File stratified_sampling_example_wise.hpp

template<typename LabelMatrix, typename IndexIterator>
class ExampleWiseStratification
#include <stratified_sampling_example_wise.hpp>

Implements stratified sampling, where distinct label vectors are treated as individual classes.

Template Parameters:

  • LabelMatrix – The type of the label matrix that provides random or row-wise access to the labels of the training examples

  • IndexIterator – The type of the iterator that provides access to the indices of the examples that should be considered

Public Functions

ExampleWiseStratification(std::unique_ptr<RNG> rngPtr, const LabelMatrix &labelMatrix, IndexIterator indicesBegin, IndexIterator indicesEnd)
Parameters:

  • rngPtr – An unique pointer to an object of type RNG that should be used for generating random numbers

  • labelMatrix – A reference to an object of template type LabelMatrix that provides random or row-wise access to the labels of the training examples

  • indicesBegin – An iterator to the beginning of the indices of the examples that should be considered

  • indicesEnd – An iterator to the end of the indices of the examples that should be considered

void sampleWeights(BitWeightVector &weightVector, EqualWeightVector::const_iterator weightIterator, float32 sampleSize, uint32 minSamples, uint32 maxSamples) const

Randomly selects a stratified sample of the available examples and sets the corresponding weights in a BitWeightVector to the value in the given iterator, while the remaining weights are set to 0.

Parameters:

  • weightVector – A reference to an object of type BitWeightVector, the weights should be written to

  • weightIterator – An iterator that provides access to the weights of individual training examples

  • sampleSize – The fraction of the available examples to be selected

  • minSamples – The minimum number of examples to be included in the sample. Must be at least 1

  • maxSamples – The maximum number of examples to be included in the sample. Must be at least minSamples or 0, if the number of examples should not be restricted

void sampleWeights(DenseWeightVector<float32> &weightVector, DenseWeightVector<float32>::const_iterator weightIterator, float32 sampleSize, uint32 minSamples, uint32 maxSamples) const

Randomly selects a stratified sample of the available examples and sets the corresponding weights in a DenseWeightVector<float32> to the value in the given iterator, while the remaining weights are set to 0.

Parameters:

  • weightVector – A reference to an object of type DenseWeightVector<float32>, the weights should be written to

  • weightIterator – An iterator that provides access to the weights of individual training examples

  • sampleSize – The fraction of the available examples to be selected

  • minSamples – The minimum number of examples to be included in the sample. Must be at least 1

  • maxSamples – The maximum number of examples to be included in the sample. Must be at least minSamples or 0, if the number of examples should not be restricted

void sampleBiPartition(BiPartition &partition) const

Randomly splits the available examples into two distinct sets and updates a given BiPartition accordingly.

Parameters:

partition – A reference to an object of type BiPartition to be updated

Private Members

const std::unique_ptr<RNG> rngPtr_
const uint32 numTotal_
std::vector<std::unique_ptr<std::vector<uint32>>> order_