/* multiplication between sparse and classic matrix A=D*Q
* hists: sparse matrix (typically matrix of database words)
* nhist: matrix (typically matrix of query words)
* return: hists*nhists (matrix)
*/
Mat mul(const SparseMat &hists, const Mat &nhist)
{
Mat answer = Mat::zeros(hists.size(0),1,CV_32F);
SparseMatConstIterator
it = hists.begin(),
it_end = hists.end();
for (; it!=it_end;++it)
{
const SparseMat::Node* n = it.node();
answer.at<float>(n->idx[0])+=nhist.at<float>(n->idx[1])*it.value<float>();
}
return answer;
}
void CmShow::showTinySparseMat(CStr &title, const SparseMat &A1d)
{
int N = A1d.nzcount();
if (N > 1000)
return;
struct NodeSM {
int r, c;
double v;
bool operator < (const NodeSM &n) {
if (r < n.r)
return true;
else if (r > n.r)
return false;
else
return c < n.c;
}
void print() {
if (abs(v) > 1e-8) printf("(%d, %d) %-12g\t", r, c, v);
}
};
vector<NodeSM> nodes(N);
SparseMatConstIterator it = A1d.begin();
for (int i = 0; i < N; i++, ++it) {
const int* idx = it.node()->idx;
nodes[i].r = idx[0] + 1;
nodes[i].c = idx[1] + 1;
nodes[i].v = it.value<double>();
}
sort(nodes.begin(), nodes.end());
for (int i = 0; i < N; i++)
nodes[i].print();
printf("\n");
Mat m;
A1d.convertTo(m, CV_64F);
showTinyMat(title, m);
}
/**
* Convert float cv::SparseMat to MxArray
* @param mat cv::SparseMat object
* @return MxArray object
*/
MxArray::MxArray(const cv::SparseMat& mat)
{
if (mat.dims() != 2)
mexErrMsgIdAndTxt("mexopencv:error","cv::Mat is not 2D");
if (mat.type() != CV_32FC1)
mexErrMsgIdAndTxt("mexopencv:error","cv::Mat is not float");
// Create sparse array
int m = mat.size(0), n = mat.size(1), nnz = mat.nzcount();
p_ = mxCreateSparse(m, n, nnz, mxREAL);
if (!p_)
mexErrMsgIdAndTxt("mexopencv:error","Allocation error");
mwIndex *ir = mxGetIr(p_);
mwIndex *jc = mxGetJc(p_);
if (ir==NULL || jc==NULL)
mexErrMsgIdAndTxt("mexopencv:error","Unknown error");
// Sort nodes before we put elems into mxArray
vector<const SparseMat::Node*> nodes;
nodes.reserve(nnz);
for (SparseMatConstIterator it = mat.begin(); it != mat.end(); ++it)
nodes.push_back(it.node());
sort(nodes.begin(),nodes.end(),compareSparseMatNode);
// Copy data
double *pr = mxGetPr(p_);
int i = 0;
jc[0] = 0;
for (vector<const SparseMat::Node*>::const_iterator it = nodes.begin();
it != nodes.end(); ++it)
{
mwIndex row = (*it)->idx[0], col = (*it)->idx[1];
ir[i] = row;
jc[col+1] = i+1;
pr[i] = static_cast<double>(mat.value<float>(*it));
++i;
}
}