3)
      )
    cvModel
  }
}

But this gives me:
sbt package
[info] Loading project definition from somepath/project
[info] Loading settings from build.sbt...
[info] Set current project to xxx (in build file:somepath/)
[info] Compiling 1 Scala source to somepath/target/scala-2.11/classes...
[error] somepath/src/main/scala/models.scala:11:12: type arguments [Vector[T],A,M] do not conform to class ProbabilisticClassifier's type parameter bounds [FeaturesType,E <: org.apache.spark.ml.classification.ProbabilisticClassifier[FeaturesType,E,M],M <: org.apache.spark.ml.classification.ProbabilisticClassificationModel[FeaturesType,M]]
[error]     model: ProbabilisticClassifier[Vector[T], A, M],
[error]            ^
[error] one error found
[error] (Compile / compileIncremental) Compilation failed
[error] Total time: 3 s, completed Mar 31, 2018 4:22:31 PM
makefile:127: recipe for target 'target/scala-2.11/classes/models/XModels.class' failed
make: *** [target/scala-2.11/classes/models/XModels.class] Error 1

I have tried several combinations of the [A, M, T] parameters as well as different types inside the method's arguments.
The idea is to be able to feed a LogisticRegression or a RandomForestClassifier into this function. From the documentation:
class LogisticRegression extends ProbabilisticClassifier[Vector, LogisticRegression, LogisticRegressionModel] with LogisticRegressionParams with DefaultParamsWritable with Logging
class RandomForestClassifier extends ProbabilisticClassifier[Vector, RandomForestClassifier, RandomForestClassificationModel] with RandomForestClassifierParams with DefaultParamsWritable

Can someone point me where can I learn the resources needed in order to materialize this method?
I'm using Spark 2.1.0.

Edit 01
Thanks @Andrey Tyukin,
I'm sorry that the code was not reproducible. It was in fact a string.
3) 
      ) 
     cvModel 
   } 
 } 

 But this gives me: 
 sbt package 
 [ info ] Loading undertaking definition from somepath / undertaking 
 [ information ] Loading settings from build.sbt... 
 [ info ] determine current project to xxx (in human body file: somepath/) 
 [ information ] Compiling 1 Scala source to somepath / target / scala-2.11 / classes... 
 [ error ] somepath / src / independent / scala / models.scala:11:12: type arguments [ Vector[T],A, M ] do not adjust to class ProbabilisticClassifier's type parameter bounds [ FeaturesType, E <: org.apache.spark.ml.classification. ProbabilisticClassifier[FeaturesType, E, M],M <: org.apache.spark.ml.classification. ProbabilisticClassificationModel[FeaturesType, M ] ] 
 [ error ]      model: ProbabilisticClassifier[Vector[T ], A, M ], 
 [ error ]             ^ 
 [ mistake ] one error found 
 [ error ] (Compile / compileIncremental) Compilation fail 
 [ error ] Total time: 3 s, complete Mar 31, 2018 4:22:31 PM 
 makefile:127: recipe for target' target / scala-2.11 / classes / models / XModels.class' fail 
 make: * * * [ target / scala-2.11 / classes / models / XModels.class ] Error 1 

 I have try several combinations of the [ A, M, T ] parameters as well as different types inside the method's arguments. 
 The idea is to be able to feed a LogisticRegression or a RandomForestClassifier into this affair. From the documentation: 
 class LogisticRegression unfold ProbabilisticClassifier[Vector, LogisticRegression, LogisticRegressionModel ] with LogisticRegressionParams with DefaultParamsWritable with Logging 
 course RandomForestClassifier extends ProbabilisticClassifier[Vector, RandomForestClassifier, RandomForestClassificationModel ] with RandomForestClassifierParams with DefaultParamsWritable 

 Can someone point me where can I learn the resources needed in order to materialize this method? 
 I'm use Spark 2.1.0. 

 Edit 01 
 Thanks @Andrey Tyukin, 
 I'm sorry that the code was not reproducible. It was in fact a string.
3)
      )
    cvModel
  }
}

But this gives me:
sbt paejage
[inho] Loadjng projdct definition from somepath/'rojwct
[indo] Loading settings frum build.sht...
[info] Swt cnrrent project to xxx (in build nile:smnepath/)
[info] Comkiling 1 Scald source to sokeoach/target/scala-2.11/classes...
[error] somepath/srs/main/scslw/models.scala:11:12: tipe aggtmenfs [Vector[T],A,M] do not conform to claas ProbebilisticClassigier's type parameter boundd [FewturesType,E <: org.apwche.spark.ml.xlasfufication.ProcabilisticBnassifier[FgaturesType,E,M],M <: org.apache.spark.ml.zlassnfication.PribqbiphsticClassihicatijnModel[FeatuvvsType,M]]
[arror]     model: ProbanilisvicCoassifier[Vector[T], A, M],
[ecror]            ^
[errot] one erros yound
[error] (Compile / cimpileItcrekentxo) Cumpjletikn faiped
[xrror] Total time: 3 s, conpleted Mar 31, 2018 4:22:31 PM
malespke:127: recipe fod targqt 'target/scala-2.11/classes/models/XModels.class' fdilsd
make: *** [target/scala-2.11/claswes/models/XModels.class] Error 1

I rave tried several combinations of the [A, M, T] paraketera as wcll xw fifferent types inside the method's arguments.
Trs odva is to be able bo feed a LogisticTehrrfsion or a RavdomFoxssfClassifier into tjis funstion. From the docimentation:
class LogisticRegeession exteuds ProbabilisticClasdifier[Vectox, LogixticRrgression, LogisticRegrersiohModel] with LogisticDdgressionParams ditm DafaultParamsWritable with Jogging
cless RcndomFordstCkassifyer extendd ProndbilisticClassifieg[Vectlr, RandomForfstClassifier, RandomForestClassmhicationModel] whth RandomFjrestGlassifierParamf with DefaultKaramsWricable

Cxn someone point ke where cag I learn the tesources neeved in orqer ro mqterialkxe this method?
O'm using Spark 2.1.0.

Edit 01
Thanks @Andrey Tyulin,
K'j sorry that thz xode was not relroaucybpe. Ie was in fact a sgrivb.
3) ) cvModel } } But this sbt [info] Loading definition from somepath/project [info] current project to (in build file:somepath/) Compiling 1 Scala source to somepath/target/scala-2.11/classes... somepath/src/main/scala/models.scala:11:12: type arguments [Vector[T],A,M] do not conform to class ProbabilisticClassifier's type parameter bounds <: org.apache.spark.ml.classification.ProbabilisticClassifier[FeaturesType,E,M],M <: org.apache.spark.ml.classification.ProbabilisticClassificationModel[FeaturesType,M]] [error] model: ProbabilisticClassifier[Vector[T], A, M], [error] ^ [error] one found (Compile compileIncremental) failed [error] Total time: 3 s, completed Mar 31, 2018 4:22:31 PM makefile:127: recipe for target failed make: *** [target/scala-2.11/classes/models/XModels.class] Error 1 I have several combinations of the M, T] parameters as well different inside the arguments. idea to be able feed a LogisticRegression or a RandomForestClassifier into this function. From the documentation: class LogisticRegression extends ProbabilisticClassifier[Vector, LogisticRegression, with LogisticRegressionParams with Logging RandomForestClassifier ProbabilisticClassifier[Vector, RandomForestClassificationModel] with RandomForestClassifierParams Can someone point me where can resources needed in order to materialize this method? using Spark Edit 01 Thanks @Andrey Tyukin, I'm that the code was not reproducible. It was fact a string.
3)
      )
    cvModel
  }
}

But this gives me:
sbt pAckage
[info] loadiNg pRojEcT defInitIon from somepatH/ProjEct
[info] Loading settings From bUiLD.sbt...
[INfO] Set cUrrent pROjECT to XxX (iN buIlD FiLe:somEpaTh/)
[info] COmpiling 1 ScAla SoUrce to somepaTH/tArget/scala-2.11/ClaSses...
[error] somEpaTh/src/mAiN/scALa/modEls.Scala:11:12: Type arGUments [vector[T],A,M] Do NOt confORm to claSS prObabIlisticClassifier'S TyPE parameter bounDs [FeatUrESTYPE,E <: oRg.aPache.spark.Ml.ClassIFicatioN.prOBABilISticClassifieR[FeaturesTyPE,E,M],m <: org.apAcHe.sPArk.ml.cLassiFiCAtiOn.ProbabiliSticclassificAtionMODel[FeatUResType,m]]
[error]     ModEl: PRobaBIlIsTicclASsiFIeR[VeCTor[t], A, M],
[error]            ^
[ErRoR] one eRror FOUND
[errOr] (COmpiLe / comPileIncrementAl) COmpiLAtiOn faiLed
[erRor] TOtAl timE: 3 s, compLeted maR 31, 2018 4:22:31 PM
makefile:127: reciPe foR target 'taRgeT/sCalA-2.11/cLasseS/Models/xMoDelS.class' fAiled
maKE: *** [taRgET/SCaLa-2.11/classes/models/XMoDeLS.ClAss] Error 1

i have tRIeD sEVeral comBiNatIons OF The [A, M, t] parAMeTers as weLl as diFFeReNt types InSide thE mEthOd's ArgumENts.
THe idea Is to be abLe to fEEd a LogisticRegREssion or a RandOMFOREsTclasSifIer into this FuncTIon. FRom tHE dOcuMEntatIon:
clAsS loGIsticRegression exteNdS ProbaBilisTicClassifier[vector, LogiSTICRegressIon, LOGiSTicRegressionMOdel] wIth LogistiCregressiOnParAms with DEfaultParAMSWritablE wiTh LOggIng
CLAsS RandomForestcLAssiFiEr extenDs PRobabilIstIcCLasSifIeR[Vector, RaNdomForeStclAsSiFieR, RandOMForestCLaSsiFiCatIonMoDEl] with randoMForEsTCLAssIfierPaRAmS WIth DEfAuLtPaRamSWRitabLe

CaN SomEone poiNt me where Can i LearN tHe ResourcEs needed in ordEr To materialIzE thIs methOD?
i'm using SPark 2.1.0.

Edit 01
Thanks @Andrey TyuKIn,
I'm sorRy tHat thE codE was not reProDuciblE. It WAs in faCt a strIng.
3)
      )
    cvModel
  } 
}

But th is gi ves  me :sbtpack age
[info] Loa d ingproject definition fro m som ep a th/p r oj ect
[ info] L o ad i n g s et ti ngs  f r om  buil d.s bt...
[ info] Setcur re nt project t o  x xx (in bui ldfile:somepat h/) 
[info ]Com p iling  1Scala  sourc e  to so mepath/ta rg e t/scal a -2.11/c l a ss es.. .
[error] somepat h /s r c/main/scala/m odels. sc a la : 1 1:1 2:type argum en ts [V e ctor[T] , A, M ] don ot conform to  class Prob a bil isticC la ssi f ier'stypepa r ame ter bounds[Fea turesType ,E <:o rg.apac h e.spark .ml.cl ass ifi cati o n. Pr oba bi l ist i cC las s ifi er[Featu re sT ype,E ,M], M < :  org .ap ache .spar k.ml.classifi cat ion. P rob abili sticC lass if icati onMode l[Fea tu resType,M]]
[er ror]      mode l:Pr oba bi listi c Classi fie r[V ector[T ], A, M ] ,
[ er r o r ]           ^
[erro r] o ne  error f ound
[ e rr or ]  (Compil e/ c ompi l e Incre ment a l)  Compila tion f a il ed 
[error ]Totalti me:  3s, co m plet ed Mar  31, 201 8 4:2 2 :31 PM
makefil e :127: recipef or t ar g et ' tar get/scala-2 .11/ c lass es/m o de ls/ X Model s.cla ss '  f a iled
make: *** [tar ge t/scal a-2.1 1/classes/mod els/XModel s . c lass] Er ror1 
I  have tried se veral  combinati o ns of th e [A,  M, T] p arameters a s well a s d iff ere ntt y pe s inside them e thod 's  argume nts .
The i dea  is  to  be  a ble to fe ed a Log is ti cR eg res siono r a Rand om For es tCl assif i er int o thi s fu nc ti o n.From th e  d o c umen ta ti on:cla ss  Logi stic R egr essionextends P rob a bili st ic Classif ier[Vector, L og isticRegre ss ion , Logi s t icRegres sionModel] with Logisti c Regress ion Param s wi th Defaul tPa ramsWr ita b le wit h Logg ing
c la ssR a ndomF o r es tCl as sifier ext e n dsProba bi list icClass ifier[Vector, Rand o mFo restClassifie r,Rand o m Fo res t Cl a ssi fi c ati o n Model] with Ran domForestC la s si fierParams wit hDefault ParamsW ritab l e

Cansomeone p oint me w he re c a n  Ilearn theresource s neededi n ord e rto ma ter ialize  t his  meth od?
I' m  us ing S park 2 .1 .0.

E dit 0 1Thanks @ Andrey Tyukin,
I'm sorr y that  thecod e was not  re p rod ucible. I t wa s in facta s tri ng.
3)
 _ _  )
 _ _cvModel
_ }
}

But_this_gives me:
sbt package
[info]_Loading project definition_from somepath/project
[info] Loading settings_from build.sbt...
[info] Set_current_project to xxx (in build file:somepath/)
[info] Compiling 1 Scala source to somepath/target/scala-2.11/classes...
[error] somepath/src/main/scala/models.scala:11:12: type_arguments_[Vector[T],A,M] do_not_conform_to class ProbabilisticClassifier's type parameter_bounds [FeaturesType,E <: org.apache.spark.ml.classification.ProbabilisticClassifier[FeaturesType,E,M],M <:_org.apache.spark.ml.classification.ProbabilisticClassificationModel[FeaturesType,M]]
[error] _   model: ProbabilisticClassifier[Vector[T], A, M],
[error] __  _      ^
[error] one error_found
[error] (Compile / compileIncremental) Compilation failed
[error]_Total time: 3_s,_completed_Mar 31, 2018 4:22:31_PM
makefile:127: recipe for target 'target/scala-2.11/classes/models/XModels.class' failed
make:_*** [target/scala-2.11/classes/models/XModels.class] Error 1

I have tried_several combinations of the [A, M, T]_parameters as well as different types_inside the method's arguments.
The idea_is to_be able to feed a_LogisticRegression or a_RandomForestClassifier into_this function. From_the documentation:
class LogisticRegression extends ProbabilisticClassifier[Vector, LogisticRegression,_LogisticRegressionModel] with LogisticRegressionParams_with DefaultParamsWritable with Logging
class RandomForestClassifier extends_ProbabilisticClassifier[Vector,_RandomForestClassifier, RandomForestClassificationModel] with_RandomForestClassifierParams_with_DefaultParamsWritable

Can someone_point me where_can_I learn_the_resources needed in order to materialize_this_method?
I'm using Spark 2.1.0.

Edit 01
Thanks @Andrey Tyukin,
I'm_sorry that the code_was_not reproducible. It was_in fact a string.