Uses of Class
com.irurueta.numerical.robust.NotEnoughSamplesException

Packages that use NotEnoughSamplesException
Package
Description
com.irurueta.numerical.robust
This package contains robust estimators that can be used to discard outliers for cases where a model of the data is known (i.e. estimating lines, planes or many other geometric objects, etc.)

  • Uses of NotEnoughSamplesException in com.irurueta.numerical.robust

    Methods in com.irurueta.numerical.robust that throw NotEnoughSamplesException
    Modifier and Type
    Method
    Description
    void
    FastRandomSubsetSelector.computeRandomSubsets(int subsetSize, int[] result)
    Computes a random subset of indices within range of number of samples to be used on robust estimators.
    int[]
    SubsetSelector.computeRandomSubsets(int subsetSize)
    Computes a random subset of indices within range of number of samples to be used on robust estimators.
    abstract void
    SubsetSelector.computeRandomSubsets(int subsetSize, int[] result)
    Computes a random subset of indices within range of number of samples to be used on robust estimators.
    void
    FastRandomSubsetSelector.computeRandomSubsetsInRange(int minPos, int maxPos, int subsetSize, boolean pickLast, int[] result)
    Computes a random subset of indices within provided range of positions to be used on robust estimators.
    int[]
    SubsetSelector.computeRandomSubsetsInRange(int minPos, int maxPos, int subsetSize, boolean pickLast)
    Computes a random subset of indices within provided range of positions to be used on robust estimators.
    abstract void
    SubsetSelector.computeRandomSubsetsInRange(int minPos, int maxPos, int subsetSize, boolean pickLast, int[] result)
    Computes a random subset of indices within provided range of positions to be used on robust estimators.