Package com.irurueta.numerical.robust

Class SubsetSelector

java.lang.Object
com.irurueta.numerical.robust.SubsetSelector
Direct Known Subclasses:
FastRandomSubsetSelector
public abstract class SubsetSelector extends Object
Base class to pick subsets of samples.

  • Field Summary

    Fields
    Modifier and Type
    Field
    Description
    static final SubsetSelectorType
    DEFAULT_SUBSET_SELECTOR_TYPE
    Defines default subset selector type.
    static final int
    MIN_NUM_SAMPLES
    Constant defining minimum amount of allowed samples.
    protected int
    numSamples
    Total number of samples to pick subsets from.

  • Constructor Summary

    Constructors
    Modifier
    Constructor
    Description
    protected
    SubsetSelector(int numSamples)
    Constructor.

  • Method Summary

    Modifier and Type
    Method
    Description
    int[]
    computeRandomSubsets(int subsetSize)
    Computes a random subset of indices within range of number of samples to be used on robust estimators.
    abstract void
    computeRandomSubsets(int subsetSize, int[] result)
    Computes a random subset of indices within range of number of samples to be used on robust estimators.
    int[]
    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
    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.
    static SubsetSelector
    create(int numSamples)
    Creates a new subset selector instance using provided total number of samples and default subset selector type.
    int
    getNumSamples()
    Returns number of samples to select subsets from.
    abstract SubsetSelectorType
    getType()
    Returns type of this subset selector.
    final void
    setNumSamples(int numSamples)
    Sets number of samples to select subsets from.

    Methods inherited from class java.lang.Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

  • Field Details

    • MIN_NUM_SAMPLES

      public static final int MIN_NUM_SAMPLES
      Constant defining minimum amount of allowed samples.
      See Also:

    • DEFAULT_SUBSET_SELECTOR_TYPE

      public static final SubsetSelectorType DEFAULT_SUBSET_SELECTOR_TYPE
      Defines default subset selector type.

    • numSamples

      protected int numSamples
      Total number of samples to pick subsets from. Subsets need to be always smaller or equal than total number of samples.

  • Constructor Details

    • SubsetSelector

      protected SubsetSelector(int numSamples)
      Constructor.
      Parameters:
      numSamples - number of samples to select subsets from.
      Throws:
      IllegalArgumentException - if provided number of samples is zero or negative.

  • Method Details

    • getNumSamples

      public int getNumSamples()
      Returns number of samples to select subsets from.
      Returns:
      number of samples to select subsets from.

    • setNumSamples

      public final void setNumSamples(int numSamples)
      Sets number of samples to select subsets from.
      Parameters:
      numSamples - number of samples to select subsets from.
      Throws:
      IllegalArgumentException - if provided number of samples is zero or negative.

    • computeRandomSubsets

      public int[] computeRandomSubsets(int subsetSize) throws NotEnoughSamplesException, InvalidSubsetSizeException
      Computes a random subset of indices within range of number of samples to be used on robust estimators. If subsets need to be computed repeatedly in a small span of time then it is suggested to use computeRandomSubsets(int, int[]) for better memory usage.
      Parameters:
      subsetSize - subset size to be computed. This value must be smaller than total number of samples.
      Returns:
      array containing indices to be picked.
      Throws:
      NotEnoughSamplesException - if subset size is greater than the total number of samples.
      InvalidSubsetSizeException - if subset size is zero or if result array does not have at least a length of subsetSize.
      See Also:

    • computeRandomSubsetsInRange

      public int[] computeRandomSubsetsInRange(int minPos, int maxPos, int subsetSize, boolean pickLast) throws NotEnoughSamplesException, InvalidSubsetSizeException, InvalidSubsetRangeException
      Computes a random subset of indices within provided range of positions to be used on robust estimators. If subsets need to be computed repeatedly in a small span of time then it is suggested to use computeRandomSubsets(int, int, int, bool, int[]) for better memory usage.
      Parameters:
      minPos - minimum position to be picked. This value must be greater or equal than zero and smaller than the total number of samples and less than maxPos.
      maxPos - maximum position to be picked. This value must be greater or equal than zero and smaller than the total number of samples and greater than minPos.
      subsetSize - subset size to be computed. This value must be smaller than total number of samples.
      pickLast - true indicates that last sample in range must always be picked within subset. This is done to obtain faster execution times and greater stability on some algorithms.
      Returns:
      array containing indices to be picked.
      Throws:
      NotEnoughSamplesException - if subset size is greater than the total number of samples or if maxPos is greater than the total number of samples.
      InvalidSubsetSizeException - if subset size is zero, or if subset size is greater than the allowed range of positions to be picked.
      InvalidSubsetRangeException - if maximum position is smaller than minimum position or maximum or minimum position are negative.

    • getType

      public abstract SubsetSelectorType getType()
      Returns type of this subset selector.
      Returns:
      type of this subset selector.

    • computeRandomSubsets

      public abstract void computeRandomSubsets(int subsetSize, int[] result) throws NotEnoughSamplesException, InvalidSubsetSizeException
      Computes a random subset of indices within range of number of samples to be used on robust estimators.
      Parameters:
      subsetSize - subset size to be computed. This value must be smaller than total number of samples.
      result - array containing indices to be picked. Provided array must be at least of length subsetSize. The former subsetSize entries of the array will be modified by this method.
      Throws:
      NotEnoughSamplesException - if subset size is greater than the total number of samples.
      InvalidSubsetSizeException - if subset size is zero or if result array does not have at least a length of subsetSize.

    • computeRandomSubsetsInRange

      public abstract void computeRandomSubsetsInRange(int minPos, int maxPos, int subsetSize, boolean pickLast, int[] result) throws NotEnoughSamplesException, InvalidSubsetSizeException, InvalidSubsetRangeException
      Computes a random subset of indices within provided range of positions to be used on robust estimators.
      Parameters:
      minPos - minimum position to be picked. This value must be greater or equal than zero and smaller than the total number of samples and less than maxPos.
      maxPos - maximum position to be picked. This value must be greater or equal than zero and smaller than the total number of samples and greater than minPos.
      subsetSize - subset size to be computed. This value must be smaller than total number of samples.
      pickLast - true indicates that last sample in range must always be picked within subset. This is done to obtain faster execution times and greater stability on some algorithms.
      result - array containing indices to be picked. Provided array must be at least of length subsetSize. The former subsetSize entries of the array will be modified by this method.
      Throws:
      NotEnoughSamplesException - if subset size is greater than the total number of samples or if maxPos is greater than the total number of samples.
      InvalidSubsetSizeException - if subset size is zero or if result array does not have at least a length of subsetSize, or if subset size is greater than the allowed range of positions to be picked.
      InvalidSubsetRangeException - if maximum position is smaller than minimum position or maximum or minimum position are negative.

    • create

      public static SubsetSelector create(int numSamples)
      Creates a new subset selector instance using provided total number of samples and default subset selector type.
      Parameters:
      numSamples - number of samples to select subsets from.
      Returns:
      a subset selector.
      Throws:
      IllegalArgumentException - if provided number of samples is zero or negative.