Class/Object

scalismo.statisticalmodel.asm

FittingConfiguration

Related Docs: object FittingConfiguration | package asm

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case class FittingConfiguration(featureDistanceThreshold: Float, pointDistanceThreshold: Float, modelCoefficientBounds: Float) extends Product with Serializable

Fitting Configuration, specifying thresholds and bounds.

featureDistanceThreshold

threshold for the feature distance. If the mahalanobis distance of a candidate point's features to the corresponding profile's mean is larger than this value, then that candidate point will be ignored during fitting.

pointDistanceThreshold

threshold for point distance: If the mahalanobis distance of a candidate point to its corresponding marginal distribution is larger than this value, then that candidate point will be ignored during fitting.

modelCoefficientBounds

bounds to apply on the model coefficients. In other words, by setting this to n, all coefficients of the fitting result will be restricted to the interval [-n, n].

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Instance Constructors

  1. new FittingConfiguration(featureDistanceThreshold: Float, pointDistanceThreshold: Float, modelCoefficientBounds: Float)

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    featureDistanceThreshold

    threshold for the feature distance. If the mahalanobis distance of a candidate point's features to the corresponding profile's mean is larger than this value, then that candidate point will be ignored during fitting.

    pointDistanceThreshold

    threshold for point distance: If the mahalanobis distance of a candidate point to its corresponding marginal distribution is larger than this value, then that candidate point will be ignored during fitting.

    modelCoefficientBounds

    bounds to apply on the model coefficients. In other words, by setting this to n, all coefficients of the fitting result will be restricted to the interval [-n, n].

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

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  2. final def ##(): Int

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

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  7. val featureDistanceThreshold: Float

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    threshold for the feature distance.

    threshold for the feature distance. If the mahalanobis distance of a candidate point's features to the corresponding profile's mean is larger than this value, then that candidate point will be ignored during fitting.

  8. def finalize(): Unit

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  10. final def isInstanceOf[T0]: Boolean

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  11. val modelCoefficientBounds: Float

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    bounds to apply on the model coefficients.

    bounds to apply on the model coefficients. In other words, by setting this to n, all coefficients of the fitting result will be restricted to the interval [-n, n].

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

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  13. final def notify(): Unit

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  14. final def notifyAll(): Unit

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  15. val pointDistanceThreshold: Float

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    threshold for point distance: If the mahalanobis distance of a candidate point to its corresponding marginal distribution is larger than this value, then that candidate point will be ignored during fitting.

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

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  17. final def wait(): Unit

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

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