void calc_edgelks_beagle(t_edge *b, t_tree *tree)
{
assert(UNINITIALIZED != tree->b_inst);
//Compute the edge likelihood
int parents[1] = {b->p_lk_left_idx};
int children[1] = {b->rght->tax?b->p_lk_tip_idx:b->p_lk_rght_idx};
int pmats[1] = {b->Pij_rr_idx};
int other[1] = {0};//Category Weights and State Frequencies both have a single buffer, hence they are both indexed at 0
double lnL[1] = {UNINITIALIZED};
// DUMP_I(parents[0], children[0], pmats[0], b->num, b->rght->tax);
int ret=beagleCalculateEdgeLogLikelihoods(tree->b_inst, parents, children, pmats, NULL, NULL, other, other, NULL, 1, lnL, NULL, NULL);
int i;
if(ret<0){
fprintf(stderr, "beagleCalculateEdgeLogLikelihoods() on instance %i failed:%i\n\n",tree->b_inst,ret);
Exit("");
}
tree->c_lnL = sizeof(phydbl)==sizeof(float)?(float)lnL[0]:lnL[0];
//Retrieve the site likelihoods that were computed during the previous edge likelihood computation
ret = beagleGetSiteLogLikelihoods(tree->b_inst,tree->cur_site_lk);//TODO: Handle when cur_site_lk is float
if(ret<0){
fprintf(stderr, "beagleGetSiteLogLikelihoods() on instance %i failed:%i\n\n",tree->b_inst,ret);
Exit("");
}
//Transform
for(i=0;i<tree->n_pattern;++i)
tree->cur_site_lk[i]=EXP(tree->cur_site_lk[i]);
}
/*
* Class: beagle_BeagleJNIWrapper
* Method: getSiteLogLikelihoods
* Signature: (I[D)I
*/
JNIEXPORT jint JNICALL Java_beagle_BeagleJNIWrapper_getSiteLogLikelihoods
(JNIEnv *env, jobject obj, jint instance, jdoubleArray outSiteLogLikelihoods) {
jdouble *siteLogLikelihoods = env->GetDoubleArrayElements(outSiteLogLikelihoods, NULL);
jint errCode = (jint)beagleGetSiteLogLikelihoods(instance, (double *)siteLogLikelihoods);
// not using JNI_ABORT flag here because we want the values to be copied back...
env->ReleaseDoubleArrayElements(outSiteLogLikelihoods, siteLogLikelihoods, 0);
return errCode;
}
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
}
