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Isolated main colamd call in function templated on key type.

release/4.3a0
Frank Dellaert 2009-09-12 03:38:55 +00:00
parent a5ae9c12ef
commit 78ac705d05
1 changed files with 40 additions and 24 deletions
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64
cpp/LinearFactorGraph.cpp
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@ -230,38 +230,27 @@ pair<Matrix,Vector> LinearFactorGraph::matrix(const Ordering& ordering) const {
/* ************************************************************************* */
Ordering LinearFactorGraph::getOrdering() const {
/**
* Call colamd given a column-major symbolic matrix A
* @param n_col colamd arg 1: number of rows in A
* @param n_row colamd arg 2: number of columns in A
* @param nrNonZeros number of non-zero entries in A
* @param columns map from keys to a sparse column of non-zero row indices
*/
template <class Key>
Ordering colamd(int n_col, int n_row, int nrNonZeros, const map<Key, vector<int> >& columns) {
// A factor graph is really laid out in row-major format, each factor a row
// Below, we compute a symbolic matrix stored in sparse columns.
typedef string Key; // default case with string keys
map<Key, vector<int> > columns; // map from keys to a sparse column of non-zero row indices
int nrNonZeros = 0; // number of non-zero entries
int n_row = factors_.size(); /* colamd arg 1: number of rows in A */
// loop over all factors = rows
for (int i = 0; i < n_row; i++) {
shared_factor factor = factors_[i];
for (map<Key, Matrix>::const_iterator lit = factor->begin(); lit != factor->end(); lit++) {
const Key& key = lit->first;
columns[key].push_back(i);
nrNonZeros++;
}
}
int n_col = (int)(columns.size()); /* colamd arg 2: number of columns in A */
if(n_col == 0) return Ordering(); // empty ordering
// Now convert to compressed column major format colamd wants it in (== MATLAB format!)
// Convert to compressed column major format colamd wants it in (== MATLAB format!)
vector<Key> initialOrder;
int Alen = nrNonZeros*30; /* colamd arg 3: size of the array A */
int Alen = nrNonZeros*30; /* colamd arg 3: size of the array A TODO: use Tim's function ! */
int * A = new int[Alen]; /* colamd arg 4: row indices of A, of size Alen */
int * p = new int[n_col + 1]; /* colamd arg 5: column pointers of A, of size n_col+1 */
{
p[0] = 0;
int j = 1;
int count = 0;
for(map<Key, vector<int> >::const_iterator it = columns.begin(); it != columns.end(); it++)
typedef typename map<Key, vector<int> >::const_iterator iterator;
for(iterator it = columns.begin(); it != columns.end(); it++)
{
const Key& key = it->first;
const vector<int>& column = it->second;
@ -290,3 +279,30 @@ Ordering LinearFactorGraph::getOrdering() const {
}
/* ************************************************************************* */
Ordering LinearFactorGraph::getOrdering() const {
// A factor graph is really laid out in row-major format, each factor a row
// Below, we compute a symbolic matrix stored in sparse columns.
typedef string Key; // default case with string keys
map<Key, vector<int> > columns; // map from keys to a sparse column of non-zero row indices
int nrNonZeros = 0; // number of non-zero entries
int n_row = factors_.size(); /* colamd arg 1: number of rows in A */
// loop over all factors = rows
for (int i = 0; i < n_row; i++) {
shared_factor factor = factors_[i];
for (map<Key, Matrix>::const_iterator lit = factor->begin(); lit != factor->end(); lit++) {
const Key& key = lit->first;
columns[key].push_back(i);
nrNonZeros++;
}
}
int n_col = (int)(columns.size()); /* colamd arg 2: number of columns in A */
if(n_col == 0)
return Ordering(); // empty ordering
else
return colamd(n_col, n_row, nrNonZeros, columns);
}
/* ************************************************************************* */