在使用编译代码之后 sbt package 在spark中提交：

sudo -u spark spark-submit  --master yarn --deploy-mode client --executor-memory 2G --num-executors 6 --class viterbiAlgorithm.viterbiAlgo ./target/scala-2.11/vibertialgo_2.11-1.3.4.jar

我有个错误：

Exception in thread "main" java.lang.NoSuchMethodError: breeze.linalg.DenseVector$.tabulate$mDc$sp(ILscala/Function1;Lscala/reflect/ClassTag;)Lbreeze/linalg/DenseVector;
    at viterbiAlgorithm.User$$anonfun$eval$2.apply(viterbiAlgo.scala:84)
    at viterbiAlgorithm.User$$anonfun$eval$2.apply(viterbiAlgo.scala:80)
    at scala.collection.immutable.Range.foreach(Range.scala:160)
    at viterbiAlgorithm.User.eval(viterbiAlgo.scala:80)
    at viterbiAlgorithm.viterbiAlgo$.main(viterbiAlgo.scala:28)
    at viterbiAlgorithm.viterbiAlgo.main(viterbiAlgo.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at org.apache.spark.deploy.JavaMainApplication.start(SparkApplication.scala:52)
    at org.apache.spark.deploy.SparkSubmit.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:851)
    at org.apache.spark.deploy.SparkSubmit.doRunMain$1(SparkSubmit.scala:167)
    at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:195)
    at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:86)
    at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:926)
    at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:935)
    at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

sbt构建文件如下所示：

name := "vibertiAlgo"  
version := "1.3.4"  
scalaVersion := "2.11.2"  

libraryDependencies  ++= Seq(  
    "org.scalanlp" %% "breeze" % "1.0",  
    "org.apache.spark" %% "spark-core" % "2.4.0",  
    "org.apache.spark" %% "spark-sql" % "2.4.0")

我可以成功地在本地运行代码，虽然与 sbt run ，所以我不知道我的代码有什么问题。另外，scala和spark的编译和运行时版本是相同的。
的代码 viterbiAlgo.scala 是：

package viterbiAlgorithm

import breeze.linalg._
// import org.apache.spark.sql.SparkSession

object viterbiAlgo {
  def main(arg: Array[String]) {

    val A = DenseMatrix((0.5,0.2,0.3), 
      (0.3,0.5,0.2),
      (0.2,0.3,0.5))
    val B = DenseMatrix((0.5,0.5), 
      (0.4,0.6),
      (0.7,0.3))
    val pi = DenseVector(0.2,0.4,0.4) 

    val o = DenseVector[Int](0,1,0) //Hive time + cell_id
    val model = new Model(A,B,pi) 
    val user = new User("Jack", model, o) //Hive 
    user.eval() // run algorithm
    user.printResult()

    //spark sql
    // val warehouseLocation = "spark-warehouse"
    // val spark = SparkSession.builder().appName("Spark.sql.warehouse.dir").config("spark.sql.warehouse.dir", warehouseLocation).enableHiveSupport().getOrCreate()
    // import spark.implicits._
    // import spark.sql

    // val usr = "1"
    // val model = new Model(A,B,pi) 
    // val get_statement = "SELECT * FROM viterbi.observation"
    // val df = sql(get_statement)
    // val o = DenseVector(df.filter(df("usr")===usr).select(df("obs")).collect().map(_.getInt(0)))
    // val user = new User(usr, model, o)
    // user.eval()
    // user.printResult()
  }
}

class Model (val A: DenseMatrix[Double], val B:DenseMatrix[Double], val pi: DenseVector[Double]) {
  def info():Unit = {
    println("The model is:")
    println("A:")
    println(A)
    println("B:")
    println(B)
    println("Pi:")
    println(pi)
  }
}

class User (val usr_name: String, val model: Model, val o:DenseVector[Int]) {
  val N = model.A.rows // state number
  val M = model.B.cols // observation state
  val T = o.length // time 
  val delta = DenseMatrix.zeros[Double](N,T)
  val psi = DenseMatrix.zeros[Int](N,T)
  val best_route = DenseVector.zeros[Int](T)

  def eval():Unit = {
    //1. Initialization
    delta(::,0) := model.pi * model.B(::, o(0))
    psi(::,0) := DenseVector.zeros[Int](N)

    /*2. Induction
    */
    val tempDelta = DenseMatrix.zeros[Double](N,N)// Initialization
    val tempB = DenseMatrix.zeros[Double](N,N)// Initialization
    for (t <- 1 to T-1) { 
      // Delta
      tempDelta := DenseMatrix.tabulate(N, N){case (i, j) => delta(i,t-1)}
      tempB := DenseMatrix.tabulate(N, N){case (i, j) => model.B(j, o(t))} 
      delta(::, t) := DenseVector.tabulate(N){i => max((tempDelta *:* model.A *:* tempB).t.t(::,i))} 
    }

    //3. Maximum
    val P_star = max(delta(::, T-1))
    val i_star_T = argmax(delta(::, T-1))
    best_route(T-1) = i_star_T

    //4. Backward

    for (t <- T-2 to 0 by -1) {
      best_route(t) = psi(best_route(t+1),t+1)
    }
  }

  def printResult():Unit = {
    println("User: " + usr_name)
    model.info()
    println
    println("Observed: ")
    printRoute(o)
    println("Best_route is: ")
    printRoute(best_route)
    println("delta is")
    println(delta)
    println("psi is: ")
    println(psi)
  }

  def printRoute(v: DenseVector[Int]):Unit = {
    for (i <- v(0 to -2)){
      print(i + "->")
    }
    println(v(-1))
  }

}

我也试过了 --jars 参数并传递了breeze库的位置，但得到了相同的错误。
我需要提到的是，我在服务器上“本地”测试了代码，并在sparkshell上测试了所有方法（我可以在服务器上的sparkshell上导入breeze库）。
服务器scala版本与sbt构建文件中的版本匹配。尽管spark版本是2.4.0-cdh6.2.1，如果我在“2.4.0”之后添加“cdh6.2.1”，sbt将不会编译。
我尝试了维克托提供的两种可能的解决办法，但没有成功。但是，我将sbt构建文件中的breeze版本更改为 0.13.2 从 1.0 ，一切正常。但我不知道出了什么问题。