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org.incal.spark_ml

SparkUtil

Related Doc: package spark_ml

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object SparkUtil

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  1. implicit class VectorMap extends AnyRef

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  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. def assembleVectors(vv: Seq[Vector]): Vector

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  6. def clone(): AnyRef

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  7. final def eq(arg0: AnyRef): Boolean

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  8. def equals(arg0: Any): Boolean

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  9. def finalize(): Unit

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  10. final def getClass(): Class[_]

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  11. def hashCode(): Int

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  12. def indexStringCols(columnNameWithEnumLabels: Seq[(String, Seq[String])])(df: DataFrame): DataFrame

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  13. final def isInstanceOf[T0]: Boolean

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  14. def joinByOrder(df1: DataFrame, df2: DataFrame): DataFrame

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  15. implicit def kryoEncoder[A](implicit ct: ClassTag[A]): Encoder[A]

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  16. def localCsvToDataFrame(fileName: String, header: Boolean = true)(session: SparkSession): DataFrame

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  17. final def ne(arg0: AnyRef): Boolean

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  18. final def notify(): Unit

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  19. final def notifyAll(): Unit

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  20. def prepFeaturesDataFrame(featureFieldNames: Set[String], outputFieldName: Option[String], dropFeatureCols: Boolean = true, dropNaValues: Boolean = true)(df: DataFrame): DataFrame

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  21. def remoteCsvToDataFrame(url: String, header: Boolean = true)(session: SparkSession): DataFrame

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

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  23. def toString(): String

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  24. def transformInPlace(outputColumnToStage: (String) ⇒ PipelineStage, inputOutputCol: String): Estimator[PipelineModel]

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  25. def transformInPlaceWithParamGrids(outputColumnToStage: (String) ⇒ (PipelineStage, Traversable[ParamGrid[_]]), inputOutputCol: String): (Estimator[PipelineModel], Traversable[ParamGrid[_]])

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  26. def transposeVectors(session: SparkSession, columnNames: Traversable[String], df: DataFrame): DataFrame

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  27. final def wait(): Unit

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

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  29. final def wait(arg0: Long): Unit

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