/* ************************************************************************* */
std::pair<DiscreteConditional::shared_ptr, DecisionTreeFactor::shared_ptr> //
EliminateDiscrete(const DiscreteFactorGraph& factors, const Ordering& frontalKeys) {
// PRODUCT: multiply all factors
gttic(product);
DecisionTreeFactor product;
BOOST_FOREACH(const DiscreteFactor::shared_ptr& factor, factors)
product = (*factor) * product;
gttoc(product);
// sum out frontals, this is the factor on the separator
gttic(sum);
DecisionTreeFactor::shared_ptr sum = product.sum(frontalKeys);
gttoc(sum);
// Ordering keys for the conditional so that frontalKeys are really in front
Ordering orderedKeys;
orderedKeys.insert(orderedKeys.end(), frontalKeys.begin(), frontalKeys.end());
orderedKeys.insert(orderedKeys.end(), sum->keys().begin(), sum->keys().end());
// now divide product/sum to get conditional
gttic(divide);
DiscreteConditional::shared_ptr cond(new DiscreteConditional(product, *sum, orderedKeys));
gttoc(divide);
return std::make_pair(cond, sum);
}
/* ************************************************************************* */
VerticalBlockMatrix SymmetricBlockMatrix::choleskyPartial(
DenseIndex nFrontals) {
// Do dense elimination
if (blockStart() != 0)
throw std::invalid_argument(
"Can only do Cholesky when the SymmetricBlockMatrix is not a restricted view, i.e. when blockStart == 0.");
if (!gtsam::choleskyPartial(matrix_, offset(nFrontals)))
throw CholeskyFailed();
// Split conditional
// Create one big conditionals with many frontal variables.
gttic(Construct_conditional);
const size_t varDim = offset(nFrontals);
VerticalBlockMatrix Ab = VerticalBlockMatrix::LikeActiveViewOf(*this, varDim);
Ab.full() = matrix_.topRows(varDim);
Ab.full().triangularView<Eigen::StrictlyLower>().setZero();
gttoc(Construct_conditional);
gttic(Remaining_factor);
// Take lower-right block of Ab_ to get the remaining factor
blockStart() = nFrontals;
gttoc(Remaining_factor);
return Ab;
}
/* ************************************************************************* */
std::pair<DiscreteConditional::shared_ptr, DecisionTreeFactor::shared_ptr> //
EliminateDiscrete(const FactorGraph<DiscreteFactor>& factors, size_t num) {
// PRODUCT: multiply all factors
gttic(product);
DecisionTreeFactor product;
BOOST_FOREACH(const DiscreteFactor::shared_ptr& factor, factors)
product = (*factor) * product;
gttoc(product);
// sum out frontals, this is the factor on the separator
gttic(sum);
DecisionTreeFactor::shared_ptr sum = product.sum(num);
gttoc(sum);
// now divide product/sum to get conditional
gttic(divide);
DiscreteConditional::shared_ptr cond(new DiscreteConditional(product, *sum));
gttoc(divide);
return std::make_pair(cond, sum);
}
/* ************************************************************************* */
Ordering Ordering::ColamdConstrained(const VariableIndex& variableIndex,
std::vector<int>& cmember) {
gttic(Ordering_COLAMDConstrained);
gttic(Prepare);
const size_t nEntries = variableIndex.nEntries(), nFactors =
variableIndex.nFactors(), nVars = variableIndex.size();
// Convert to compressed column major format colamd wants it in (== MATLAB format!)
const size_t Alen = ccolamd_recommended((int) nEntries, (int) nFactors,
(int) nVars); /* colamd arg 3: size of the array A */
vector<int> A = vector<int>(Alen); /* colamd arg 4: row indices of A, of size Alen */
vector<int> p = vector<int>(nVars + 1); /* colamd arg 5: column pointers of A, of size n_col+1 */
// Fill in input data for COLAMD
p[0] = 0;
int count = 0;
vector<Key> keys(nVars); // Array to store the keys in the order we add them so we can retrieve them in permuted order
size_t index = 0;
for (auto key_factors: variableIndex) {
// Arrange factor indices into COLAMD format
const VariableIndex::Factors& column = key_factors.second;
for(size_t factorIndex: column) {
A[count++] = (int) factorIndex; // copy sparse column
}
p[index + 1] = count; // column j (base 1) goes from A[j-1] to A[j]-1
// Store key in array and increment index
keys[index] = key_factors.first;
++index;
}
assert((size_t)count == variableIndex.nEntries());
//double* knobs = NULL; /* colamd arg 6: parameters (uses defaults if NULL) */
double knobs[CCOLAMD_KNOBS];
ccolamd_set_defaults(knobs);
knobs[CCOLAMD_DENSE_ROW] = -1;
knobs[CCOLAMD_DENSE_COL] = -1;
int stats[CCOLAMD_STATS]; /* colamd arg 7: colamd output statistics and error codes */
gttoc(Prepare);
// call colamd, result will be in p
/* returns (1) if successful, (0) otherwise*/
if (nVars > 0) {
gttic(ccolamd);
int rv = ccolamd((int) nFactors, (int) nVars, (int) Alen, &A[0], &p[0],
knobs, stats, &cmember[0]);
if (rv != 1)
throw runtime_error(
(boost::format("ccolamd failed with return value %1%") % rv).str());
}
// ccolamd_report(stats);
// Convert elimination ordering in p to an ordering
gttic(Fill_Ordering);
Ordering result;
result.resize(nVars);
for (size_t j = 0; j < nVars; ++j)
result[j] = keys[p[j]];
gttoc(Fill_Ordering);
return result;
}
