Delayed subsetting of a matrix with sorted, unique indices. More...

#include <DelayedSubsetSortedUnique.hpp>

Inheritance diagram for tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >:

Collaboration diagram for tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >:

Public Member Functions
	DelayedSubsetSortedUnique (std::shared_ptr< const Matrix< Value_, Index_ > > matrix, SubsetStorage_ subset, bool by_row, bool check=true)

Index_	nrow () const

Index_	ncol () const

bool	is_sparse () const

double	is_sparse_proportion () const

bool	prefer_rows () const

double	prefer_rows_proportion () const

bool	uses_oracle (bool row) const

std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > >	dense (bool row, const Options &opt) const

std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > >	dense (bool row, Index_ block_start, Index_ block_length, const Options &opt) const

std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > >	dense (bool row, VectorPtr< Index_ > indices_ptr, const Options &opt) const

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > >	sparse (bool row, const Options &opt) const

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > >	sparse (bool row, Index_ block_start, Index_ block_length, const Options &opt) const

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > >	sparse (bool row, VectorPtr< Index_ > indices_ptr, const Options &opt) const

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > >	dense (bool row, std::shared_ptr< const Oracle< Index_ > > oracle, const Options &opt) const

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > >	dense (bool row, std::shared_ptr< const Oracle< Index_ > > oracle, Index_ block_start, Index_ block_length, const Options &opt) const

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > >	dense (bool row, std::shared_ptr< const Oracle< Index_ > > oracle, VectorPtr< Index_ > indices_ptr, const Options &opt) const

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > >	sparse (bool row, std::shared_ptr< const Oracle< Index_ > > oracle, const Options &opt) const

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > >	sparse (bool row, std::shared_ptr< const Oracle< Index_ > > oracle, Index_ block_start, Index_ block_length, const Options &opt) const

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > >	sparse (bool row, std::shared_ptr< const Oracle< Index_ > > oracle, VectorPtr< Index_ > indices_ptr, const Options &opt) const

Public Member Functions inherited from tatami::Matrix< Value_, Index_ >
std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > >	dense_row (const Options &opt) const

std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > >	dense_row (Index_ block_start, Index_ block_length, const Options &opt) const

std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > >	dense_row (VectorPtr< Index_ > indices_ptr, const Options &opt) const

std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > >	dense_row (std::vector< Index_ > indices, const Options &opt) const

std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > >	dense_row () const

std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > >	dense_row (Index_ block_start, Index_ block_length) const

std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > >	dense_row (VectorPtr< Index_ > indices_ptr) const

std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > >	dense_row (std::vector< Index_ > indices) const

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > >	dense_row (std::shared_ptr< const Oracle< Index_ > > oracle, const Options &opt) const

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > >	dense_row (std::shared_ptr< const Oracle< Index_ > > oracle, Index_ block_start, Index_ block_length, const Options &opt) const

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > >	dense_row (std::shared_ptr< const Oracle< Index_ > > oracle, VectorPtr< Index_ > indices_ptr, const Options &opt) const

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > >	dense_row (std::shared_ptr< const Oracle< Index_ > > oracle, std::vector< Index_ > indices, const Options &opt) const

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > >	dense_row (std::shared_ptr< const Oracle< Index_ > > oracle) const

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > >	dense_row (std::shared_ptr< const Oracle< Index_ > > oracle, Index_ block_start, Index_ block_length) const

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > >	dense_row (std::shared_ptr< const Oracle< Index_ > > oracle, VectorPtr< Index_ > indices_ptr) const

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > >	dense_row (std::shared_ptr< const Oracle< Index_ > > oracle, std::vector< Index_ > indices) const

std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > >	dense_column (const Options &opt) const

std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > >	dense_column (Index_ block_start, Index_ block_length, const Options &opt) const

std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > >	dense_column (VectorPtr< Index_ > indices_ptr, const Options &opt) const

std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > >	dense_column (std::vector< Index_ > indices, const Options &opt) const

std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > >	dense_column () const

std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > >	dense_column (Index_ block_start, Index_ block_length) const

std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > >	dense_column (VectorPtr< Index_ > indices_ptr) const

std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > >	dense_column (std::vector< Index_ > indices) const

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > >	dense_column (std::shared_ptr< const Oracle< Index_ > > oracle, const Options &opt) const

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > >	dense_column (std::shared_ptr< const Oracle< Index_ > > oracle, Index_ block_start, Index_ block_length, const Options &opt) const

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > >	dense_column (std::shared_ptr< const Oracle< Index_ > > oracle, VectorPtr< Index_ > indices_ptr, const Options &opt) const

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > >	dense_column (std::shared_ptr< const Oracle< Index_ > > oracle, std::vector< Index_ > indices, const Options &opt) const

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > >	dense_column (std::shared_ptr< const Oracle< Index_ > > oracle) const

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > >	dense_column (std::shared_ptr< const Oracle< Index_ > > oracle, Index_ block_start, Index_ block_length) const

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > >	dense_column (std::shared_ptr< const Oracle< Index_ > > oracle, VectorPtr< Index_ > indices_ptr) const

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > >	dense_column (std::shared_ptr< const Oracle< Index_ > > oracle, std::vector< Index_ > indices) const

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > >	sparse_row (const Options &opt) const

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > >	sparse_row (Index_ block_start, Index_ block_length, const Options &opt) const

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > >	sparse_row (VectorPtr< Index_ > indices_ptr, const Options &opt) const

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > >	sparse_row (std::vector< Index_ > indices, const Options &opt) const

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > >	sparse_row () const

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > >	sparse_row (Index_ block_start, Index_ block_length) const

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > >	sparse_row (VectorPtr< Index_ > indices_ptr) const

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > >	sparse_row (std::vector< Index_ > indices) const

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > >	sparse_row (std::shared_ptr< const Oracle< Index_ > > oracle, const Options &opt) const

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > >	sparse_row (std::shared_ptr< const Oracle< Index_ > > oracle, Index_ block_start, Index_ block_length, const Options &opt) const

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > >	sparse_row (std::shared_ptr< const Oracle< Index_ > > oracle, VectorPtr< Index_ > indices_ptr, const Options &opt) const

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > >	sparse_row (std::shared_ptr< const Oracle< Index_ > > oracle, std::vector< Index_ > indices, const Options &opt) const

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > >	sparse_row (std::shared_ptr< const Oracle< Index_ > > oracle) const

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > >	sparse_row (std::shared_ptr< const Oracle< Index_ > > oracle, Index_ block_start, Index_ block_length) const

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > >	sparse_row (std::shared_ptr< const Oracle< Index_ > > oracle, VectorPtr< Index_ > indices_ptr) const

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > >	sparse_row (std::shared_ptr< const Oracle< Index_ > > oracle, std::vector< Index_ > indices) const

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > >	sparse_column (const Options &opt) const

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > >	sparse_column (Index_ block_start, Index_ block_length, const Options &opt) const

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > >	sparse_column (VectorPtr< Index_ > indices_ptr, const Options &opt) const

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > >	sparse_column (std::vector< Index_ > indices, const Options &opt) const

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > >	sparse_column () const

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > >	sparse_column (Index_ block_start, Index_ block_length) const

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > >	sparse_column (VectorPtr< Index_ > indices_ptr) const

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > >	sparse_column (std::vector< Index_ > indices) const

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > >	sparse_column (std::shared_ptr< const Oracle< Index_ > > oracle, const Options &opt) const

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > >	sparse_column (std::shared_ptr< const Oracle< Index_ > > oracle, Index_ block_start, Index_ block_length, const Options &opt) const

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > >	sparse_column (std::shared_ptr< const Oracle< Index_ > > oracle, VectorPtr< Index_ > indices_ptr, const Options &opt) const

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > >	sparse_column (std::shared_ptr< const Oracle< Index_ > > oracle, std::vector< Index_ > indices, const Options &opt) const

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > >	sparse_column (std::shared_ptr< const Oracle< Index_ > > oracle) const

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > >	sparse_column (std::shared_ptr< const Oracle< Index_ > > oracle, Index_ block_start, Index_ block_length) const

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > >	sparse_column (std::shared_ptr< const Oracle< Index_ > > oracle, VectorPtr< Index_ > indices_ptr) const

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > >	sparse_column (std::shared_ptr< const Oracle< Index_ > > oracle, std::vector< Index_ > indices) const

Additional Inherited Members
Public Types inherited from tatami::Matrix< Value_, Index_ >
typedef Value_	value_type

typedef Index_	index_type

Detailed Description

template<typename Value_, typename Index_, class SubsetStorage_>
class tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >

Delayed subsetting of a matrix with sorted, unique indices.

Implements delayed subsetting (i.e., slicing) on the rows or columns of a matrix, given a vector of sorted and unique indices. This operation is "delayed" in that it is only evaluated when data is requested from the matrix.

Template Parameters

Value_	Type of matrix value.
Index_	Type of index value.
SubsetStorage_	Vector containing the subset indices.

Constructor & Destructor Documentation

◆ DelayedSubsetSortedUnique()

template<typename Value_ , typename Index_ , class SubsetStorage_ >

tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >::DelayedSubsetSortedUnique	(	std::shared_ptr< const Matrix< Value_, Index_ > >	matrix,
		SubsetStorage_	subset,
		bool	by_row,
		bool	check = `true`
	)

inline

Parameters

matrix	Pointer to the underlying (pre-subset) matrix.
subset	Vector of 0-based indices to use for subsetting on the rows (if `by_row = true`) or columns (otherwise). This should be sorted and unique.
by_row	Whether to apply the subset to the rows. If false, the subset is applied to the columns.
check	Whether to check `subset` for sorted and unique values.

Member Function Documentation

◆ nrow()

template<typename Value_ , typename Index_ , class SubsetStorage_ >

Index_ tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >::nrow ( ) const

inlinevirtual

Returns: Number of rows.

Implements tatami::Matrix< Value_, Index_ >.

◆ ncol()

template<typename Value_ , typename Index_ , class SubsetStorage_ >

Index_ tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >::ncol ( ) const

inlinevirtual

Returns: Number of columns.

Implements tatami::Matrix< Value_, Index_ >.

◆ is_sparse()

template<typename Value_ , typename Index_ , class SubsetStorage_ >

bool tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >::is_sparse ( ) const

inlinevirtual

Returns: Boolean indicating whether this matrix is sparse.

This can be used to choose between dense and sparse outputs.

Implements tatami::Matrix< Value_, Index_ >.

◆ is_sparse_proportion()

template<typename Value_ , typename Index_ , class SubsetStorage_ >

double tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >::is_sparse_proportion ( ) const

inlinevirtual

Returns: Approximate proportion of the matrix that is sparse.

This is defined as the proportion of matrix elements that lie within sparse submatrices. It is intended for use in Matrix representations that consist of combinations of multiple submatrices (e.g., DelayedBind), allowing them to derive a suitable value for is_sparse() based on whether most of its submatrices are sparse. (A more granular approach would be to report the density of structural non-zero elements, but this may not be known by all representations at construction time.)

Implements tatami::Matrix< Value_, Index_ >.

◆ prefer_rows()

template<typename Value_ , typename Index_ , class SubsetStorage_ >

bool tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >::prefer_rows ( ) const

inlinevirtual

Returns: The preferred dimension for extracting values. If true, row-wise extraction is preferred; if false, column-wise extraction is preferred.

Implements tatami::Matrix< Value_, Index_ >.

◆ prefer_rows_proportion()

template<typename Value_ , typename Index_ , class SubsetStorage_ >

double tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >::prefer_rows_proportion ( ) const

inlinevirtual

Returns: Approximate proportion of the matrix that prefers row-level access.

This is defined as the proportion of matrix elements that lie within submatrices that prefer row-level access. It is useful for determining the return value of prefer_rows() in combined matrices consisting of both row- and column-preferred submatrices. In such cases, the net preference can be determined based on the combined size of the submatrices for each preference. (A more granular approach would be to report the iteration cost on each dimension, but this is difficult to estimate.)

Implements tatami::Matrix< Value_, Index_ >.

◆ uses_oracle()

template<typename Value_ , typename Index_ , class SubsetStorage_ >

bool tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >::uses_oracle ( bool row ) const

inlinevirtual

Parameters

row	Row access if `true`, column access otherwise.

Returns: Whether this matrix's tatami::Extractor classes make use of oracle predictions for row (if row = true) or column access (otherwise).

The output of this method indicates whether callers should construct an oracle for use in ExtractorBase::set_oracle(). If false, callers should not bother to pass an oracle as it will be ignored.

Implements tatami::Matrix< Value_, Index_ >.

◆ dense() [1/6]

template<typename Value_ , typename Index_ , class SubsetStorage_ >

std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > > tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >::dense	(	bool	row,
		const Options &	opt
	)		const

inlinevirtual

Create an extractor that retrieves the full extent of the non-target dimension in dense form.

Parameters

row	Whether to create a row-wise extractor, i.e., the rows are the target dimension.
opt	Options for extraction.

Returns: Object for extracting each row (if row = true) or column (otherwise) in dense form. This should not outlive the parent Matrix from which it was created.

Implements tatami::Matrix< Value_, Index_ >.

◆ dense() [2/6]

template<typename Value_ , typename Index_ , class SubsetStorage_ >

std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > > tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >::dense	(	bool	row,
		Index_	block_start,
		Index_	block_length,
		const Options &	opt
	)		const

inlinevirtual

Create an extractor that retrieves a contiguous block of the non-target dimension in dense form.

Parameters

row	Whether to create a row-wise extractor, i.e., the rows are the target dimension.
block_start	Index of the column (if `row = true`) or row (otherwise) at the start of the block.
block_length	Number of columns (if `row = true`) or rows (otherwise) in the block.
opt	Options for extraction.

Returns: Object for extracting a contiguous block from each row (if row = true) or column (otherwise) in dense form. This should not outlive the parent Matrix from which it was created.

Implements tatami::Matrix< Value_, Index_ >.

◆ dense() [3/6]

template<typename Value_ , typename Index_ , class SubsetStorage_ >

std::unique_ptr< MyopicDenseExtractor< Value_, Index_ > > tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >::dense	(	bool	row,
		VectorPtr< Index_ >	indices_ptr,
		const Options &	opt
	)		const

inlinevirtual

Create an extractor that retrieves an indexed subset of the non-target dimension in dense form.

Parameters

row	Whether to create a row-wise extractor, i.e., the rows are the target dimension.
indices_ptr	Pointer to a vector of sorted and unique column indices (if `row = true`) or row indices (otherwise). This should be non-NULL.
opt	Options for extraction.

Returns: Object for extracting an indexed subset from each row (if row = true) or column (otherwise) in dense form. This should not outlive the parent Matrix from which it was created.

Implements tatami::Matrix< Value_, Index_ >.

◆ sparse() [1/6]

template<typename Value_ , typename Index_ , class SubsetStorage_ >

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > > tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >::sparse	(	bool	row,
		const Options &	opt
	)		const

inlinevirtual

Create an extractor that retrieves the full extent of the non-target dimension in sparse form.

Parameters

row	Whether to create a row-wise extractor, i.e., the rows are the target dimension.
opt	Options for extraction.

Returns: Object for extracting each row (if row = true) or columns (otherwise) in sparse form. This should not outlive the parent Matrix from which it was created.

Implements tatami::Matrix< Value_, Index_ >.

◆ sparse() [2/6]

template<typename Value_ , typename Index_ , class SubsetStorage_ >

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > > tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >::sparse	(	bool	row,
		Index_	block_start,
		Index_	block_length,
		const Options &	opt
	)		const

inlinevirtual

Create an extractor that retrieves a contiguous block of the non-target dimension in sparse form.

Parameters

row	Whether to create a row-wise extractor, i.e., the rows are the target dimension.
block_start	Index of the column (if `row = true`) or row (otherwise) at the start of the block.
block_length	Number of columns (if `row = true`) or rows (otherwise) in the block.
opt	Options for extraction.

Returns: Object for extracting a contiguous block from each row (if row = true) or column (otherwise) in sparse form. This should not outlive the parent Matrix from which it was created.

Implements tatami::Matrix< Value_, Index_ >.

◆ sparse() [3/6]

template<typename Value_ , typename Index_ , class SubsetStorage_ >

std::unique_ptr< MyopicSparseExtractor< Value_, Index_ > > tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >::sparse	(	bool	row,
		VectorPtr< Index_ >	indices_ptr,
		const Options &	opt
	)		const

inlinevirtual

Create an extractor that retrieves an indexed subset of the non-target dimension in sparse form.

Parameters

row	Whether to create a row-wise extractor, i.e., the rows are the target dimension.
indices_ptr	Pointer to a vector of sorted and unique column indices (if `row = true`) or row indices (otherwise). This should be non-NULL.
opt	Options for extraction.

Returns: Object for extracting an indexed subset from each row (if row = true) or column (otherwise) in sparse form. This should not outlive the parent Matrix from which it was created.

Implements tatami::Matrix< Value_, Index_ >.

◆ dense() [4/6]

template<typename Value_ , typename Index_ , class SubsetStorage_ >

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > > tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >::dense	(	bool	row,
		std::shared_ptr< const Oracle< Index_ > >	oracle,
		const Options &	opt
	)		const

inlinevirtual

Create an oracle-aware extractor that retrieves the full extent of the non-target dimension in dense form.

Parameters

row	Whether to create a row-wise extractor, i.e., the rows are the target dimension.
oracle	An oracle supplying predictions of the next requested row (if `row = true`) or column (otherwise).
opt	Options for extraction.

Returns: Object for extracting each row (if row = true) or columns (otherwise) in dense form. This should not outlive the parent Matrix from which it was created.

Implements tatami::Matrix< Value_, Index_ >.

◆ dense() [5/6]

template<typename Value_ , typename Index_ , class SubsetStorage_ >

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > > tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >::dense	(	bool	row,
		std::shared_ptr< const Oracle< Index_ > >	oracle,
		Index_	block_start,
		Index_	block_length,
		const Options &	opt
	)		const

inlinevirtual

Create an oracle-aware extractor that retrieves a contiguous block of the non-target dimension in dense form.

Parameters

row	Whether to create a row-wise extractor, i.e., the rows are the target dimension.
oracle	An oracle supplying predictions of the next requested row (if `row = true`) or column (otherwise).
block_start	Index of the column (if `row = true`) or row (otherwise) at the start of the block.
block_length	Number of columns (if `row = true`) or rows (otherwise) in the block.
opt	Options for extraction.

Returns: Object for extracting a contiguous block from each row (if row = true) or column (otherwise) in dense form. This should not outlive the parent Matrix from which it was created.

Implements tatami::Matrix< Value_, Index_ >.

◆ dense() [6/6]

template<typename Value_ , typename Index_ , class SubsetStorage_ >

std::unique_ptr< OracularDenseExtractor< Value_, Index_ > > tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >::dense	(	bool	row,
		std::shared_ptr< const Oracle< Index_ > >	oracle,
		VectorPtr< Index_ >	indices_ptr,
		const Options &	opt
	)		const

inlinevirtual

Create an oracle-aware extractor that retrieves an indexed subset of the non-target dimension in dense form.

Parameters

row	Whether to create a row-wise extractor, i.e., the rows are the target dimension.
oracle	An oracle supplying predictions of the next requested row (if `row = true`) or column (otherwise).
indices_ptr	Pointer to a vector of sorted and unique column indices (if `row = true`) or row indices (otherwise). This should not be NULL.
opt	Options for extraction.

Returns: Object for extracting an indexed subset from each row (if row = true) or column (otherwise) in dense form. This should not outlive the parent Matrix from which it was created.

Implements tatami::Matrix< Value_, Index_ >.

◆ sparse() [4/6]

template<typename Value_ , typename Index_ , class SubsetStorage_ >

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > > tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >::sparse	(	bool	row,
		std::shared_ptr< const Oracle< Index_ > >	oracle,
		const Options &	opt
	)		const

inlinevirtual

Create an oracle-aware extractor that retrieves the full extent of the non-target dimension in sparse form.

Parameters

row	Whether to create a row-wise extractor, i.e., the rows are the target dimension.
oracle	An oracle supplying predictions of the next requested row (if `row = true`) or column (otherwise).
opt	Options for extraction.

Returns: Object for extracting each row (if row = true) or columns (otherwise) in sparse form. This should not outlive the parent Matrix from which it was created.

Implements tatami::Matrix< Value_, Index_ >.

◆ sparse() [5/6]

template<typename Value_ , typename Index_ , class SubsetStorage_ >

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > > tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >::sparse	(	bool	row,
		std::shared_ptr< const Oracle< Index_ > >	oracle,
		Index_	block_start,
		Index_	block_length,
		const Options &	opt
	)		const

inlinevirtual

Create an oracle-aware extractor that retrieves a contiguous block of the non-target dimension in sparse form.

Parameters

row	Whether to create a row-wise extractor, i.e., the rows are the target dimension.
oracle	An oracle supplying predictions of the next requested row (if `row = true`) or column (otherwise).
block_start	Index of the column (if `row = true`) or row (otherwise) at the start of the block.
block_length	Number of columns (if `row = true`) or rows (otherwise) in the block.
opt	Options for extraction.

Returns: Object for extracting a contiguous block from each row (if row = true) or column (otherwise) in dense form. This should not outlive the parent Matrix from which it was created.

Implements tatami::Matrix< Value_, Index_ >.

◆ sparse() [6/6]

template<typename Value_ , typename Index_ , class SubsetStorage_ >

std::unique_ptr< OracularSparseExtractor< Value_, Index_ > > tatami::DelayedSubsetSortedUnique< Value_, Index_, SubsetStorage_ >::sparse	(	bool	row,
		std::shared_ptr< const Oracle< Index_ > >	oracle,
		VectorPtr< Index_ >	indices_ptr,
		const Options &	opt
	)		const

inlinevirtual

Create an oracle-aware extractor that retrieves an indexed subset of the non-target dimension in sparse form.

Parameters

row	Whether to create a row-wise extractor, i.e., the rows are the target dimension.
oracle	An oracle supplying predictions of the next requested row (if `row = true`) or column (otherwise).
indices_ptr	Pointer to a vector of sorted and unique column indices (if `row = true`) or row indices (otherwise). This should not be NULL.
opt	Options for extraction.

Returns: Object for extracting an indexed subset from each row (if row = true) or column (otherwise) in sparse form. This should not outlive the parent Matrix from which it was created.

Implements tatami::Matrix< Value_, Index_ >.

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

tatami/subset/DelayedSubsetSortedUnique.hpp

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ DelayedSubsetSortedUnique()

Member Function Documentation

◆ nrow()

◆ ncol()

◆ is_sparse()

◆ is_sparse_proportion()

◆ prefer_rows()

◆ prefer_rows_proportion()

◆ uses_oracle()

◆ dense() [1/6]

◆ dense() [2/6]

◆ dense() [3/6]

◆ sparse() [1/6]

◆ sparse() [2/6]

◆ sparse() [3/6]

◆ dense() [4/6]

◆ dense() [5/6]

◆ dense() [6/6]

◆ sparse() [4/6]

◆ sparse() [5/6]

◆ sparse() [6/6]