TrainingData

Value Members

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

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

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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

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6. final def asInstanceOf[T0]: T0

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7. def asSvmLight(signature: FeatureEncoding): String

   Expresses this training data in "SVMlight" format, which is <line> .

   Expresses this training data in "SVMlight" format, which is <line> .=. <target> <feature>:<value> ... <feature>:<value> # <info> <target> .=. +1 | -1 | 0 | <float> <feature> .=. <integer> | "qid" <value> .=. <float> <info> .=. <string>

   signature

   the signature to use for encoding feature names as integer

   returns

   the training data in SVMlight format

8. def binarize(margin: Double): BinaryTrainingData

   Creates "positive" and "negative" feature vectors according to whether the feature cost is greater than margin or less than -margin, respectively.

   Creates "positive" and "negative" feature vectors according to whether the feature cost is greater than margin or less than -margin, respectively.

   Feature vectors that are within margin of zero are filtered from the traing data.

   margin

   the absolute threshold that determines whether a vector is kept

   returns

   a TrainingData instance where all costs are -1 or 1

9. def clone(): AnyRef

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

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11. lazy val featureNames: Set[FeatureName]

    The set of feature names found in the training data.

12. def finalize(): Unit

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

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14. final def isInstanceOf[T0]: Boolean

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15. val labeledVectors: Iterable[(FeatureVector, Double)]

    a sequence of feature vectors labeled with doubles

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 svmLightLabel(label: Double): String

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

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21. final def wait(): Unit

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

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

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