int main( int argc, const char* argv[] )
{
// print resource list
BeagleResourceList* rList;
rList = beagleGetResourceList();
fprintf(stdout, "Available resources:\n");
for (int i = 0; i < rList->length; i++) {
fprintf(stdout, "\tResource %i:\n\t\tName : %s\n", i, rList->list[i].name);
fprintf(stdout, "\t\tDesc : %s\n", rList->list[i].description);
fprintf(stdout, "\t\tFlags:");
printFlags(rList->list[i].supportFlags);
fprintf(stdout, "\n");
}
fprintf(stdout, "\n");
bool manualScaling = false;
bool autoScaling = false;
bool gRates = false; // generalized rate categories, separate root buffers
// is nucleotides...
int stateCount = 4;
// get the number of site patterns
int nPatterns = strlen(human);
int rateCategoryCount = 4;
int nRateCats = (gRates ? 1 : rateCategoryCount);
int nRootCount = (!gRates ? 1 : rateCategoryCount);
int nPartBuffs = 4 + nRootCount;
int scaleCount = (manualScaling ? 2 + nRootCount : 0);
// initialize the instance
BeagleInstanceDetails instDetails;
// create an instance of the BEAGLE library
int instance = beagleCreateInstance(
3, /**< Number of tip data elements (input) */
nPartBuffs, /**< Number of partials buffers to create (input) */
0, /**< Number of compact state representation buffers to create (input) */
stateCount, /**< Number of states in the continuous-time Markov chain (input) */
nPatterns, /**< Number of site patterns to be handled by the instance (input) */
1, /**< Number of rate matrix eigen-decomposition buffers to allocate (input) */
4, /**< Number of rate matrix buffers (input) */
nRateCats, /**< Number of rate categories (input) */
scaleCount, /**< Number of scaling buffers */
NULL, /**< List of potential resource on which this instance is allowed (input, NULL implies no restriction */
0, /**< Length of resourceList list (input) */
BEAGLE_FLAG_PRECISION_DOUBLE | BEAGLE_FLAG_PROCESSOR_GPU | (autoScaling ? BEAGLE_FLAG_SCALING_AUTO : 0), /**< Bit-flags indicating preferred implementation charactertistics, see BeagleFlags (input) */
0, /**< Bit-flags indicating required implementation characteristics, see BeagleFlags (input) */
&instDetails);
if (instance < 0) {
fprintf(stderr, "Failed to obtain BEAGLE instance\n\n");
exit(1);
}
int rNumber = instDetails.resourceNumber;
fprintf(stdout, "Using resource %i:\n", rNumber);
fprintf(stdout, "\tRsrc Name : %s\n",instDetails.resourceName);
fprintf(stdout, "\tImpl Name : %s\n", instDetails.implName);
fprintf(stdout, "\tImpl Desc : %s\n", instDetails.implDescription);
fprintf(stdout, "\tFlags:");
printFlags(instDetails.flags);
fprintf(stdout, "\n\n");
if (!(instDetails.flags & BEAGLE_FLAG_SCALING_AUTO))
autoScaling = false;
// beagleSetTipStates(instance, 0, getStates(human));
// beagleSetTipStates(instance, 1, getStates(chimp));
// beagleSetTipStates(instance, 2, getStates(gorilla));
// set the sequences for each tip using partial likelihood arrays
double *humanPartials = getPartials(human);
double *chimpPartials = getPartials(chimp);
double *gorillaPartials = getPartials(gorilla);
beagleSetTipPartials(instance, 0, humanPartials);
beagleSetTipPartials(instance, 1, chimpPartials);
beagleSetTipPartials(instance, 2, gorillaPartials);
//#ifdef _WIN32
// std::vector<double> rates(rateCategoryCount);
//#else
// double rates[rateCategoryCount];
//#endif
// for (int i = 0; i < rateCategoryCount; i++) {
// rates[i] = 1.0;
// }
double rates[4] = { 0.03338775, 0.25191592, 0.82026848, 2.89442785 };
// create base frequency array
double freqs[16] = { 0.25, 0.25, 0.25, 0.25,
0.25, 0.25, 0.25, 0.25,
0.25, 0.25, 0.25, 0.25,
0.25, 0.25, 0.25, 0.25 };
// create an array containing site category weights
double* weights = (double*) malloc(sizeof(double) * rateCategoryCount);
for (int i = 0; i < rateCategoryCount; i++) {
weights[i] = 1.0/rateCategoryCount;
}
double* patternWeights = (double*) malloc(sizeof(double) * nPatterns);
for (int i = 0; i < nPatterns; i++) {
patternWeights[i] = 1.0;
}
// an eigen decomposition for the JC69 model
double evec[4 * 4] = {
1.0, 2.0, 0.0, 0.5,
1.0, -2.0, 0.5, 0.0,
1.0, 2.0, 0.0, -0.5,
1.0, -2.0, -0.5, 0.0
};
double ivec[4 * 4] = {
0.25, 0.25, 0.25, 0.25,
0.125, -0.125, 0.125, -0.125,
0.0, 1.0, 0.0, -1.0,
1.0, 0.0, -1.0, 0.0
};
double eval[4] = { 0.0, -1.3333333333333333, -1.3333333333333333, -1.3333333333333333 };
// set the Eigen decomposition
beagleSetEigenDecomposition(instance, 0, evec, ivec, eval);
beagleSetStateFrequencies(instance, 0, freqs);
beagleSetCategoryWeights(instance, 0, weights);
beagleSetPatternWeights(instance, patternWeights);
// a list of indices and edge lengths
int nodeIndices[4] = { 0, 1, 2, 3 };
double edgeLengths[4] = { 0.1, 0.1, 0.2, 0.1 };
int* rootIndices = (int*) malloc(sizeof(int) * nRootCount);
int* categoryWeightsIndices = (int*) malloc(sizeof(int) * nRootCount);
int* stateFrequencyIndices = (int*) malloc(sizeof(int) * nRootCount);
int* cumulativeScalingIndices = (int*) malloc(sizeof(int) * nRootCount);
for (int i = 0; i < nRootCount; i++) {
rootIndices[i] = 4 + i;
categoryWeightsIndices[i] = 0;
stateFrequencyIndices[i] = 0;
cumulativeScalingIndices[i] = (manualScaling ? 2 + i : BEAGLE_OP_NONE);
beagleSetCategoryRates(instance, &rates[i]);
// tell BEAGLE to populate the transition matrices for the above edge lengths
beagleUpdateTransitionMatrices(instance, // instance
0, // eigenIndex
nodeIndices, // probabilityIndices
NULL, // firstDerivativeIndices
NULL, // secondDerivativeIndices
edgeLengths, // edgeLengths
4); // count
// create a list of partial likelihood update operations
// the order is [dest, destScaling, source1, matrix1, source2, matrix2]
BeagleOperation operations[2] = {
3, (manualScaling ? 0 : BEAGLE_OP_NONE), BEAGLE_OP_NONE, 0, 0, 1, 1,
rootIndices[i], (manualScaling ? 1 : BEAGLE_OP_NONE), BEAGLE_OP_NONE, 2, 2, 3, 3
};
if (manualScaling)
beagleResetScaleFactors(instance, cumulativeScalingIndices[i]);
// update the partials
beagleUpdatePartials(instance, // instance
operations, // eigenIndex
2, // operationCount
cumulativeScalingIndices[i]);// cumulative scaling index
}
if (autoScaling) {
int scaleIndices[2] = {3, 4};
beagleAccumulateScaleFactors(instance, scaleIndices, 2, BEAGLE_OP_NONE);
}
double *patternLogLik = (double*)malloc(sizeof(double) * nPatterns);
double logL = 0.0;
int returnCode = 0;
// calculate the site likelihoods at the root node
returnCode = beagleCalculateRootLogLikelihoods(instance, // instance
(const int *)rootIndices,// bufferIndices
(const int *)categoryWeightsIndices, // weights
(const int *)stateFrequencyIndices, // stateFrequencies
cumulativeScalingIndices,// cumulative scaling index
nRootCount, // count
&logL); // outLogLikelihoods
if (returnCode < 0) {
fprintf(stderr, "Failed to calculate root likelihood\n\n");
} else {
beagleGetSiteLogLikelihoods(instance, patternLogLik);
double sumLogL = 0.0;
for (int i = 0; i < nPatterns; i++) {
sumLogL += patternLogLik[i] * patternWeights[i];
// std::cerr << "site lnL[" << i << "] = " << patternLogLik[i] << '\n';
}
fprintf(stdout, "logL = %.5f (PAUP logL = -1498.89812)\n", logL);
fprintf(stdout, "sumLogL = %.5f\n", sumLogL);
}
// no rate heterogeneity:
// fprintf(stdout, "logL = %.5f (PAUP logL = -1574.63623)\n\n", logL);
free(weights);
free(patternWeights);
free(rootIndices);
free(categoryWeightsIndices);
free(stateFrequencyIndices);
free(cumulativeScalingIndices);
free(patternLogLik);
free(humanPartials);
free(chimpPartials);
free(gorillaPartials);
beagleFinalizeInstance(instance);
#ifdef _WIN32
std::cout << "\nPress ENTER to exit...\n";
fflush( stdout);
fflush( stderr);
getchar();
#endif
}
int main( int argc, const char* argv[] )
{
bool scaling = true;
// is nucleotides...
int stateCount = 4;
// get the number of site patterns
int nPatterns = strlen(human);
int rateCategoryCount = 4;
int scaleCount = (scaling ? 3 : 0);
BeagleInstanceDetails instDetails;
// create an instance of the BEAGLE library
int instance = beagleCreateInstance(
3, /**< Number of tip data elements (input) */
5, /**< Number of partials buffers to create (input) */
0, /**< Number of compact state representation buffers to create (input) */
stateCount, /**< Number of states in the continuous-time Markov chain (input) */
nPatterns, /**< Number of site patterns to be handled by the instance (input) */
1, /**< Number of rate matrix eigen-decomposition buffers to allocate (input) */
4, /**< Number of rate matrix buffers (input) */
rateCategoryCount,/**< Number of rate categories (input) */
scaleCount, /**< Number of scaling buffers */
NULL, /**< List of potential resource on which this instance is allowed (input, NULL implies no restriction */
0, /**< Length of resourceList list (input) */
BEAGLE_FLAG_PROCESSOR_GPU, /**< Bit-flags indicating preferred implementation charactertistics, see BeagleFlags (input) */
0
#ifndef JC
| BEAGLE_FLAG_EIGEN_COMPLEX
#endif
, /**< Bit-flags indicating required implementation characteristics, see BeagleFlags (input) */
&instDetails);
if (instance < 0) {
fprintf(stderr, "Failed to obtain BEAGLE instance\n\n");
exit(1);
}
int rNumber = instDetails.resourceNumber;
fprintf(stdout, "Using resource %i:\n", rNumber);
fprintf(stdout, "\tRsrc Name : %s\n",instDetails.resourceName);
fprintf(stdout, "\tImpl : %s\n", instDetails.implName);
fprintf(stdout, "\tImpl Desc : %s\n", instDetails.implDescription);
fprintf(stdout, "\n");
// set the sequences for each tip using partial likelihood arrays
double *humanPartials = getPartials(human);
double *chimpPartials = getPartials(chimp);
double *gorillaPartials = getPartials(gorilla);
beagleSetTipPartials(instance, 0, humanPartials);
beagleSetTipPartials(instance, 1, chimpPartials);
beagleSetTipPartials(instance, 2, gorillaPartials);
#ifdef _WIN32
std::vector<double> rates(rateCategoryCount);
#else
double rates[rateCategoryCount];
#endif
for (int i = 0; i < rateCategoryCount; i++) {
rates[i] = 1.0;
}
beagleSetCategoryRates(instance, &rates[0]);
double* patternWeights = (double*) malloc(sizeof(double) * nPatterns);
for (int i = 0; i < nPatterns; i++) {
patternWeights[i] = 1.0;
}
beagleSetPatternWeights(instance, patternWeights);
// create base frequency array
double freqs[4] = { 0.25, 0.25, 0.25, 0.25 };
beagleSetStateFrequencies(instance, 0, freqs);
// create an array containing site category weights
#ifdef _WIN32
std::vector<double> weights(rateCategoryCount);
#else
double weights[rateCategoryCount];
#endif
for (int i = 0; i < rateCategoryCount; i++) {
weights[i] = 1.0/rateCategoryCount;
}
beagleSetCategoryWeights(instance, 0, &weights[0]);
#ifndef JC
// an eigen decomposition for the 4-state 1-step circulant infinitesimal generator
double evec[4 * 4] = {
-0.5, 0.6906786606674509, 0.15153543380548623, 0.5,
0.5, -0.15153543380548576, 0.6906786606674498, 0.5,
-0.5, -0.6906786606674498, -0.15153543380548617, 0.5,
0.5, 0.15153543380548554, -0.6906786606674503, 0.5
};
double ivec[4 * 4] = {
-0.5, 0.5, -0.5, 0.5,
0.6906786606674505, -0.15153543380548617, -0.6906786606674507, 0.15153543380548645,
0.15153543380548568, 0.6906786606674509, -0.15153543380548584, -0.6906786606674509,
0.5, 0.5, 0.5, 0.5
};
double eval[8] = { -2.0, -1.0, -1.0, 0, 0, 1, -1, 0 };
#else
// an eigen decomposition for the JC69 model
double evec[4 * 4] = {
1.0, 2.0, 0.0, 0.5,
1.0, -2.0, 0.5, 0.0,
1.0, 2.0, 0.0, -0.5,
1.0, -2.0, -0.5, 0.0
};
double ivec[4 * 4] = {
0.25, 0.25, 0.25, 0.25,
0.125, -0.125, 0.125, -0.125,
0.0, 1.0, 0.0, -1.0,
1.0, 0.0, -1.0, 0.0
};
double eval[8] = { 0.0, -1.3333333333333333, -1.3333333333333333, -1.3333333333333333, 0.0, 0.0, 0.0, 0.0 };
#endif
// a list of indices and edge lengths
int nodeIndices[4] = { 0, 1, 2, 3 };
double edgeLengths[4] = { 0.1, 0.1, 0.2, 0.1 };
// // set the Eigen decomposition
// beagleSetEigenDecomposition(instance, 0, evec, ivec, eval);
//
// // tell BEAGLE to populate the transition matrices for the above edge lengths
// beagleUpdateTransitionMatrices(instance, // instance
// 0, // eigenIndex
// nodeIndices, // probabilityIndices
// NULL, // firstDerivativeIndices
// NULL, // secondDervativeIndices
// edgeLengths, // edgeLengths
// 4); // count
// set transitionMatrices
double* transitionMatrix = (double*) malloc(4 * 4 * 4 * rateCategoryCount * sizeof(double));
double* paddedValues = (double*) malloc(4*sizeof(double));
for(int b=0; b<4; b++) {
getTransitionMatrix(eval,
evec,
ivec,
4,
rateCategoryCount,
&rates[0],
edgeLengths[b],
transitionMatrix + b*4*4*rateCategoryCount);
paddedValues[b] = 1.0;
}
beagleSetTransitionMatrices(instance,
nodeIndices,
transitionMatrix,
paddedValues,
4);
free(transitionMatrix);
// create a list of partial likelihood update operations
// the order is [dest, destScaling, source1, matrix1, source2, matrix2]
BeagleOperation operations[2] = {
3, (scaling ? 0 : BEAGLE_OP_NONE), BEAGLE_OP_NONE, 0, 0, 1, 1,
4, (scaling ? 1 : BEAGLE_OP_NONE), BEAGLE_OP_NONE, 2, 2, 3, 3
};
int rootIndex = 4;
// update the partials
beagleUpdatePartials(instance, // instance
operations, // eigenIndex
2, // operationCount
BEAGLE_OP_NONE); // cumulative scaling index
double *patternLogLik = (double*)malloc(sizeof(double) * nPatterns);
int cumulativeScalingIndex = (scaling ? 2 : BEAGLE_OP_NONE);
if (scaling) {
int scalingFactorsCount = 2;
int scalingFactorsIndices[2] = {0, 1};
beagleResetScaleFactors(instance,
cumulativeScalingIndex);
beagleAccumulateScaleFactors(instance,
scalingFactorsIndices,
scalingFactorsCount,
cumulativeScalingIndex);
}
int categoryWeightsIndex = 0;
int stateFrequencyIndex = 0;
double logL = 0.0;
// calculate the site likelihoods at the root node
beagleCalculateRootLogLikelihoods(instance, // instance
(const int *)&rootIndex,// bufferIndices
&categoryWeightsIndex, // weights
&stateFrequencyIndex, // stateFrequencies
&cumulativeScalingIndex,// cumulative scaling index
1, // count
&logL); // outLogLikelihoods
#ifndef JC
fprintf(stdout, "logL = %.5f (BEAST = -1665.38544)\n\n", logL);
#else
fprintf(stdout, "logL = %.5f (PAUP = -1574.63623)\n\n", logL);
#endif
free(patternWeights);
free(patternLogLik);
free(humanPartials);
free(chimpPartials);
free(gorillaPartials);
beagleFinalizeInstance(instance);
#ifdef _WIN32
std::cout << "\nPress ENTER to exit...\n";
fflush( stdout);
fflush( stderr);
getchar();
#endif
}
/*-----------------------------------------------------------------------------
| Calculates the log likelihood by calling the beagle functions
| updateTransitionMatrices, updatePartials and calculateEdgeLogLikelihoods.
*/
double calcLnL(world_fmt *world, boolean instance)
{
beagle_fmt *beagle = world->beagle;
double logL = 0.0;
unsigned long i;
unsigned long j;
//unsigned long z;
long locus = world->locus;
long ii;
double *patternloglike = (double *) calloc(world->maxnumpattern[locus],sizeof(double));
double *outlike = (double *) calloc(10*world->maxnumpattern[locus],sizeof(double));
int rootIndex = beagle->operations[BEAGLE_PARTIALS * (beagle->numoperations-1)];//world->root->next->back->id;
for(i=0; i<world->nummutationmodels[locus]; i++)
{
ii = world->sublocistarts[locus] + i;
int code = beagleUpdateTransitionMatrices(beagle->instance_handle[i], // instance,
0, // eigenIndex,
(const int *) beagle->branch_indices, // indicators transitionrates for each branch,
NULL, // firstDerivativeIndices,
NULL, // secondDervativeIndices,
beagle->branch_lengths, // edgeLengths,
beagle->numbranches); // number branches to update, count
if (code != 0)
usererror("updateTransitionMatrices encountered a problem");
int cumulativeScalingFactorIndex = 0; //BEAGLE_OP_NONE; //this would be the index of the root scaling location
beagleResetScaleFactors(beagle->instance_handle[i],
cumulativeScalingFactorIndex);
beagleAccumulateScaleFactors(beagle->instance_handle[i],
beagle->scalingfactorsindices,
beagle->scalingfactorscount,
cumulativeScalingFactorIndex);
code = beagleUpdatePartials((const int *) &beagle->instance_handle[i], // instance
1, // instanceCount
beagle->operations, // operations
beagle->numoperations, // operationCount
cumulativeScalingFactorIndex);//BEAGLE_OP_NONE); // connected to accumulate....
#ifdef BEAGLEDEBUG
for(j=0;j<2*(world->sumtips * 2 - 1); j++)
{
beagleGetPartials(beagle->instance_handle[i],j,BEAGLE_OP_NONE, outlike);
if(j==world->sumtips * 2 - 1)
printf("-----------------------\n");
printf("%li: {%f, %f, %f, %f}\n",j, outlike[0],outlike[1],outlike[2],outlike[3]);
}
#endif
if (code != 0)
usererror("updatePartials encountered a problem");
if(beagle->weights==NULL)
beagle->weights = (double *) mycalloc(1,sizeof(double));
// else
// beagle->weights = (double *) myrealloc(beagle->weights,beagle->numoperations * sizeof(double));
beagle->weights[0]= 1.0;
// calculate the site likelihoods at the root node
code = beagleCalculateRootLogLikelihoods(beagle->instance_handle[i], // instance
(const int *) &rootIndex, // bufferIndices
(const double *) world->mutationmodels[ii].siteprobs, // weights
(const double *) world->mutationmodels[ii].basefreqs,// stateFrequencies
&cumulativeScalingFactorIndex, //scalingfactors index,
1, // count is this correct
patternloglike); // outLogLikelihoods
//trash from function above &beagle->scalingfactorscount,//size of the scaling factor index
if (code != 0)
usererror("calculateRootLogLikelihoods encountered a problem");
for (j = 0; j < world->mutationmodels[ii].numsites; j++)
{
logL += beagle->allyweights[j] * patternloglike[j];
// printf("%.1f ",patternloglike[j]);
}
}
printf("Log LnL=%f (instance=%li)\n",logL,(long) instance);
#ifdef BEAGLEDEBUG
debug_beagle(beagle);
#endif
myfree(patternloglike);
myfree(outlike);
return logL;
}
/*
* Class: beagle_BeagleJNIWrapper
* Method: resetScaleFactors
* Signature: (II)I
*/
JNIEXPORT jint JNICALL Java_beagle_BeagleJNIWrapper_resetScaleFactors
(JNIEnv *env, jobject obj, jint instance, jint cumulativeScalingIndex) {
jint errCode = (jint)beagleResetScaleFactors(instance, cumulativeScalingIndex);
return errCode;
}
/*-----------------------------------------------------------------
|
| LaunchBEAGLELogLikeForDivision: calculate the log likelihood
| of the new state of the chain for a single division
|
-----------------------------------------------------------------*/
void LaunchBEAGLELogLikeForDivision(int chain, int d, ModelInfo* m, Tree* tree, MrBFlt* lnL) {
int i, rescaleFreqNew;
int *isScalerNode;
TreeNode *p;
if (beagleScalingScheme == MB_BEAGLE_SCALE_ALWAYS)
{
#if defined (DEBUG_MB_BEAGLE_FLOW)
printf("ALWAYS RESCALING\n");
#endif
/* Flip and copy or reset site scalers */
FlipSiteScalerSpace(m, chain);
if (m->upDateAll == YES) {
for (i=0; i<m->nCijkParts; i++) {
beagleResetScaleFactors(m->beagleInstance, m->siteScalerIndex[chain] + i);
}
}
else
CopySiteScalers(m, chain);
TreeTiProbs_Beagle(tree, d, chain);
TreeCondLikes_Beagle(tree, d, chain);
TreeLikelihood_Beagle(tree, d, chain, lnL, (chainId[chain] % chainParams.numChains));
}
else
{ /* MB_BEAGLE_SCALE_DYNAMIC */
/* This flag is only valid within this block */
m->rescaleBeagleAll = NO;
TreeTiProbs_Beagle(tree, d, chain);
if( m->succesCount[chain] > 1000 )
{
m->succesCount[chain] = 10;
m->rescaleFreq[chain]++; /* increase rescaleFreq independent of whether we accept or reject new state*/
m->rescaleFreqOld = rescaleFreqNew = m->rescaleFreq[chain];
for (i=0; i<tree->nIntNodes; i++)
{
p = tree->intDownPass[i];
if ( p->upDateCl == YES ) {
/* flip to the new workspace since TreeCondLikes_Beagle_Rescale_All() does not do it for
(p->upDateCl == YES) since it assumes that TreeCondLikes_Beagle_No_Rescale() did it */
FlipCondLikeSpace (m, chain, p->index);
}
}
goto rescale_all;
}
if( beagleScalingFrequency != 0 &&
m->beagleComputeCount[chain] % (beagleScalingFrequency) == 0 )
{
m->rescaleFreqOld = rescaleFreqNew = m->rescaleFreq[chain];
for (i=0; i<tree->nIntNodes; i++)
{
p = tree->intDownPass[i];
if ( p->upDateCl == YES ) {
/* flip to the new workspace since TreeCondLikes_Beagle_Rescale_All() does not do it for (p->upDateCl == YES) since it assumes that TreeCondLikes_Beagle_No_Rescale() did it*/
FlipCondLikeSpace (m, chain, p->index);
}
}
goto rescale_all;
}
TreeCondLikes_Beagle_No_Rescale(tree, d, chain);
/* Check if likelihood is valid */
if( TreeLikelihood_Beagle(tree, d, chain, lnL, (chainId[chain] % chainParams.numChains)) == BEAGLE_ERROR_FLOATING_POINT )
{
m->rescaleFreqOld = rescaleFreqNew = m->rescaleFreq[chain];
if(rescaleFreqNew > 1 && m->succesCount[chain] < 40)
{
if( m->succesCount[chain] < 10 )
{
if( m->succesCount[chain] < 4 )
{
rescaleFreqNew-= rescaleFreqNew >> 3; /* <== we cut up to 12,5% of rescaleFreq */
if( m->succesCount[chain] < 2 )
{
rescaleFreqNew-= rescaleFreqNew >> 3;
/* to avoid situation when we may stack at high rescaleFreq when new states do not get accepted because of low liklihood but there proposed frequency is high we reduce rescaleFreq even if we reject the last move*/
/* basically the higher probability of proposing of low liklihood state which needs smaller rescaleFreq would lead to higher probability of hitting this code which should reduce rescaleFreqOld thus reduce further probability of hitting this code */
/* at some point this negative feedback mechanism should get in balance with the mechanism of periodically increasing rescaleFreq when long sequence of successes is achieved*/
m->rescaleFreqOld-= m->rescaleFreqOld >> 3;
}
m->rescaleFreqOld-= m->rescaleFreqOld >> 3;
m->rescaleFreqOld--;
m->rescaleFreqOld = ( m->rescaleFreqOld ? m->rescaleFreqOld:1);
m->recalculateScalers = YES;
recalcScalers = YES;
}
}
rescaleFreqNew--;
rescaleFreqNew = ( rescaleFreqNew ? rescaleFreqNew:1);
}
void runBeagle(int resource,
int stateCount,
int ntaxa,
int nsites,
bool manualScaling,
bool autoScaling,
bool dynamicScaling,
int rateCategoryCount,
int nreps,
bool fullTiming,
bool requireDoublePrecision,
bool requireSSE,
int compactTipCount,
int randomSeed,
int rescaleFrequency,
bool unrooted,
bool calcderivs,
bool logscalers,
int eigenCount,
bool eigencomplex,
bool ievectrans,
bool setmatrix)
{
int edgeCount = ntaxa*2-2;
int internalCount = ntaxa-1;
int partialCount = ((ntaxa+internalCount)-compactTipCount)*eigenCount;
int scaleCount = ((manualScaling || dynamicScaling) ? ntaxa : 0);
BeagleInstanceDetails instDetails;
// create an instance of the BEAGLE library
int instance = beagleCreateInstance(
ntaxa, /**< Number of tip data elements (input) */
partialCount, /**< Number of partials buffers to create (input) */
compactTipCount, /**< Number of compact state representation buffers to create (input) */
stateCount, /**< Number of states in the continuous-time Markov chain (input) */
nsites, /**< Number of site patterns to be handled by the instance (input) */
eigenCount, /**< Number of rate matrix eigen-decomposition buffers to allocate (input) */
(calcderivs ? (3*edgeCount*eigenCount) : edgeCount*eigenCount),/**< Number of rate matrix buffers (input) */
rateCategoryCount,/**< Number of rate categories */
scaleCount*eigenCount, /**< scaling buffers */
&resource, /**< List of potential resource on which this instance is allowed (input, NULL implies no restriction */
1, /**< Length of resourceList list (input) */
0, /**< Bit-flags indicating preferred implementation charactertistics, see BeagleFlags (input) */
(ievectrans ? BEAGLE_FLAG_INVEVEC_TRANSPOSED : BEAGLE_FLAG_INVEVEC_STANDARD) |
(logscalers ? BEAGLE_FLAG_SCALERS_LOG : BEAGLE_FLAG_SCALERS_RAW) |
(eigencomplex ? BEAGLE_FLAG_EIGEN_COMPLEX : BEAGLE_FLAG_EIGEN_REAL) |
(dynamicScaling ? BEAGLE_FLAG_SCALING_DYNAMIC : 0) |
(autoScaling ? BEAGLE_FLAG_SCALING_AUTO : 0) |
(requireDoublePrecision ? BEAGLE_FLAG_PRECISION_DOUBLE : BEAGLE_FLAG_PRECISION_SINGLE) |
(requireSSE ? BEAGLE_FLAG_VECTOR_SSE : BEAGLE_FLAG_VECTOR_NONE), /**< Bit-flags indicating required implementation characteristics, see BeagleFlags (input) */
&instDetails);
if (instance < 0) {
fprintf(stderr, "Failed to obtain BEAGLE instance\n\n");
return;
}
int rNumber = instDetails.resourceNumber;
fprintf(stdout, "Using resource %i:\n", rNumber);
fprintf(stdout, "\tRsrc Name : %s\n",instDetails.resourceName);
fprintf(stdout, "\tImpl Name : %s\n", instDetails.implName);
if (!(instDetails.flags & BEAGLE_FLAG_SCALING_AUTO))
autoScaling = false;
// set the sequences for each tip using partial likelihood arrays
gt_srand(randomSeed); // fix the random seed...
for(int i=0; i<ntaxa; i++)
{
if (i >= compactTipCount) {
double* tmpPartials = getRandomTipPartials(nsites, stateCount);
beagleSetTipPartials(instance, i, tmpPartials);
free(tmpPartials);
} else {
int* tmpStates = getRandomTipStates(nsites, stateCount);
beagleSetTipStates(instance, i, tmpStates);
free(tmpStates);
}
}
#ifdef _WIN32
std::vector<double> rates(rateCategoryCount);
#else
double rates[rateCategoryCount];
#endif
for (int i = 0; i < rateCategoryCount; i++) {
rates[i] = gt_rand() / (double) GT_RAND_MAX;
}
beagleSetCategoryRates(instance, &rates[0]);
double* patternWeights = (double*) malloc(sizeof(double) * nsites);
for (int i = 0; i < nsites; i++) {
patternWeights[i] = gt_rand() / (double) GT_RAND_MAX;
}
beagleSetPatternWeights(instance, patternWeights);
free(patternWeights);
// create base frequency array
#ifdef _WIN32
std::vector<double> freqs(stateCount);
#else
double freqs[stateCount];
#endif
// create an array containing site category weights
#ifdef _WIN32
std::vector<double> weights(rateCategoryCount);
#else
double weights[rateCategoryCount];
#endif
for (int eigenIndex=0; eigenIndex < eigenCount; eigenIndex++) {
for (int i = 0; i < rateCategoryCount; i++) {
weights[i] = gt_rand() / (double) GT_RAND_MAX;
}
beagleSetCategoryWeights(instance, eigenIndex, &weights[0]);
}
double* eval;
if (!eigencomplex)
eval = (double*)malloc(sizeof(double)*stateCount);
else
eval = (double*)malloc(sizeof(double)*stateCount*2);
double* evec = (double*)malloc(sizeof(double)*stateCount*stateCount);
double* ivec = (double*)malloc(sizeof(double)*stateCount*stateCount);
for (int eigenIndex=0; eigenIndex < eigenCount; eigenIndex++) {
if (!eigencomplex && ((stateCount & (stateCount-1)) == 0)) {
for (int i=0; i<stateCount; i++) {
freqs[i] = 1.0 / stateCount;
}
// an eigen decomposition for the general state-space JC69 model
// If stateCount = 2^n is a power-of-two, then Sylvester matrix H_n describes
// the eigendecomposition of the infinitesimal rate matrix
double* Hn = evec;
Hn[0*stateCount+0] = 1.0; Hn[0*stateCount+1] = 1.0;
Hn[1*stateCount+0] = 1.0; Hn[1*stateCount+1] = -1.0; // H_1
for (int k=2; k < stateCount; k <<= 1) {
// H_n = H_1 (Kronecker product) H_{n-1}
for (int i=0; i<k; i++) {
for (int j=i; j<k; j++) {
double Hijold = Hn[i*stateCount + j];
Hn[i *stateCount + j + k] = Hijold;
Hn[(i+k)*stateCount + j ] = Hijold;
Hn[(i+k)*stateCount + j + k] = -Hijold;
Hn[j *stateCount + i + k] = Hn[i *stateCount + j + k];
Hn[(j+k)*stateCount + i ] = Hn[(i+k)*stateCount + j ];
Hn[(j+k)*stateCount + i + k] = Hn[(i+k)*stateCount + j + k];
}
}
}
// Since evec is Hadamard, ivec = (evec)^t / stateCount;
for (int i=0; i<stateCount; i++) {
for (int j=i; j<stateCount; j++) {
ivec[i*stateCount+j] = evec[j*stateCount+i] / stateCount;
ivec[j*stateCount+i] = ivec[i*stateCount+j]; // Symmetric
}
}
eval[0] = 0.0;
for (int i=1; i<stateCount; i++) {
eval[i] = -stateCount / (stateCount - 1.0);
}
} else if (!eigencomplex) {
for (int i=0; i<stateCount; i++) {
freqs[i] = gt_rand() / (double) GT_RAND_MAX;
}
double** qmat=New2DArray<double>(stateCount, stateCount);
double* relNucRates = new double[(stateCount * stateCount - stateCount) / 2];
int rnum=0;
for(int i=0;i<stateCount;i++){
for(int j=i+1;j<stateCount;j++){
relNucRates[rnum] = gt_rand() / (double) GT_RAND_MAX;
qmat[i][j]=relNucRates[rnum] * freqs[j];
qmat[j][i]=relNucRates[rnum] * freqs[i];
rnum++;
}
}
//set diags to sum rows to 0
double sum;
for(int x=0;x<stateCount;x++){
sum=0.0;
for(int y=0;y<stateCount;y++){
if(x!=y) sum+=qmat[x][y];
}
qmat[x][x]=-sum;
}
double* eigvalsimag=new double[stateCount];
double** eigvecs=New2DArray<double>(stateCount, stateCount);//eigenvecs
double** teigvecs=New2DArray<double>(stateCount, stateCount);//temp eigenvecs
double** inveigvecs=New2DArray<double>(stateCount, stateCount);//inv eigenvecs
int* iwork=new int[stateCount];
double* work=new double[stateCount];
EigenRealGeneral(stateCount, qmat, eval, eigvalsimag, eigvecs, iwork, work);
memcpy(*teigvecs, *eigvecs, stateCount*stateCount*sizeof(double));
InvertMatrix(teigvecs, stateCount, work, iwork, inveigvecs);
for(int x=0;x<stateCount;x++){
for(int y=0;y<stateCount;y++){
evec[x * stateCount + y] = eigvecs[x][y];
if (ievectrans)
ivec[x * stateCount + y] = inveigvecs[y][x];
else
ivec[x * stateCount + y] = inveigvecs[x][y];
}
}
Delete2DArray(qmat);
delete relNucRates;
delete eigvalsimag;
Delete2DArray(eigvecs);
Delete2DArray(teigvecs);
Delete2DArray(inveigvecs);
delete iwork;
delete work;
} else if (eigencomplex && stateCount==4 && eigenCount==1) {
// create base frequency array
double temp_freqs[4] = { 0.25, 0.25, 0.25, 0.25 };
// an eigen decomposition for the 4-state 1-step circulant infinitesimal generator
double temp_evec[4 * 4] = {
-0.5, 0.6906786606674509, 0.15153543380548623, 0.5,
0.5, -0.15153543380548576, 0.6906786606674498, 0.5,
-0.5, -0.6906786606674498, -0.15153543380548617, 0.5,
0.5, 0.15153543380548554, -0.6906786606674503, 0.5
};
double temp_ivec[4 * 4] = {
-0.5, 0.5, -0.5, 0.5,
0.6906786606674505, -0.15153543380548617, -0.6906786606674507, 0.15153543380548645,
0.15153543380548568, 0.6906786606674509, -0.15153543380548584, -0.6906786606674509,
0.5, 0.5, 0.5, 0.5
};
double temp_eval[8] = { -2.0, -1.0, -1.0, 0, 0, 1, -1, 0 };
for(int x=0;x<stateCount;x++){
freqs[x] = temp_freqs[x];
eval[x] = temp_eval[x];
eval[x+stateCount] = temp_eval[x+stateCount];
for(int y=0;y<stateCount;y++){
evec[x * stateCount + y] = temp_evec[x * stateCount + y];
if (ievectrans)
ivec[x * stateCount + y] = temp_ivec[x + y * stateCount];
else
ivec[x * stateCount + y] = temp_ivec[x * stateCount + y];
}
}
} else {
abort("should not be here");
}
beagleSetStateFrequencies(instance, eigenIndex, &freqs[0]);
if (!setmatrix) {
// set the Eigen decomposition
beagleSetEigenDecomposition(instance, eigenIndex, &evec[0], &ivec[0], &eval[0]);
}
}
free(eval);
free(evec);
free(ivec);
// a list of indices and edge lengths
int* edgeIndices = new int[edgeCount*eigenCount];
int* edgeIndicesD1 = new int[edgeCount*eigenCount];
int* edgeIndicesD2 = new int[edgeCount*eigenCount];
for(int i=0; i<edgeCount*eigenCount; i++) {
edgeIndices[i]=i;
edgeIndicesD1[i]=(edgeCount*eigenCount)+i;
edgeIndicesD2[i]=2*(edgeCount*eigenCount)+i;
}
double* edgeLengths = new double[edgeCount];
for(int i=0; i<edgeCount; i++) {
edgeLengths[i]=gt_rand() / (double) GT_RAND_MAX;
}
// create a list of partial likelihood update operations
// the order is [dest, destScaling, source1, matrix1, source2, matrix2]
int* operations = new int[(internalCount)*BEAGLE_OP_COUNT*eigenCount];
int* scalingFactorsIndices = new int[(internalCount)*eigenCount]; // internal nodes
for(int i=0; i<internalCount*eigenCount; i++){
operations[BEAGLE_OP_COUNT*i+0] = ntaxa+i;
operations[BEAGLE_OP_COUNT*i+1] = (dynamicScaling ? i : BEAGLE_OP_NONE);
operations[BEAGLE_OP_COUNT*i+2] = (dynamicScaling ? i : BEAGLE_OP_NONE);
int child1Index;
if (((i % internalCount)*2) < ntaxa)
child1Index = (i % internalCount)*2;
else
child1Index = i*2 - internalCount * (int)(i / internalCount);
operations[BEAGLE_OP_COUNT*i+3] = child1Index;
operations[BEAGLE_OP_COUNT*i+4] = child1Index;
int child2Index;
if (((i % internalCount)*2+1) < ntaxa)
child2Index = (i % internalCount)*2+1;
else
child2Index = i*2+1 - internalCount * (int)(i / internalCount);
operations[BEAGLE_OP_COUNT*i+5] = child2Index;
operations[BEAGLE_OP_COUNT*i+6] = child2Index;
scalingFactorsIndices[i] = i;
// printf("i %d dest %d c1 %d c2 %d\n", i, ntaxa+i, child1Index, child2Index);
if (autoScaling)
scalingFactorsIndices[i] += ntaxa;
}
int* rootIndices = new int[eigenCount];
int* lastTipIndices = new int[eigenCount];
int* categoryWeightsIndices = new int[eigenCount];
int* stateFrequencyIndices = new int[eigenCount];
int* cumulativeScalingFactorIndices = new int[eigenCount];
for (int eigenIndex=0; eigenIndex < eigenCount; eigenIndex++) {
rootIndices[eigenIndex] = ntaxa+(internalCount*(eigenIndex+1))-1;//ntaxa*2-2;
lastTipIndices[eigenIndex] = ntaxa-1;
categoryWeightsIndices[eigenIndex] = eigenIndex;
stateFrequencyIndices[eigenIndex] = 0;
cumulativeScalingFactorIndices[eigenIndex] = ((manualScaling || dynamicScaling) ? (scaleCount*eigenCount-1)-eigenCount+eigenIndex+1 : BEAGLE_OP_NONE);
if (dynamicScaling)
beagleResetScaleFactors(instance, cumulativeScalingFactorIndices[eigenIndex]);
}
// start timing!
struct timeval time1, time2, time3, time4, time5;
double bestTimeUpdateTransitionMatrices, bestTimeUpdatePartials, bestTimeAccumulateScaleFactors, bestTimeCalculateRootLogLikelihoods, bestTimeTotal;
double logL = 0.0;
double deriv1 = 0.0;
double deriv2 = 0.0;
double previousLogL = 0.0;
double previousDeriv1 = 0.0;
double previousDeriv2 = 0.0;
for (int i=0; i<nreps; i++){
if (manualScaling && (!(i % rescaleFrequency) || !((i-1) % rescaleFrequency))) {
for(int j=0; j<internalCount*eigenCount; j++){
operations[BEAGLE_OP_COUNT*j+1] = (((manualScaling && !(i % rescaleFrequency))) ? j : BEAGLE_OP_NONE);
operations[BEAGLE_OP_COUNT*j+2] = (((manualScaling && (i % rescaleFrequency))) ? j : BEAGLE_OP_NONE);
}
}
gettimeofday(&time1,NULL);
for (int eigenIndex=0; eigenIndex < eigenCount; eigenIndex++) {
if (!setmatrix) {
// tell BEAGLE to populate the transition matrices for the above edge lengths
beagleUpdateTransitionMatrices(instance, // instance
eigenIndex, // eigenIndex
&edgeIndices[eigenIndex*edgeCount], // probabilityIndices
(calcderivs ? &edgeIndicesD1[eigenIndex*edgeCount] : NULL), // firstDerivativeIndices
(calcderivs ? &edgeIndicesD2[eigenIndex*edgeCount] : NULL), // secondDerivativeIndices
edgeLengths, // edgeLengths
edgeCount); // count
} else {
double* inMatrix = new double[stateCount*stateCount*rateCategoryCount];
for (int matrixIndex=0; matrixIndex < edgeCount; matrixIndex++) {
for(int z=0;z<rateCategoryCount;z++){
for(int x=0;x<stateCount;x++){
for(int y=0;y<stateCount;y++){
inMatrix[z*stateCount*stateCount + x*stateCount + y] = gt_rand() / (double) GT_RAND_MAX;
}
}
}
beagleSetTransitionMatrix(instance, edgeIndices[eigenIndex*edgeCount + matrixIndex], inMatrix, 1);
if (calcderivs) {
beagleSetTransitionMatrix(instance, edgeIndicesD1[eigenIndex*edgeCount + matrixIndex], inMatrix, 0);
beagleSetTransitionMatrix(instance, edgeIndicesD2[eigenIndex*edgeCount + matrixIndex], inMatrix, 0);
}
}
}
}
gettimeofday(&time2, NULL);
// update the partials
beagleUpdatePartials( instance, // instance
(BeagleOperation*)operations, // eigenIndex
internalCount*eigenCount, // operationCount
(dynamicScaling ? internalCount : BEAGLE_OP_NONE)); // cumulative scaling index
gettimeofday(&time3, NULL);
int scalingFactorsCount = internalCount;
for (int eigenIndex=0; eigenIndex < eigenCount; eigenIndex++) {
if (manualScaling && !(i % rescaleFrequency)) {
beagleResetScaleFactors(instance,
cumulativeScalingFactorIndices[eigenIndex]);
beagleAccumulateScaleFactors(instance,
&scalingFactorsIndices[eigenIndex*internalCount],
scalingFactorsCount,
cumulativeScalingFactorIndices[eigenIndex]);
} else if (autoScaling) {
beagleAccumulateScaleFactors(instance, &scalingFactorsIndices[eigenIndex*internalCount], scalingFactorsCount, BEAGLE_OP_NONE);
}
}
gettimeofday(&time4, NULL);
// calculate the site likelihoods at the root node
if (!unrooted) {
beagleCalculateRootLogLikelihoods(instance, // instance
rootIndices,// bufferIndices
categoryWeightsIndices, // weights
stateFrequencyIndices, // stateFrequencies
cumulativeScalingFactorIndices,
eigenCount, // count
&logL); // outLogLikelihoods
} else {
// calculate the site likelihoods at the root node
beagleCalculateEdgeLogLikelihoods(instance, // instance
rootIndices,// bufferIndices
lastTipIndices,
lastTipIndices,
(calcderivs ? edgeIndicesD1 : NULL),
(calcderivs ? edgeIndicesD2 : NULL),
categoryWeightsIndices, // weights
stateFrequencyIndices, // stateFrequencies
cumulativeScalingFactorIndices,
eigenCount, // count
&logL, // outLogLikelihood
(calcderivs ? &deriv1 : NULL),
(calcderivs ? &deriv2 : NULL));
}
// end timing!
gettimeofday(&time5,NULL);
if (i == 0 || getTimeDiff(time1, time2) < bestTimeUpdateTransitionMatrices)
bestTimeUpdateTransitionMatrices = getTimeDiff(time1, time2);
if (i == 0 || getTimeDiff(time2, time3) < bestTimeUpdatePartials)
bestTimeUpdatePartials = getTimeDiff(time2, time3);
if (i == 0 || getTimeDiff(time3, time4) < bestTimeAccumulateScaleFactors)
bestTimeAccumulateScaleFactors = getTimeDiff(time3, time4);
if (i == 0 || getTimeDiff(time4, time5) < bestTimeUpdateTransitionMatrices)
bestTimeCalculateRootLogLikelihoods = getTimeDiff(time4, time5);
if (i == 0 || getTimeDiff(time1, time5) < bestTimeTotal)
bestTimeTotal = getTimeDiff(time1, time5);
if (!(logL - logL == 0.0))
abort("error: invalid lnL");
if (i > 0 && abs(logL - previousLogL) > MAX_DIFF)
abort("error: large lnL difference between reps");
if (calcderivs) {
if (!(deriv1 - deriv1 == 0.0) || !(deriv2 - deriv2 == 0.0))
abort("error: invalid deriv");
if (i > 0 && ((abs(deriv1 - previousDeriv1) > MAX_DIFF) || (abs(deriv2 - previousDeriv2) > MAX_DIFF)) )
abort("error: large deriv difference between reps");
}
previousLogL = logL;
previousDeriv1 = deriv1;
previousDeriv2 = deriv2;
}
if (resource == 0) {
cpuTimeUpdateTransitionMatrices = bestTimeUpdateTransitionMatrices;
cpuTimeUpdatePartials = bestTimeUpdatePartials;
cpuTimeAccumulateScaleFactors = bestTimeAccumulateScaleFactors;
cpuTimeCalculateRootLogLikelihoods = bestTimeCalculateRootLogLikelihoods;
cpuTimeTotal = bestTimeTotal;
}
if (!calcderivs)
fprintf(stdout, "logL = %.5f \n", logL);
else
fprintf(stdout, "logL = %.5f d1 = %.5f d2 = %.5f\n", logL, deriv1, deriv2);
std::cout.setf(std::ios::showpoint);
std::cout.setf(std::ios::floatfield, std::ios::fixed);
int timePrecision = 6;
int speedupPrecision = 2;
int percentPrecision = 2;
std::cout << "best run: ";
printTiming(bestTimeTotal, timePrecision, resource, cpuTimeTotal, speedupPrecision, 0, 0, 0);
if (fullTiming) {
std::cout << " transMats: ";
printTiming(bestTimeUpdateTransitionMatrices, timePrecision, resource, cpuTimeUpdateTransitionMatrices, speedupPrecision, 1, bestTimeTotal, percentPrecision);
std::cout << " partials: ";
printTiming(bestTimeUpdatePartials, timePrecision, resource, cpuTimeUpdatePartials, speedupPrecision, 1, bestTimeTotal, percentPrecision);
if (manualScaling || autoScaling) {
std::cout << " accScalers: ";
printTiming(bestTimeAccumulateScaleFactors, timePrecision, resource, cpuTimeAccumulateScaleFactors, speedupPrecision, 1, bestTimeTotal, percentPrecision);
}
std::cout << " rootLnL: ";
printTiming(bestTimeCalculateRootLogLikelihoods, timePrecision, resource, cpuTimeCalculateRootLogLikelihoods, speedupPrecision, 1, bestTimeTotal, percentPrecision);
}
std::cout << "\n";
beagleFinalizeInstance(instance);
}
