keystoneml.evaluation

AugmentedExamplesEvaluator

class AugmentedExamplesEvaluator[T] extends Evaluator[DenseVector[Double], Int, MulticlassMetrics] with Serializable

Linear Supertypes
Serializable, Serializable, Evaluator[DenseVector[Double], Int, MulticlassMetrics], AnyRef, Any
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  1. AugmentedExamplesEvaluator
  2. Serializable
  3. Serializable
  4. Evaluator
  5. AnyRef
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Instance Constructors

  1. new AugmentedExamplesEvaluator(names: RDD[T], numClasses: Int, policy: AggregationPolicyType.Value = AggregationPolicyType.average)(implicit arg0: ClassTag[T])

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 averagePolicy(preds: Array[DenseVector[Double]]): DenseVector[Double]

  8. def bordaPolicy(preds: Array[DenseVector[Double]]): DenseVector[Double]

    Borda averaging works as follows: Let s(k) be the ordering of patch k.

    Borda averaging works as follows: Let s(k) be the ordering of patch k. For i in images, For k in patches, score[i] += s(k)[i]

  9. def clone(): AnyRef

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

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

    Definition Classes
    AnyRef

  11. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  12. def evaluate(predicted: RDD[DenseVector[Double]], actualLabels: RDD[Int]): MulticlassMetrics

    Generate an evaluation.

    Generate an evaluation.

    returns

    An evaluation.

    Definition Classes
    AugmentedExamplesEvaluator → Evaluator

  13. def evaluate(predictions: PipelineDataset[DenseVector[Double]], labels: PipelineDataset[Int]): MulticlassMetrics

    Definition Classes
    Evaluator

  14. def evaluate(predictions: RDD[DenseVector[Double]], labels: PipelineDataset[Int]): MulticlassMetrics

    Definition Classes
    Evaluator

  15. def evaluate(predictions: PipelineDataset[DenseVector[Double]], labels: RDD[Int]): MulticlassMetrics

    Definition Classes
    Evaluator

  16. def finalize(): Unit

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

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

    Definition Classes
    AnyRef → Any

  18. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  19. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

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

    Definition Classes
    AnyRef

  21. final def notify(): Unit

    Definition Classes
    AnyRef

  22. final def notifyAll(): Unit

    Definition Classes
    AnyRef

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

    Definition Classes
    AnyRef

  24. def toString(): String

    Definition Classes
    AnyRef → Any

  25. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from Evaluator[DenseVector[Double], Int, MulticlassMetrics]

Inherited from AnyRef

Inherited from Any

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