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#ifndef INCLUDED_NJN_LOCALMAXSTATUTIL
#define INCLUDED_NJN_LOCALMAXSTATUTIL
/* $Id: $
* ===========================================================================
*
* PUBLIC DOMAIN NOTICE
* National Center for Biotechnology Information
*
* This software/database is a "United States Government Work" under the
* terms of the United States Copyright Act. It was written as part of
* the author's offical duties as a United States Government employee and
* thus cannot be copyrighted. This software/database is freely available
* to the public for use. The National Library of Medicine and the U.S.
* Government have not placed any restriction on its use or reproduction.
*
* Although all reasonable efforts have been taken to ensure the accuracy
* and reliability of the software and data, the NLM and the U.S.
* Government do not and cannot warrant the performance or results that
* may be obtained by using this software or data. The NLM and the U.S.
* Government disclaim all warranties, express or implied, including
* warranties of performance, merchantability or fitness for any particular
* purpose.
*
* Please cite the author in any work or product based on this material.
*
* ===========================================================================*/
/*****************************************************************************
File name: njn_localmaxstatutil.hpp
Author: John Spouge
Contents: Random walk parameters
******************************************************************************/
#include "njn_matrix.hpp"
namespace Njn {
namespace LocalMaxStatUtil {
const double REL_TOL = 1.0e-6;
void flatten ( // allocates memory for linear probabilities and scores
size_t dimension_, // dimension of equilProb_
const long int *const *scoreMatrix_, // packed scoring matrix [0...dimension_)[0...dimension_)
const double *const *prob_, // prob_ [0...dimension_)[0...dimension_) : distribution of scores sum to 1.0
size_t *dim_, // dimension of p_
long int **score_, // score [0...dim_) in increasing order
double **p_, // linear p_ [0...dim_) : distribution of scores
size_t dimension2_ = 0); // dimension2 of equilProb_ : defaults to dimension_
// asserts (sum (p_) == 1.0);
double lambda (
size_t dimMatrix_, // dimension of equilProb_
const long int *const *scoreMatrix_, // packed scoring matrix [0...dimension_)[0...dimension_)
const double *q_); // q_ [0...dimension_) : distribution of independent letters
double mu (
size_t dimension_, // #(distinct values)
const long int *score_, // scores in increasing order
const double *prob_); // probability of corresponding value
double lambda (
size_t dimension_, // #(distinct values)
const long int *score_, // scores in increasing order
const double *prob_); // probability of corresponding value
// assumes logarithmic regime
double muAssoc (
size_t dimension_, // #(distinct values) of scores & probabilities (which are paired)
const long int *score_, // scores in increasing order
const double *prob_, // corresponding probabilities
double lambda_ = 0.0); // lambda
double muPowerAssoc (
size_t dimension_, // #(distinct values) of scores & probabilities (which are paired)
const long int *score_, // scores in increasing order
const double *prob_, // corresponding probabilities
double lambda_ = 0.0, // lambda
long int power_ = 1); // power
double thetaMin ( // minimizing value for r(theta)
size_t dimension_, // #(distinct values)
const long int *score_, // scores in increasing order
const double *prob_, // probability of corresponding value
double lambda_ = 0.0); // lambda
// assumes logarithmic regime
double rMin ( // minimum value of r(theta)
size_t dimension_, // #(distinct values)
const long int *score_, // scores in increasing order
const double *prob_, // probability of corresponding value
double lambda_ = 0.0, // lambda
double thetaMin_ = 0.0); // argument of rate
// assumes logarithmic regime
double r ( // r(theta)
size_t dimension_, // #(distinct values)
const long int *score_, // scores in increasing order
const double *prob_, // probability of corresponding value
double theta_); // argument of rate
// assumes logarithmic regime
long int delta ( // theta [minus delta] for ungapped sequence comparison
size_t dimension_, // #(distinct values) of scores & probabilities (which are paired)
const long int *score_); // scores
double thetaMinusDelta ( // theta [minus delta] for ungapped sequence comparison
double lambda_, // lambda, the exponential rate for the local maximum
size_t dimension_, // #(distinct values) of scores & probabilities (which are paired)
const long int *score_); // scores
void descendingLadderEpoch (
size_t dimension_, // #(distinct values)
const long int *score_, // values
const double *prob_, // probability of corresponding value
double *eSumAlpha_ = 0, // expectation (sum [alpha])
double *eOneMinusExpSumAlpha_ = 0, // expectation [1.0 - exp (sum [alpha])]
bool isStrict_ = false, // ? is this a strict descending ladder epoch
double lambda0_ = 0.0, // lambda for flattened distribution (avoid recomputation)
double mu0_ = 0.0, // mean of flattened distribution (avoid recomputation)
double muAssoc0_ = 0.0, // mean of associated flattened distribution (avoid recomputation)
double thetaMin0_ = 0.0, // thetaMin of flattened distribution (avoid recomputation)
double rMin0_ = 0.0, // rMin of flattened distribution (avoid recomputation)
double time_ = 0.0, // get time for the dynamic programming computation
bool *terminated_ = 0); // ? Was the dynamic programming computation terminated prematurely ?
void descendingLadderEpochRepeat (
size_t dimension_, // #(distinct values)
const long int *score_, // values
const double *prob_, // probability of corresponding value
double *eSumAlpha_ = 0, // expectation (sum [alpha])
double *eOneMinusExpSumAlpha_ = 0, // expectation [1.0 - exp (sum [alpha])]
bool isStrict_ = false, // ? is this a strict descending ladder epoch
double lambda_ = 0.0, // lambda for repeats : default is lambda0_ below
size_t endW_ = 0, // maximum w plus 1
double *pAlphaW_ = 0, // probability {alpha = w} : pAlphaW_ [0, wEnd)
double *eOneMinusExpSumAlphaW_ = 0, // expectation [1.0 - exp (sum [alpha]); alpha = w] : eOneMinusExpSumAlphaW_ [0, wEnd)
double lambda0_ = 0.0, // lambda for flattened distribution (avoid recomputation)
double mu0_ = 0.0, // mean of flattened distribution (avoid recomputation)
double muAssoc0_ = 0.0, // mean of associated flattened distribution (avoid recomputation)
double thetaMin0_ = 0.0, // thetaMin of flattened distribution (avoid recomputation)
double rMin0_ = 0.0, // rMin of flattened distribution (avoid recomputation)
double time_ = 0.0, // get time for the dynamic programming computation
bool *terminated_ = 0); // ? Was the dynamic programming computation terminated prematurely ?
// assumes logarithmic regime
bool isProbDist (
size_t dimension_, // #(distinct values) of scores & probabilities (which are paired)
const double *prob_); // corresponding probabilities
bool isScoreIncreasing (
size_t dimension_, // #(distinct values)
const long int *score_); // scores in increasing order
bool isLogarithmic (
size_t dimension_, // #(distinct values)
const long int *score_, // scores in increasing order
const double *prob_); // probability of corresponding value
}
}
#endif
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