#include <scores.h>

Public Member Functions
	ScoreModel (const DatasetFrames &p)

bool	operator== (const ScoreModel &other) const

float	operator[] (FrameId i) const

void	reset (float val=1.0F)

float	adjust (FrameId i, float prob)
	Multiplies the relevance score with the provided value. More...

float	adjust (size_t temp, FrameId i, float prob)
	Multiplies the relevance score of temporal part with the provided value. More...

float	set (FrameId i, float prob)
	Hard-sets the score with the provided value (normalization required). More...

const float *	v () const
	Pointer to the begin of the data. More...

const float *	temp (size_t temp) const

size_t	size () const
	Returns number of scores stored. More...

void	apply_temporals (size_t depth, const DatasetFrames &_dataset_frames, const float power)
	Transforms temporal inverse score into temporal scores and aggregates into full image scores. More...

void	normalize (size_t depth=MAX_TEMPORAL_SIZE)
	Normalizes the score distribution. More...

void	normalize (float *scores, size_t size)

void	invalidate_cache ()

void	reset_mask ()

bool	is_masked (FrameId ID) const
	Returns the current value for the frame. More...

bool	set_mask (FrameId ID, bool val)
	Sets the mask value for the frame. More...

void	apply_bayes (std::set< FrameId > likes, const std::set< FrameId > &screen, const PrimaryFrameFeatures &features)
	Applies relevance feedback rescore based on the Bayesian update rule. More...

const std::vector< FrameId > &	top_n (const DatasetFrames &_dataset_frames, size_t _size, size_t from_vid_limit=0, size_t from_shot_limit=0) const
	Gets the images with the highest scores but respecting the provided limits. More...

std::vector< FrameId >	top_n_with_context (const DatasetFrames &_dataset_frames, size_t _size, size_t from_vid_limit, size_t from_shot_limit) const
	Gets the images with the highest scores while respecting the provided limits and each frame is wrapped by it's context based on the number of frames per line. More...

std::vector< FrameId >	weighted_sample (size_t _size, float pow=1) const
	Samples `n` random frames from the current scores distribution. More...

FrameId	weighted_example (const std::vector< FrameId > &subset) const
	Samples a random frame from the current scores distribution. More...

size_t	frame_rank (FrameId i) const
	Returns the current rank of the provided frame (starts from 0). More...

Static Public Member Functions
static StdVector< std::pair< FrameId, float > >	sort_by_score (const StdVector< float > &scores)
	Sorts images by given score vector. More...

Private Attributes
std::vector< float >	_scores
	Current score distribution for the frames. More...

StdMatrix< float >	_temporal_scores

std::vector< bool >	_mask
	Frames mask telling what frames should be placed inside the results. More...

std::vector< FrameId >	_topn_cache

bool	_cache_dirty

std::vector< FrameId >	_topn_ctx_cache

bool	_cache_ctx_dirty

Constructor & Destructor Documentation

◆ ScoreModel()

sh::ScoreModel::ScoreModel ( const DatasetFrames & p )

inline

Member Function Documentation

◆ adjust() [1/2]

float ScoreModel::adjust	(	FrameId	i,
		float	prob
	)

Multiplies the relevance score with the provided value.

Does not update temporal scores.

Here is the call graph for this function:

◆ adjust() [2/2]

float ScoreModel::adjust	(	size_t	temp,
		FrameId	i,
		float	prob
	)

Multiplies the relevance score of temporal part with the provided value.

Here is the call graph for this function:

◆ apply_bayes()

void ScoreModel::apply_bayes	(	std::set< FrameId >	likes,
		const std::set< FrameId > &	screen,
		const PrimaryFrameFeatures &	features
	)

Applies relevance feedback rescore based on the Bayesian update rule.

Here is the call graph for this function:

◆ apply_temporals()

void ScoreModel::apply_temporals	(	size_t	depth,
		const DatasetFrames &	_dataset_frames,
		const float	power
	)

Transforms temporal inverse score into temporal scores and aggregates into full image scores.

Depth parameter defines depth of temporal query

Here is the call graph for this function:

◆ frame_rank()

size_t ScoreModel::frame_rank ( FrameId i ) const

Returns the current rank of the provided frame (starts from 0).

◆ invalidate_cache()

void sh::ScoreModel::invalidate_cache ( )

inline

◆ is_masked()

bool sh::ScoreModel::is_masked ( FrameId ID ) const

inline

Returns the current value for the frame.

◆ normalize() [1/2]

void ScoreModel::normalize	(	float *	scores,
		size_t	size
	)

Here is the call graph for this function:

◆ normalize() [2/2]

void ScoreModel::normalize ( size_t depth = MAX_TEMPORAL_SIZE )

Normalizes the score distribution.

Here is the call graph for this function:

◆ operator==()

bool ScoreModel::operator== ( const ScoreModel & other ) const

◆ operator[]()

float sh::ScoreModel::operator[] ( FrameId i ) const

inline

◆ reset()

void ScoreModel::reset ( float val = 1.0F )

Here is the call graph for this function:

◆ reset_mask()

void sh::ScoreModel::reset_mask ( )

inline

Here is the call graph for this function:

◆ set()

float ScoreModel::set	(	FrameId	i,
		float	prob
	)

Hard-sets the score with the provided value (normalization required).

Here is the call graph for this function:

◆ set_mask()

bool sh::ScoreModel::set_mask	(	FrameId	ID,
		bool	val
	)

inline

Sets the mask value for the frame.

Here is the call graph for this function:

◆ size()

size_t sh::ScoreModel::size ( ) const

inline

Returns number of scores stored.

◆ sort_by_score()

std::vector< std::pair< FrameId, float > > ScoreModel::sort_by_score ( const StdVector< float > & scores )

static

Sorts images by given score vector.

◆ temp()

const float* sh::ScoreModel::temp ( size_t temp ) const

inline

Here is the call graph for this function:

◆ top_n()

const std::vector< FrameId > & ScoreModel::top_n	(	const DatasetFrames &	_dataset_frames,
		size_t	_size,
		size_t	from_vid_limit = `0`,
		size_t	from_shot_limit = `0`
	)		const

Gets the images with the highest scores but respecting the provided limits.

Here is the call graph for this function:

◆ top_n_with_context()

std::vector< FrameId > ScoreModel::top_n_with_context	(	const DatasetFrames &	_dataset_frames,
		size_t	_size,
		size_t	from_vid_limit,
		size_t	from_shot_limit
	)		const

Gets the images with the highest scores while respecting the provided limits and each frame is wrapped by it's context based on the number of frames per line.

Here is the call graph for this function:

◆ v()

const float* sh::ScoreModel::v ( ) const

inline

Pointer to the begin of the data.

◆ weighted_example()

FrameId ScoreModel::weighted_example ( const std::vector< FrameId > & subset ) const

Samples a random frame from the current scores distribution.

◆ weighted_sample()

std::vector< FrameId > ScoreModel::weighted_sample	(	size_t	_size,
		float	pow = `1`
	)		const

Samples n random frames from the current scores distribution.

Member Data Documentation

◆ _cache_ctx_dirty

bool sh::ScoreModel::_cache_ctx_dirty

mutableprivate

◆ _cache_dirty

bool sh::ScoreModel::_cache_dirty

mutableprivate

◆ _mask

std::vector<bool> sh::ScoreModel::_mask

private

Frames mask telling what frames should be placed inside the results.

true <=> present in the result set false <=> filtered out

◆ _scores

std::vector<float> sh::ScoreModel::_scores

private

Current score distribution for the frames.

◆ _temporal_scores

StdMatrix<float> sh::ScoreModel::_temporal_scores

private

◆ _topn_cache

std::vector<FrameId> sh::ScoreModel::_topn_cache

mutableprivate

◆ _topn_ctx_cache

std::vector<FrameId> sh::ScoreModel::_topn_ctx_cache

mutableprivate

The documentation for this class was generated from the following files:

src/somhunter/searches/scores.h
src/somhunter/searches/scores.cpp

Public Member Functions

Static Public Member Functions

Private Attributes

Constructor & Destructor Documentation

◆ ScoreModel()

Member Function Documentation

◆ adjust() [1/2]

◆ adjust() [2/2]

◆ apply_bayes()

◆ apply_temporals()

◆ frame_rank()

◆ invalidate_cache()

◆ is_masked()

◆ normalize() [1/2]

◆ normalize() [2/2]

◆ operator==()

◆ operator[]()

◆ reset()

◆ reset_mask()

◆ set()

◆ set_mask()

◆ size()

◆ sort_by_score()

◆ temp()

◆ top_n()

◆ top_n_with_context()

◆ v()

◆ weighted_example()

◆ weighted_sample()

Member Data Documentation

◆ _cache_ctx_dirty

◆ _cache_dirty

◆ _mask

◆ _scores

◆ _temporal_scores

◆ _topn_cache

◆ _topn_ctx_cache