org.allenai.nlpstack.parse.poly.ml

BinaryTrainingData

class BinaryTrainingData extends TrainingData

A subinstance of TrainingData whose labels are -1 or 1.

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TrainingData, Serializable, Serializable, Product, Equals, AnyRef, Any
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  1. BinaryTrainingData
  2. TrainingData
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  4. Serializable
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Instance Constructors

  1. new BinaryTrainingData(labeledVectors: Iterable[(FeatureVector, Double)])

    labeledVectors

    a sequence of feature vectors labeled with doubles

Value Members

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  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

    Definition Classes
    TrainingData

  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

    Definition Classes
    TrainingData

  9. def clone(): AnyRef

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

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

    Definition Classes
    AnyRef

  11. lazy val featureNames: Set[FeatureName]

    The set of feature names found in the training data.

    The set of feature names found in the training data.

    Definition Classes
    TrainingData

  12. def finalize(): Unit

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

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

    Definition Classes
    AnyRef → Any

  14. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

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

    a sequence of feature vectors labeled with doubles

    a sequence of feature vectors labeled with doubles

    Definition Classes
    BinaryTrainingData → TrainingData

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

    Definition Classes
    AnyRef

  17. final def notify(): Unit

    Definition Classes
    AnyRef

  18. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  19. def svmLightLabel(label: Double): String

    Definition Classes
    BinaryTrainingData → TrainingData

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

    Definition Classes
    AnyRef

  21. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from TrainingData

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from AnyRef

Inherited from Any

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