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likelihood.c
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/*
* MrBayes 3
*
* (c) 2002-2023
*
* John P. Huelsenbeck
* Dept. Integrative Biology
* University of California, Berkeley
* Berkeley, CA 94720-3140
*
* Fredrik Ronquist
* Swedish Museum of Natural History
* Box 50007
* SE-10405 Stockholm, SWEDEN
*
* With important contributions by
*
* Paul van der Mark ([email protected])
* Maxim Teslenko ([email protected])
* Chi Zhang ([email protected])
*
* and by many users (run 'acknowledgments' to see more info)
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU General Public License
* as published by the Free Software Foundation; either version 2
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details (www.gnu.org).
*
*/
#include "bayes.h"
#include "likelihood.h"
#include "mbbeagle.h"
#include "model.h"
#include "utils.h"
#define LIKE_EPSILON 1.0e-300
/* global variables declared here */
CLFlt *preLikeL; /* precalculated cond likes for left descendant */
CLFlt *preLikeR; /* precalculated cond likes for right descendant*/
CLFlt *preLikeA; /* precalculated cond likes for ancestor */
/* global variables used here but declared elsewhere */
extern int *chainId;
extern int numLocalChains;
extern int rateProbRowSize; /* size of rate probs for one chain one state */
extern MrBFlt **rateProbs; /* pointers to rate probs used by adgamma model */
/* local prototypes */
MrBFlt GetRate (int division, int chain);
int RemoveNodeScalers(TreeNode *p, int division, int chain);
#if defined (SSE_ENABLED)
int RemoveNodeScalers_SSE(TreeNode *p, int division, int chain);
#endif
#if defined (AVX_ENABLED)
int RemoveNodeScalers_AVX(TreeNode *p, int division, int chain);
#endif
int SetBinaryQMatrix (MrBFlt **a, int whichChain, int division);
int SetNucQMatrix (MrBFlt **a, int n, int whichChain, int division, MrBFlt rateMult, MrBFlt *rA, MrBFlt *rS);
int SetStdQMatrix (MrBFlt **a, int nStates, MrBFlt *bs, int cType);
int SetProteinQMatrix (MrBFlt **a, int n, int whichChain, int division, MrBFlt rateMult);
int UpDateCijk (int whichPart, int whichChain);
/*----------------------------------------------------------------
|
| CondLikeDown_Bin: binary model with or without rate variation
|
-----------------------------------------------------------------*/
int CondLikeDown_Bin (TreeNode *p, int division, int chain)
{
int c, k;
CLFlt *clL, *clR, *clP, *pL, *pR, *tiPL, *tiPR;
ModelInfo *m;
/* find model settings for this division */
m = &modelSettings[division];
/* Flip conditional likelihood space */
FlipCondLikeSpace (m, chain, p->index);
/* find conditional likelihood pointers */
clL = m->condLikes[m->condLikeIndex[chain][p->left->index ]];
clR = m->condLikes[m->condLikeIndex[chain][p->right->index]];
clP = m->condLikes[m->condLikeIndex[chain][p->index ]];
/* find transition probabilities */
pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]];
pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]];
tiPL = pL;
tiPR = pR;
for (k=0; k<m->numRateCats; k++)
{
for (c=0; c<m->numChars; c++)
{
*(clP++) = (tiPL[0]*clL[0] + tiPL[1]*clL[1])
*(tiPR[0]*clR[0] + tiPR[1]*clR[1]);
*(clP++) = (tiPL[2]*clL[0] + tiPL[3]*clL[1])
*(tiPR[2]*clR[0] + tiPR[3]*clR[1]);
clL += 2;
clR += 2;
}
tiPL += 4;
tiPR += 4;
}
return NO_ERROR;
}
#if defined (SSE_ENABLED)
/*----------------------------------------------------------------
|
| CondLikeDown_Bin_SSE: binary model with or without rate
| variation
|
-----------------------------------------------------------------*/
int CondLikeDown_Bin_SSE (TreeNode *p, int division, int chain)
{
int c, k;
CLFlt *pL, *pR, *tiPL, *tiPR;
__m128 *clL, *clR, *clP;
__m128 m1, m2, m3, m4, m5, m6;
ModelInfo *m;
m = &modelSettings[division];
/* flip state of node so that we are not overwriting old cond likes */
FlipCondLikeSpace (m, chain, p->index);
/* find conditional likelihood pointers */
clL = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->left->index ]];
clR = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->right->index]];
clP = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->index ]];
/* find transition probabilities */
pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]];
pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]];
tiPL = pL;
tiPR = pR;
for (k=0; k<m->numRateCats; k++)
{
for (c=0; c<m->numVecChars; c++)
{
m1 = _mm_load1_ps (&tiPL[0]);
m2 = _mm_load1_ps (&tiPR[0]);
m5 = _mm_mul_ps (m1, clL[0]);
m6 = _mm_mul_ps (m2, clR[0]);
m1 = _mm_load1_ps (&tiPL[1]);
m2 = _mm_load1_ps (&tiPR[1]);
m3 = _mm_mul_ps (m1, clL[1]);
m4 = _mm_mul_ps (m2, clR[1]);
m5 = _mm_add_ps (m3, m5);
m6 = _mm_add_ps (m4, m6);
*clP++ = _mm_mul_ps (m5, m6);
m1 = _mm_load1_ps (&tiPL[2]);
m2 = _mm_load1_ps (&tiPR[2]);
m5 = _mm_mul_ps (m1, clL[0]);
m6 = _mm_mul_ps (m2, clR[0]);
m1 = _mm_load1_ps (&tiPL[3]);
m2 = _mm_load1_ps (&tiPR[3]);
m3 = _mm_mul_ps (m1, clL[1]);
m4 = _mm_mul_ps (m2, clR[1]);
m5 = _mm_add_ps (m3, m5);
m6 = _mm_add_ps (m4, m6);
*clP++ = _mm_mul_ps (m5, m6);
clL += 2;
clR += 2;
}
tiPL += 4;
tiPR += 4;
}
return NO_ERROR;
}
#endif
/*----------------------------------------------------------------
|
| CondLikeDown_Gen: general n-state model with or without rate
| variation
|
-----------------------------------------------------------------*/
int CondLikeDown_Gen (TreeNode *p, int division, int chain)
{
int a, b, c, h, i, k, j, shortCut, *lState=NULL, *rState=NULL,
nObsStates, nStates, nStatesSquared, preLikeJump;
CLFlt likeL, likeR, *pL, *pR, *tiPL, *tiPR, *clL, *clR, *clP;
ModelInfo *m;
# if !defined (DEBUG_NOSHORTCUTS)
int catStart;
# endif
/* find model settings for this division and nStates, nStatesSquared */
m = &modelSettings[division];
nObsStates = m->numStates;
nStates = m->numModelStates;
nStatesSquared = nStates * nStates;
preLikeJump = nObsStates * nStates;
/* flip conditional likelihood space */
FlipCondLikeSpace (m, chain, p->index);
/* find conditional likelihood pointers */
clL = m->condLikes[m->condLikeIndex[chain][p->left->index ]];
clR = m->condLikes[m->condLikeIndex[chain][p->right->index]];
clP = m->condLikes[m->condLikeIndex[chain][p->index ]];
/* find transition probabilities */
pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]];
pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]];
/* find likelihoods of site patterns for left branch if terminal */
shortCut = 0;
# if !defined (DEBUG_NOSHORTCUTS)
if (p->left->left == NULL && m->isPartAmbig[p->left->index] == NO)
{
shortCut |= 1;
lState = m->termState[p->left->index];
tiPL = pL;
for (k=a=0; k<m->numRateCats; k++)
{
catStart = a;
for (i=0; i<nObsStates; i++)
for (j=i; j<nStatesSquared; j+=nStates)
preLikeL[a++] = tiPL[j];
for (b=1; b<nStates/nObsStates; b++)
{
a = catStart;
for (i=0; i<nObsStates; i++)
{
for (j=i+b*nObsStates; j<nStatesSquared; j+=nStates)
preLikeL[a++] += tiPL[j];
}
}
/* for ambiguous */
for (i=0; i<nStates; i++)
preLikeL[a++] = 1.0;
tiPL += nStatesSquared;
}
}
/* find likelihoods of site patterns for right branch if terminal */
if (p->right->left == NULL && m->isPartAmbig[p->right->index] == NO)
{
shortCut |= 2;
rState = m->termState[p->right->index];
tiPR = pR;
for (k=a=0; k<m->numRateCats; k++)
{
catStart = a;
for (i=0; i<nObsStates; i++)
for (j=i; j<nStatesSquared; j+=nStates)
preLikeR[a++] = tiPR[j];
for (b=1; b<nStates/nObsStates; b++)
{
a = catStart;
for (i=0; i<nObsStates; i++)
{
for (j=i+b*nObsStates; j<nStatesSquared; j+=nStates)
preLikeR[a++] += tiPR[j];
}
}
/* for ambiguous */
for (i=0; i<nStates; i++)
preLikeR[a++] = 1.0;
tiPR += nStatesSquared;
}
}
# endif
switch (shortCut)
{
case 0:
tiPL = pL;
tiPR = pR;
for (k=0; k<m->numRateCats; k++)
{
for (c=0; c<m->numChars; c++)
{
for (i=h=0; i<nStates; i++)
{
likeL = likeR = 0.0;
for (j=0; j<nStates; j++)
{
likeL += tiPL[h]*clL[j];
likeR += tiPR[h++]*clR[j];
}
*(clP++) = likeL * likeR;
}
clL += nStates;
clR += nStates;
}
tiPL += nStatesSquared;
tiPR += nStatesSquared;
}
break;
case 1:
tiPR = pR;
for (k=0; k<m->numRateCats; k++)
{
for (c=0; c<m->numChars; c++)
{
a = lState[c] + k*(preLikeJump+nStates);
for (i=h=0; i<nStates; i++)
{
likeR = 0.0;
for (j=0; j<nStates; j++)
{
likeR += tiPR[h++]*clR[j];
}
*(clP++) = preLikeL[a++] * likeR;
}
clR += nStates;
}
tiPR += nStatesSquared;
}
break;
case 2:
tiPL = pL;
for (k=0; k<m->numRateCats; k++)
{
for (c=0; c<m->numChars; c++)
{
a = rState[c] + k*(preLikeJump+nStates);
for (i=h=0; i<nStates; i++)
{
likeL = 0.0;
for (j=0; j<nStates; j++)
{
likeL += tiPL[h++]*clL[j];
}
*(clP++) = preLikeR[a++] * likeL;
}
clL += nStates;
}
tiPL += nStatesSquared;
}
break;
case 3:
for (k=0; k<m->numRateCats; k++)
{
for (c=0; c<m->numChars; c++)
{
a = rState[c] + k*(preLikeJump+nStates);
b = lState[c] + k*(preLikeJump+nStates);
for (i=0; i<nStates; i++)
{
*(clP++) = preLikeR[a++] * preLikeL[b++];
}
}
}
break;
}
return NO_ERROR;
}
#if defined (SSE_ENABLED)
/*----------------------------------------------------------------
|
| CondLikeDown_Gen_SSE: general n-state model with or without rate
| variation
|
-----------------------------------------------------------------*/
int CondLikeDown_Gen_SSE (TreeNode *p, int division, int chain)
{
int c, c1, h, i, j, k, t, shortCut, *lState=NULL, *rState=NULL, nStates, nStatesSquared, nObsStates, preLikeJump;
CLFlt *pL, *pR, *tiPL, *tiPR;
__m128 *clL, *clR, *clP;
__m128 mTiPL, mTiPR, mL, mR, mAcumL, mAcumR;
ModelInfo *m;
CLFlt *preLikeRV[4] = {0};
CLFlt *preLikeLV[4] = {0};
# if !defined (DEBUG_NOSHORTCUTS)
int a, b, catStart;
# endif
/* find model settings for this division and nStates, nStatesSquared */
m = &modelSettings[division];
nObsStates = m->numStates;
nStates = m->numModelStates;
nStatesSquared = nStates * nStates;
preLikeJump = nObsStates * nStates;
/* Flip conditional likelihood space */
FlipCondLikeSpace (m, chain, p->index);
/* find conditional likelihood pointers */
clL = (__m128 *)m->condLikes[m->condLikeIndex[chain][p->left->index ]];
clR = (__m128 *)m->condLikes[m->condLikeIndex[chain][p->right->index]];
clP = (__m128 *)m->condLikes[m->condLikeIndex[chain][p->index ]];
/* find transition probabilities */
pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]];
pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]];
/* find likelihoods of site patterns for left branch if terminal */
shortCut = 0;
# if !defined (DEBUG_NOSHORTCUTS)
if (p->left->left == NULL && m->isPartAmbig[p->left->index] == NO)
{
shortCut |= 1;
lState = m->termState[p->left->index];
tiPL = pL;
for (k=a=0; k<m->numRateCats; k++)
{
catStart = a;
for (i=0; i<nObsStates; i++)
for (j=i; j<nStatesSquared; j+=nStates)
preLikeL[a++] = tiPL[j];
for (b=1; b<nStates/nObsStates; b++)
{
a = catStart;
for (i=0; i<nObsStates; i++)
{
for (j=i+b*nObsStates; j<nStatesSquared; j+=nStates)
preLikeL[a++] += tiPL[j];
}
}
/* for ambiguous */
for (i=0; i<nStates; i++)
preLikeL[a++] = 1.0;
tiPL += nStatesSquared;
}
}
/* find likelihoods of site patterns for right branch if terminal */
if (p->right->left == NULL && m->isPartAmbig[p->right->index] == NO)
{
shortCut |= 2;
rState = m->termState[p->right->index];
tiPR = pR;
for (k=a=0; k<m->numRateCats; k++)
{
catStart = a;
for (i=0; i<nObsStates; i++)
for (j=i; j<nStatesSquared; j+=nStates)
preLikeR[a++] = tiPR[j];
for (b=1; b<nStates/nObsStates; b++)
{
a = catStart;
for (i=0; i<nObsStates; i++)
{
for (j=i+b*nObsStates; j<nStatesSquared; j+=nStates)
preLikeR[a++] += tiPR[j];
}
}
/* for ambiguous */
for (i=0; i<nStates; i++)
preLikeR[a++] = 1.0;
tiPR += nStatesSquared;
}
}
# endif
switch (shortCut)
{
case 0:
tiPL = pL;
tiPR = pR;
for (k=0; k<m->numRateCats; k++)
{
for (c=0; c<m->numVecChars; c++)
{
for (i=h=0; i<nStates; i++)
{
mAcumL = _mm_setzero_ps();
mAcumR = _mm_setzero_ps();
for (j=0; j<nStates; j++)
{
mTiPL = _mm_load1_ps (&tiPL[h]);
mTiPR = _mm_load1_ps (&tiPR[h++]);
mL = _mm_mul_ps (mTiPL, clL[j]);
mR = _mm_mul_ps (mTiPR, clR[j]);
mAcumL = _mm_add_ps (mL, mAcumL);
mAcumR = _mm_add_ps (mR, mAcumR);
}
*(clP++) = _mm_mul_ps (mAcumL, mAcumR);
}
clL += nStates;
clR += nStates;
}
tiPL += nStatesSquared;
tiPR += nStatesSquared;
}
break;
case 1:
tiPR = pR;
for (k=0; k<m->numRateCats; k++)
{
for (c=t=0; c<m->numVecChars; c++)
{
for (c1=0; c1<m->numFloatsPerVec; c1++,t++)
{
preLikeLV[c1] = &preLikeL[lState[t] + k*(preLikeJump+nStates)];
}
for (i=h=0; i<nStates; i++)
{
mAcumL = _mm_set_ps (*(preLikeLV[3]++), *(preLikeLV[2]++), *(preLikeLV[1]++), *(preLikeLV[0]++));
mAcumR = _mm_setzero_ps();
for (j=0; j<nStates; j++)
{
mTiPR = _mm_load1_ps (&tiPR[h++]);
mR = _mm_mul_ps (mTiPR, clR[j]);
mAcumR = _mm_add_ps (mR, mAcumR);
}
*(clP++) = _mm_mul_ps (mAcumL,mAcumR);
}
clR += nStates;
}
tiPR += nStatesSquared;
}
break;
case 2:
tiPL = pL;
for (k=0; k<m->numRateCats; k++)
{
for (c=t=0; c<m->numVecChars; c++)
{
for (c1=0; c1<m->numFloatsPerVec; c1++,t++)
{
preLikeRV[c1] = &preLikeR[rState[t] + k*(preLikeJump+nStates)];
}
for (i=h=0; i<nStates; i++)
{
mAcumR = _mm_set_ps (*(preLikeRV[3]++), *(preLikeRV[2]++), *(preLikeRV[1]++), *(preLikeRV[0]++));
mAcumL = _mm_setzero_ps();
for (j=0; j<nStates; j++)
{
mTiPL = _mm_load1_ps (&tiPL[h++]);
mL = _mm_mul_ps (mTiPL, clL[j]);
mAcumL = _mm_add_ps (mL, mAcumL);
}
*(clP++) = _mm_mul_ps (mAcumL,mAcumR);
}
clL += nStates;
}
tiPL += nStatesSquared;
}
break;
case 3:
for (k=0; k<m->numRateCats; k++)
{
for (c=t=0; c<m->numVecChars; c++)
{
for (c1=0; c1<m->numFloatsPerVec; c1++,t++)
{
preLikeRV[c1] = &preLikeR[rState[t] + k*(preLikeJump+nStates)];
preLikeLV[c1] = &preLikeL[lState[t] + k*(preLikeJump+nStates)];
}
for (i=0; i<nStates; i++)
{
assert (m->numFloatsPerVec == 4); /* In the following 2 statments we assume that SSE register can hold exactly 4 ClFlts. */
mL = _mm_set_ps (*(preLikeLV[3]++), *(preLikeLV[2]++), *(preLikeLV[1]++), *(preLikeLV[0]++));
mR = _mm_set_ps (*(preLikeRV[3]++), *(preLikeRV[2]++), *(preLikeRV[1]++), *(preLikeRV[0]++));
*(clP++) = _mm_mul_ps (mL,mR);
}
}
}
break;
}
return NO_ERROR;
}
#endif
/*----------------------------------------------------------------
|
| CondLikeDown_Gen_GibbsGamma: general n-state model with rate
| variation modeled using discrete gamma with Gibbs resampling
|
-----------------------------------------------------------------*/
int CondLikeDown_Gen_GibbsGamma (TreeNode *p, int division, int chain)
{
int a, b, c, i, j, r, *rateCat, shortCut, *lState=NULL, *rState=NULL,
nObsStates, nStates, nStatesSquared, nGammaCats;
CLFlt likeL, likeR, *pL, *pR, *tiPL, *tiPR, *clL, *clR, *clP;
ModelInfo *m;
# if !defined (DEBUG_NOSHORTCUTS)
int k, catStart;
# endif
/* find model settings for this division and nStates, nStatesSquared */
m = &modelSettings[division];
nObsStates = m->numStates;
nStates = m->numModelStates;
nStatesSquared = nStates * nStates;
/* flip conditional likelihood space */
FlipCondLikeSpace (m, chain, p->index);
/* find conditional likelihood pointers */
clL = m->condLikes[m->condLikeIndex[chain][p->left->index ]];
clR = m->condLikes[m->condLikeIndex[chain][p->right->index]];
clP = m->condLikes[m->condLikeIndex[chain][p->index ]];
/* find transition probabilities */
pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]];
pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]];
/* find rate category index and number of gamma categories */
rateCat = m->tiIndex + chain * m->numChars;
nGammaCats = m->numRateCats;
/* find likelihoods of site patterns for left branch if terminal */
shortCut = 0;
# if !defined (DEBUG_NOSHORTCUTS)
if (p->left->left == NULL && m->isPartAmbig[p->left->index] == NO)
{
shortCut |= 1;
lState = m->termState[p->left->index];
tiPL = pL;
for (k=a=0; k<nGammaCats; k++)
{
catStart = a;
for (i=0; i<nObsStates; i++)
for (j=i; j<nStatesSquared; j+=nStates)
preLikeL[a++] = tiPL[j];
for (b=1; b<nStates/nObsStates; b++)
{
a = catStart;
for (i=0; i<nObsStates; i++)
{
for (j=i+b*nObsStates; j<nStatesSquared; j+=nStates)
preLikeL[a++] += tiPL[j];
}
}
/* for ambiguous */
for (i=0; i<nStates; i++)
preLikeL[a++] = 1.0;
tiPL += nStatesSquared;
}
}
/* find likelihoods of site patterns for right branch if terminal */
if (p->right->left == NULL && m->isPartAmbig[p->right->index] == NO)
{
shortCut |= 2;
rState = m->termState[p->right->index];
tiPR = pR;
for (k=a=0; k<nGammaCats; k++)
{
catStart = a;
for (i=0; i<nObsStates; i++)
for (j=i; j<nStatesSquared; j+=nStates)
preLikeR[a++] = tiPR[j];
for (b=1; b<nStates/nObsStates; b++)
{
a = catStart;
for (i=0; i<nObsStates; i++)
{
for (j=i+b*nObsStates; j<nStatesSquared; j+=nStates)
preLikeR[a++] += tiPR[j];
}
}
/* for ambiguous */
for (i=0; i<nStates; i++)
preLikeR[a++] = 1.0;
tiPR += nStatesSquared;
}
}
# endif
switch (shortCut)
{
case 0:
for (c=0; c<m->numChars; c++)
{
r = (*rateCat++);
if (r < nGammaCats)
{
tiPL = pL + r*nStatesSquared;
tiPR = pR + r*nStatesSquared;
for (i=0; i<nStates; i++)
{
likeL = likeR = 0.0;
for (j=0; j<nStates; j++)
{
likeL += (*tiPL++) * clL[j];
likeR += (*tiPR++) * clR[j];
}
*(clP++) = likeL * likeR;
}
}
else
clP += nStates;
clL += nStates;
clR += nStates;
}
break;
case 1:
for (c=0; c<m->numChars; c++)
{
r = (*rateCat++);
if (r < nGammaCats)
{
tiPR = pR + r*nStatesSquared;
a = lState[c] + r*(nStatesSquared+nStates);
for (i=0; i<nStates; i++)
{
likeR = 0.0;
for (j=0; j<nStates; j++)
{
likeR += (*tiPR++)*clR[j];
}
*(clP++) = preLikeL[a++] * likeR;
}
}
else
clP += nStates;
clR += nStates;
}
break;
case 2:
for (c=0; c<m->numChars; c++)
{
r = (*rateCat++);
if (r < nGammaCats)
{
tiPL = pL + r*nStatesSquared;
a = rState[c] + r*(nStatesSquared+nStates);
for (i=0; i<nStates; i++)
{
likeL = 0.0;
for (j=0; j<nStates; j++)
{
likeL += (*tiPL++)*clL[j];
}
*(clP++) = preLikeR[a++] * likeL;
}
}
else
clP += nStates;
clL += nStates;
}
break;
case 3:
for (c=0; c<m->numChars; c++)
{
r = (*rateCat++);
if (r < nGammaCats)
{
a = lState[c] + r*(nStatesSquared+nStates);
b = rState[c] + r*(nStatesSquared+nStates);
for (i=0; i<nStates; i++)
*(clP++) = preLikeL[a++]*preLikeR[b++];
}
else
clP += nStates;
}
break;
}
return NO_ERROR;
}
/*----------------------------------------------------------------
|
| CondLikeDown_NUC4: 4by4 nucleotide model with or without rate
| variation
|
-----------------------------------------------------------------*/
int CondLikeDown_NUC4 (TreeNode *p, int division, int chain)
{
int c, h, i, j, k, shortCut, *lState=NULL, *rState=NULL;
CLFlt *clL, *clR, *clP, *pL, *pR, *tiPL, *tiPR;
ModelInfo *m;
m = &modelSettings[division];
/* flip space so that we do not overwrite old cond likes */
FlipCondLikeSpace (m, chain, p->index);
/* find conditional likelihood pointers */
clL = m->condLikes[m->condLikeIndex[chain][p->left->index ]];
clR = m->condLikes[m->condLikeIndex[chain][p->right->index]];
clP = m->condLikes[m->condLikeIndex[chain][p->index ]];
/* find transition probabilities */
pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]];
pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]];
/* find likelihoods of site patterns for left branch if terminal */
shortCut = 0;
# if !defined (DEBUG_NOSHORTCUTS)
if (p->left->left == NULL && m->isPartAmbig[p->left->index] == NO)
{
shortCut |= 1;
lState = m->termState[p->left->index];
tiPL = pL;
for (k=j=0; k<m->numRateCats; k++)
{
for (i=0; i<4; i++)
{
preLikeL[j++] = tiPL[0];
preLikeL[j++] = tiPL[4];
preLikeL[j++] = tiPL[8];
preLikeL[j++] = tiPL[12];
tiPL++;
}
/* for ambiguous */
for (i=0; i<4; i++)
preLikeL[j++] = 1.0;
tiPL += 12;
}
}
/* find likelihoods of site patterns for right branch if terminal */
if (p->right->left == NULL && m->isPartAmbig[p->right->index] == NO)
{
shortCut |= 2;
rState = m->termState[p->right->index];
tiPR = pR;
for (k=j=0; k<m->numRateCats; k++)
{
for (i=0; i<4; i++)
{
preLikeR[j++] = tiPR[0];
preLikeR[j++] = tiPR[4];
preLikeR[j++] = tiPR[8];
preLikeR[j++] = tiPR[12];
tiPR++;
}
/* for ambiguous */
for (i=0; i<4; i++)
preLikeR[j++] = 1.0;
tiPR += 12;
}
}
# endif
switch (shortCut)
{
case 0:
tiPL = pL;
tiPR = pR;
for (k=h=0; k<m->numRateCats; k++)
{
for (c=0; c<m->numChars; c++)
{
clP[h++] = (tiPL[AA]*clL[A] + tiPL[AC]*clL[C] + tiPL[AG]*clL[G] + tiPL[AT]*clL[T])
*(tiPR[AA]*clR[A] + tiPR[AC]*clR[C] + tiPR[AG]*clR[G] + tiPR[AT]*clR[T]);
clP[h++] = (tiPL[CA]*clL[A] + tiPL[CC]*clL[C] + tiPL[CG]*clL[G] + tiPL[CT]*clL[T])
*(tiPR[CA]*clR[A] + tiPR[CC]*clR[C] + tiPR[CG]*clR[G] + tiPR[CT]*clR[T]);
clP[h++] = (tiPL[GA]*clL[A] + tiPL[GC]*clL[C] + tiPL[GG]*clL[G] + tiPL[GT]*clL[T])
*(tiPR[GA]*clR[A] + tiPR[GC]*clR[C] + tiPR[GG]*clR[G] + tiPR[GT]*clR[T]);
clP[h++] = (tiPL[TA]*clL[A] + tiPL[TC]*clL[C] + tiPL[TG]*clL[G] + tiPL[TT]*clL[T])
*(tiPR[TA]*clR[A] + tiPR[TC]*clR[C] + tiPR[TG]*clR[G] + tiPR[TT]*clR[T]);
clL += 4;
clR += 4;
}
tiPL += 16;
tiPR += 16;
}
break;
case 1:
tiPR = pR;
for (k=h=0; k<m->numRateCats; k++)
{
for (c=0; c<m->numChars; c++)
{
i = lState[c] + k*20;
clP[h++] = preLikeL[i++]
*(tiPR[AA]*clR[A] + tiPR[AC]*clR[C] + tiPR[AG]*clR[G] + tiPR[AT]*clR[T]);
clP[h++] = preLikeL[i++]
*(tiPR[CA]*clR[A] + tiPR[CC]*clR[C] + tiPR[CG]*clR[G] + tiPR[CT]*clR[T]);
clP[h++] = preLikeL[i++]
*(tiPR[GA]*clR[A] + tiPR[GC]*clR[C] + tiPR[GG]*clR[G] + tiPR[GT]*clR[T]);
clP[h++] = preLikeL[i++]
*(tiPR[TA]*clR[A] + tiPR[TC]*clR[C] + tiPR[TG]*clR[G] + tiPR[TT]*clR[T]);
clR += 4;
}
tiPR += 16;
}
break;
case 2:
tiPL = pL;
for (k=h=0; k<m->numRateCats; k++)
{
for (c=0; c<m->numChars; c++)
{
i = rState[c] + k*20;
clP[h++] = (tiPL[AA]*clL[A] + tiPL[AC]*clL[C] + tiPL[AG]*clL[G] + tiPL[AT]*clL[T])
*preLikeR[i++];
clP[h++] = (tiPL[CA]*clL[A] + tiPL[CC]*clL[C] + tiPL[CG]*clL[G] + tiPL[CT]*clL[T])
*preLikeR[i++];
clP[h++] = (tiPL[GA]*clL[A] + tiPL[GC]*clL[C] + tiPL[GG]*clL[G] + tiPL[GT]*clL[T])
*preLikeR[i++];
clP[h++] = (tiPL[TA]*clL[A] + tiPL[TC]*clL[C] + tiPL[TG]*clL[G] + tiPL[TT]*clL[T])
*preLikeR[i++];
clL += 4;
}
tiPL += 16;
}
break;
case 3:
for (k=h=0; k<m->numRateCats; k++)
{
for (c=0; c<m->numChars; c++)
{
i = j = k*20;
i += lState[c];
j += rState[c];
clP[h++] = preLikeL[i++]*preLikeR[j++];
clP[h++] = preLikeL[i++]*preLikeR[j++];
clP[h++] = preLikeL[i++]*preLikeR[j++];
clP[h++] = preLikeL[i++]*preLikeR[j++];
}
}
}
return NO_ERROR;
}
/*----------------------------------------------------------------
|
| CondLikeDown_NUC4_GibbsGamma: 4by4 nucleotide model with rate
| variation approximated using Gibbs sampling of gamma
|
-----------------------------------------------------------------*/
int CondLikeDown_NUC4_GibbsGamma (TreeNode *p, int division, int chain)
{
int c, h, i, j, r, *rateCat, shortCut, *lState=NULL, *rState=NULL,
nGammaCats;
CLFlt *clL, *clR, *clP, *pL, *pR, *tiPL, *tiPR;
ModelInfo *m;
# if !defined (DEBUG_NOSHORTCUTS)
int k;
# endif
m = &modelSettings[division];
/* flip conditional likelihood space */
FlipCondLikeSpace (m, chain, p->index);
/* find conditional likelihood pointers */
clL = m->condLikes[m->condLikeIndex[chain][p->left->index ]];
clR = m->condLikes[m->condLikeIndex[chain][p->right->index]];
clP = m->condLikes[m->condLikeIndex[chain][p->index ]];
/* find transition probabilities */
pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]];
pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]];
/* find rate category index and number of gamma categories */
rateCat = m->tiIndex + chain * m->numChars;
nGammaCats = m->numRateCats;
/* find likelihoods of site patterns for left branch if terminal */
shortCut = 0;
# if !defined (DEBUG_NOSHORTCUTS)
if (p->left->left == NULL && m->isPartAmbig[p->left->index] == NO)
{
shortCut |= 1;
lState = m->termState[p->left->index];
tiPL = pL;
for (k=j=0; k<nGammaCats; k++)
{
for (i=0; i<4; i++)
{
preLikeL[j++] = tiPL[0];
preLikeL[j++] = tiPL[4];
preLikeL[j++] = tiPL[8];
preLikeL[j++] = tiPL[12];
tiPL++;
}
/* for ambiguous */
for (i=0; i<4; i++)
preLikeL[j++] = 1.0;
tiPL += 12;
}
}
/* find likelihoods of site patterns for right branch if terminal */
if (p->right->left == NULL && m->isPartAmbig[p->right->index] == NO)
{