breeze.classify.LogisticClassifier.Trainer

ObjectiveFunction

class ObjectiveFunction extends BatchDiffFunction[LFMatrix[L, TF]]

Attributes
protected
Linear Supertypes
BatchDiffFunction[LFMatrix[L, TF]], (LFMatrix[L, TF], IndexedSeq[Int]) ⇒ Double, DiffFunction[LFMatrix[L, TF]], StochasticDiffFunction[LFMatrix[L, TF]], (LFMatrix[L, TF]) ⇒ Double, AnyRef, Any
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Inherited
  1. ObjectiveFunction
  2. BatchDiffFunction
  3. Function2
  4. DiffFunction
  5. StochasticDiffFunction
  6. Function1
  7. AnyRef
  8. Any
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Visibility
  1. Public
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Instance Constructors

  1. new ObjectiveFunction(data: IndexedSeq[Example[L, TF]], labelIndex: Index[L])

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. def andThen[A](g: (Double) ⇒ A): (LFMatrix[L, TF]) ⇒ A

    Definition Classes
    Function1
    Annotations
    @unspecialized()
  7. def apply(x: LFMatrix[L, TF], batch: IndexedSeq[Int]): Double

    Definition Classes
    BatchDiffFunction → Function2
  8. final def apply(x: LFMatrix[L, TF]): Double

    Definition Classes
    StochasticDiffFunction → Function1
  9. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  10. def calculate(weights: LFMatrix[L, TF], range: IndexedSeq[Int]): (Double, LFMatrix[L, TF])

    Calculates the value and gradient of the function on a subset of the data;

    Calculates the value and gradient of the function on a subset of the data;

    Definition Classes
    ObjectiveFunctionBatchDiffFunction
  11. def calculate(x: LFMatrix[L, TF]): (Double, LFMatrix[L, TF])

    Calculates both the value and the gradient at a point

    Calculates both the value and the gradient at a point

    Definition Classes
    BatchDiffFunctionStochasticDiffFunction
  12. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws()
  13. def compose[A](g: (A) ⇒ LFMatrix[L, TF]): (A) ⇒ Double

    Definition Classes
    Function1
    Annotations
    @unspecialized()
  14. def curried: (LFMatrix[L, TF]) ⇒ (IndexedSeq[Int]) ⇒ Double

    Definition Classes
    Function2
    Annotations
    @unspecialized()
  15. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  16. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  17. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws()
  18. val fullRange: Range

    The full size of the data

    The full size of the data

    Definition Classes
    ObjectiveFunctionBatchDiffFunction
  19. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  20. def gradientAt(x: LFMatrix[L, TF]): LFMatrix[L, TF]

    calculates the gradient at a point

    calculates the gradient at a point

    Definition Classes
    BatchDiffFunctionStochasticDiffFunction
  21. def gradientAt(x: LFMatrix[L, TF], batch: IndexedSeq[Int]): LFMatrix[L, TF]

    Calculates the gradient of the function on a subset of the data

    Calculates the gradient of the function on a subset of the data

    Definition Classes
    BatchDiffFunction
  22. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  23. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  24. def logScores(weights: LFMatrix[L, TF], datum: TF): DenseVector[Double]

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

    Definition Classes
    AnyRef
  26. final def notify(): Unit

    Definition Classes
    AnyRef
  27. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  28. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  29. def throughLens[U](implicit l: Isomorphism[LFMatrix[L, TF], U]): DiffFunction[U]

    Lenses provide a way of mapping between two types, which we typically use to convert something to a DenseVector or other Tensor for optimization purposes.

    Lenses provide a way of mapping between two types, which we typically use to convert something to a DenseVector or other Tensor for optimization purposes.

    Definition Classes
    StochasticDiffFunction
  30. def toString(): String

    Definition Classes
    Function2 → AnyRef → Any
  31. def tupled: ((LFMatrix[L, TF], IndexedSeq[Int])) ⇒ Double

    Definition Classes
    Function2
    Annotations
    @unspecialized()
  32. def valueAt(x: LFMatrix[L, TF]): Double

    calculates the value at a point

    calculates the value at a point

    Definition Classes
    BatchDiffFunctionStochasticDiffFunction
  33. def valueAt(x: LFMatrix[L, TF], batch: IndexedSeq[Int]): Double

    Calculates the value of the function on a subset of the data

    Calculates the value of the function on a subset of the data

    Definition Classes
    BatchDiffFunction
  34. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws()
  35. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws()
  36. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws()
  37. def withRandomBatches(size: Int): StochasticDiffFunction[LFMatrix[L, TF]]

    Definition Classes
    BatchDiffFunction
  38. def withScanningBatches(size: Int): StochasticDiffFunction[LFMatrix[L, TF]]

    Definition Classes
    BatchDiffFunction

Inherited from BatchDiffFunction[LFMatrix[L, TF]]

Inherited from (LFMatrix[L, TF], IndexedSeq[Int]) ⇒ Double

Inherited from DiffFunction[LFMatrix[L, TF]]

Inherited from StochasticDiffFunction[LFMatrix[L, TF]]

Inherited from (LFMatrix[L, TF]) ⇒ Double

Inherited from AnyRef

Inherited from Any

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