdfs://master:9000/ml/data/Article_03.txt", vocuRow)
　　vocuRow = df_vocabulary.collect()
　　insertVocabulary("hdfs://master:9000/ml/data/Article_04.txt", vocuRow)
　　vocuRow = df_vocabulary.collect()
　　insertVocabulary("hdfs://master:9000/ml/data/Article_05.txt", vocuRow)
　　vocuRow = df_vocabulary.collect()
　　insertVocabulary("hdfs://master:9000/ml/data/Article_06.txt", vocuRow)
　　vocuRow = df_vocabulary.collect()
　　insertVocabulary("hdfs://master:9000/ml/data/Article_07.txt", vocuRow)
　　vocuRow = df_vocabulary.collect()
　　insertVocabulary("hdfs://master:9000/ml/data/Article_08.txt", vocuRow)
　　vocuRow = df_vocabulary.collect()
　　insertVocabulary("hdfs://master:9000/ml/data/Article_09.txt", vocuRow)
　　vocuRow = df_vocabulary.collect()
　　insertVocabulary("hdfs://master:9000/ml/data/Article_10.txt", vocuRow)
　　vocuRow = df_vocabulary.collect()

    // 插入stopword表
　　insertStopWord("hdfs://master:9000/ml/data/stop_words_eng.txt")
　　stopRow = df_stopword.collect()

    // 从Vocabulary表中删除StopWord表里面的词
    compareAndDelete(vocuRow, stopRow)
    vocuRow = df_vocabulary.collect()

    // 生成LDA用的RDD
    val ldaBufferArrayBuffer = ArrayBuffer[Array[Double]]()
    ldaBufferArrayBuffer += getFrequency("hdfs://master:9000/ml/data/Article_01.txt", vocuRow)
    ldaBufferArrayBuffer += getFrequency("hdfs://master:9000/ml/data/Article_02.txt", vocuRow)
    ldaBufferArrayBuffer += getFrequency("hdfs://master:9000/ml/data/Article_03.txt", vocuRow)
    ldaBufferArrayBuffer += getFrequency("hdfs://master:9000/ml/data/Article_04.txt", vocuRow)
    ldaBufferArrayBuffer += getFrequency("hdfs://master:9000/ml/data/Article_05.txt", vocuRow)
    ldaBufferArrayBuffer += getFrequency("hdfs://master:9000/ml/data/Article_06.txt", vocuRow)
    ldaBufferArrayBuffer += getFrequency("hdfs://master:9000/ml/data/Article_07.txt", vocuRow)
    ldaBufferArrayBuffer += getFrequency("hdfs://master:9000/ml/data/Article_08.txt", vocuRow)
    ldaBufferArrayBuffer += getFrequency("hdfs://master:9000/ml/data/Article_09.txt", vocuRow)
    ldaBufferArrayBuffer += getFrequency("hdfs://master:9000/ml/data/Article_10.txt", vocuRow)

    val parsedLdaArray = ldaBufferArrayBuffer.toArray.map(s => Vectors.dense(s))
    val ldaRDD = sc.parallelize(parsedLdaArray).zipWithIndex().map(_.swap).cache()

    println("LDA is training...")
    val ldaModel = new LDA()
      // 主题数量
      .setK(3)
      // 超参数(伪计数)
      .setDocConcentration(1.1)
      // 超参数(伪计数)
      .setTopicConcentration(1.1)
      // 最大迭代次数
      .setMaxIterations(100)
      .setSeed(0L)
      .setCheckpointInterval(10)
      .setOptimizer("em")
      .run(ldaRDD)
    println("LDA finishes training...")

    println("Learned topics (as distributions over vocab of " + ldaModel.vocabSize + " words):")
    // 主题分布
    val topics = ldaModel.topicsMatrix
    try {
      for (topic <- Range(0, 3)) {
        print("Topic " + topic + ":")
        for (word <- Range(0, ldaModel.vocabSize)) {
          print(" " + word + "(" + topics(word, topic) + ")");
        }
        println()
      }
    } catch {
      case ex : Exception => println(ex)
    }

    // 主题分布排序
    val topics2 = ldaModel.describeTopics(10)
    println("The topics described by their top-weighted terms:")
    try {
      for (topic2 <- Range(0, topics2.length)) {
        print("Topic " + topic2 + ":")
        for (word <- Range(0, topics2.apply(topic2)._1.size)) {
          print(word + "(" + topics2.apply(topic2)._2.apply(word) + ") ")
        }
        println
      }
    } catch {
      case ex : Exception => println(ex)
    }

    // 文档分布
    println("The documentation distribution:")
    val distLDAModel = ldaModel.asInstanceOf[DistributedLDAModel]
    val docs = distLDAModel.topicDistributions.collect
    val docs2 = docs.sortBy(_._1)
    try{
      for (doc <- Range(0, docs2.length)){
          println(docs2.apply(doc)._1 + " " + docs2.apply(doc)._2)
      }
    } catch {
      case ex : Exception => println(ex)
    }

    sc.stop
  }

统计每个文档的每个单词出现的次数:

  def getFrequency(path : String, recordRow : Array[Row]) : Array[Double] = {
    println(path + "'s frequency is getting...")
    val res = ArrayBuffer[Double]()
    val textRDD = sc.textFile(path)
    val wordRDD = textRDD.flatMap(line => line.split("\s+"))
      .map(word => {
        // 对word进行处理,包括去除符号,转换成小写等
        var s = ""
        word.foreach(c => {
          var ch = c
          if (ch.isLetter) {
            if (Character.isUpperCase(ch)) {
              ch = ch.toLower
            }
            s = s + ch
          }
        })
        (s, 1)})
      .reduceByKey((x, y) => x + y)
      .sortByKey(true)
      .collect()

    for (i <- Range(0,recordRow.size)){
      try {
        val r = recordRow.apply(i).getString(1)
        var isFound = false
        breakable {
          wordRDD.foreach(w => {
            if (r.equals(w._1)) {
              res += w._2.toDouble
              isFound = true
              break
            }
          })
        }
        if (!isFound)
          res += 0.0
      } catch {
        case ex : Exception => println(ex)
      }
    }

    println("Frequency finishes geting!")
    res.toArray
  }

从Vocabulary表中删除StopWord表里面的词:

  def compareAndDelete(vRow : Array[Row], sRow : Array[Row]) : Unit = {
    println("CompareAndDelete is running...")
    var conn: Connection = null
    var ps: PreparedStatement = null
    val sql = "delete from vocabulary where Word = ?"
    conn = DriverManager.getConnection("jdbc:mysql://192.168.1.101:3306/machinelearning", "hadoop", "root")
    val vocuArray = new ArrayBuffer[String]()
    val stopArray = new ArrayBuffer[String]()
    val delArray = new ArrayBuffer[String]()
    vRow.foreach(v => {
      vocuArray += v.getString(1)
    })
    sRow.foreach(s => {
      stopArray += s.getString(1)
    })
    vocuArray.foreach(v => {
      stopArray.foreach(s => {
        if (v.equals(s))
          delArray += v
      })
    })
    delArray.foreach(d => {
      ps = conn.prepareStatement(sql)
      ps.setString(1, d)
      ps.executeUpdate()
    })
    println("CompareAndDelete finishes running!")
  }

把停用词插入stopword表：

  def insertStopWord(path : String): Unit = {
    println("Stopword is inserting...")
    val textRDD = sc.textFile(path)
    val stopWordRDD = textRDD.flatMap(line => line.split("\s+"))
      .map(word => (word, 1))
      .reduceByKey((x, y) => x + y)
      .sortByKey(true)

    // 写入vocabulary表
    var conn: Connection = null
    var ps: PreparedStatement = null
    val sql = "insert into stopword(Word) values (?)"
    conn = DriverManager.getConnection("jdbc:mysql://192.168.1.101:3306/machinelearning", "hadoop", "root")
    stopWordRDD.collect().foreach(p => {
      // 该单词写入表vocabulary
      ps = conn.prepareStatement(sql)
      ps.setString(1, p._1)
      ps.executeUpdate()
    })
    println("Stopword finishes inserting!")

  }

把文档的单词插入vocabulary表：

  def insertVocabulary(path : String, recordRow : Array[Row]): Unit = {
    println(path + " is inserting...")
    val textRDD = sc.textFile(path)
    val wordRDD = textRDD.flatMap(line => line.split("\s+"))
      .map(word => {
        // 对word进行处理,包括去除符号,转换成小写等
        var s = ""
        word.foreach(c => {
          var ch = c
          if (ch.isLetter) {
            if (Character.isUpperCase(ch)) {
              ch = ch.toLower
            }
            s = s + ch
          }
        })
        (s, 1)})
      .reduceByKey((x, y) => x + y)
    .sortByKey(true)

    // 写入vocabulary表
    var conn: Connection = null
    var ps: PreparedStatement = null
    val sql = "insert into vocabulary(Word) values (?)"
    conn = DriverManager.getConnection("jdbc:mysql://192.168.1.101:3306/machinelearning", "hadoop", "root")
    wordRDD.collect().foreach(p => {
      println(p)
      var isWrite = true
      breakable{
        recordRow.foreach(q => {
          if (p._1 == q.getString(1)){
            // 已经存在这个单词则不写入
            isWrite = false
            break
          }
        }
        )
      }
      // 该单词写入表vocabulary
      if (isWrite){
        ps = conn.prepareStatement(sql)
        ps.setString(1, p._1)
        ps.executeUpdate()
      }
    })
    println(path + " finishes inserting!")
  }

运行结果：

Learned topics (as distributions over vocab of 1324 words):
Topic 0: 0(2.5145205580836416E-5) 1(2.5145205580836416E-5) 2(1.1291049227313227E-4) 3(2.5145205580836416E-5) 4(0.998745917983795) 5(2.5145205580836416E-5) 6(2.5145205580836416E-5) 7(2.5145205580836416E-5) 8(2.5145205580836416E-5) 9(0.9997669538548448) 10(2.7449336165991658E-5) 11(2.9975368739414727) 12(0.9996985895091448) 13(2.5145205580836416E-5) 14(4.936699969890295E-5) 15(9.273504406339597E-5) 16(2.5145205580836416E-5) 17(2.000014204168829) 18(2.5145205580836416E-5) 19(0.9991911071252098) 20(4.936699969890295E-5) 21(0.9985711249060892) 22(0.9993494841023468) 23(2.6342996410253656E-5) 24(2.676801478412973E-5) 25(2.998715335627293) 26(0.9997669538548448) 27(2.5145205580836416E-5) 28(2.5145205580836416E-5) 29(0.9993494841023468) 30(2.5145205580836416E-5) 31(2.5145205580836416E-5) 32(3.979229408939385E-5) 33(5.113882938317321E-5) 34(2.5145205580836416E-5) 35(2.5145205580836416E-5) 36(2.5145205580836416E-5) 37(2.6342996410253656E-5) 38(2.5145205580836416E-5) 39(2.9998155642939417) 40(4.999433111714826) 41(3.387405036548482E-5) 42(2.5145205580836416E-5) 43(3.979229408939385E-5) 44(5.973997183679773E-5) 45(2.5145205580836416E-5) 46(2.5145205580836416E-5) 47(4.936699969890295E-5) 48(2.5145205580836416E-5) 49(4.000545295615069) 50(2.5145205580836416E-5) 51(2.5145205580836416E-5) 52(2.5145205580836416E-5) 53(2.5145205580836416E-5) 54(2.6342996410253656E-5) 55(2.5145205580836416E-5) 56(2.5145205580836416E-5) 57(3.0003584697399495) 58(2.5145205580836416E-5) 59(5.056468298445796E-5) 60(2.5145205580836416E-5) 61(2.5145205580836416E-5) 62(2.5145205580836416E-5) 63(2.5145205580836416E-5) 64(0.9993494841023468) 65(0.9993494841023468) 66(2.5145205580836416E-5) 67(6.997591263562541) 68(2.5145205580836416E-5) 69(2.5145205580836416E-5) 70(0.9990237539827164) 71(2.5145205580836416E-5) 72(2.5145205580836416E-5) 73(2.5145205580836416E-5) 74(2.5145205580836416E-5) 75(3.587854320991863E-5) 76(2.6342996410253656E-5) 77(3.587854320991863E-5) 78(2.5145205580836416E-5) 79(2.5145205580836416E-5) 80(0.9997669538548448) 81(2.5145205580836416E-5) 82(2.5145205580836416E-5) 83(2.5145205580836416E-5) 84(3.0020395447940484) 85(2.5145205580836416E-5) 86(6.401160673356124E-5) 87(1.9981605849279473) 88(7.081780287742211E-5) 89(2.5145205580836416E-5) 90(1.0796362026414354E-4) 91(2.5145205580836416E-5) 92(2.5145205580836416E-5) 93(2.5145205580836416E-5) 94(2.5145205580836416E-5) 95(6.401160673356124E-5) 96(2.6342996410253656E-5) 97(4.499085544043795E-5) 98(5.276346455586639E-5) 99(2.5145205580836416E-5) 100(2.5145205580836416E-5) 101(2.5145205580836416E-5) 102(2.6342996410253656E-5) 103(2.5145205580836416E-5) 104(2.5145205580836416E-5) 105(2.5145205580836416E-5) 106(4.936699969890295E-5) 107(2.5145205580836416E-5) 108(2.5145205580836416E-5) 109(4.000070835486271) 110(8.27125254893778E-5) 111(2.5145205580836416E-5) 112(4.000177459372059) 113(0.9985100590613034) 114(6.401160673356124E-5) 115(3.080557709345904E-5) 116(2.5145205580836416E-5) 117(2.698570609408248E-5) 118(2.6342996410253656E-5) 119(2.5145205580836416E-5) 120(2.5145205580836416E-5) 121(2.5145205580836416E-5) 122(7.001432387508433) 123(2.5145205580836416E-5) 124(2.676801478412973E-5) 125(2.6342996410253656E-5) 126(2.5145205580836416E-5) 127(2.5145205580836416E-5) 128(2.5145205580836416E-5) 129(0.9992394425337842) 130(2.5145205580836416E-5) 131(7.926846928146479E-5) 132(2.5145205580836416E-5) 133(4.936699969890295E-5) 134(2.5145205580836416E-5) 135(2.5145205580836416E-5) 136(0.9997405100542728) 137(2.5145205580836416E-5) 138(2.5145205580836416E-5) 139(0.999008000950605) 140(2.5145205580836416E-5) 141(2.5145205580836416E-5) 142(2.5145205580836416E-5) 143(2.5145205580836416E-5) 144(2.5145205580836416E-5) 145(5.056468298445796E-5) 146(2.5145205580836416E-5) 147(2.5145205580836416E-5) 148(2.5145205580836416E-5) 149(2.5145205580836416E-5) 150(2.5145205580836416E-5) 151(2.5145205580836416E-5) 152(2.6342996410253656E-5) 153(0.9953188882008727) 154(2.7270849902875906E-5) 155(2.6342996410253656E-5) 156(7.737822026514119E-5) 157(2.6342996410253656E-5) 158(2.5145205580836416E-5) 159(2.5145205580836416E-5) 160(2.6342996410253656E-5) 161(6.91451329402625E-5) 162(2.5145205580836416E-5) 163(2.5145205580836416E-5) 164(6.5453085305864E-5) 165(2.5145205580836416E-5) 166(1.9995297272663017) 167(2.000216218038889) 168(2.676801478412973E-5) 169(2.5145205580836416E-5) 170(2.731896045227702E-5) 171(12.010168692813501) 172(0.9981211283744875) 173(2.5145205580836416E-5) 174(2.5145205580836416E-5) 175(8.27125254893778E-5) 176(2.676801478412973E-5) 177(1.997622908860093) 178(4.936699969890295E-5) 179(5.153769989016098E-5) 180(2.5145205580836416E-5) 181(3.0002836115187366) 182(2.5145205580836416E-5) 183(1.999568052126521) 184(0.9997669538548448) 185(2.5145205580836416E-5) 186(17.007642787151525) 187(5.0520671517072685E-5) 188(2.676801478412973E-5) 189(2.5145205580836416E-5) 190(2.5145205580836416E-5) 191(5.001730677979211) 192(9.39351853021172E-5) 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1139(0.9997612002573364) 1140(0.9997612002573364) 1141(1.9997498106588625) 1142(0.9997612002573364) 1143(0.9997612002573364) 1144(0.9997612002573364) 1145(0.9997612002573364) 1146(0.9997612002573364) 1147(4.5122508499873454E-5) 1148(4.5122508499873454E-5) 1149(4.307037755864134E-5) 1150(0.99906235393172) 1151(4.5122508499873454E-5) 1152(4.307037755864134E-5) 1153(4.5122508499873454E-5) 1154(4.307037755864134E-5) 1155(4.307037755864134E-5) 1156(4.307037755864134E-5) 1157(4.307037755864134E-5) 1158(4.307037755864134E-5) 1159(4.5122508499873454E-5) 1160(4.307037755864134E-5) 1161(4.5122508499873454E-5) 1162(1.9982312939058595) 1163(0.9955554235351355) 1164(4.585067347419394E-5) 1165(4.5122508499873454E-5) 1166(4.5122508499873454E-5) 1167(4.307037755864134E-5) 1168(4.307037755864134E-5) 1169(4.307037755864134E-5) 1170(0.99906235393172) 1171(4.5122508499873454E-5) 1172(4.307037755864134E-5) 1173(4.715514062624739E-5) 1174(4.698804088714326E-5) 1175(4.307037755864134E-5) 1176(4.307037755864134E-5) 1177(4.307037755864134E-5) 1178(4.307037755864134E-5) 1179(4.307037755864134E-5) 1180(4.307037755864134E-5) 1181(4.307037755864134E-5) 1182(4.307037755864134E-5) 1183(4.307037755864134E-5) 1184(4.307037755864134E-5) 1185(4.307037755864134E-5) 1186(4.307037755864134E-5) 1187(4.307037755864134E-5) 1188(4.307037755864134E-5) 1189(4.307037755864134E-5) 1190(4.307037755864134E-5) 1191(4.307037755864134E-5) 1192(4.307037755864134E-5) 1193(4.307037755864134E-5) 1194(4.307037755864134E-5) 1195(4.307037755864134E-5) 1196(4.307037755864134E-5) 1197(4.6450333568493035E-5) 1198(4.307037755864134E-5) 1199(4.307037755864134E-5) 1200(4.585067347419394E-5) 1201(4.307037755864134E-5) 1202(4.307037755864134E-5) 1203(4.307037755864134E-5) 1204(4.307037755864134E-5) 1205(4.307037755864134E-5) 1206(4.307037755864134E-5) 1207(4.307037755864134E-5) 1208(4.585067347419394E-5) 1209(4.307037755864134E-5) 1210(4.6450333568493035E-5) 1211(4.585067347419394E-5) 1212(4.307037755864134E-5) 1213(4.5122508499873454E-5) 1214(4.5122508499873454E-5) 1215(4.307037755864134E-5) 1216(4.307037755864134E-5) 1217(4.307037755864134E-5) 1218(4.5122508499873454E-5) 1219(4.307037755864134E-5) 1220(4.307037755864134E-5) 1221(4.307037755864134E-5) 1222(4.307037755864134E-5) 1223(4.5122508499873454E-5) 1224(4.5122508499873454E-5) 1225(4.307037755864134E-5) 1226(4.679458055842222E-5) 1227(4.5122508499873454E-5) 1228(4.307037755864134E-5) 1229(0.9979041404486393) 1230(4.307037755864134E-5) 1231(4.307037755864134E-5) 1232(4.307037755864134E-5) 1233(4.307037755864134E-5) 1234(4.307037755864134E-5) 1235(4.307037755864134E-5) 1236(4.307037755864134E-5) 1237(4.307037755864134E-5) 1238(4.307037755864134E-5) 1239(4.307037755864134E-5) 1240(4.307037755864134E-5) 1241(4.307037755864134E-5) 1242(4.585067347419394E-5) 1243(4.307037755864134E-5) 1244(4.307037755864134E-5) 1245(4.585067347419394E-5) 1246(4.307037755864134E-5) 1247(4.5122508499873454E-5) 1248(4.6223633387041405E-5) 1249(4.5122508499873454E-5) 1250(4.307037755864134E-5) 1251(4.307037755864134E-5) 1252(4.307037755864134E-5) 1253(0.99906235393172) 1254(4.307037755864134E-5) 1255(4.307037755864134E-5) 1256(4.307037755864134E-5) 1257(4.307037755864134E-5) 1258(4.307037755864134E-5) 1259(4.307037755864134E-5) 1260(4.679458055842222E-5) 1261(4.307037755864134E-5) 1262(4.6223633387041405E-5) 1263(0.99906235393172) 1264(4.307037755864134E-5) 1265(4.307037755864134E-5) 1266(4.307037755864134E-5) 1267(4.307037755864134E-5) 1268(4.307037755864134E-5) 1269(4.307037755864134E-5) 1270(0.99906235393172) 1271(4.307037755864134E-5) 1272(0.999769651168907) 1273(0.999769651168907) 1274(1.9997586652601287) 1275(0.999769651168907) 1276(0.999769651168907) 1277(0.999769651168907) 1278(0.999769651168907) 1279(0.999769651168907) 1280(0.999769651168907) 1281(1.9997586652601287) 1282(0.999769651168907) 1283(2.9997547672248634) 1284(0.999769651168907) 1285(0.999769651168907) 1286(0.999769651168907) 1287(0.999769651168907) 1288(0.999769651168907) 1289(0.999769651168907) 1290(0.999769651168907) 1291(2.9997547672248634) 1292(0.999769651168907) 1293(0.999769651168907) 1294(8.999749370150024) 1295(0.999769651168907) 1296(0.999769651168907) 1297(0.999769651168907) 1298(0.999769651168907) 1299(0.999769651168907) 1300(0.999769651168907) 1301(0.999769651168907) 1302(0.999769651168907) 1303(0.999769651168907) 1304(5.999750741513635) 1305(0.999769651168907) 1306(0.999769651168907) 1307(0.999769651168907) 1308(0.999769651168907) 1309(0.999769651168907) 1310(0.999769651168907) 1311(0.999769651168907) 1312(0.999769651168907) 1313(0.999769651168907) 1314(0.999769651168907) 1315(1.9997586652601287) 1316(0.999769651168907) 1317(0.999769651168907) 1318(0.999769651168907) 1319(0.999769651168907) 1320(0.999769651168907) 1321(6.99975015562379) 1322(1.9997586652601287) 1323(1.9997586652601287)
The topics described by their top-weighted terms:
Topic 0:0(0.026537654308753947) 1(0.022291595584459593) 2(0.01805376454632414) 3(0.016994449901753302) 4(0.012748901211959393) 5(0.012740514417610937) 6(0.012738038629205008) 7(0.01273801561924325) 8(0.011676529759440337) 9(0.010614979074646718) 
Topic 1:0(0.014607438152180853) 1(0.014598111550937454) 2(0.013559476460060267) 3(0.013555391429087675) 4(0.011473295929068187) 5(0.010427262939480266) 6(0.010427262939480266) 7(0.010427185065023511) 8(0.00938853183624946) 9(0.009385227376273907) 
Topic 2:0(0.013807670229605759) 1(0.012821404351493447) 2(0.011834519149445158) 3(0.010848808634841414) 4(0.010848808634841414) 5(0.009862562012887958) 6(0.009862542832894608) 7(0.008877277286893682) 8(0.008876340901933325) 9(0.008876145972864815) 
The documentation distribution:
0 [7.333146435036974E-5,6.080960819974437E-5,0.99986585892745]
1 [2.6663949298565034E-4,0.999649289033629,8.407147338540428E-5]
2 [0.9998175992795761,1.1652366091318298E-4,6.587705951070862E-5]
3 [1.4469275471657022E-4,0.9996555194522557,1.997877930276974E-4]
4 [0.9996659842219435,1.942152940732567E-4,1.3980048398321528E-4]
5 [6.57922549992311E-5,0.9998590561541768,7.515159082395228E-5]
6 [1.5473658550027247E-4,2.315996735072716E-4,0.9996136637409924]
7 [2.3259757591235534E-4,1.851791507569187E-4,0.9995822232733307]
8 [0.9998317528382852,3.189857050859758E-5,1.363485912062577E-4]
9 [2.909973281987435E-4,2.4861458428830874E-4,0.999460388087513]

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