Class

io.github.edouardfouche.generators

HyperSphere

Related Doc: package generators

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case class HyperSphere(nDim: Int, noise: Double, noisetype: String, discretize: Int) extends DataGenerator with Product with Serializable

Linear Supertypes
Serializable, Serializable, Product, Equals, DataGenerator, AnyRef, Any
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Inherited
  1. HyperSphere
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. DataGenerator
  7. AnyRef
  8. Any
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Visibility
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Instance Constructors

  1. new HyperSphere(nDim: Int, noise: Double, noisetype: String, discretize: Int)

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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 addGaussianNoise(x: Double, noise: Double): Double

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    Definition Classes
    DataGenerator

  5. def addUniformNoise(x: Double, noise: Double): Double

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    Definition Classes
    DataGenerator

  6. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  7. def clone(): AnyRef

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

  8. val discretize: Int

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    Definition Classes
    HyperSphere → DataGenerator

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

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    Definition Classes
    AnyRef

  10. def finalize(): Unit

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

  11. def generate(n: Int): Array[Array[Double]]

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    Definition Classes
    DataGenerator

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

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    Definition Classes
    AnyRef → Any

  13. def getPoints(n: Int): Array[Array[Double]]

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    Definition Classes
    HyperSphere → DataGenerator

  14. lazy val id: String

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    Definition Classes
    DataGenerator

  15. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  16. def linearNormalization(x: Double): Double

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    Definition Classes
    DataGenerator

  17. val nDim: Int

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    Definition Classes
    HyperSphere → DataGenerator

  18. val name: String

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    Definition Classes
    HyperSphere → DataGenerator

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

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    Definition Classes
    AnyRef

  20. val noise: Double

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    Definition Classes
    HyperSphere → DataGenerator

  21. def noiselessPowerNormalization(x: Double, pow: Double): Double

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    Definition Classes
    DataGenerator

  22. val noisetype: String

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    Definition Classes
    HyperSphere → DataGenerator

  23. final def notify(): Unit

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    Definition Classes
    AnyRef

  24. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  25. def plot(n: Int, path: String = ..., filename: String = this.id): Unit

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    Definition Classes
    DataGenerator

  26. def postprocess(data: Array[Array[Double]], noise: Double, t: String = "gaussian"): Array[Array[Double]]

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    Definition Classes
    DataGenerator

  27. def powerNormalization(x: Double, pow: Double): Double

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    Definition Classes
    DataGenerator

  28. def save(n: Int, path: String = System.getProperty("user.dir"), filename: String = this.id): Unit

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    Definition Classes
    DataGenerator

  29. lazy val shortname: String

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    Definition Classes
    DataGenerator

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

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    Definition Classes
    AnyRef

  31. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from DataGenerator

Inherited from AnyRef

Inherited from Any

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