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support for c(L1_w, L1_h) and c(L2_w, L2_h)
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@zdebruine
zdebruine committed Dec 18, 2023
1 parent a4aaba0 commit 1ac9ee2
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Showing 4 changed files with 194 additions and 28 deletions.
16 changes: 12 additions & 4 deletions R/RcppExports.R
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,10 @@ rowwise_compress_dense <- function(A, n = 10L, threads = 0L) {
.Call(`_singlet_rowwise_compress_dense`, A, n, threads)
}

calc_L1_matrix <- function(h, batch_id) {
.Call(`_singlet_calc_L1_matrix`, h, batch_id)
}

Rcpp_predict <- function(A, w, L1, L2, threads) {
.Call(`_singlet_Rcpp_predict`, A, w, L1, L2, threads)
}
Expand All @@ -21,8 +25,12 @@ c_project_model <- function(A, w, L1, L2, threads) {
.Call(`_singlet_c_project_model`, A, w, L1, L2, threads)
}

c_nmf <- function(A, At, tol, maxit, verbose, L1, L2, threads, w) {
.Call(`_singlet_c_nmf`, A, At, tol, maxit, verbose, L1, L2, threads, w)
c_nmf <- function(A, At, tol, maxit, verbose, L1_w, L1_h, L2_w, L2_h, threads, w) {
.Call(`_singlet_c_nmf`, A, At, tol, maxit, verbose, L1_w, L1_h, L2_w, L2_h, threads, w)
}

c_nmf_batch <- function(A, At, tol, maxit, verbose, L1, L2, threads, w, batch_id) {
.Call(`_singlet_c_nmf_batch`, A, At, tol, maxit, verbose, L1, L2, threads, w, batch_id)
}

c_nmf_sparse_list <- function(A_, At_, tol, maxit, verbose, L1, L2, threads, w) {
Expand Down Expand Up @@ -59,8 +67,8 @@ c_mu_nmf <- function(A, At, tol, maxit, verbose, L1, L2, threads, w) {
.Call(`_singlet_c_mu_nmf`, A, At, tol, maxit, verbose, L1, L2, threads, w)
}

c_nmf_dense <- function(A, At, tol, maxit, verbose, L1, L2, threads, w) {
.Call(`_singlet_c_nmf_dense`, A, At, tol, maxit, verbose, L1, L2, threads, w)
c_nmf_dense <- function(A, At, tol, maxit, verbose, L1_w, L1_h, L2_w, L2_h, threads, w) {
.Call(`_singlet_c_nmf_dense`, A, At, tol, maxit, verbose, L1_w, L1_h, L2_w, L2_h, threads, w)
}

c_linked_nmf <- function(A, At, tol, maxit, verbose, L1, L2, threads, w, link_h, link_w) {
Expand Down
14 changes: 9 additions & 5 deletions R/run_nmf.R
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Original file line number Diff line number Diff line change
Expand Up @@ -17,9 +17,6 @@
#'
run_nmf <- function(A, rank, tol = 1e-4, maxit = 100, verbose = TRUE, L1 = 0.01, L2 = 0, threads = 0, compression_level = 3) {
use_vcsc <- compression_level == 2
if (L1 >= 1) {
stop("L1 penalty must be strictly in the range (0, 1]")
}

if ("list" %in% class(A)) {
# check that number of rows is identical
Expand Down Expand Up @@ -47,12 +44,19 @@ run_nmf <- function(A, rank, tol = 1e-4, maxit = 100, verbose = TRUE, L1 = 0.01,
At <- t(A)
dense_mode <- TRUE
}

if(length(L1) != 2){
L1 <- c(L1[[1]], L1[[1]])
}
if(length(L2) != 2){
L2 <- c(L2[[1]], L2[[1]])
}

w_init <- matrix(stats::runif(nrow(A) * rank), rank, nrow(A))
if (dense_mode) {
model <- c_nmf_dense(A, At, tol, maxit, verbose, L1, L2, threads, w_init)
model <- c_nmf_dense(A, At, tol, maxit, verbose, L1[[1]], L1[[2]], L2[[1]], L2[[2]], threads, w_init)
} else {
model <- c_nmf(A, At, tol, maxit, verbose, L1, L2, threads, w_init)
model <- c_nmf(A, At, tol, maxit, verbose, L1[[1]], L1[[2]], L2[[1]], L2[[2]], threads, w_init)
}
rn <- rownames(A)
cn <- colnames(A)
Expand Down
58 changes: 48 additions & 10 deletions src/RcppExports.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -51,6 +51,18 @@ BEGIN_RCPP
return rcpp_result_gen;
END_RCPP
}
// calc_L1_matrix
Eigen::MatrixXd calc_L1_matrix(const Eigen::MatrixXd& h, Rcpp::IntegerVector batch_id);
RcppExport SEXP _singlet_calc_L1_matrix(SEXP hSEXP, SEXP batch_idSEXP) {
BEGIN_RCPP
Rcpp::RObject rcpp_result_gen;
Rcpp::RNGScope rcpp_rngScope_gen;
Rcpp::traits::input_parameter< const Eigen::MatrixXd& >::type h(hSEXP);
Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type batch_id(batch_idSEXP);
rcpp_result_gen = Rcpp::wrap(calc_L1_matrix(h, batch_id));
return rcpp_result_gen;
END_RCPP
}
// Rcpp_predict
Eigen::MatrixXd Rcpp_predict(Rcpp::SparseMatrix A, Eigen::MatrixXd w, const double L1, const double L2, const int threads);
RcppExport SEXP _singlet_Rcpp_predict(SEXP ASEXP, SEXP wSEXP, SEXP L1SEXP, SEXP L2SEXP, SEXP threadsSEXP) {
Expand Down Expand Up @@ -82,8 +94,29 @@ BEGIN_RCPP
END_RCPP
}
// c_nmf
Rcpp::List c_nmf(Rcpp::SparseMatrix& A, Rcpp::SparseMatrix& At, const double tol, const uint16_t maxit, const bool verbose, const double L1, const double L2, const uint16_t threads, Eigen::MatrixXd w);
RcppExport SEXP _singlet_c_nmf(SEXP ASEXP, SEXP AtSEXP, SEXP tolSEXP, SEXP maxitSEXP, SEXP verboseSEXP, SEXP L1SEXP, SEXP L2SEXP, SEXP threadsSEXP, SEXP wSEXP) {
Rcpp::List c_nmf(Rcpp::SparseMatrix& A, Rcpp::SparseMatrix& At, const double tol, const uint16_t maxit, const bool verbose, const double L1_w, const double L1_h, const double L2_w, const double L2_h, const uint16_t threads, Eigen::MatrixXd w);
RcppExport SEXP _singlet_c_nmf(SEXP ASEXP, SEXP AtSEXP, SEXP tolSEXP, SEXP maxitSEXP, SEXP verboseSEXP, SEXP L1_wSEXP, SEXP L1_hSEXP, SEXP L2_wSEXP, SEXP L2_hSEXP, SEXP threadsSEXP, SEXP wSEXP) {
BEGIN_RCPP
Rcpp::RObject rcpp_result_gen;
Rcpp::RNGScope rcpp_rngScope_gen;
Rcpp::traits::input_parameter< Rcpp::SparseMatrix& >::type A(ASEXP);
Rcpp::traits::input_parameter< Rcpp::SparseMatrix& >::type At(AtSEXP);
Rcpp::traits::input_parameter< const double >::type tol(tolSEXP);
Rcpp::traits::input_parameter< const uint16_t >::type maxit(maxitSEXP);
Rcpp::traits::input_parameter< const bool >::type verbose(verboseSEXP);
Rcpp::traits::input_parameter< const double >::type L1_w(L1_wSEXP);
Rcpp::traits::input_parameter< const double >::type L1_h(L1_hSEXP);
Rcpp::traits::input_parameter< const double >::type L2_w(L2_wSEXP);
Rcpp::traits::input_parameter< const double >::type L2_h(L2_hSEXP);
Rcpp::traits::input_parameter< const uint16_t >::type threads(threadsSEXP);
Rcpp::traits::input_parameter< Eigen::MatrixXd >::type w(wSEXP);
rcpp_result_gen = Rcpp::wrap(c_nmf(A, At, tol, maxit, verbose, L1_w, L1_h, L2_w, L2_h, threads, w));
return rcpp_result_gen;
END_RCPP
}
// c_nmf_batch
Rcpp::List c_nmf_batch(Rcpp::SparseMatrix& A, Rcpp::SparseMatrix& At, const double tol, const uint16_t maxit, const bool verbose, const double L1, const double L2, const uint16_t threads, Eigen::MatrixXd w, Rcpp::IntegerVector batch_id);
RcppExport SEXP _singlet_c_nmf_batch(SEXP ASEXP, SEXP AtSEXP, SEXP tolSEXP, SEXP maxitSEXP, SEXP verboseSEXP, SEXP L1SEXP, SEXP L2SEXP, SEXP threadsSEXP, SEXP wSEXP, SEXP batch_idSEXP) {
BEGIN_RCPP
Rcpp::RObject rcpp_result_gen;
Rcpp::RNGScope rcpp_rngScope_gen;
Expand All @@ -96,7 +129,8 @@ BEGIN_RCPP
Rcpp::traits::input_parameter< const double >::type L2(L2SEXP);
Rcpp::traits::input_parameter< const uint16_t >::type threads(threadsSEXP);
Rcpp::traits::input_parameter< Eigen::MatrixXd >::type w(wSEXP);
rcpp_result_gen = Rcpp::wrap(c_nmf(A, At, tol, maxit, verbose, L1, L2, threads, w));
Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type batch_id(batch_idSEXP);
rcpp_result_gen = Rcpp::wrap(c_nmf_batch(A, At, tol, maxit, verbose, L1, L2, threads, w, batch_id));
return rcpp_result_gen;
END_RCPP
}
Expand Down Expand Up @@ -204,8 +238,8 @@ BEGIN_RCPP
END_RCPP
}
// c_nmf_dense
Rcpp::List c_nmf_dense(Eigen::MatrixXd& A, Eigen::MatrixXd& At, const double tol, const uint16_t maxit, const bool verbose, const double L1, const double L2, const uint16_t threads, Eigen::MatrixXd w);
RcppExport SEXP _singlet_c_nmf_dense(SEXP ASEXP, SEXP AtSEXP, SEXP tolSEXP, SEXP maxitSEXP, SEXP verboseSEXP, SEXP L1SEXP, SEXP L2SEXP, SEXP threadsSEXP, SEXP wSEXP) {
Rcpp::List c_nmf_dense(Eigen::MatrixXd& A, Eigen::MatrixXd& At, const double tol, const uint16_t maxit, const bool verbose, const double L1_w, const double L1_h, const double L2_w, const double L2_h, const uint16_t threads, Eigen::MatrixXd w);
RcppExport SEXP _singlet_c_nmf_dense(SEXP ASEXP, SEXP AtSEXP, SEXP tolSEXP, SEXP maxitSEXP, SEXP verboseSEXP, SEXP L1_wSEXP, SEXP L1_hSEXP, SEXP L2_wSEXP, SEXP L2_hSEXP, SEXP threadsSEXP, SEXP wSEXP) {
BEGIN_RCPP
Rcpp::RObject rcpp_result_gen;
Rcpp::RNGScope rcpp_rngScope_gen;
Expand All @@ -214,11 +248,13 @@ BEGIN_RCPP
Rcpp::traits::input_parameter< const double >::type tol(tolSEXP);
Rcpp::traits::input_parameter< const uint16_t >::type maxit(maxitSEXP);
Rcpp::traits::input_parameter< const bool >::type verbose(verboseSEXP);
Rcpp::traits::input_parameter< const double >::type L1(L1SEXP);
Rcpp::traits::input_parameter< const double >::type L2(L2SEXP);
Rcpp::traits::input_parameter< const double >::type L1_w(L1_wSEXP);
Rcpp::traits::input_parameter< const double >::type L1_h(L1_hSEXP);
Rcpp::traits::input_parameter< const double >::type L2_w(L2_wSEXP);
Rcpp::traits::input_parameter< const double >::type L2_h(L2_hSEXP);
Rcpp::traits::input_parameter< const uint16_t >::type threads(threadsSEXP);
Rcpp::traits::input_parameter< Eigen::MatrixXd >::type w(wSEXP);
rcpp_result_gen = Rcpp::wrap(c_nmf_dense(A, At, tol, maxit, verbose, L1, L2, threads, w));
rcpp_result_gen = Rcpp::wrap(c_nmf_dense(A, At, tol, maxit, verbose, L1_w, L1_h, L2_w, L2_h, threads, w));
return rcpp_result_gen;
END_RCPP
}
Expand Down Expand Up @@ -409,17 +445,19 @@ static const R_CallMethodDef CallEntries[] = {
{"_singlet_weight_by_split", (DL_FUNC) &_singlet_weight_by_split, 3},
{"_singlet_rowwise_compress_sparse", (DL_FUNC) &_singlet_rowwise_compress_sparse, 3},
{"_singlet_rowwise_compress_dense", (DL_FUNC) &_singlet_rowwise_compress_dense, 3},
{"_singlet_calc_L1_matrix", (DL_FUNC) &_singlet_calc_L1_matrix, 2},
{"_singlet_Rcpp_predict", (DL_FUNC) &_singlet_Rcpp_predict, 5},
{"_singlet_c_project_model", (DL_FUNC) &_singlet_c_project_model, 5},
{"_singlet_c_nmf", (DL_FUNC) &_singlet_c_nmf, 9},
{"_singlet_c_nmf", (DL_FUNC) &_singlet_c_nmf, 11},
{"_singlet_c_nmf_batch", (DL_FUNC) &_singlet_c_nmf_batch, 10},
{"_singlet_c_nmf_sparse_list", (DL_FUNC) &_singlet_c_nmf_sparse_list, 9},
{"_singlet_write_IVCSC", (DL_FUNC) &_singlet_write_IVCSC, 2},
{"_singlet_save_IVSparse", (DL_FUNC) &_singlet_save_IVSparse, 2},
{"_singlet_build_IVCSC2", (DL_FUNC) &_singlet_build_IVCSC2, 2},
{"_singlet_read_IVSparse", (DL_FUNC) &_singlet_read_IVSparse, 0},
{"_singlet_run_nmf_on_sparsematrix_list", (DL_FUNC) &_singlet_run_nmf_on_sparsematrix_list, 9},
{"_singlet_c_mu_nmf", (DL_FUNC) &_singlet_c_mu_nmf, 9},
{"_singlet_c_nmf_dense", (DL_FUNC) &_singlet_c_nmf_dense, 9},
{"_singlet_c_nmf_dense", (DL_FUNC) &_singlet_c_nmf_dense, 11},
{"_singlet_c_linked_nmf", (DL_FUNC) &_singlet_c_linked_nmf, 11},
{"_singlet_c_ard_nmf", (DL_FUNC) &_singlet_c_ard_nmf, 13},
{"_singlet_c_ard_nmf_sparse_list", (DL_FUNC) &_singlet_c_ard_nmf_sparse_list, 13},
Expand Down
134 changes: 125 additions & 9 deletions src/singlet.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -226,14 +226,14 @@ void scale(Eigen::MatrixXd& w, Eigen::VectorXd& d) {

// NNLS SOLVER OF THE FORM ax=b ---------------------------------------------------------------------------------
// optimized and modified from github.com/linxihui/NNLM "c_nnls" function
inline void nnls(Eigen::MatrixXd& a, Eigen::VectorXd& b, Eigen::MatrixXd& x, const size_t col, const double L1 = 0, const double L2 = 0) {
inline void nnls(Eigen::MatrixXd& a, Eigen::VectorXd& b, Eigen::MatrixXd& x, const size_t col,const double L1 = 0, const double L2 = 0) {
double tol = 1;
for (uint8_t it = 0; it < 100 && (tol / b.size()) > 1e-8; ++it) {
tol = 0;
for (size_t i = 0; i < x.rows(); ++i) {
double diff = b(i) / a(i, i);
if (L1 != 0) diff -= L1;
if (L2 != 0) diff -= L2 * x(i, col);
if (L2 != 0) diff += L2 * x(i, col);
if (-diff > x(i, col)) {
if (x(i, col) != 0) {
b -= a.col(i) * -x(i, col);
Expand All @@ -249,6 +249,84 @@ inline void nnls(Eigen::MatrixXd& a, Eigen::VectorXd& b, Eigen::MatrixXd& x, con
}
}

// NNLS SOLVER OF THE FORM ax=b ---------------------------------------------------------------------------------
// optimized and modified from github.com/linxihui/NNLM "c_nnls" function
inline void nnls_L1_matrix(Eigen::MatrixXd& a, Eigen::VectorXd& b, Eigen::MatrixXd& x, const size_t col, const Eigen::MatrixXd& L1_matrix, const double L1 = 0, const double L2 = 0) {
double tol = 1;
for (uint8_t it = 0; it < 100 && (tol / b.size()) > 1e-8; ++it) {
tol = 0;
for (size_t i = 0; i < x.rows(); ++i) {
double diff = b(i) / a(i, i);
diff -= L1_matrix(i, col);
if (L1 != 0) diff -= L1;
if (L2 != 0) diff += L2 * x(i, col);
if (-diff > x(i, col)) {
if (x(i, col) != 0) {
b -= a.col(i) * -x(i, col);
tol = 1;
x(i, col) = 0;
}
} else if (diff != 0) {
x(i, col) += diff;
b -= a.col(i) * diff;
tol += std::abs(diff / (x(i, col) + 1e-15));
}
}
}
}

// batch_id is a vector with values between 1 and n_batch_ids, the result of
// as.numeric(as.factor(metadata_item_vector))
//[[Rcpp::export]]
Eigen::MatrixXd calc_L1_matrix(const Eigen::MatrixXd& h, Rcpp::IntegerVector batch_id) {
const size_t n_batch_ids = Rcpp::max(batch_id);
if (batch_id.size() != h.cols()) Rcpp::stop("batch_id vector must be of the same length as the number of columns in the NMF 'h' matrix");
Eigen::MatrixXd L1_matrix(h.rows(), n_batch_ids);

// calculate the mean factor loading for each batch
for (size_t i = 1; i < n_batch_ids; ++i) {
size_t n_samples_in_batch = 0;
for (size_t j = 0; j < batch_id.size(); ++j) {
if (batch_id[j] == i) {
L1_matrix.col(i).array() += h.col(j).array();
++n_samples_in_batch;
}
}
L1_matrix.col(i).array() /= n_samples_in_batch;
}

// calculate the difference in the mean factor loading for each batch vs. all other batches
for (size_t i = 0; i < L1_matrix.cols(); ++i) {
Eigen::VectorXd mean_other_batches = Eigen::VectorXd::Zero(L1_matrix.rows());
for (size_t j = 0; j < L1_matrix.cols(); ++j) {
if (j != i) {
mean_other_batches.array() += L1_matrix.col(j).array();
}
}
mean_other_batches /= (L1_matrix.cols() - 1);
L1_matrix.col(i) -= mean_other_batches;
}

return L1_matrix;
}

// update h given A and w, balanced for batch identity
inline void predict_L1_matrix(Rcpp::SparseMatrix A, const Eigen::MatrixXd& w, Eigen::MatrixXd& h, const double L1, const double L2, const int threads, Rcpp::IntegerVector batch_id) {
Eigen::MatrixXd a = AAt(w);
// calculate difference between mean loading of in-batch factor vs. out-of-batch factors
Eigen::MatrixXd L1_matrix = calc_L1_matrix(h, batch_id);
#ifdef _OPENMP
#pragma omp parallel for num_threads(threads)
#endif
for (size_t i = 0; i < h.cols(); ++i) {
if (A.p[i] == A.p[i + 1]) continue;
Eigen::VectorXd b = Eigen::VectorXd::Zero(h.rows());
for (Rcpp::SparseMatrix::InnerIterator it(A, i); it; ++it)
b += it.value() * w.col(it.row());
nnls_L1_matrix(a, b, h, i, L1_matrix, L1, L2);
}
}

// NMF PROJECTION FUNCTIONS ------------------------------------------------------------------------------

// update h given A and w
Expand Down Expand Up @@ -558,7 +636,7 @@ inline double mse_test(const Eigen::MatrixXd& A, const Eigen::MatrixXd& w, Eigen
// NMF FUNCTIONS ---------------------------------------------------------------------------------------
// no linking or masking
template <class Matrix>
Rcpp::List c_nmf_base(Matrix& A, Matrix& At, const double tol, const uint16_t maxit, const bool verbose, const double L1, const double L2, const uint16_t threads, Eigen::MatrixXd w) {
Rcpp::List c_nmf_base(Matrix& A, Matrix& At, const double tol, const uint16_t maxit, const bool verbose, const double L1_w, const double L1_h, const double L2_w, const double L2_h, const uint16_t threads, Eigen::MatrixXd w) {
Eigen::MatrixXd h = Eigen::MatrixXd::Zero(w.rows(), A.cols());
Eigen::VectorXd d = Eigen::VectorXd::Ones(w.rows());
double tol_ = 1;
Expand All @@ -569,11 +647,11 @@ Rcpp::List c_nmf_base(Matrix& A, Matrix& At, const double tol, const uint16_t ma
for (uint16_t iter_ = 0; iter_ < maxit && tol_ > tol; ++iter_) {
Eigen::MatrixXd w_it = w;
// update h
predict(A, w, h, L1, L2, threads);
predict(A, w, h, L1_h, L2_h, threads);
scale(h, d);
Rcpp::checkUserInterrupt();
// update w
predict(At, h, w, L1, L2, threads);
predict(At, h, w, L1_w, L2_w, threads);
scale(w, d);

// calculate tolerance of the model fit to detect convergence
Expand All @@ -589,8 +667,46 @@ Rcpp::List c_nmf_base(Matrix& A, Matrix& At, const double tol, const uint16_t ma

//[[Rcpp::export]]
Rcpp::List c_nmf(Rcpp::SparseMatrix& A, Rcpp::SparseMatrix& At, const double tol, const uint16_t maxit, const bool verbose,
const double L1, const double L2, const uint16_t threads, Eigen::MatrixXd w) {
return c_nmf_base(A, At, tol, maxit, verbose, L1, L2, threads, w);
const double L1_w, const double L1_h, const double L2_w, const double L2_h, const uint16_t threads, Eigen::MatrixXd w) {
return c_nmf_base(A, At, tol, maxit, verbose, L1_w, L1_h, L2_w, L2_h, threads, w);
}

// L1 MATRIX-BASED BATCH CORRECTION (EXPERIMENTAL)

template <class Matrix>
Rcpp::List c_nmf_base_batch(Matrix& A, Matrix& At, const double tol, const uint16_t maxit, const bool verbose, const double L1, const double L2, const uint16_t threads, Eigen::MatrixXd w, Rcpp::IntegerVector batch_id) {
Eigen::MatrixXd h = Eigen::MatrixXd::Zero(w.rows(), A.cols());
Eigen::VectorXd d = Eigen::VectorXd::Ones(w.rows());
double tol_ = 1;
if (verbose)
Rprintf("\n%4s | %8s \n---------------\n", "iter", "tol");

// alternating least squares update loop
for (uint16_t iter_ = 0; iter_ < maxit && tol_ > tol; ++iter_) {
Eigen::MatrixXd w_it = w;
// update h
predict_L1_matrix(A, w, h, L1, L2, threads, batch_id);
scale(h, d);
Rcpp::checkUserInterrupt();
// update w
predict(At, h, w, L1, L2, threads);
scale(w, d);

// calculate tolerance of the model fit to detect convergence
// correlation between "w" across consecutive iterations
tol_ = cor(w, w_it);

if (verbose)
Rprintf("%4d | %8.2e\n", iter_ + 1, tol_);
Rcpp::checkUserInterrupt();
}
return Rcpp::List::create(Rcpp::Named("w") = w, Rcpp::Named("d") = d, Rcpp::Named("h") = h);
}

//[[Rcpp::export]]
Rcpp::List c_nmf_batch(Rcpp::SparseMatrix& A, Rcpp::SparseMatrix& At, const double tol, const uint16_t maxit, const bool verbose,
const double L1, const double L2, const uint16_t threads, Eigen::MatrixXd w, Rcpp::IntegerVector batch_id) {
return c_nmf_base_batch(A, At, tol, maxit, verbose, L1, L2, threads, w, batch_id);
}

// NMF FUNCTIONS ---------------------------------------------------------------------------------------
Expand Down Expand Up @@ -933,8 +1049,8 @@ Rcpp::List c_mu_nmf(Rcpp::SparseMatrix& A, Rcpp::SparseMatrix& At, const double
}

//[[Rcpp::export]]
Rcpp::List c_nmf_dense(Eigen::MatrixXd& A, Eigen::MatrixXd& At, const double tol, const uint16_t maxit, const bool verbose, const double L1, const double L2, const uint16_t threads, Eigen::MatrixXd w) {
return c_nmf_base(A, At, tol, maxit, verbose, L1, L2, threads, w);
Rcpp::List c_nmf_dense(Eigen::MatrixXd& A, Eigen::MatrixXd& At, const double tol, const uint16_t maxit, const bool verbose, const double L1_w, const double L1_h, const double L2_w, const double L2_h, const uint16_t threads, Eigen::MatrixXd w) {
return c_nmf_base(A, At, tol, maxit, verbose, L1_w, L1_h, L2_w, L2_h, threads, w);
}

// NMF FUNCTION ---------------------------------------------------------------------------------------
Expand Down

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