dgCMatrix.c
#include "dgCMatrix.h"
/* for Csparse_transpose() : */
#include "Csparse.h"
#include "chm_common.h"
/* -> Mutils.h / SPQR ... */
/* FIXME -- we "forget" about dimnames almost everywhere : */
/* for dgCMatrix _and_ lgCMatrix and others (but *not* ngC...) : */
SEXP xCMatrix_validate(SEXP x)
{
/* Almost everything now in Csparse_validate ( ./Csparse.c )
* *but* the checking of the 'x' slot : */
if (length(GET_SLOT(x, Matrix_iSym)) !=
length(GET_SLOT(x, Matrix_xSym)))
return mkString(_("lengths of slots 'i' and 'x' must match"));
return ScalarLogical(1);
}
/* for dgRMatrix _and_ lgRMatrix and others (but *not* ngC...) : */
SEXP xRMatrix_validate(SEXP x)
{
/* Almost everything now in Rsparse_validate ( ./Csparse.c )
* *but* the checking of the 'x' slot : */
if (length(GET_SLOT(x, Matrix_jSym)) !=
length(GET_SLOT(x, Matrix_xSym)))
return mkString(_("lengths of slots 'j' and 'x' must match"));
return ScalarLogical(1);
}
/* This and the following R_to_CMatrix() lead to memory-not-mapped seg.faults
* only with {32bit + R-devel + enable-R-shlib} -- no idea why */
SEXP compressed_to_TMatrix(SEXP x, SEXP colP)
{
int col = asLogical(colP); /* 1 if "C"olumn compressed; 0 if "R"ow */
/* however, for Csparse, we now effectively use the cholmod-based
* Csparse_to_Tsparse() in ./Csparse.c ; maybe should simply write
* an as_cholmod_Rsparse() function and then do "as there" ...*/
SEXP indSym = col ? Matrix_iSym : Matrix_jSym,
ans, indP = GET_SLOT(x, indSym),
pP = GET_SLOT(x, Matrix_pSym);
int npt = length(pP) - 1;
char *ncl = strdup(class_P(x));
static const char *valid[] = { MATRIX_VALID_Csparse, MATRIX_VALID_Rsparse, ""};
int ctype = Matrix_check_class_etc(x, valid);
if (ctype < 0)
error(_("invalid class(x) '%s' in compressed_to_TMatrix(x)"), ncl);
/* replace 'C' or 'R' with 'T' :*/
ncl[2] = 'T';
ans = PROTECT(NEW_OBJECT(MAKE_CLASS(ncl)));
slot_dup(ans, x, Matrix_DimSym);
if((ctype / 3) % 4 != 2) /* not n..Matrix */
slot_dup(ans, x, Matrix_xSym);
if(ctype % 3) { /* s(ymmetric) or t(riangular) : */
slot_dup(ans, x, Matrix_uploSym);
if(ctype % 3 == 2) /* t(riangular) : */
slot_dup(ans, x, Matrix_diagSym);
}
SET_DimNames(ans, x);
SET_SLOT(ans, indSym, duplicate(indP));
expand_cmprPt(npt, INTEGER(pP),
INTEGER(ALLOC_SLOT(ans, col ? Matrix_jSym : Matrix_iSym,
INTSXP, length(indP))));
free(ncl);
UNPROTECT(1);
return ans;
}
SEXP R_to_CMatrix(SEXP x)
{
SEXP ans, tri = PROTECT(allocVector(LGLSXP, 1));
char *ncl = strdup(class_P(x));
static const char *valid[] = { MATRIX_VALID_Rsparse, ""};
int ctype = Matrix_check_class_etc(x, valid);
int *x_dims = INTEGER(GET_SLOT(x, Matrix_DimSym)), *a_dims;
PROTECT_INDEX ipx;
if (ctype < 0)
error(_("invalid class(x) '%s' in R_to_CMatrix(x)"), ncl);
/* replace 'R' with 'C' : */
ncl[2] = 'C';
PROTECT_WITH_INDEX(ans = NEW_OBJECT(MAKE_CLASS(ncl)), &ipx);
a_dims = INTEGER(ALLOC_SLOT(ans, Matrix_DimSym, INTSXP, 2));
/* reversed dim() since we will transpose: */
a_dims[0] = x_dims[1];
a_dims[1] = x_dims[0];
/* triangular: */ LOGICAL(tri)[0] = 0;
if((ctype / 3) != 2) /* not n..Matrix */
slot_dup(ans, x, Matrix_xSym);
if(ctype % 3) { /* s(ymmetric) or t(riangular) : */
SET_SLOT(ans, Matrix_uploSym,
mkString((*uplo_P(x) == 'U') ? "L" : "U"));
if(ctype % 3 == 2) { /* t(riangular) : */
LOGICAL(tri)[0] = 1;
slot_dup(ans, x, Matrix_diagSym);
}
}
SET_SLOT(ans, Matrix_iSym, duplicate(GET_SLOT(x, Matrix_jSym)));
slot_dup(ans, x, Matrix_pSym);
REPROTECT(ans = Csparse_transpose(ans, tri), ipx);
SET_DimNames(ans, x);
free(ncl);
UNPROTECT(2);
return ans;
}
/** Return a 2 column matrix '' cbind(i, j) '' of 0-origin index vectors (i,j)
* which entirely correspond to the (i,j) slots of
* as(x, "TsparseMatrix") :
*/
SEXP compressed_non_0_ij(SEXP x, SEXP colP)
{
int col = asLogical(colP); /* 1 if "C"olumn compressed; 0 if "R"ow */
SEXP ans, indSym = col ? Matrix_iSym : Matrix_jSym;
SEXP indP = GET_SLOT(x, indSym),
pP = GET_SLOT(x, Matrix_pSym);
int i, *ij;
int nouter = INTEGER(GET_SLOT(x, Matrix_DimSym))[col ? 1 : 0],
n_el = INTEGER(pP)[nouter]; /* is only == length(indP), if the
inner slot is not over-allocated */
ij = INTEGER(ans = PROTECT(allocMatrix(INTSXP, n_el, 2)));
/* expand the compressed margin to 'i' or 'j' : */
expand_cmprPt(nouter, INTEGER(pP), &ij[col ? n_el : 0]);
/* and copy the other one: */
if (col)
for(i = 0; i < n_el; i++)
ij[i] = INTEGER(indP)[i];
else /* row compressed */
for(i = 0; i < n_el; i++)
ij[i + n_el] = INTEGER(indP)[i];
UNPROTECT(1);
return ans;
}
#if 0 /* unused */
SEXP dgCMatrix_lusol(SEXP x, SEXP y)
{
SEXP ycp = PROTECT((TYPEOF(y) == REALSXP) ?
duplicate(y) : coerceVector(y, REALSXP));
CSP xc = AS_CSP__(x);
R_CheckStack();
if (xc->m != xc->n || xc->m <= 0)
error(_("dgCMatrix_lusol requires a square, non-empty matrix"));
if (LENGTH(ycp) != xc->m)
error(_("Dimensions of system to be solved are inconsistent"));
if (!cs_lusol(/*order*/ 1, xc, REAL(ycp), /*tol*/ 1e-7))
error(_("cs_lusol failed"));
UNPROTECT(1);
return ycp;
}
#endif
SEXP dgCMatrix_qrsol(SEXP x, SEXP y, SEXP ord)
{
/* FIXME: extend this to work in multivariate case, i.e. y a matrix with > 1 column ! */
SEXP ycp = PROTECT((TYPEOF(y) == REALSXP) ?
duplicate(y) : coerceVector(y, REALSXP));
CSP xc = AS_CSP(x); /* <--> x may be dgC* or dtC* */
int order = INTEGER(ord)[0];
#ifdef _not_yet_do_FIXME__
const char *nms[] = {"L", "coef", "Xty", "resid", ""};
SEXP ans = PROTECT(Rf_mkNamed(VECSXP, nms));
#endif
R_CheckStack();
if (order < 0 || order > 3)
error(_("dgCMatrix_qrsol(., order) needs order in {0,..,3}"));
/* --> cs_amd() --- order 0: natural, 1: Chol, 2: LU, 3: QR */
if (LENGTH(ycp) != xc->m)
error(_("Dimensions of system to be solved are inconsistent"));
/* FIXME? Note that qr_sol() would allow *under-determined systems;
* In general, we'd need LENGTH(ycp) = max(n,m)
* FIXME also: multivariate y (see above)
*/
if (xc->m < xc->n || xc->n <= 0)
error(_("dgCMatrix_qrsol(<%d x %d>-matrix) requires a 'tall' rectangular matrix"),
xc->m, xc->n);
/* cs_qrsol(): Tim Davis (2006) .. "8.2 Using a QR factorization", p.136f , calling
* ------- cs_sqr(order, ..), see p.76 */
/* MM: FIXME: write our *OWN* version of - the first case (m >= n) - of cs_qrsol()
* --------- which will (1) work with a *multivariate* y
* (2) compute coefficients properly, not overwriting RHS
*/
if (!cs_qrsol(order, xc, REAL(ycp)))
/* return value really is 0 or 1 - no more info there */
error(_("cs_qrsol() failed inside dgCMatrix_qrsol()"));
/* Solution is only in the first part of ycp -- cut its length back to n : */
ycp = lengthgets(ycp, (R_len_t) xc->n);
UNPROTECT(1);
return ycp;
}
// Modified version of Tim Davis's cs_qr_mex.c file for MATLAB (in CSparse)
// Usage: [V,beta,p,R,q] = cs_qr(A) ;
SEXP dgCMatrix_QR(SEXP Ap, SEXP order)
{
CSP A = AS_CSP__(Ap), D;
int io = INTEGER(order)[0];
Rboolean verbose = (io < 0);// verbose=TRUE, encoded with negative 'order'
int m = A->m, n = A->n, ord = asLogical(order) ? 3 : 0, *p;
R_CheckStack();
if (m < n) error(_("A must have #{rows} >= #{columns}")) ;
SEXP ans = PROTECT(NEW_OBJECT(MAKE_CLASS("sparseQR")));
int *dims = INTEGER(ALLOC_SLOT(ans, Matrix_DimSym, INTSXP, 2));
dims[0] = m; dims[1] = n;
css *S = cs_sqr(ord, A, 1); /* symbolic QR ordering & analysis*/
if (!S) error(_("cs_sqr failed"));
if(verbose && S->m2 > m) // in ./cs.h , m2 := # of rows for QR, after adding fictitious rows
Rprintf("Symbolic QR(): Matrix structurally rank deficient (m2-m = %d)\n",
S->m2 - m);
csn *N = cs_qr(A, S); /* numeric QR factorization */
if (!N) error(_("cs_qr failed")) ;
cs_dropzeros(N->L); /* drop zeros from V and sort */
D = cs_transpose(N->L, 1); cs_spfree(N->L);
N->L = cs_transpose(D, 1); cs_spfree(D);
cs_dropzeros(N->U); /* drop zeros from R and sort */
D = cs_transpose(N->U, 1); cs_spfree(N->U) ;
N->U = cs_transpose(D, 1); cs_spfree(D);
m = N->L->m; /* m may be larger now */
// MM: m := S->m2 also counting the ficticious rows (Tim Davis, p.72, 74f)
p = cs_pinv(S->pinv, m); /* p = pinv' */
SET_SLOT(ans, install("V"),
Matrix_cs_to_SEXP(N->L, "dgCMatrix", 0));
Memcpy(REAL(ALLOC_SLOT(ans, install("beta"),
REALSXP, n)), N->B, n);
Memcpy(INTEGER(ALLOC_SLOT(ans, Matrix_pSym,
INTSXP, m)), p, m);
SET_SLOT(ans, install("R"),
Matrix_cs_to_SEXP(N->U, "dgCMatrix", 0));
if (ord)
Memcpy(INTEGER(ALLOC_SLOT(ans, install("q"),
INTSXP, n)), S->q, n);
else
ALLOC_SLOT(ans, install("q"), INTSXP, 0);
cs_nfree(N);
cs_sfree(S);
cs_free(p);
UNPROTECT(1);
return ans;
}
#ifdef Matrix_with_SPQR
/**
* Return a SuiteSparse QR factorization of the sparse matrix A
*
* @param Ap (pointer to) a [m x n] dgCMatrix
* @param ordering integer SEXP specifying the ordering strategy to be used
* see SPQR/Include/SuiteSparseQR_definitions.h
* @param econ integer SEXP ("economy"): number of rows of R and columns of Q
* to return. The default is m. Using n gives the standard economy form.
* A value less than the estimated rank r is set to r, so econ=0 gives the
* "rank-sized" factorization, where nrow(R)==nnz(diag(R))==r.
* @param tol double SEXP: if tol <= -2 use SPQR's default,
* if -2 < tol < 0, then no tol is used; otherwise,
* tol > 0, use as tolerance: columns with 2-norm <= tol treated as 0
*
*
* @return SEXP "SPQR" object with slots (Q, R, p, rank, Dim):
* Q: dgCMatrix; R: dgCMatrix [subject to change to dtCMatrix FIXME ?]
* p: integer: 0-based permutation (or length 0 <=> identity);
* rank: integer, the "revealed" rank Dim: integer, original matrix dim.
*/
SEXP dgCMatrix_SPQR(SEXP Ap, SEXP ordering, SEXP econ, SEXP tol)
{
/* SEXP ans = PROTECT(allocVector(VECSXP, 4)); */
SEXP ans = PROTECT(NEW_OBJECT(MAKE_CLASS("SPQR")));
CHM_SP A = AS_CHM_SP(Ap), Q, R;
UF_long *E, rank;/* not always = int FIXME (Windows_64 ?) */
if ((rank = SuiteSparseQR_C_QR(asInteger(ordering),
asReal(tol),/* originally had SPQR_DEFAULT_TOL */
(UF_long)asInteger(econ),/* originally had 0 */
A, &Q, &R, &E, &cl)) == -1)
error(_("SuiteSparseQR_C_QR returned an error code"));
SET_SLOT(ans, Matrix_DimSym, duplicate(GET_SLOT(Ap, Matrix_DimSym)));
/* SET_VECTOR_ELT(ans, 0, */
/* chm_sparse_to_SEXP(Q, 0, 0, 0, "", R_NilValue)); */
SET_SLOT(ans, install("Q"),
chm_sparse_to_SEXP(Q, 0, 0, 0, "", R_NilValue));
/* Also gives a dgCMatrix (not a dtC* *triangular*) :
* may make sense if to be used in the "spqr_solve" routines .. ?? */
/* SET_VECTOR_ELT(ans, 1, */
/* chm_sparse_to_SEXP(R, 0, 0, 0, "", R_NilValue)); */
SET_SLOT(ans, install("R"),
chm_sparse_to_SEXP(R, 0, 0, 0, "", R_NilValue));
cholmod_free_sparse(&Al, &cl);
cholmod_free_sparse(&R, &cl);
cholmod_free_sparse(&Q, &cl);
if (E) {
int *Er;
SET_VECTOR_ELT(ans, 2, allocVector(INTSXP, A->ncol));
Er = INTEGER(VECTOR_ELT(ans, 2));
for (int i = 0; i < A->ncol; i++) Er[i] = (int) E[i];
Free(E);
} else SET_VECTOR_ELT(ans, 2, allocVector(INTSXP, 0));
SET_VECTOR_ELT(ans, 3, ScalarInteger((int)rank));
UNPROTECT(1);
return ans;
}
#endif
/* Matrix_with_SPQR */
/* Modified version of Tim Davis's cs_lu_mex.c file for MATLAB */
void install_lu(SEXP Ap, int order, double tol, Rboolean err_sing)
{
// (order, tol) == (1, 1) by default, when called from R.
SEXP ans;
css *S;
csn *N;
int n, *p, *dims;
CSP A = AS_CSP__(Ap), D;
R_CheckStack();
n = A->n;
if (A->m != n)
error(_("LU decomposition applies only to square matrices"));
if (order) { /* not using natural order */
order = (tol == 1) ? 2 /* amd(S'*S) w/dense rows or I */
: 1; /* amd (A+A'), or natural */
}
S = cs_sqr(order, A, /*qr = */ 0); /* symbolic ordering */
N = cs_lu(A, S, tol); /* numeric factorization */
if (!N) {
if(err_sing)
error(_("cs_lu(A) failed: near-singular A (or out of memory)"));
else {
/* No warning: The useR should be careful :
* Put NA into "LU" factor */
set_factors(Ap, ScalarLogical(NA_LOGICAL), "LU");
return;
}
}
cs_dropzeros(N->L); /* drop zeros from L and sort it */
D = cs_transpose(N->L, 1);
cs_spfree(N->L);
N->L = cs_transpose(D, 1);
cs_spfree(D);
cs_dropzeros(N->U); /* drop zeros from U and sort it */
D = cs_transpose(N->U, 1);
cs_spfree(N->U);
N->U = cs_transpose(D, 1);
cs_spfree(D);
p = cs_pinv(N->pinv, n); /* p=pinv' */
ans = PROTECT(NEW_OBJECT(MAKE_CLASS("sparseLU")));
dims = INTEGER(ALLOC_SLOT(ans, Matrix_DimSym, INTSXP, 2));
dims[0] = n; dims[1] = n;
SET_SLOT(ans, install("L"),
Matrix_cs_to_SEXP(N->L, "dtCMatrix", 0));
SET_SLOT(ans, install("U"),
Matrix_cs_to_SEXP(N->U, "dtCMatrix", 0));
Memcpy(INTEGER(ALLOC_SLOT(ans, Matrix_pSym, /* "p" */
INTSXP, n)), p, n);
if (order)
Memcpy(INTEGER(ALLOC_SLOT(ans, install("q"),
INTSXP, n)), S->q, n);
cs_nfree(N);
cs_sfree(S);
cs_free(p);
UNPROTECT(1);
set_factors(Ap, ans, "LU");
}
SEXP dgCMatrix_LU(SEXP Ap, SEXP orderp, SEXP tolp, SEXP error_on_sing)
{
SEXP ans;
Rboolean err_sing = asLogical(error_on_sing);
/* FIXME: dgCMatrix_LU should check ans for consistency in
* permutation type with the requested value - Should have two
* classes or two different names in the factors list for LU with
* permuted columns or not.
* OTOH, currently (order, tol) === (1, 1) always.
* It is true that length(LU@q) does flag the order argument.
*/
if (!isNull(ans = get_factors(Ap, "LU")))
return ans;
install_lu(Ap, asInteger(orderp), asReal(tolp), err_sing);
return get_factors(Ap, "LU");
}
SEXP dgCMatrix_matrix_solve(SEXP Ap, SEXP b, SEXP give_sparse)
{
Rboolean sparse = asLogical(give_sparse);
if(sparse) {
// FIXME: implement this
error(_("dgCMatrix_matrix_solve(.., sparse=TRUE) not yet implemented"));
/* Idea: in the for(j = 0; j < nrhs ..) loop below, build the *sparse* result matrix
* ----- *column* wise -- which is perfect for dgCMatrix
* --> build (i,p,x) slots "increasingly" [well, allocate in batches ..]
*
* --> maybe first a protoype in R
*/
}
SEXP ans = PROTECT(dup_mMatrix_as_dgeMatrix(b)),
lu, qslot;
CSP L, U;
int *bdims = INTEGER(GET_SLOT(ans, Matrix_DimSym)), *p, *q;
int j, n = bdims[0], nrhs = bdims[1];
double *ax = REAL(GET_SLOT(ans, Matrix_xSym)),
*x = Alloca(n, double);
R_CheckStack();
if (isNull(lu = get_factors(Ap, "LU"))) {
install_lu(Ap, /* order = */ 1, /* tol = */ 1.0, /* err_sing = */ TRUE);
lu = get_factors(Ap, "LU");
}
qslot = GET_SLOT(lu, install("q"));
L = AS_CSP__(GET_SLOT(lu, install("L")));
U = AS_CSP__(GET_SLOT(lu, install("U")));
R_CheckStack();
p = INTEGER(GET_SLOT(lu, Matrix_pSym));
q = LENGTH(qslot) ? INTEGER(qslot) : (int *) NULL;
if (U->n != n || nrhs < 1 || n < 1)
error(_("Dimensions of system to be solved are inconsistent"));
for (j = 0; j < nrhs; j++) {
cs_pvec(p, ax + j * n, x, n); /* x = b(p) */
cs_lsolve(L, x); /* x = L\x */
cs_usolve(U, x); /* x = U\x */
if (q) /* r(q) = x , hence r = Q' U{^-1} L{^-1} P b = A^{-1} b */
cs_ipvec(q, x, ax + j * n, n);
else
Memcpy(ax + j * n, x, n);
}
UNPROTECT(1);
return ans;
}
SEXP dgCMatrix_cholsol(SEXP x, SEXP y)
{
/* Solve Sparse Least Squares X %*% beta ~= y with dense RHS y,
* where X = t(x) i.e. we pass x = t(X) as argument,
* via "Cholesky(X'X)" .. well not really:
* cholmod_factorize("x", ..) finds L in X'X = L'L directly */
CHM_SP cx = AS_CHM_SP(x);
/* FIXME: extend this to work in multivariate case, i.e. y a matrix with > 1 column ! */
CHM_DN cy = AS_CHM_DN(coerceVector(y, REALSXP)), rhs, cAns, resid;
CHM_FR L;
int n = cx->ncol;/* #{obs.} {x = t(X) !} */
double one[] = {1,0}, zero[] = {0,0}, neg1[] = {-1,0};
const char *nms[] = {"L", "coef", "Xty", "resid", ""};
SEXP ans = PROTECT(Rf_mkNamed(VECSXP, nms));
R_CheckStack();
if (n < cx->nrow || n <= 0)
error(_("dgCMatrix_cholsol requires a 'short, wide' rectangular matrix"));
if (cy->nrow != n)
error(_("Dimensions of system to be solved are inconsistent"));
rhs = cholmod_allocate_dense(cx->nrow, 1, cx->nrow, CHOLMOD_REAL, &c);
/* cholmod_sdmult(A, transp, alpha, beta, X, Y, &c):
* Y := alpha*(A*X) + beta*Y or alpha*(A'*X) + beta*Y ;
* here: rhs := 1 * x %*% y + 0 = x %*% y = X'y */
if (!(cholmod_sdmult(cx, 0 /* trans */, one, zero, cy, rhs, &c)))
error(_("cholmod_sdmult error (rhs)"));
L = cholmod_analyze(cx, &c);
if (!cholmod_factorize(cx, L, &c))
error(_("cholmod_factorize failed: status %d, minor %d from ncol %d"),
c.status, L->minor, L->n);
/* FIXME: Do this in stages so an "effects" vector can be calculated */
if (!(cAns = cholmod_solve(CHOLMOD_A, L, rhs, &c)))
error(_("cholmod_solve (CHOLMOD_A) failed: status %d, minor %d from ncol %d"),
c.status, L->minor, L->n);
/* L : */
SET_VECTOR_ELT(ans, 0, chm_factor_to_SEXP(L, 0));
/* coef : */
SET_VECTOR_ELT(ans, 1, allocVector(REALSXP, cx->nrow));
Memcpy(REAL(VECTOR_ELT(ans, 1)), (double*)(cAns->x), cx->nrow);
/* X'y : */
/* FIXME: Change this when the "effects" vector is available */
SET_VECTOR_ELT(ans, 2, allocVector(REALSXP, cx->nrow));
Memcpy(REAL(VECTOR_ELT(ans, 2)), (double*)(rhs->x), cx->nrow);
/* resid := y */
resid = cholmod_copy_dense(cy, &c);
/* cholmod_sdmult(A, transp, alp, bet, X, Y, &c):
* Y := alp*(A*X) + bet*Y or alp*(A'*X) + beta*Y ;
* here: resid := -1 * x' %*% coef + 1 * y = y - X %*% coef */
if (!(cholmod_sdmult(cx, 1/* trans */, neg1, one, cAns, resid, &c)))
error(_("cholmod_sdmult error (resid)"));
/* FIXME: for multivariate case, i.e. resid *matrix* with > 1 column ! */
SET_VECTOR_ELT(ans, 3, allocVector(REALSXP, n));
Memcpy(REAL(VECTOR_ELT(ans, 3)), (double*)(resid->x), n);
cholmod_free_factor(&L, &c);
cholmod_free_dense(&rhs, &c);
cholmod_free_dense(&cAns, &c);
UNPROTECT(1);
return ans;
}
/* Define all of
* dgCMatrix_colSums(....)
* igCMatrix_colSums(....)
* lgCMatrix_colSums_d(....)
* lgCMatrix_colSums_i(....)
* ngCMatrix_colSums_d(....)
* ngCMatrix_colSums_i(....)
*/
#define _dgC_
#include "t_gCMatrix_colSums.c"
#define _igC_
#include "t_gCMatrix_colSums.c"
#define _lgC_
#include "t_gCMatrix_colSums.c"
#define _ngC_
#include "t_gCMatrix_colSums.c"
#define _lgC_mn
#include "t_gCMatrix_colSums.c"
#define _ngC_mn
#include "t_gCMatrix_colSums.c"
SEXP lgCMatrix_colSums(SEXP x, SEXP NArm, SEXP spRes, SEXP trans, SEXP means)
{
if(asLogical(means)) /* ==> result will be "double" / "dsparseVector" */
return lgCMatrix_colSums_d(x, NArm, spRes, trans, means);
else
return lgCMatrix_colSums_i(x, NArm, spRes, trans, means);
}
SEXP ngCMatrix_colSums(SEXP x, SEXP NArm, SEXP spRes, SEXP trans, SEXP means)
{
if(asLogical(means)) /* ==> result will be "double" / "dsparseVector" */
return ngCMatrix_colSums_d(x, NArm, spRes, trans, means);
else
return ngCMatrix_colSums_i(x, NArm, spRes, trans, means);
}
