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optimizeModel.c
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optimizeModel.c
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/* RAxML-VI-HPC (version 2.2) a program for sequential and parallel estimation of phylogenetic trees
* Copyright August 2006 by Alexandros Stamatakis
*
* Partially derived from
* fastDNAml, a program for estimation of phylogenetic trees from sequences by Gary J. Olsen
*
* and
*
* Programs of the PHYLIP package by Joe Felsenstein.
*
* This program is free software; you may redistribute it and/or modify its
* 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.
*
*
* For any other enquiries send an Email to Alexandros Stamatakis
* Alexandros.Stamatakis@epfl.ch
*
* When publishing work that is based on the results from RAxML-VI-HPC please cite:
*
* Alexandros Stamatakis:"RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands
* of taxa and mixed models".
* Bioinformatics 2006; doi: 10.1093/bioinformatics/btl446
*/
#ifndef WIN32
#include <unistd.h>
#endif
#include <math.h>
#include <time.h>
#include <stdlib.h>
#include <stdio.h>
#include <ctype.h>
#include <string.h>
#include "axml.h"
static const double MNBRAK_GOLD = 1.618034;
static const double MNBRAK_TINY = 1.e-20;
static const double MNBRAK_GLIMIT = 100.0;
static const double BRENT_ZEPS = 1.e-5;
static const double BRENT_CGOLD = 0.3819660;
extern int optimizeRatesInvocations;
extern int optimizeRateCategoryInvocations;
extern int optimizeAlphaInvocations;
extern int optimizeInvarInvocations;
extern double masterTime;
extern char ratesFileName[1024];
extern char workdir[1024];
extern char run_id[128];
extern char lengthFileName[1024];
extern char lengthFileNameModel[1024];
extern char *protModels[NUM_PROT_MODELS];
#ifdef _USE_PTHREADS
extern volatile int NumberOfThreads;
extern volatile double *reductionBuffer;
#endif
/* TODO remove at some point */
#define _DEBUG_MODEL_OPTIMIZATION
static void brentGeneric(double *ax, double *bx, double *cx, double *fb, double tol, double *xmin, double *result, int numberOfModels,
int whichFunction, int rateNumber, tree *tr, linkageList *ll, double lim_inf, double lim_sup);
static int brakGeneric(double *param, double *ax, double *bx, double *cx, double *fa, double *fb,
double *fc, double lim_inf, double lim_sup,
int numberOfModels, int rateNumber, int whichFunction, tree *tr, linkageList *ll, double modelEpsilon);
/*********************FUNCTIONS FOOR EXACT MODEL OPTIMIZATION UNDER GTRGAMMA ***************************************/
static void setRateModel(tree *tr, int model, double rate, int position)
{
int
states = tr->partitionData[model].states,
numRates = (states * states - states) / 2;
if(tr->partitionData[model].dataType == DNA_DATA)
assert(position >= 0 && position < (numRates - 1));
else
assert(position >= 0 && position < numRates);
assert(tr->partitionData[model].dataType != BINARY_DATA);
if(!(tr->partitionData[model].dataType == SECONDARY_DATA ||
tr->partitionData[model].dataType == SECONDARY_DATA_6 ||
tr->partitionData[model].dataType == SECONDARY_DATA_7))
assert(rate >= RATE_MIN && rate <= RATE_MAX);
if(tr->partitionData[model].nonGTR)
{
int
i,
k = tr->partitionData[model].symmetryVector[position];
assert(tr->partitionData[model].dataType == SECONDARY_DATA ||
tr->partitionData[model].dataType == SECONDARY_DATA_6 ||
tr->partitionData[model].dataType == SECONDARY_DATA_7);
if(k == -1)
tr->partitionData[model].substRates[position] = 0.0;
else
{
if(k == tr->partitionData[model].symmetryVector[numRates - 1])
{
for(i = 0; i < numRates - 1; i++)
if(tr->partitionData[model].symmetryVector[i] == k)
tr->partitionData[model].substRates[position] = 1.0;
}
else
{
for(i = 0; i < numRates - 1; i++)
{
if(tr->partitionData[model].symmetryVector[i] == k)
tr->partitionData[model].substRates[i] = rate;
}
}
}
}
else
tr->partitionData[model].substRates[position] = rate;
}
static linkageList* initLinkageList(int *linkList, tree *tr)
{
int
k,
partitions,
numberOfModels = 0,
i,
pos;
linkageList* ll = (linkageList*)rax_malloc(sizeof(linkageList));
for(i = 0; i < tr->NumberOfModels; i++)
{
if(linkList[i] > numberOfModels)
numberOfModels = linkList[i];
}
numberOfModels++;
ll->entries = numberOfModels;
ll->ld = (linkageData*)rax_malloc(sizeof(linkageData) * numberOfModels);
for(i = 0; i < numberOfModels; i++)
{
ll->ld[i].valid = TRUE;
partitions = 0;
for(k = 0; k < tr->NumberOfModels; k++)
if(linkList[k] == i)
partitions++;
ll->ld[i].partitions = partitions;
ll->ld[i].partitionList = (int*)rax_malloc(sizeof(int) * partitions);
for(k = 0, pos = 0; k < tr->NumberOfModels; k++)
if(linkList[k] == i)
ll->ld[i].partitionList[pos++] = k;
}
return ll;
}
static linkageList* initLinkageListGTR(tree *tr)
{
int
i,
*links = (int*)rax_malloc(sizeof(int) * tr->NumberOfModels),
firstAA = tr->NumberOfModels + 2,
countGTR = 0,
countUnlinkedGTR = 0,
countOtherModel = 0;
linkageList*
ll;
for(i = 0; i < tr->NumberOfModels; i++)
{
if(tr->partitionData[i].dataType == AA_DATA)
{
if(tr->partitionData[i].protModels == GTR)
{
if(i < firstAA)
firstAA = i;
countGTR++;
}
else
{
if(tr->partitionData[i].protModels == GTR_UNLINKED)
countUnlinkedGTR++;
else
countOtherModel++;
}
}
}
/*
TODO need to think what we actually want here !
Shall we mix everything: linked, unlinked WAG etc?
*/
assert((countGTR > 0 && countOtherModel == 0) || (countGTR == 0 && countOtherModel > 0) || (countGTR == 0 && countOtherModel == 0));
if(countGTR == 0)
{
for(i = 0; i < tr->NumberOfModels; i++)
links[i] = i;
}
else
{
for(i = 0; i < tr->NumberOfModels; i++)
{
switch(tr->partitionData[i].dataType)
{
case DNA_DATA:
case BINARY_DATA:
case GENERIC_32:
case GENERIC_64:
case SECONDARY_DATA:
case SECONDARY_DATA_6:
case SECONDARY_DATA_7:
links[i] = i;
break;
case AA_DATA:
links[i] = firstAA;
break;
default:
assert(0);
}
}
}
ll = initLinkageList(links, tr);
rax_free(links);
return ll;
}
static void freeLinkageList( linkageList* ll)
{
int i;
for(i = 0; i < ll->entries; i++)
rax_free(ll->ld[i].partitionList);
rax_free(ll->ld);
rax_free(ll);
}
#define ALPHA_F 0
#define INVAR_F 1
#define RATE_F 2
#define SCALER_F 3
#define LXRATE_F 4
#define LXWEIGHT_F 5
static void updateWeights(tree *tr, int model, int rate, double value)
{
int
j;
double
w = 0.0;
tr->partitionData[model].weightExponents[rate] = value;
for(j = 0; j < 4; j++)
w += exp(tr->partitionData[model].weightExponents[j]);
for(j = 0; j < 4; j++)
tr->partitionData[model].weights[j] = exp(tr->partitionData[model].weightExponents[j]) / w;
}
static void optLG4X_Weights(tree *tr, linkageList *ll, int numberOfModels, int weightNumber, double modelEpsilon)
{
int
pos,
i;
double
lim_inf = -1000000.0,
lim_sup = 200.0,
*startLH = (double *)rax_malloc(sizeof(double) * numberOfModels),
*endLH = (double *)rax_malloc(sizeof(double) * numberOfModels),
*startWeights = (double *)rax_malloc(sizeof(double) * numberOfModels * 4),
*startExponents = (double *)rax_malloc(sizeof(double) * numberOfModels * 4),
*endWeights = (double *)rax_malloc(sizeof(double) * numberOfModels),
*endExponents = (double *)rax_malloc(sizeof(double) * numberOfModels),
*_a = (double *)rax_malloc(sizeof(double) * numberOfModels),
*_b = (double *)rax_malloc(sizeof(double) * numberOfModels),
*_c = (double *)rax_malloc(sizeof(double) * numberOfModels),
*_fa = (double *)rax_malloc(sizeof(double) * numberOfModels),
*_fb = (double *)rax_malloc(sizeof(double) * numberOfModels),
*_fc = (double *)rax_malloc(sizeof(double) * numberOfModels),
*_param = (double *)rax_malloc(sizeof(double) * numberOfModels),
*result = (double *)rax_malloc(sizeof(double) * numberOfModels),
*_x = (double *)rax_malloc(sizeof(double) * numberOfModels);
evaluateGeneric(tr, tr->start);
#ifdef _DEBUG_MODEL_OPTIMIZATION
double
initialLH = tr->likelihood;
#endif
assert(weightNumber >= 0 && weightNumber < 4);
/*
at this point here every worker has the traversal data it needs for the
search, so we won't re-distribute it he he :-)
*/
for(i = 0, pos = 0; i < ll->entries; i++)
{
if(ll->ld[i].valid)
{
int
index = ll->ld[i].partitionList[0];
assert(ll->ld[i].partitions == 1);
memcpy(&startWeights[pos * 4], tr->partitionData[index].weights, 4 * sizeof(double));
memcpy(&startExponents[pos * 4], tr->partitionData[index].weightExponents, 4 * sizeof(double));
_a[pos] = startExponents[pos * 4 + weightNumber] + 0.1;
_b[pos] = startExponents[pos * 4 + weightNumber] - 0.1;
if(_a[pos] < lim_inf)
_a[pos] = lim_inf;
if(_a[pos] > lim_sup)
_a[pos] = lim_sup;
if(_b[pos] < lim_inf)
_b[pos] = lim_inf;
if(_b[pos] > lim_sup)
_b[pos] = lim_sup;
startLH[pos] = tr->perPartitionLH[index];
endLH[pos] = unlikely;
pos++;
}
}
#ifdef _USE_PTHREADS
masterBarrier(THREAD_COPY_LG4X_RATES, tr);
#endif
assert(pos == numberOfModels);
brakGeneric(_param, _a, _b, _c, _fa, _fb, _fc, lim_inf, lim_sup, numberOfModels, weightNumber, LXWEIGHT_F, tr, ll, modelEpsilon);
brentGeneric(_a, _b, _c, _fb, modelEpsilon, _x, result, numberOfModels, LXWEIGHT_F, weightNumber, tr, ll, lim_inf, lim_sup);
/* now calculate the likelihood with the optimized rate */
for(i = 0, pos = 0; i < ll->entries; i++)
{
if(ll->ld[i].valid)
{
int
index = ll->ld[i].partitionList[0];
assert(ll->ld[i].partitions == 1);
tr->executeModel[index] = TRUE;
updateWeights(tr, index, weightNumber, _x[pos]);
pos++;
}
}
#ifdef _USE_PTHREADS
masterBarrier(THREAD_COPY_LG4X_RATES, tr);
#endif
evaluateGeneric(tr, tr->start);
for(i = 0, pos = 0; i < ll->entries; i++)
{
if(ll->ld[i].valid)
{
int
index = ll->ld[i].partitionList[0];
assert(ll->ld[i].partitions == 1);
if(tr->executeModel[index])
{
endLH[pos] = tr->perPartitionLH[index];
if(startLH[pos] > endLH[pos])
{
memcpy(tr->partitionData[index].weights, &startWeights[pos * 4], sizeof(double) * 4);
memcpy(tr->partitionData[index].weightExponents, &startExponents[pos * 4], sizeof(double) * 4);
}
}
pos++;
}
}
assert(pos == numberOfModels);
#ifdef _USE_PTHREADS
masterBarrier(THREAD_COPY_LG4X_RATES, tr);
#endif
#ifdef _DEBUG_MODEL_OPTIMIZATION
evaluateGeneric(tr, tr->start);
if(tr->likelihood < initialLH)
printf("%f %f\n", tr->likelihood, initialLH);
assert(tr->likelihood >= initialLH);
#endif
rax_free(endExponents);
rax_free(startExponents);
rax_free(startLH);
rax_free(endLH);
rax_free(startWeights);
rax_free(endWeights);
rax_free(result);
rax_free(_a);
rax_free(_b);
rax_free(_c);
rax_free(_fa);
rax_free(_fb);
rax_free(_fc);
rax_free(_param);
rax_free(_x);
}
static void optimizeWeights(tree *tr, double modelEpsilon, linkageList *ll, int numberOfModels)
{
int
i;
double
initialLH = 0.0,
finalLH = 0.0;
evaluateGeneric(tr, tr->start);
initialLH = tr->likelihood;
//printf("W: %f %f [%f] ->", tr->perPartitionLH[0], tr->perPartitionLH[1], initialLH);
for(i = 0; i < 4; i++)
optLG4X_Weights(tr, ll, numberOfModels, i, modelEpsilon);
#ifdef _USE_PTHREADS
masterBarrier(THREAD_COPY_LG4X_RATES, tr);
#endif
evaluateGenericInitrav(tr, tr->start);
finalLH = tr->likelihood;
if(finalLH < initialLH)
printf("Final: %f initial: %f\n", finalLH, initialLH);
assert(finalLH >= initialLH);
//printf("%f %f [%f]\n", tr->perPartitionLH[0], tr->perPartitionLH[1], finalLH);
}
static void evaluateChange(tree *tr, int rateNumber, double *value, double *result, boolean* converged, int whichFunction, int numberOfModels, linkageList *ll, double modelEpsilon)
{
int i, k, pos;
switch(whichFunction)
{
case ALPHA_F:
for(i = 0, pos = 0; i < ll->entries; i++)
{
int
index = ll->ld[i].partitionList[0];
assert(ll->ld[i].partitions == 1);
if(ll->ld[i].valid)
{
if(converged[pos])
tr->executeModel[index] = FALSE;
else
{
tr->executeModel[index] = TRUE;
tr->partitionData[index].alpha = value[pos];
makeGammaCats(tr->partitionData[index].alpha, tr->partitionData[index].gammaRates, 4, tr->useGammaMedian);
}
pos++;
}
else
tr->executeModel[index] = FALSE;
}
assert(pos == numberOfModels);
#ifdef _USE_PTHREADS
{
volatile double result;
masterBarrier(THREAD_OPT_ALPHA, tr);
if(tr->NumberOfModels == 1)
{
for(i = 0, result = 0.0; i < NumberOfThreads; i++)
result += reductionBuffer[i];
tr->perPartitionLH[0] = result;
}
else
{
int j;
volatile double partitionResult;
result = 0.0;
for(j = 0; j < tr->NumberOfModels; j++)
{
for(i = 0, partitionResult = 0.0; i < NumberOfThreads; i++)
partitionResult += reductionBuffer[i * tr->NumberOfModels + j];
result += partitionResult;
tr->perPartitionLH[j] = partitionResult;
}
}
}
#else
evaluateGenericInitrav(tr, tr->start);
#endif
for(i = 0, pos = 0; i < ll->entries; i++)
{
int
index = ll->ld[i].partitionList[0];
assert(ll->ld[i].partitions == 1);
if(ll->ld[i].valid)
{
result[pos] = 0.0;
assert(tr->perPartitionLH[index] <= 0.0);
result[pos] -= tr->perPartitionLH[index];
pos++;
}
tr->executeModel[index] = TRUE;
}
assert(pos == numberOfModels);
break;
case INVAR_F:
for(i = 0; i < ll->entries; i++)
{
if(converged[i])
{
for(k = 0; k < ll->ld[i].partitions; k++)
tr->executeModel[ll->ld[i].partitionList[k]] = FALSE;
}
else
{
for(k = 0; k < ll->ld[i].partitions; k++)
{
int index = ll->ld[i].partitionList[k];
tr->executeModel[index] = TRUE;
tr->partitionData[index].propInvariant = value[i];
}
}
}
#ifdef _USE_PTHREADS
{
volatile double result;
masterBarrier(THREAD_OPT_INVAR, tr);
if(tr->NumberOfModels == 1)
{
for(i = 0, result = 0.0; i < NumberOfThreads; i++)
result += reductionBuffer[i];
tr->perPartitionLH[0] = result;
}
else
{
int j;
volatile double partitionResult;
result = 0.0;
for(j = 0; j < tr->NumberOfModels; j++)
{
for(i = 0, partitionResult = 0.0; i < NumberOfThreads; i++)
partitionResult += reductionBuffer[i * tr->NumberOfModels + j];
result += partitionResult;
tr->perPartitionLH[j] = partitionResult;
}
}
}
#else
evaluateGeneric(tr, tr->start);
#endif
for(i = 0; i < ll->entries; i++)
{
result[i] = 0.0;
for(k = 0; k < ll->ld[i].partitions; k++)
{
int index = ll->ld[i].partitionList[k];
assert(tr->perPartitionLH[index] <= 0.0);
result[i] -= tr->perPartitionLH[index];
tr->executeModel[index] = TRUE;
}
}
break;
case RATE_F:
for(i = 0, pos = 0; i < ll->entries; i++)
{
if(ll->ld[i].valid)
{
if(converged[pos])
{
for(k = 0; k < ll->ld[i].partitions; k++)
tr->executeModel[ll->ld[i].partitionList[k]] = FALSE;
}
else
{
for(k = 0; k < ll->ld[i].partitions; k++)
{
int index = ll->ld[i].partitionList[k];
setRateModel(tr, index, value[pos], rateNumber);
initReversibleGTR(tr, index);
}
}
pos++;
}
else
{
for(k = 0; k < ll->ld[i].partitions; k++)
tr->executeModel[ll->ld[i].partitionList[k]] = FALSE;
}
}
assert(pos == numberOfModels);
if(tr->useBrLenScaler)
determineFullTraversal(tr->start, tr);
#ifdef _USE_PTHREADS
{
volatile double result;
masterBarrier(THREAD_OPT_RATE, tr);
if(tr->NumberOfModels == 1)
{
for(i = 0, result = 0.0; i < NumberOfThreads; i++)
result += reductionBuffer[i];
tr->perPartitionLH[0] = result;
}
else
{
int j;
volatile double partitionResult;
result = 0.0;
for(j = 0; j < tr->NumberOfModels; j++)
{
for(i = 0, partitionResult = 0.0; i < NumberOfThreads; i++)
partitionResult += reductionBuffer[i * tr->NumberOfModels + j];
result += partitionResult;
tr->perPartitionLH[j] = partitionResult;
}
}
}
#else
evaluateGenericInitrav(tr, tr->start);
#endif
for(i = 0, pos = 0; i < ll->entries; i++)
{
if(ll->ld[i].valid)
{
result[pos] = 0.0;
for(k = 0; k < ll->ld[i].partitions; k++)
{
int index = ll->ld[i].partitionList[k];
assert(tr->perPartitionLH[index] <= 0.0);
result[pos] -= tr->perPartitionLH[index];
}
pos++;
}
for(k = 0; k < ll->ld[i].partitions; k++)
{
int index = ll->ld[i].partitionList[k];
tr->executeModel[index] = TRUE;
}
}
assert(pos == numberOfModels);
break;
case SCALER_F:
assert(ll->entries == tr->NumberOfModels);
assert(ll->entries == tr->numBranches);
for(i = 0; i < ll->entries; i++)
{
if(converged[i])
{
for(k = 0; k < ll->ld[i].partitions; k++)
tr->executeModel[ll->ld[i].partitionList[k]] = FALSE;
}
else
{
for(k = 0; k < ll->ld[i].partitions; k++)
{
int
index = ll->ld[i].partitionList[k];
tr->executeModel[index] = TRUE;
tr->partitionData[index].brLenScaler = value[i];
scaleBranches(tr, FALSE);
}
}
}
#ifdef _USE_PTHREADS
{
volatile
double result;
/* need to call this here because we need to copy the changed branch lengths
(due to changed) scalers into the traversal descriptor */
determineFullTraversal(tr->start, tr);
masterBarrier(THREAD_OPT_SCALER, tr);
if(tr->NumberOfModels == 1)
{
for(i = 0, result = 0.0; i < NumberOfThreads; i++)
result += reductionBuffer[i];
tr->perPartitionLH[0] = result;
}
else
{
int j;
volatile double partitionResult;
result = 0.0;
for(j = 0; j < tr->NumberOfModels; j++)
{
for(i = 0, partitionResult = 0.0; i < NumberOfThreads; i++)
partitionResult += reductionBuffer[i * tr->NumberOfModels + j];
result += partitionResult;
tr->perPartitionLH[j] = partitionResult;
}
}
}
#else
evaluateGenericInitrav(tr, tr->start);
#endif
for(i = 0; i < ll->entries; i++)
{
result[i] = 0.0;
for(k = 0; k < ll->ld[i].partitions; k++)
{
int index = ll->ld[i].partitionList[k];
assert(tr->perPartitionLH[index] <= 0.0);
result[i] -= tr->perPartitionLH[index];
tr->executeModel[index] = TRUE;
}
}
break;
case LXRATE_F:
{
boolean
atLeastOnePartition = FALSE;
assert(rateNumber != -1);
for(i = 0, pos = 0; i < ll->entries; i++)
{
int
index = ll->ld[i].partitionList[0];
assert(ll->ld[i].partitions == 1);
if(ll->ld[i].valid)
{
if(converged[pos])
tr->executeModel[index] = FALSE;
else
{
atLeastOnePartition = TRUE;
tr->executeModel[index] = TRUE;
tr->partitionData[index].gammaRates[rateNumber] = value[pos];
}
pos++;
}
else
tr->executeModel[index] = FALSE;
}
assert(pos == numberOfModels);
#ifdef _USE_PTHREADS
{
volatile double result;
masterBarrier(THREAD_OPT_LG4X_RATES, tr);
if(tr->NumberOfModels == 1)
{
for(i = 0, result = 0.0; i < NumberOfThreads; i++)
result += reductionBuffer[i];
tr->perPartitionLH[0] = result;
}
else
{
int j;
volatile double partitionResult;
result = 0.0;
for(j = 0; j < tr->NumberOfModels; j++)
{
for(i = 0, partitionResult = 0.0; i < NumberOfThreads; i++)
partitionResult += reductionBuffer[i * tr->NumberOfModels + j];
result += partitionResult;
tr->perPartitionLH[j] = partitionResult;
}
}
}
#else
evaluateGenericInitrav(tr, tr->start);
#endif
if(atLeastOnePartition)
{
boolean
*buffer = (boolean*)rax_malloc(tr->NumberOfModels * sizeof(boolean));
memcpy(buffer, tr->executeModel, sizeof(boolean) * tr->NumberOfModels);
for(i = 0; i < tr->NumberOfModels; i++)
tr->executeModel[i] = FALSE;
for(i = 0, pos = 0; i < ll->entries; i++)
{
int
index = ll->ld[i].partitionList[0];
if(ll->ld[i].valid)
tr->executeModel[index] = TRUE;
}
optimizeWeights(tr, modelEpsilon, ll, numberOfModels);
memcpy(tr->executeModel, buffer, sizeof(boolean) * tr->NumberOfModels);
rax_free(buffer);
}
for(i = 0, pos = 0; i < ll->entries; i++)
{
int
index = ll->ld[i].partitionList[0];
assert(ll->ld[i].partitions == 1);
if(ll->ld[i].valid)
{
result[pos] = 0.0;
assert(tr->perPartitionLH[index] <= 0.0);
result[pos] -= tr->perPartitionLH[index];
pos++;
}
tr->executeModel[index] = TRUE;
}
assert(pos == numberOfModels);
}
break;
case LXWEIGHT_F:
assert(rateNumber != -1);
//printf("%d %d\n", tr->executeModel[0], tr->executeModel[1]);
for(i = 0, pos = 0; i < ll->entries; i++)
{
int
index = ll->ld[i].partitionList[0];
assert(ll->ld[i].partitions == 1);
if(ll->ld[i].valid)
{
if(converged[pos])
tr->executeModel[index] = FALSE;
else
{
tr->executeModel[index] = TRUE;
updateWeights(tr, index, rateNumber, value[pos]);
}
pos++;
}
else
tr->executeModel[index] = FALSE;
}
assert(pos == numberOfModels);
#ifdef _USE_PTHREADS
{
volatile double result;
masterBarrier(THREAD_OPT_LG4X_RATES, tr);
if(tr->NumberOfModels == 1)
{
for(i = 0, result = 0.0; i < NumberOfThreads; i++)
result += reductionBuffer[i];
tr->perPartitionLH[0] = result;
}
else
{
int j;
volatile double partitionResult;
result = 0.0;
for(j = 0; j < tr->NumberOfModels; j++)
{
for(i = 0, partitionResult = 0.0; i < NumberOfThreads; i++)
partitionResult += reductionBuffer[i * tr->NumberOfModels + j];
result += partitionResult;
tr->perPartitionLH[j] = partitionResult;
}
}
}
#else
evaluateGeneric(tr, tr->start);
#endif
//printf("weights Like: %f\n", tr->perPartitionLH[0]);
for(i = 0, pos = 0; i < ll->entries; i++)
{
int
index = ll->ld[i].partitionList[0];
assert(ll->ld[i].partitions == 1);
if(ll->ld[i].valid)
{