Running sums from sparse data. More...

#include <sums.hpp>

Public Member Functions
	RunningSparse (Output_ *sum, bool skip_nan, Index_ subtract=0)

void	add (const Value_ value, const Index_ index, Index_ number)

Detailed Description

template<typename Output_, typename Value_, typename Index_>
class tatami_stats::sums::RunningSparse< Output_, Value_, Index_ >

Running sums from sparse data.

Compute running sums from sparse data. This is the counterpart to RunningDense, but for sparse observed vectors.

Template Parameters

Output_	Type of the output data.
Value_	Type of the input value.
Index_	Type of the row/column indices.

Constructor & Destructor Documentation

template<typename Output_ , typename Value_ , typename Index_ >

tatami_stats::sums::RunningSparse< Output_, Value_, Index_ >::RunningSparse	(	Output_ *	sum,
		bool	skip_nan,
		Index_	subtract = 0 )

inline

Parameters

[out]	sum	Pointer to an output array of length equal to the number of objective vectors. This should be zeroed on input, and will store the running sums after each `add()`.
	skip_nan	See `Options::skip_nan` for details.
	subtract	Offset to subtract from each element of `index` before using it to index into `mean` and friends. Only relevant if `mean` and friends hold statistics for a contiguous subset of objective vectors, e.g., during task allocation for parallelization.

template<typename Output_ , typename Value_ , typename Index_ >

void tatami_stats::sums::RunningSparse< Output_, Value_, Index_ >::add	(	const Value_ *	value,
		const Index_ *	index,
		Index_	number )

inline

Add the next observed vector to the running sums.

Parameters

[in]	value	Value of structural non-zero elements.
[in]	index	Index of structural non-zero elements. This does not have to be sorted.
	number	Number of non-zero elements in `value` and `index`.

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