mrw_gmm.c

来自「LastWave」· C语言 代码 · 共 123 行

//
// 
//  File that implements GMM for MRW
//
//


#include "lastwave.h"
#include "signals.h"

#define EULER .577215664901

static LWFLOAT lntau;
static SIGNAL m = NULL;
static SIGNAL m2 = NULL;
static LWFLOAT var2;
static SIGNAL theLags = NULL;

/* The GMM Vector */
#define  GMMV_Var2  0
#define  GMMV_VarLn  -1
#define  GMMV_AutoCorLn  1 /* !!!!!!! Must be the last one and must be right after GMMV_VarLn */ 

/* The Parameters  */
#define  GMMP_Sigma2  0
#define  GMMP_Lambda2  1
#define  GMMP_LnT  2
#define  GMMP_Last  3


/* Ln epsilon theoretical variance */
static LWFLOAT VarianceLnEps(SIGNAL params)
{
  return(M_PI*M_PI/8);
}

/* Ln epsilon theoretical order 4 centered moment */
static LWFLOAT M4LnEps(SIGNAL params)
{
  return(M_PI*M_PI*M_PI*M_PI*7./64.);
}

/* Ln epsilon theoretical order 3 centered moment */
static LWFLOAT M3LnEps(SIGNAL params)
{
  return(-2.1036);
}

/* Mean of epsilon^2 * (centered_ln(epsilon)) */
static LWFLOAT MeanEps2lnEps(SIGNAL params)
{
  return(1.0);
}

/* Mean of epsilon^2 * (centered_ln(epsilon)^2) */
static LWFLOAT MeanEps2lnEps2(SIGNAL params)
{
  return(1.2337);
}

/* omega theoretical auto-correlation */
// ??????? A CHANGER 
// LWFLOAT CovLnSqrtVol (int m1, int n1, int m2, int n2, LWFLOAT lambda2, LWFLOAT T)
static LWFLOAT AutoCovarianceOm(SIGNAL params,int lag)
{
  LWFLOAT lambda2 = params->Y[GMMP_Lambda2];
  LWFLOAT lnT = params->Y[GMMP_LnT];
  
  lag = (lag<0 ? -lag : lag);
  
  switch(lag) {
  
  case 0: 
    return(lambda2*(lnT-lntau+1.5));
  
  case 1:
    return(lambda2*(
             lnT-lntau+1.5
             -.5*(lag+1)*(lag+1)*log(lag+1)
             ));

  default:  
    if (lag>=exp(lnT)) return(0);
    return(lambda2*(
             lnT-lntau+1.5+lag*lag*log(lag)
             -.5*(lag-1)*(lag-1)*log(lag-1)
             -.5*(lag+1)*(lag+1)*log(lag+1)
           ));
  }         
}

/* log(mrw) theoretical auto-correlation */
// ????? A CHANGER
// LWFLOAT CovLnX (int n, int m, LWFLOAT lambda2, LWFLOAT T)
static LWFLOAT AutoCovarianceLn(SIGNAL params,int lag)
{
  if (lag != 0) return(AutoCovarianceOm(params,lag));
  else return(AutoCovarianceOm(params,lag)+VarianceLnEps(params));
}

/* 
 * log(mrw) theoretical gradient autocorrelation  according to parameter number i
 */
static LWFLOAT DAutoCovarianceLn(SIGNAL params, int lag, int i)
{
  LWFLOAT lambda2 = params->Y[GMMP_Lambda2];
  LWFLOAT lnT = params->Y[GMMP_LnT];
  
  switch (i) {
  
  case GMMP_Sigma2:
    return(0);

  case GMMP_Lambda2:
      switch(lag) {
    case 0: return(lnT-lntau+1.5);
    default: return(AutoCovarianceLn(params,lag)/lambda2);
    }
    
  case GMMP_LnT: // ???? A CHANGER Pas tout