Class/Object

com.ibm.sparktc.sparkbench.datageneration.mlgenerator

LinearRegressionDataGen

Related Docs: object LinearRegressionDataGen | package mlgenerator

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case class LinearRegressionDataGen(numRows: Int, numCols: Int, input: Option[String] = None, output: Option[String], saveMode: String, eps: Double, intercepts: Double, numPartitions: Int) extends Workload with Product with Serializable

Linear Supertypes
Serializable, Serializable, Product, Equals, Workload, AnyRef, Any
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  1. LinearRegressionDataGen
  2. Serializable
  3. Serializable
  4. Product
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  6. Workload
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Instance Constructors

  1. new LinearRegressionDataGen(numRows: Int, numCols: Int, input: Option[String] = None, output: Option[String], saveMode: String, eps: Double, intercepts: Double, numPartitions: Int)

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Value Members

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

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any

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

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    Definition Classes
    AnyRef → Any

  4. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  5. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  6. def doWorkload(df: Option[DataFrame] = None, spark: SparkSession): DataFrame

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    Actually run the workload.

    Actually run the workload. Takes an optional DataFrame as input if the user supplies an inputDir, and returns the generated results DataFrame.

    Definition Classes
    LinearRegressionDataGen → Workload

  7. val eps: Double

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

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    Definition Classes
    AnyRef

  9. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

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    Definition Classes
    AnyRef → Any

  11. val input: Option[String]

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    Definition Classes
    LinearRegressionDataGen → Workload

  12. val intercepts: Double

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  13. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

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

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    Definition Classes
    AnyRef

  15. final def notify(): Unit

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    Definition Classes
    AnyRef

  16. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  17. val numCols: Int

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  18. val numPartitions: Int

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  19. val numRows: Int

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  20. val output: Option[String]

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    Definition Classes
    LinearRegressionDataGen → Workload

  21. def reconcileSchema(dataFrame: DataFrame): DataFrame

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    Validate that the data set has a correct schema and fix if necessary.

    Validate that the data set has a correct schema and fix if necessary. This is to solve issues such as the KMeans load-from-disk pathway returning a DataFrame with all the rows as StringType instead of DoubleType.

    Definition Classes
    Workload

  22. def run(spark: SparkSession): DataFrame

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    Definition Classes
    Workload

  23. val saveMode: String

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    Definition Classes
    LinearRegressionDataGen → Workload

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

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    Definition Classes
    AnyRef

  25. def toMap: Map[String, Any]

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    Definition Classes
    Workload

  26. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
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    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from Workload

Inherited from AnyRef

Inherited from Any

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