Class

com.salesforce.op.dsl.RichTextFeature

RichBase64Feature

Related Doc: package RichTextFeature

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implicit class RichBase64Feature extends AnyRef

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Instance Constructors

  1. new RichBase64Feature(f: FeatureLike[Base64])

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Value Members

  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 clone(): AnyRef

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  6. def detectMimeTypes(typeHint: Option[String] = None): FeatureLike[Text]

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    Detect MIME type for Base64 encoded binary data

    Detect MIME type for Base64 encoded binary data

    typeHint

    MIME type hint, i.e. 'application/json', 'text/plain' etc.

    returns

    mime type as text

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

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  8. def equals(arg0: Any): Boolean

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  9. val f: FeatureLike[Base64]

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  10. def finalize(): Unit

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

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  12. def hashCode(): Int

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  13. final def isInstanceOf[T0]: Boolean

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

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  15. final def notify(): Unit

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  16. final def notifyAll(): Unit

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

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  18. def toString(): String

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  19. def vectorize(topK: Int, minSupport: Int, cleanText: Boolean, trackNulls: Boolean = TransmogrifierDefaults.TrackNulls, typeHint: Option[String] = None, others: Array[FeatureLike[Base64]] = Array.empty, maxPctCardinality: Double = ...): FeatureLike[OPVector]

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    Extracts Base64 features (MIME type etc.), then converts those into PickList features and vectorizes them.

    Extracts Base64 features (MIME type etc.), then converts those into PickList features and vectorizes them.

    topK

    number of values to keep for each key

    minSupport

    min times a value must occur to be retained in pivot

    cleanText

    clean text before pivoting

    trackNulls

    keep an extra column that indicated if feature was null

    typeHint

    MIME type hint, i.e. 'application/json', 'text/plain' etc.

    others

    other features of the same type

    maxPctCardinality

    max percentage of distinct values a categorical feature can have (between 0.0 and 1.00)

    returns

    result feature of type vector

  20. final def wait(): Unit

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

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

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