Sequence::_PolySNPImpl Struct Reference

#include <PolySNPimpl.hpp>

Public Member Functions
void	preprocess (void)
	_PolySNPImpl (const Sequence::PolyTable *data, const bool &haveOutgroup, const unsigned &outgroup, const bool &totMuts)

Public Attributes
const PolyTable *	_data
unsigned	_nsites
unsigned	_nsam
unsigned	_outgroup
bool	_haveOutgroup
bool	_totMuts
unsigned	_totsam
unsigned	_DVK
double	_DVH
bool	_counted_singletons
bool	_know_pi
bool	_CalculatedDandV
double	_pi
unsigned	_singletons
unsigned	_walls_Bprime
unsigned	_NumPoly
double	_walls_B
double	_walls_Q
bool	_calculated_wall_stats
std::vector< Sequence::stateCounter >	_counts
std::vector< std::pair< bool, Sequence::stateCounter > >	_derivedCounts
std::mutex	instance_lock
bool	_preprocessed

Detailed Description

Implementation details for PolySNP. This class is visible so that it can be accessed from classes derived from PolySNP. A PolySNP object contains a pointer to an instance of this class that is storage class protected.

Definition at line 30 of file PolySNPimpl.hpp.

Member Function Documentation

preprocess()

void Sequence::_PolySNPImpl::preprocess

(

void

)

This routine takes the data and obtains count information for each possible character state at each site. The reason for doing this is that the various summary statistics that depend on frequency spectrum information are all O(n x S) to compute, where n is the sample size and S the number of sites. However, all of these statistics depend basically on the same features of the data, so it inefficient to recalculate them every time. This function obtains the counts for each site. Note that the efficiency gain is huge (especially for large data sets) as each summary statistic is reduced from an O(n x S) to an O(S) calculation. The increase in run-time efficiency comes at the cost of allocating 2 vectors whose sizes are linear in S

Definition at line 92 of file PolySNP.cc.

---

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

• PolySNPimpl.hpp
• PolySNP.cc