Trait

com.github.gradientgmm.optim

WeightsTransformation

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trait WeightsTransformation extends Serializable

Contains the basic functionality for any mapping to be used to fit the weights of a mixture model

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  1. abstract 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

  2. abstract 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

  3. abstract 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

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