double COriginDragDlg::NormDampingSliderPos(double Damping)
{
const CEditSliderCtrl::INFO& info = m_SliderInfo[SL_DAMPING];
double b = info.LogBase;
double s = info.SliderScale;
return(s - LogNorm(Damping, b, s));
}
/* Function: P7PriorifyEmissionVector()
*
* Purpose: Add prior pseudocounts to an observed
* emission count vector and renormalize.
*
* Can return the posterior mixture probabilities
* P(q | counts) if ret_mix[MAXDCHLET] is passed.
* Else, pass NULL.
*
* Args: vec - the 4 or 20-long vector of counts to modify
* pri - prior data structure
* num - pri->mnum or pri->inum; # of mixtures
* eq - pri->mq or pri->iq; prior mixture probabilities
* e - pri->i or pri->m; Dirichlet components
* ret_mix - filled with posterior mixture probabilities, or NULL
*
* Return: (void)
* The counts in vec are changed and normalized to probabilities.
*/
void
P7PriorifyEmissionVector(float *vec, struct p7prior_s *pri,
int num, float eq[MAXDCHLET], float e[MAXDCHLET][MAXABET],
float *ret_mix)
{
int x; /* counter over vec */
int q; /* counter over mixtures */
float mix[MAXDCHLET]; /* posterior distribution over mixtures */
float totc; /* total counts */
float tota; /* total alpha terms */
float xi; /* X_i term, Sjolander eq. 41 */
/* Calculate mix[], which is the posterior probability
* P(q | n) of mixture component q given the count vector n
*
* (side effect note: note that an insert vector in a PAM prior
* is passed with num = 1, bypassing pam prior code; this means
* that inserts cannot be mixture Dirichlets...)
* [SRE, 12/24/00: the above comment is cryptic! what the hell does that
* mean, inserts can't be mixtures? doesn't seem to be true. it
* may mean that in a PAM prior, you can't have a mixture for inserts,
* but I don't even understand that. The insert vectors aren't passed
* with num=1!!]
*/
mix[0] = 1.0;
if (pri->strategy == PRI_DCHLET && num > 1)
{
for (q = 0; q < num; q++)
{
mix[q] = eq[q] > 0.0 ? log(eq[q]) : -999.;
mix[q] += Logp_cvec(vec, Alphabet_size, e[q]);
}
LogNorm(mix, num); /* now mix[q] is P(component_q | n) */
}
else if (pri->strategy == PRI_PAM && num > 1)
{ /* pam prior uses aa frequencies as `P(q|n)' */
for (q = 0; q < Alphabet_size; q++)
mix[q] = vec[q];
FNorm(mix, Alphabet_size);
}
/* Convert the counts to probabilities, following Sjolander (1996)
*/
totc = FSum(vec, Alphabet_size);
for (x = 0; x < Alphabet_size; x++) {
xi = 0.0;
for (q = 0; q < num; q++) {
tota = FSum(e[q], Alphabet_size);
xi += mix[q] * (vec[x] + e[q][x]) / (totc + tota);
}
vec[x] = xi;
}
FNorm(vec, Alphabet_size);
if (ret_mix != NULL)
for (q = 0; q < num; q++)
ret_mix[q] = mix[q];
}
/* Function: P7PriorifyTransitionVector()
*
* Purpose: Add prior pseudocounts to transition vector,
* which contains three different probability vectors
* for m, d, and i.
*
* Args: t - state transitions, counts: 3 for M, 2 for I, 2 for D.
* prior - Dirichlet prior information
* tq - prior distribution over Dirichlet components.
* (overrides prior->iq[]; used for alternative
* methods of conditioning prior on structural data)
*
* Return: (void)
* t is changed, and renormalized -- comes back as
* probability vectors.
*/
void
P7PriorifyTransitionVector(float *t, struct p7prior_s *prior,
float tq[MAXDCHLET])
{
int ts;
int q;
float mix[MAXDCHLET];
float totm, totd, toti; /* total counts in three transition vecs */
float xi; /* Sjolander's X_i term */
mix[0] = 1.0; /* default is simple one component */
if ((prior->strategy == PRI_DCHLET || prior->strategy == PRI_PAM) && prior->mnum > 1)
{
for (q = 0; q < prior->tnum; q++)
{
mix[q] = tq[q] > 0.0 ? log(tq[q]) : -999.;
mix[q] += Logp_cvec(t, 3, prior->t[q]); /* 3 match */
mix[q] += Logp_cvec(t+3, 2, prior->t[q]+3); /* 2 insert */
mix[q] += Logp_cvec(t+5, 2, prior->t[q]+5); /* 2 delete */
}
LogNorm(mix, prior->tnum); /* mix[q] is now P(q | counts) */
}
/* precalc some denominators */
totm = FSum(t,3);
toti = t[TIM] + t[TII];
totd = t[TDM] + t[TDD];
for (ts = 0; ts < 7; ts++)
{
xi = 0.0;
for (q = 0; q < prior->tnum; q++)
{
switch (ts) {
case TMM: case TMI: case TMD:
xi += mix[q] * (t[ts] + prior->t[q][ts]) /
(totm + FSum(prior->t[q], 3));
break;
case TIM: case TII:
xi += mix[q] * (t[ts] + prior->t[q][ts]) /
(toti + prior->t[q][TIM] + prior->t[q][TII]);
break;
case TDM: case TDD:
xi += mix[q] * (t[ts] + prior->t[q][ts]) /
(totd + prior->t[q][TDM] + prior->t[q][TDD]);
break;
}
}
t[ts] = xi;
}
FNorm(t, 3); /* match */
FNorm(t+3, 2); /* insert */
FNorm(t+5, 2); /* delete */
}
double CPosNormVal::Norm(double x) const
{
return(LogNorm(x, m_LogBase, m_Scale));
}
