Object/Class

com.intel.analytics.bigdl.dataset.datamining

RowTransformer

Related Docs: class RowTransformer | package datamining

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object RowTransformer extends Serializable

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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. def apply(schemas: Seq[RowTransformSchema], rowSize: Int = 0): RowTransformer

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  5. final def asInstanceOf[T0]: T0

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  6. def atomic(indices: Seq[Int], rowSize: Int): RowTransformer

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    A RowTransformer which transform each selected columns to a size(1) Tensor.

    A RowTransformer which transform each selected columns to a size(1) Tensor. The keys of output Table are indices of selected columns.

    indices

    indices of selected columns

    rowSize

    size of Row transformed by this transformer

  7. def atomic(fieldNames: Seq[String]): RowTransformer

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    A RowTransformer which transform each selected columns to a size(1) Tensor.

    A RowTransformer which transform each selected columns to a size(1) Tensor. The keys of output Table are fieldNames of selected columns.

    fieldNames

    field names of selected columns

  8. def atomicWithNumeric[T](atomicFields: Seq[String], numericFields: Map[String, Seq[String]])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): RowTransformer

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    A RowTransformer which contains both atomic schemas and numeric schemas.

    A RowTransformer which contains both atomic schemas and numeric schemas.

    atomicFields

    field names of selected columns

    numericFields

    Map<schemaKey, fieldNames of selected columns> of numeric fields

  9. def clone(): AnyRef

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

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  11. def equals(arg0: Any): Boolean

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  12. def finalize(): Unit

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

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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  19. def numeric[T](numericFields: Map[String, Seq[String]])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): RowTransformer

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    A RowTransformer which concat values of selected columns to one Tensor.

    A RowTransformer which concat values of selected columns to one Tensor. It means you will get a Table with keys of numericFields. Values of Table are Tensors concatenated by selected columns of the keys.

    numericFields

    Map<schemaKey, fieldNames of selected columns> of numeric fields

  20. def numeric[T](schemaKey: String = "all")(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): RowTransformer

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    A RowTransformer which concat values of all columns to one Tensor.

    A RowTransformer which concat values of all columns to one Tensor. It means you will get a Table with single key-value pair after transformation. The unique key is schemaKey. The unique value is a size(length of Row) Tensor.

    schemaKey

    key of the schema, default value is "all"

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

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  22. def toString(): String

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  23. final def wait(): Unit

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

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

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