XKMeansModel

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 clear(param: Param[_]): XKMeansModel.this.type

def clone(): AnyRef

def clusterCenters: Array[Vector]

def copy(extra: ParamMap): XKMeansModel

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

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

final val distanceMeasure: Param[String]

val distanceSummary: ClusteringDistanceSummary

final val distanceToCentroidCol: Param[String]

Param for distanceToCentroid column name.

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

final val featureNames: Param[Option[Seq[String]]]

Set the names of the features corresponding to each dimension of the input vectors column

final val featuresCol: Param[String]

val featuresSummary: ClusteringFeaturesSummary

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]

final def getDistanceMeasure: String

final def getDistanceToCentroidCol: String

def getFeatureNames: Option[Seq[String]]

final def getFeaturesCol: String

def getInitMode: String

def getInitSteps: Int

def getK: Int

final def getMaxIter: Int

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

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

final def getPredictionCol: String

final def getProbabilityByFeatureCol: String

final def getProbabilityCol: String

final def getSeed: Long

final def getTol: Double

def getXSigma: Double

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

def hasParam(paramName: String): Boolean

def hasParent: Boolean

def hasSummary: Boolean

def hashCode(): Int

final val initMode: Param[String]

final val initSteps: IntParam

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

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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

final def isInstanceOf[T0]: Boolean

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

def isTraceEnabled(): Boolean

final val k: IntParam

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 val maxIter: IntParam

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

lazy val params: Array[Param[_]]

var parent: Estimator[KMeansModel]

final val predictionCol: Param[String]

final val probabilityByFeatureCol: Param[String]

Param for probabilityByFeature column name.

final val probabilityCol: Param[String]

def save(path: String): Unit

final val seed: LongParam

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

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

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

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

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

def setFeaturesCol(value: String): XKMeansModel.this.type

def setParent(parent: Estimator[KMeansModel]): KMeansModel

def setPredictionCol(value: String): XKMeansModel.this.type

def setXSigma(value: Double): XKMeansModel.this.type

def summary: KMeansSummary

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

def toString(): String

final val tol: DoubleParam

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

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

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

def transformSchema(schema: StructType): StructType

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

val uid: String

def validateAndTransformSchema(schema: StructType): StructType

final def wait(): Unit

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

final def wait(arg0: Long): Unit

def write: GeneralMLWriter

final val xSigma: DoubleParam

The ratio of standard deviation used to compute probability

def computeCost(dataset: Dataset[_]): Double