//PROTOTYPE IMPL!!!!!!!!
//BUG BUG Result ought to be dense/sparse/diag according as M is....
matrix apply(RingHom phi, ConstMatrixView M)
{
CoCoA_ASSERT(domain(phi) == RingOf(M));
matrix NewM(NewDenseMat(codomain(phi), NumRows(M), NumCols(M)));
for (long i=0; i < NumRows(M); ++i)
for (long j=0; j < NumCols(M); ++j)
SetEntry(NewM, i, j, phi(M(i,j)));
return NewM;
}
void CNumMat::operator-=(const CNumMat &m)
{
assert(NumCols()==m.NumCols());
assert(NumRows()==m.NumRows());
if(gsl_matrix_sub(m_mat,m.m_mat))
throw BPException("gsl_matrix_sub");
}
const double &CNumMat::operator()(unsigned row, unsigned col) const
{
assert(m_mat!=NULL);
assert(row<NumRows());
assert(col<NumCols());
return(*gsl_matrix_ptr(m_mat,row, col));
}
// Simple rather than efficient (esp. the call to eval)
std::vector<RingElem> BM_generic(const SparsePolyRing& P, const ConstMatrixView& pts)
{
if (CoeffRing(P) != RingOf(pts)) CoCoA_ERROR(ERR::MixedRings, "Buchberger-Moeller");
if (NumIndets(P) < NumCols(pts)) CoCoA_ERROR(ERR::IncompatDims, "Buchberger-Moeller");
const long NumPts = NumRows(pts);
const long dim = NumCols(pts);
const ring k = CoeffRing(P);
vector<RingElem> GB;
const PPMonoid TT = PPM(P);
QBGenerator QBG(TT);
QBG.myCornerPPIntoQB(one(TT));
matrix M = NewDenseMat(k, 1, NumPts);
// Fill first row with 1:
for (int i=0; i<NumPts; ++i) SetEntry(M,0,i, 1);
// The next loop removes the last indets from consideration.
for (int i=dim; i < NumIndets(TT); ++i)
QBG.myCornerPPIntoAvoidSet(indet(TT,i));
while (!QBG.myCorners().empty())
{
const PPMonoidElem t = QBG.myCorners().front();
const vector<RingElem> v = eval(t, pts);
ConstMatrixView NewRow = RowMat(v);
const matrix a = LinSolve(transpose(M), transpose(NewRow));
if (IsValidSolution(a))
{
QBG.myCornerPPIntoAvoidSet(t);
RingElem NewGBElem = monomial(P, one(k), t);
const vector<PPMonoidElem>& QB = QBG.myQB();
for (int i=0; i < NumRows(M); ++i)
NewGBElem -= monomial(P, a(i,0), QB[i]);
GB.push_back(NewGBElem);
}
else
{
QBG.myCornerPPIntoQB(t);
M = NewDenseMat(ConcatVer(M, NewRow));
}
}
return GB;
}
// I bet this makes lots of wasteful copies...
matrix NewDenseMat(ConstMatrixView M)
{
const long r = NumRows(M);
const long c = NumCols(M);
matrix ans(new DenseMatImpl(RingOf(M), r, c));
for (long i=0; i < r; ++i)
for (long j=0; j < c; ++j)
SetEntry(ans, i, j, M(i, j));
return ans;
}
void CNumMat::resize(unsigned rows, unsigned cols)
{
assert(rows>0 && cols>0);
if(m_mat!=NULL)
{
if(NumRows()==rows && NumCols()==cols)
return;
gsl_matrix_free(m_mat);
}
m_mat=gsl_matrix_alloc(rows,cols);
}
ideal IdealOfPoints(const SparsePolyRing& P, const ConstMatrixView& M)
{
if (CoeffRing(P) != RingOf(M)) CoCoA_ERROR(ERR::MixedRings, "IdealOfPoints");
if (NumIndets(P) != NumCols(M)) CoCoA_ERROR(ERR::BadMatrixSize, "IdealOfPoints");
if (DuplicateRows(M)) CoCoA_ERROR("Duplicate points", "IdealOfPoints");
ideal I(P, std::vector<RingElem>(0));
if (IsFiniteField(CoeffRing(P))) I = ideal(P, BM_modp(P, M));
else if (IsQQ(CoeffRing(P))) I = ideal(P, BM_QQ(P, M));
// generic case
else I = ideal(P, BM_generic(P, M));
SetGBasisAsGens(I);
return I;
}
/* CloneSMatrix: return a clone of given Matrix */
SMatrix CloneSMatrix(MemHeap *hmem, SMatrix s, Boolean sharing)
{
SMatrix t; /* the target */
if (s==NULL) return NULL;
if (GetUse(s)>0 && sharing) {
IncUse(s);
return s;
}
t = CreateSMatrix(hmem,NumRows(s),NumCols(s));
CopyMatrix(s,t);
return t;
}
bool DuplicateRows(const ConstMatrixView& M)
{
const long nrows = NumRows(M);
const long ncols = NumCols(M);
for (long i1=0; i1 < nrows; ++i1)
for (long i2=i1+1; i2 < nrows; ++i2)
{
bool NoDifference = true;
for (long j=0; NoDifference && j < ncols; ++j)
NoDifference = (M(i1,j) == M(i2,j));
if (NoDifference) return true;
}
return false;
}
PODVector<CubeTerrain*>* Terrain::GetColumnCubes(CubeTerrain *cube, DIR dir)
{
const int dRow[8] = { 0, -1, -1, -1, 0, 1, 1, 1};
const int dCol[8] = {-1, -1, 0, 1, 1, 1, 0, -1};
uint row = (uint)((int)cube->row + dRow[dir]);
uint col = (uint)((int)cube->col + dCol[dir]);
if(row > NumRows() - 1 || col > NumCols() - 1)
{
return nullptr;
}
return &columnsCubes[row][col];
}
std::vector<RingElem> BM_modp(const SparsePolyRing& P, const ConstMatrixView& pts)
{
ring Fp = CoeffRing(P);
const int NumPts = NumRows(pts);
const int NumVars = NumCols(pts);
const long p = ConvertTo<long>(characteristic(Fp));
FF FFp = FFctor(p);
FFselect(FFp);
FFelem** points_p = (FFelem**)malloc(NumPts*sizeof(FFelem*));
for (int i=0; i < NumPts; ++i)
{
points_p[i] = (FFelem*)malloc(NumVars*sizeof(FFelem));
for (int j=0; j < NumVars; ++j)
{
points_p[i][j] = ConvertTo<FFelem>(LeastNNegRemainder(ConvertTo<BigInt>(pts(i,j)), p));
}
}
pp_cmp_PPM = &PPM(P);
const BM modp = BM_affine_mod_p(NumVars, NumPts, points_p, pp_cmp);
if (modp == NULL) return std::vector<RingElem>(); // empty list means error
const int GBsize = modp->GBsize;
std::vector<RingElem> GB(GBsize);
vector<long> expv(NumVars);
for (int i=0; i < GBsize; ++i)
{
for (int var = 0; var < NumVars; ++var)
expv[var] = modp->pp[modp->GB[i]][var];
RingElem GBelem = monomial(P, 1, expv);
for (int j=0; j < NumPts; ++j)
{
const int c = modp->M[modp->GB[i]][j+NumPts];
if (c == 0) continue;
for (int var = 0; var < NumVars; ++var)
expv[var] = modp->pp[modp->sep[j]][var];
GBelem += monomial(P, c, expv);
}
GB[i] = GBelem;
}
BM_dtor(modp);
return GB;
}
/* CheckLRTransP
determine wheter transition matrix is left-to-right, i.e. no backward transitions
*/
static Boolean CheckLRTransP (SMatrix transP)
{
int r,c,N;
N = NumCols (transP);
assert (N == NumRows (transP));
for (r = 1; r <= N; ++r) {
for (c = 1; c < r; ++c) {
if (transP[r][c] > LSMALL)
return FALSE;
}
for (c = r+2; c < r; ++c) {
if (transP[r][c] > LSMALL)
return FALSE;
}
}
return TRUE;
}
std::vector<RingElem> BM_QQ(const SparsePolyRing& P, const ConstMatrixView& pts_in)
{
const long NumPts = NumRows(pts_in);
const long dim = NumCols(pts_in);
matrix pts = NewDenseMat(RingQQ(), NumPts, dim);
for (long i=0; i < NumPts; ++i)
for (long j=0; j < dim; ++j)
{
BigRat q;
if (!IsRational(q, pts_in(i,j))) throw 999;
SetEntry(pts,i,j, q);
}
// Ensure input pts have integer coords by using
// scale factors for each indet.
vector<BigInt> ScaleFactor(dim, BigInt(1));
for (long j=0; j < dim; ++j)
for (long i=0; i < NumPts; ++i)
ScaleFactor[j] = lcm(ScaleFactor[j], ConvertTo<BigInt>(den(pts(i,j))));
mpz_t **points = (mpz_t**)malloc(NumPts*sizeof(mpz_t*));
for (long i=0; i < NumPts; ++i)
{
points[i] = (mpz_t*)malloc(dim*sizeof(mpz_t));
for (long j=0; j < dim; ++j) mpz_init(points[i][j]);
for (long j=0; j < dim; ++j)
{
mpz_set(points[i][j], mpzref(ConvertTo<BigInt>(ScaleFactor[j]*pts(i,j))));
}
}
BMGB char0; // these will be "filled in" by BM_affine below
BM modp; //
pp_cmp_PPM = &PPM(P); // not threadsafe!
BM_affine(&char0, &modp, dim, NumPts, points, pp_cmp); // THIS CALL DOES THE REAL WORK!!!
pp_cmp_PPM = NULL;
for (long i=NumPts-1; i >=0 ; --i)
{
for (long j=0; j < dim; ++j) mpz_clear(points[i][j]);
free(points[i]);
}
free(points);
if (modp == NULL) { if (char0 != NULL) BMGB_dtor(char0); CoCoA_ERROR("Something went wrong", "BM_QQ"); }
// Now extract the answer...
const int GBsize = char0->GBsize;
std::vector<RingElem> GB(GBsize);
const long NumVars = dim;
vector<long> expv(NumVars); // buffer for creating monomials
for (int i=0; i < GBsize; ++i)
{
BigInt denom(1); // scale factor needed to make GB elem monic.
for (int var = 0; var < NumVars; ++var)
{
expv[var] = modp->pp[modp->GB[i]][var];
denom *= power(ScaleFactor[var], expv[var]);
}
RingElem GBelem = monomial(P, 1, expv);
for (int j=0; j < NumPts; ++j)
{
if (mpq_sgn(char0->GB[i][j])==0) continue;
BigRat c(char0->GB[i][j]);
for (int var = 0; var < NumVars; ++var)
{
expv[var] = modp->pp[modp->sep[j]][var];
c *= power(ScaleFactor[var], expv[var]);
}
GBelem += monomial(P, c/denom, expv);
}
GB[i] = GBelem;
}
BMGB_dtor(char0);
BM_dtor(modp);
return GB;
// ignoring separators for the moment
}
int exampleSolver(const std::string file_input,
const int prunecut,
const int max_concurrency,
const int nrhs,
const int mb,
const int nb,
const bool verbose) {
typedef typename
Kokkos::Impl::is_space<DeviceSpaceType>::host_mirror_space::execution_space HostSpaceType ;
const bool detail = false;
std::cout << "DeviceSpace:: "; DeviceSpaceType::print_configuration(std::cout, detail);
std::cout << "HostSpace:: "; HostSpaceType::print_configuration(std::cout, detail);
typedef Solver<value_type,ordinal_type,size_type,DeviceSpaceType> SolverType;
#ifdef HAVE_SHYLUTACHO_VTUNE
__itt_pause();
#endif
int r_val = 0;
Kokkos::Impl::Timer timer;
SolverType tacho("Tacho::CholSolver");
tacho.setPolicy(max_concurrency);
tacho.setBlocksize(mb, nb);
///
/// Read from matrix market
///
/// input - file
/// output - AA
///
struct {
size_type *ap;
ordinal_type *aj;
value_type *ax;
value_type *b;
bool is_allocated;
} data;
// non standard initialization and icpc complains
//= { .ap = NULL, .aj = NULL, .ax = NULL,
//.b = NULL, .is_allocated = false };
timer.reset();
if (file_input.compare("null") != 0) {
data.is_allocated = false;
std::cout << "Solver:: "
<< "Matrix market input is selected"
<< std::endl;
// matrix market example
typename SolverType::CrsMatrixBaseHostType AA("AA");
std::ifstream in;
in.open(file_input);
if (!in.good()) {
std::cout << "Failed in open the file: " << file_input << std::endl;
return -1;
}
MatrixMarket::read(AA, in);
typename SolverType::DenseMatrixBaseHostType BB("BB", AA.NumRows(), nrhs), XX("XX");
XX.createConfTo(BB);
srand(time(NULL));
const ordinal_type m = BB.NumRows();
for (auto rhs=0;rhs<nrhs;++rhs)
for (auto i=0;i<m;++i)
BB.Value(i, rhs) = ((value_type)rand()/(RAND_MAX));
//tacho.setProblem(AA, BB, XX);
tacho.setMatrix(AA);
tacho.setLeftHandSide(XX);
tacho.setRightHandSide(BB);
} else {
data.is_allocated = true;
std::cout << "Solver:: "
<< "user data input is selected"
<< std::endl;
const ordinal_type m = 12, nnz = 46;
data.ap = new size_type[m+1]{
0, 3, 7, 11, 15, 20, 23, 26, 31, 35, 39, 43, 46
};
data.aj = new ordinal_type[nnz]{
0, 1, 2,
0, 1, 3, 4,
0, 2, 4, 5,
1, 3, 6, 7,
1, 2, 4, 7, 8,
2, 5, 8,
3, 6, 9,
3, 4, 7, 9, 10,
4, 5, 8, 10,
6, 7, 9, 11,
7, 8, 10, 11,
9, 10, 11
};
data.ax = new value_type[nnz]{
10, 1, 1,
1, 10, 1, 2,
1, 10, 1, 1,
1, 10, 1, 1,
2, 1, 10, 2, 2,
1, 10, 1,
1, 10, 2,
1, 2, 10, 2, 1,
2, 1, 10, 1,
2, 2, 10, 2,
1, 1, 10, 1,
2, 1, 10
};
data.b = new value_type[m*nrhs];
srand(time(NULL));
for (auto rhs=0;rhs<nrhs;++rhs)
for (auto i=0;i<m;++i)
data.b[i+m*rhs] = ((value_type)rand()/(RAND_MAX));
typename SolverType::CrsMatrixBaseHostType AA("AA", m, m, nnz);
// copy user data
for (auto i=0;i<m;++i) {
AA.RowPtrBegin(i) = data.ap[i];
AA.RowPtrEnd(i) = data.ap[i+1];
}
for (auto k=0;k<nnz;++k) {
AA.Col(k) = data.aj[k];
AA.Value(k) = data.ax[k];
}
typename SolverType::DenseMatrixBaseHostType BB("BB", m, nrhs), XX("XX");
XX.createConfTo(BB);
for (auto rhs=0;rhs<nrhs;++rhs)
for (auto i=0;i<m;++i)
BB.Value(i, rhs) = data.b[i+m*rhs];
tacho.setProblem(AA, BB, XX);
}
const double t_input = timer.seconds();
std::cout << "Solver:: "
<< "input generation = " << t_input << " [sec] "
<< std::endl;
if (verbose) {
tacho.getA().showMe(std::cout) << std::endl;
tacho.getB().showMe(std::cout) << std::endl;
tacho.getX().showMe(std::cout) << std::endl;
}
///
/// Solver interface
///
TACHO_SOLVER_RUN(tacho.reorder(prunecut), t_reorder);
TACHO_SOLVER_RUN(tacho.analyze(), t_analyze);
#ifdef HAVE_SHYLUTACHO_VTUNE
__itt_resume();
#endif
TACHO_SOLVER_RUN(tacho.factorize(), t_factorize);
#ifdef HAVE_SHYLUTACHO_VTUNE
__itt_pause();
#endif
TACHO_SOLVER_RUN(tacho.solve(), t_solve);
double norm, error;
tacho.check(norm, error);
///
/// Print out
///
{
const auto prec = std::cout.precision();
std::cout.precision(4);
const auto AA = tacho.getA();
const auto UU = tacho.getFlatU();
const auto HU = tacho.getHierU();
std::cout << std::scientific;
std::cout << "Solver:: given matrix = " << AA.NumRows() << " x " << AA.NumCols() << ", nnz = " << AA.NumNonZeros() << std::endl;
std::cout << "Solver:: factored matrix = " << UU.NumRows() << " x " << UU.NumCols() << ", nnz = " << UU.NumNonZeros() << std::endl;
std::cout << "Solver:: hier matrix = " << HU.NumRows() << " x " << HU.NumCols() << ", nnz = " << HU.NumNonZeros() << std::endl;
std::cout << "Solver:: "
<< "input generation = " << t_input << " [sec], "
<< std::endl
<< "Solver:: "
<< "reorder = " << t_reorder << " [sec] "
<< std::endl
<< "Solver:: "
<< "analyze = " << t_analyze << " [sec] "
<< std::endl
<< "Solver:: "
<< "factorize = " << t_factorize << " [sec] "
<< std::endl
<< "Solver:: "
<< "solve = " << t_solve << " [sec] "
<< std::endl
<< "Solver:: "
<< "norm = " << norm << ", error = " << error << ", rel error = " << (error/norm)
<< std::endl;
std::cout << std::endl;
std::cout.unsetf(std::ios::scientific);
std::cout.precision(prec);
}
if (data.is_allocated) {
delete []data.ap;
delete []data.aj;
delete []data.ax;
delete []data.b;
}
return r_val;
}
