OpProbabilisticClassifierModel

abstract def predictRawMirror: scala.reflect.api.JavaUniverse.MethodMirror

abstract def probability2predictionMirror: scala.reflect.api.JavaUniverse.MethodMirror

abstract def raw2probabilityMirror: scala.reflect.api.JavaUniverse.MethodMirror

final def !=(arg0: Any): Boolean

final def ##(): Int

final def $[T](param: Param[T]): T

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

final def checkInputLength(features: Array[_]): Boolean

final def checkSerializable: Try[Unit]

final def clear(param: Param[_]): OpProbabilisticClassifierModel.this.type

def clone(): AnyRef

final def copy(extra: ParamMap): OpProbabilisticClassifierModel.this.type

def copyValues[T <: Params](to: T, extra: ParamMap): T

final def defaultCopy[T <: Params](extra: ParamMap): T

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def explainParam(param: Param[_]): String

def explainParams(): String

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

def finalize(): Unit

final def get[T](param: Param[T]): Option[T]

final def getClass(): Class[_]

final def getDefault[T](param: Param[T]): Option[T]

def getInputColParamNames(): Array[String]

final def getInputFeature[T <: FeatureType](i: Int): Option[FeatureLike[T]]

final def getInputFeatures(): Array[OPFeature]

final def getInputSchema(): StructType

final def getMetadata(): Metadata

final def getOrDefault[T](param: Param[T]): T

def getOutput(): FeatureLike[Prediction]

def getOutputColParamNames(): Array[String]

final def getOutputFeatureName: String

def getParam(paramName: String): Param[Any]

def getSparkMlStage(): Option[T]

def getStageSavePath(): Option[String]

final def getTransientFeature(i: Int): Option[TransientFeature]

final def getTransientFeatures(): Array[TransientFeature]

final def hasDefault[T](param: Param[T]): Boolean

def hasParam(paramName: String): Boolean

def hasParent: Boolean

def hashCode(): Int

final def in1: TransientFeature

final def in2: TransientFeature

def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

final def inputAsArray(in: InputFeatures): Array[OPFeature]

final def isDefined(param: Param[_]): Boolean

final def isInstanceOf[T0]: Boolean

final def isSet(param: Param[_]): Boolean

def isTraceEnabled(): Boolean

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def onGetMetadata(): Unit

def onSetInput(): Unit

val operationName: String

final def outputAsArray(out: OutputFeatures): Array[OPFeature]

def outputFeatureUid: String

def outputIsResponse: Boolean

lazy val params: Array[Param[_]]

var parent: Estimator[OpPredictorWrapperModel[T]]

def predictRaw(features: Vector): Vector

def probability2prediction(prob: Vector): Double

def raw2probability(raw: Vector): Vector

def save(path: String): Unit

final def set(paramPair: ParamPair[_]): OpProbabilisticClassifierModel.this.type

final def set(param: String, value: Any): OpProbabilisticClassifierModel.this.type

final def set[T](param: Param[T], value: T): OpProbabilisticClassifierModel.this.type

final def setDefault(paramPairs: ParamPair[_]*): OpProbabilisticClassifierModel.this.type

final def setDefault[T](param: Param[T], value: T): OpProbabilisticClassifierModel.this.type

final def setInput(features: InputFeatures): OpProbabilisticClassifierModel.this.type

final def setInputFeatures[S <: OPFeature](features: Array[S]): OpProbabilisticClassifierModel.this.type

final def setMetadata(m: Metadata): OpProbabilisticClassifierModel.this.type

def setOutputFeatureName(name: String): OpProbabilisticClassifierModel.this.type

def setParent(parent: Estimator[OpPredictorWrapperModel[T]]): OpPredictorWrapperModel[T]

def setSparkMlStage(stage: Option[T]): OpProbabilisticClassifierModel.this.type

def setStageSavePath(path: String): OpProbabilisticClassifierModel.this.type

final val sparkInputColParamNames: StringArrayParam

final val sparkMlStage: SparkStageParam[T]

val sparkModel: T

final val sparkOutputColParamNames: StringArrayParam

final def stageName: String

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

def transform(dataset: Dataset[_]): DataFrame

def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

def transformFn: (RealNN, OPVector) ⇒ Prediction

Function used to convert input to output

lazy val transformKeyValue: (KeyValue) ⇒ Any

def transformMap: (Map[String, Any]) ⇒ Any

def transformRow: (Row) ⇒ Any

final def transformSchema(schema: StructType): StructType

def transformSchema(schema: StructType, logging: Boolean): StructType

implicit val tti1: scala.reflect.api.JavaUniverse.TypeTag[RealNN]

implicit val tti2: scala.reflect.api.JavaUniverse.TypeTag[OPVector]

implicit val tto: scala.reflect.api.JavaUniverse.TypeTag[Prediction]

implicit val ttov: scala.reflect.api.JavaUniverse.TypeTag[Map[String, Double]]

val uid: String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

final def write: MLWriter