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njn_localmaxstat.hpp
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njn_localmaxstat.hpp
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#ifndef INCLUDED_NJN_LOCALMAXSTAT
#define INCLUDED_NJN_LOCALMAXSTAT
/* $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_localmaxstat.hpp
Author: John Spouge
Contents: Random walk parameters
******************************************************************************/
#include <math.h>
#include <vector>
#include <assert.h>
#include <ostream>
namespace Njn {
class LocalMaxStat {
// calculates the statistical parameters for the local maximum in a random walk
//
// The scores are uniqued and
// with the correspondence to probabilities maintained, placed in ascending order.
public:
// The following subroutines control the time for the dynamic programming computation.
// The default time_ = 0.0 permits the computation to run forever.
static void setTime (double time_ = 0.0) {assert (time_ >= 0.0); s_time = time_;} // set time for the dynamic programming computation
static double getTime () {return s_time;} // get time for the dynamic programming computation
// For an object o,
// if the computation is terminated before it finishes,
// o.getTerminated () == true.
inline LocalMaxStat (
size_t dimension_ = 0, // #(distinct values)
const long int *score_ = 0, // scores in increasing order
const double *prob_ = 0) // probability of corresponding value
: d_dimension (0), d_score_p (0), d_prob_p (0),
d_lambda (0.0), d_k (0.0), d_c (0.0), d_thetaMin (0.0), d_rMin (0.0),
d_delta (0), d_thetaMinusDelta (0.0),
d_mu (0.0), d_sigma (0.0), d_muAssoc (0.0), d_sigmaAssoc (0.0),
d_meanWDLE (0.0), d_terminated (false)
{
copy (dimension_, score_, prob_);
}
inline LocalMaxStat (const LocalMaxStat &localMaxStat_) // random walk parameters
: d_dimension (0), d_score_p (0), d_prob_p (0),
d_lambda (0.0), d_k (0.0), d_c (0.0), d_thetaMin (0.0), d_rMin (0.0),
d_delta (0), d_thetaMinusDelta (0.0),
d_mu (0.0), d_sigma (0.0), d_muAssoc (0.0), d_sigmaAssoc (0.0),
d_meanWDLE (0.0), d_terminated (false)
{
copy (localMaxStat_);
}
inline ~LocalMaxStat () {free2 ();}
inline operator bool () // ? is the object ready for computation ?
const {
return d_dimension != 0;
}
inline LocalMaxStat &operator= (const LocalMaxStat &localMaxStat_) // random walk parameters
{
if (this != &localMaxStat_) copy (localMaxStat_);
return *this;
}
void copy (
size_t dimension_, // #(distinct values) of scores & probabilities (which are paired)
const long int *score_, // scores in increasing order
const double *prob_); // probabilities
inline void copy (const LocalMaxStat &localMaxStat_)
{
copy (localMaxStat_.getDimension (), localMaxStat_.getScore (), localMaxStat_.getProb (),
localMaxStat_.getLambda (), localMaxStat_.getK (), localMaxStat_.getC (),
localMaxStat_.getThetaMin (), localMaxStat_.getRMin (),
localMaxStat_.getDelta (), localMaxStat_.getThetaMinusDelta (),
localMaxStat_.getMu (), localMaxStat_.getSigma (), localMaxStat_.getMuAssoc (), localMaxStat_.getSigmaAssoc (),
localMaxStat_.getMeanWDLE (), localMaxStat_.getTerminated ());
}
void copy (
size_t dimension_, // #(distinct values) of scores & probabilities (which are paired)
const long int *score_, // scores in increasing order
const double *prob_, // probabilities
double lambda_, // lambda for associated random walk
double k_, // k for random walk : exponential prefactor
double c_, // c for random walk : exponential prefactor (global alignment)
double thetaMin_, // theta for minimum expectation (exp (theta * score))
double rMin_, // minimum expectation (exp (theta * score))
long int delta_, // span
double thetaMinusDelta_, // renewal span parameter
double mu_, // step mean for random walk
double sigma_, // step standard deviation for random walk
double muAssoc_, // step mean for associated random walk (relative entropy)
double sigmaAssoc_, // step standard deviation for associated random walk
double meanDLE_, // expected renewal length
bool terminated_ = false); // ? Was the dynamic programming computation terminated prematurely ?
inline std::ostream &out (std::ostream &ostr_) const {return ostr_;} // output
double getR (double theta_) const; // r (theta_) : dominant eigenvalue for theta_
double getA () const {return getMuAssoc () == 0 ? HUGE_VAL : 1.0 / getMuAssoc ();} // expected [length / y] for achieving y
double getAlpha () const {return getSigmaAssoc () * getSigmaAssoc () * getA () * getA () * getA ();} // var [length] / y for achieving y
inline size_t getDimension () const {return d_dimension;} // #(distinct values) of scores & probabilities (which are paired)
inline const long int *getScore () const {return d_score_p;} // scores in increasing order
inline const double *getProb () const {return d_prob_p;} // probabilities
inline double getLambda () const {return d_lambda;} // lambda for associated random walk
inline double getK () const {return d_k;} // k for random walk : exponential prefactor
inline double getC () const {return d_c;} // c for random walk : exponential prefactor (global alignment)
inline double getThetaMin () const {return d_thetaMin;} // theta for minimum expectation (exp (theta * score))
inline double getRMin () const {return d_rMin;} // minimum expectation (exp (theta * score))
inline long int getDelta () const {return d_delta;} // span
inline double getThetaMinusDelta () const {return d_thetaMinusDelta;} // renewal span parameter
inline double getMu () const {return d_mu;} // step mean for random walk
inline double getSigma () const {return d_sigma;} // step standard deviation for random walk
inline double getMuAssoc () const {return d_muAssoc;} // step mean for associated random walk (relative entropy)
inline double getSigmaAssoc () const {return d_sigmaAssoc;} // step standard deviation for associated random walk
inline double getMeanWDLE () const {return d_meanWDLE;} // expected renewal length for weak ladder epochs
inline bool getTerminated () const {return d_terminated;} // ? Was the dynamic programming computation terminated prematurely ?
private:
// random walk distribution
size_t d_dimension; // #(distinct values) of scores & probabilities (which are paired)
long int *d_score_p; // scores in increasing order
double *d_prob_p; // probabilities
// Karlin-Altschul parameters
double d_lambda; // lambda for associated random walk
double d_k; // k for random walk : exponential prefactor
double d_c; // c for random walk : exponential prefactor (global alignment)
double d_thetaMin; // theta for minimum expectation (exp (theta * score))
double d_rMin; // minimum expectation (exp (theta * score))
long int d_delta; // span
double d_thetaMinusDelta; // renewal span parameter
double d_mu; // step mean for random walk
double d_sigma; // step standard deviation for random walk
double d_muAssoc; // step mean for associated random walk (relative entropy)
double d_sigmaAssoc; // step standard deviation for associated random walk
double d_meanWDLE; // expected renewal length for weak ladder epochs
bool d_terminated; // ? Was the dynamic programming computation terminated prematurely ?
void init (size_t dimension_);
void free2 ();
void clear ();
void dynProgCalc ();
// k for random walk : exponential prefactor
// expected renewal length for weak ladder epochs
static double s_time;
};
}
#endif