Class

axle.ml

LogisticRegression

Related Doc: package ml

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case class LogisticRegression[D, M](examples: List[D], numFeatures: Int, featureExtractor: (D) ⇒ List[Double], objectiveExtractor: (D) ⇒ Boolean, α: Double = 0.1, numIterations: Int = 100)(implicit la: LinearAlgebra[M, Int, Int, Double]) extends (List[Double]) ⇒ Double with Product with Serializable

Linear Supertypes
Serializable, Serializable, Product, Equals, (List[Double]) ⇒ Double, AnyRef, Any
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Inherited
  1. LogisticRegression
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. Function1
  7. AnyRef
  8. Any
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Visibility
  1. Public
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Instance Constructors

  1. new LogisticRegression(examples: List[D], numFeatures: Int, featureExtractor: (D) ⇒ List[Double], objectiveExtractor: (D) ⇒ Boolean, α: Double = 0.1, numIterations: Int = 100)(implicit la: LinearAlgebra[M, Int, Int, Double])

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Value Members

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

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any

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

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    Definition Classes
    AnyRef → Any

  4. def Jθ(X: M, θ: M, y: M): Double

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  5. val X: M

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  6. def andThen[A](g: (Double) ⇒ A): (List[Double]) ⇒ A

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    Definition Classes
    Function1
    Annotations
    @unspecialized()

  7. def apply(features: List[Double]): Double

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    Definition Classes
    LogisticRegression → Function1

  8. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  9. def boolean2double(b: Boolean): Double

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  10. def clone(): AnyRef

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

  11. def compose[A](g: (A) ⇒ List[Double]): (A) ⇒ Double

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    Definition Classes
    Function1
    Annotations
    @unspecialized()

  12. def cost(xi: M, θ: M, yi: Double): Double

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  13. def dθ(X: M, y: M, θ: M): M

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

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    Definition Classes
    AnyRef

  15. val examples: List[D]

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  16. val featureExtractor: (D) ⇒ List[Double]

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  17. val featureNormalizer: LinearFeatureNormalizer[M]

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  18. def finalize(): Unit

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

  19. val finalθ: M

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

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    Definition Classes
    AnyRef → Any

  21. def gradientDescent(X: M, y: M, α: Double, iterations: Int): M

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    Gradient descent's objective: minimize (over θ) the value of Jθ

  22. def h(xi: M, θ: M): Double

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  23. val inputX: M

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  24. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  25. implicit val mul: MultiplicativeSemigroup[M]

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

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    Definition Classes
    AnyRef

  27. final def notify(): Unit

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    Definition Classes
    AnyRef

  28. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  29. val numFeatures: Int

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  30. val numIterations: Int

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  31. val objectiveExtractor: (D) ⇒ Boolean

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

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    Definition Classes
    AnyRef

  33. def toString(): String

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    Definition Classes
    Function1 → AnyRef → Any

  34. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  37. val y: M

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  38. val α: Double

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Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from (List[Double]) ⇒ Double

Inherited from AnyRef

Inherited from Any

Ungrouped