Class

com.github.gradientgmm.optim

RatioWeightTransformation

Related Doc: package optim

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class RatioWeightTransformation extends WeightsTransformation

Ratio mapping to fit the weight vector

The precise mapping is w_i => w_i/w_last

Using it is NOT recommended; use the default Softmax transformation instead.

Annotations
@deprecated
Deprecated

(Since version gradientgmm >= 1.4) This is an experimental, numerically unstable transformation and should not be used

Linear Supertypes
WeightsTransformation, Serializable, Serializable, AnyRef, Any
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Inherited
  1. RatioWeightTransformation
  2. WeightsTransformation
  3. Serializable
  4. Serializable
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Instance Constructors

  1. new RatioWeightTransformation()

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Value Members

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

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  2. final def ##(): Int

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

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  4. final def asInstanceOf[T0]: T0

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  5. def bound(soft: DenseVector[Double]): DenseVector[Double]

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    Prevents underflows and overflows when optimizing the weights

    Prevents underflows and overflows when optimizing the weights

    Because of the functions involved in mapping points from and to the weight simplex sometimes over/underflows can occur and make some weights go to zero or one. To avoid this, at each iteration this function add an offset to the vector without affecting the resulting simplex points, or bound them if necessary, in this case affecting the resulting weights.

    soft

    (possibly) arbitrary vector that is going to be mapped to a valid weight vector

    returns

    rescaled/translated vector

    Definition Classes
    RatioWeightTransformationWeightsTransformation
  6. def clone(): AnyRef

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    Attributes
    protected[java.lang]
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    @throws( ... )
  7. val eps: Double

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    machine's epsilon

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

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  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  11. def fromSimplex(weights: DenseVector[Double]): DenseVector[Double]

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    Take a valid weight vector of a mixture model and map it to another in which unconstrained gradient ascent can be performed.

    Take a valid weight vector of a mixture model and map it to another in which unconstrained gradient ascent can be performed. see https://en.wikipedia.org/wiki/Simplex

    weights

    mixture weights

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

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  13. def gradient(posteriors: DenseVector[Double], weights: DenseVector[Double]): DenseVector[Double]

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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

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  20. def toSimplex(soft: DenseVector[Double]): DenseVector[Double]

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    Take an arbitrary vector and map it to the weight simplex see https://en.wikipedia.org/wiki/Simplex

    Take an arbitrary vector and map it to the weight simplex see https://en.wikipedia.org/wiki/Simplex

    returns

    valid mixture weight vector

    Definition Classes
    RatioWeightTransformationWeightsTransformation
  21. def toString(): String

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  22. final def wait(): Unit

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    @throws( ... )
  23. final def wait(arg0: Long, arg1: Int): Unit

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

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Inherited from WeightsTransformation

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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