/*
* Class: beagle_BeagleJNIWrapper
* Method: setTipStates
* Signature: (II[I)I
*/
JNIEXPORT jint JNICALL Java_beagle_BeagleJNIWrapper_setTipStates
(JNIEnv *env, jobject obj, jint instance, jint tipIndex, jintArray inTipStates)
{
jint *tipStates = env->GetIntArrayElements(inTipStates, NULL);
jint errCode = (jint)beagleSetTipStates(instance, tipIndex, (int *)tipStates);
env->ReleaseIntArrayElements(inTipStates, tipStates, JNI_ABORT);
return errCode;
}
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);
}
/*------------------------------------------------------------------------
|
| InitBeagleInstance: create and initialize a beagle instance
|
-------------------------------------------------------------------------*/
int InitBeagleInstance (ModelInfo *m, int division)
{
int i, j, k, c, s, *inStates, numPartAmbigTips;
double *inPartials;
SafeLong *charBits;
BeagleInstanceDetails details;
long preferedFlags, requiredFlags;
int resource;
if (m->useBeagle == NO)
return ERROR;
/* at least one eigen buffer needed */
if (m->nCijkParts == 0)
m->nCijkParts = 1;
/* allocate memory used by beagle */
m->logLikelihoods = (MrBFlt *) SafeCalloc ((numLocalChains)*m->numChars, sizeof(MrBFlt));
m->inRates = (MrBFlt *) SafeCalloc (m->numGammaCats, sizeof(MrBFlt));
m->branchLengths = (MrBFlt *) SafeCalloc (2*numLocalTaxa, sizeof(MrBFlt));
m->tiProbIndices = (int *) SafeCalloc (2*numLocalTaxa, sizeof(int));
m->inWeights = (MrBFlt *) SafeCalloc (m->numGammaCats*m->nCijkParts, sizeof(MrBFlt));
m->bufferIndices = (int *) SafeCalloc (m->nCijkParts, sizeof(int));
m->eigenIndices = (int *) SafeCalloc (m->nCijkParts, sizeof(int));
m->childBufferIndices = (int *) SafeCalloc (m->nCijkParts, sizeof(int));
m->childTiProbIndices = (int *) SafeCalloc (m->nCijkParts, sizeof(int));
m->cumulativeScaleIndices = (int *) SafeCalloc (m->nCijkParts, sizeof(int));
numPartAmbigTips = 0;
if (m->numStates != m->numModelStates)
numPartAmbigTips = numLocalTaxa;
else
{
for (i=0; i<numLocalTaxa; i++)
{
if (m->isPartAmbig[i] == YES)
numPartAmbigTips++;
}
}
if (beagleResourceCount == 0)
{
preferedFlags = beagleFlags;
}
else
{
resource = beagleResource[beagleInstanceCount % beagleResourceCount];
preferedFlags = beagleFlags;
}
requiredFlags = 0L;
if (beagleScalingScheme == MB_BEAGLE_SCALE_ALWAYS)
requiredFlags |= BEAGLE_FLAG_SCALERS_LOG; //BEAGLE_FLAG_SCALERS_RAW;
/* TODO: allocate fewer buffers when nCijkParts > 1 */
/* create beagle instance */
m->beagleInstance = beagleCreateInstance(numLocalTaxa,
m->numCondLikes * m->nCijkParts,
numLocalTaxa - numPartAmbigTips,
m->numModelStates,
m->numChars,
(numLocalChains + 1) * m->nCijkParts,
m->numTiProbs*m->nCijkParts,
m->numGammaCats,
m->numScalers * m->nCijkParts,
(beagleResourceCount == 0 ? NULL : &resource),
(beagleResourceCount == 0 ? 0 : 1),
preferedFlags,
requiredFlags,
&details);
if (m->beagleInstance < 0)
{
MrBayesPrint ("%s Failed to start BEAGLE instance\n", spacer);
return (ERROR);
}
else
{
MrBayesPrint( "\n%s Using BEAGLE resource %i for division %d:", spacer, details.resourceNumber, division+1);
#if defined (THREADS_ENABLED)
MrBayesPrint( " (%s)\n", (tryToUseThreads ? "threaded" : "non-threaded"));
#else
MrBayesPrint( " (non-threaded)\n");
#endif
MrBayesPrint( "%s Rsrc Name : %s\n", spacer, details.resourceName);
MrBayesPrint( "%s Impl Name : %s\n", spacer, details.implName);
MrBayesPrint( "%s Flags:", spacer);
BeaglePrintFlags(details.flags);
MrBayesPrint( "\n");
beagleInstanceCount++;
}
/* initialize tip data */
inStates = (int *) SafeMalloc (m->numChars * sizeof(int));
if (!inStates)
return ERROR;
inPartials = (double *) SafeMalloc (m->numChars * m->numModelStates * sizeof(double));
if (!inPartials)
return ERROR;
for (i=0; i<numLocalTaxa; i++)
{
if (m->isPartAmbig[i] == NO)
{
charBits = m->parsSets[i];
for (c=0; c<m->numChars; c++)
{
for (s=j=0; s<m->numModelStates; s++)
{
if (IsBitSet(s, charBits))
{
inStates[c] = s;
j++;
}
}
if (j == m->numModelStates)
inStates[c] = j;
else
assert(j==1);
charBits += m->nParsIntsPerSite;
}
beagleSetTipStates(m->beagleInstance, i, inStates);
}
else /* if (m->isPartAmbig == YES) */
{
k = 0;
charBits = m->parsSets[i];
for (c=0; c<m->numChars; c++)
{
for (s=0; s<m->numModelStates; s++)
{
if (IsBitSet(s%m->numStates, charBits))
inPartials[k++] = 1.0;
else
inPartials[k++] = 0.0;
}
charBits += m->nParsIntsPerSite;
}
beagleSetTipPartials(m->beagleInstance, i, inPartials);
}
}
free (inStates);
free (inPartials);
return NO_ERROR;
}
double calc_ln_likelihood() {
//////////////////////////////////////////////////////////////////////////////
// Setup and initialze Beagle
std::vector<PhylogeneticNode *> nodes;
this->seed_node_->get_nodes_postorder(nodes);
std::vector<PhylogeneticNode *> leaf_nodes;
this->seed_node_->get_leaf_nodes(leaf_nodes);
std::vector<PhylogeneticNode *> internal_nodes;
this->seed_node_->get_internal_nodes_postorder(internal_nodes, true);
int num_sites = leaf_nodes[0]->get_state_vector_len();
int num_nodes = nodes.size();
int num_tip_nodes = leaf_nodes.size();
int num_int_nodes = internal_nodes.size() ;
int num_partials = num_nodes;
BeagleInstanceDetails * return_info = new BeagleInstanceDetails();
int beagle_instance = beagleCreateInstance(
num_tip_nodes, // Number of tip data elements (input)
num_partials, // Number of partials buffers to create (input) -- internal node count
num_tip_nodes, // Number of compact state representation buffers to create -- for use with setTipStates (input)
4, // Number of states in the continuous-time Markov chain (input) -- DNA
num_sites, // Number of site patterns to be handled by the instance (input) -- not compressed in this case
1, // Number of eigen-decomposition buffers to allocate (input)
num_nodes, // Number of transition matrix buffers (input) -- one per edge
1, // Number of rate categories
0, // Number of scaling buffers -- can be zero if scaling is not needed
NULL, // List of potential resource on which this instance is allowed (input, NULL implies no restriction
0, // Length of resourceList list (input) -- not needed to use the default hardware config
0, // Bit-flags indicating preferred implementation charactertistics, see BeagleFlags (input)
0, // Bit-flags indicating required implementation characteristics, see BeagleFlags (input)
return_info
);
if (beagle_instance < 0) {
std::cerr << "\n\n***ERROR*** Failed to obtain BEAGLE instance\n" << std::endl;
exit(1);
}
int ret_code = 0;
for (int i; i < num_tip_nodes; ++i) {
// ret_code = beagleSetTipPartials(
// beagle_instance, // instance
// leaf_nodes[i]->get_index(), // bufferIndex
// leaf_nodes[i]->get_partials_data() // inPartials
// );
ret_code = beagleSetTipStates(
beagle_instance,
leaf_nodes[i]->get_index(),
leaf_nodes[i]->get_state_vector_data()
);
if (ret_code != 0) {
std::cerr << "\n\n***ERROR*** Failed to set tip data\n" << std::endl;
exit(1);
}
}
// let all sites have equal weight
std::vector<double> pattern_weights( num_sites, 1 );
beagleSetPatternWeights(beagle_instance, pattern_weights.data());
// create array of state background frequencies
double freqs[4] = { 0.25, 0.25, 0.25, 0.25 };
beagleSetStateFrequencies(beagle_instance, 0, freqs);
// create an array containing site category weights and rates
const double weights[1] = { 1.0 };
const double rates[1] = { 1.0 };
beagleSetCategoryWeights(beagle_instance, 0, weights);
beagleSetCategoryRates(beagle_instance, rates);
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
};
// JC69 model inverse eigenvector matrix
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
};
// JC69 model eigenvalues
double eval[4] = { 0.0, -1.3333333333333333, -1.3333333333333333, -1.3333333333333333 };
ret_code = beagleSetEigenDecomposition(
beagle_instance, // instance
0, // eigenIndex,
(const double *)evec, // inEigenVectors,
(const double *)ivec, // inInverseEigenVectors,
eval); // inEigenValues
if (ret_code != 0) {
std::cerr << "\n\n***ERROR*** Failed to set eigen decomposition\n" << std::endl;
exit(1);
}
//////////////////////////////////////////////////////////////////////////////
// Calculate log-likelihood
// a list of indices and edge lengths
// these get used to tell beagle which edge length goes with which node
std::vector<int> node_indices;
std::vector<double> edge_lens;
for (auto &nd : nodes) {
node_indices.push_back(nd->get_index());
edge_lens.push_back(nd->get_edge_len());
}
// tell BEAGLE to populate the transition matrices for the above edge lengthss
beagleUpdateTransitionMatrices(beagle_instance, // instance
0, // eigenIndex
node_indices.data(), // probabilityIndices
NULL, // firstDerivativeIndices
NULL, // secondDervativeIndices
edge_lens.data(), // edgeLengths
node_indices.size()); // count
// create a list of partial likelihood update operations
// the order is [dest, sourceScaling, destScaling, source1, matrix1, source2, matrix2]
// these operations say: first peel node 0 and 1 to calculate the per-site partial likelihoods, and store them
// in buffer 3. Then peel node 2 and buffer 3 and store the per-site partial likelihoods in buffer 4.
// BeagleOperation operations[2] = {
// {3, BEAGLE_OP_NONE, BEAGLE_OP_NONE, 0, 0, 1, 1},
// {4, BEAGLE_OP_NONE, BEAGLE_OP_NONE, 2, 2, 3, 3}
// };
std::vector<BeagleOperation> beagle_operations;
int ch1_idx = 0;
int ch2_idx = 0;
for (auto &nd : internal_nodes) {
ch1_idx = nd->get_child_node_index(0);
ch2_idx = nd->get_child_node_index(1);
std::cerr << nd->get_index() << ": " << ch1_idx << ", " << ch2_idx << std::endl;
beagle_operations.push_back(
{nd->get_index(), BEAGLE_OP_NONE, BEAGLE_OP_NONE, ch1_idx, ch1_idx, ch2_idx, ch2_idx}
);
}
// this invokes all the math to carry out the likelihood calculation
beagleUpdatePartials( beagle_instance, // instance
beagle_operations.data(), // eigenIndex
beagle_operations.size(), // operationCount
BEAGLE_OP_NONE); // cumulative scale index
// for (auto &nd : nodes) {
// std::cerr << nd->get_index() << ":";
// for (unsigned int i = 0; i < 4; ++i) {
// std::cerr << " " << nd->get_partial(i);
// }
// std::cerr << std::endl;
// }
double logL = 0;
int root_index[1] = {this->seed_node_->get_index()};
int category_weight_index[1] = {0};
int state_freq_index[1] = {0};
int cumulative_scale_index[1] = {BEAGLE_OP_NONE};
// calculate the site likelihoods at the root node
// this integrates the per-site root partial likelihoods across sites, background state frequencies, and rate categories
// results in a single log likelihood, output here into logL
beagleCalculateRootLogLikelihoods(beagle_instance, // instance
root_index,// bufferIndices
category_weight_index, // weights
state_freq_index, // stateFrequencies
cumulative_scale_index, // scaleBuffer to use
1, // count
&logL); // outLogLikelihoods
return logL;
}
