Using iterators for sparse vectors and matrices
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Iterating over a sparse vector
Consider the following vector:
idx1 <- c(2L, 0L, 4L, 0L, 7L)
A sparse representation of this vector will tell that at entries 1,3,5 (or at entries 0,2,4 if we are 0-based) we will find the values 2,4,7.
Using Eigen via
RcppEigen we
can obtain the coercion with .sparseView()
.
We can iterate over all elements (including the zeros) in a sparse
vector as follows:
// [[Rcpp::depends(RcppEigen)]] #include <RcppEigen.h> typedef Eigen::SparseVector<double> SpVec; //[[Rcpp::export]] void vec_loop1 (Eigen::VectorXi idx){ SpVec sidx = idx.sparseView(); // create sparse vector Rcpp::Rcout << "Standard looping over a sparse vector" << std::endl; for (int i=0; i<sidx.size();++i){ Rcpp::Rcout << " i=" << i << " value=" << sidx.coeff( i ) << std::endl; } } vec_loop1( idx1 ) Standard looping over a sparse vector i=0 value=2 i=1 value=0 i=2 value=4 i=3 value=0 i=4 value=7
To iterate only over the non-zero elements we can do:
// [[Rcpp::depends(RcppEigen)]] #include <RcppEigen.h> typedef Eigen::SparseVector<double> SpVec; typedef SpVec::InnerIterator InIterVec; //[[Rcpp::export]] void vec_loop2 (Eigen::VectorXi idx){ SpVec sidx = idx.sparseView(); Rcpp::Rcout << "Looping over a sparse vector using iterators" << std::endl; for (InIterVec i_(sidx); i_; ++i_){ Rcpp::Rcout << " i=" << i_.index() << " value=" << i_.value() << std::endl; } } vec_loop2( idx1 ) Looping over a sparse vector using iterators i=0 value=2 i=2 value=4 i=4 value=7
Iterating over a sparse matrix
library(Matrix) Loading required package: methods M1<- new("dgCMatrix" , i = c(1L, 2L, 3L, 0L, 2L, 3L, 0L, 1L, 3L, 0L, 1L, 2L, 4L, 5L, 3L, 5L, 3L, 4L) , p = c(0L, 3L, 6L, 9L, 14L, 16L, 18L) , Dim = c(6L, 6L) , Dimnames = list(c("a", "b", "c", "d", "e", "f"), c("a", "b", "c", "d", "e", "f")) , x = c(1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3) , factors = list()); M1 6 x 6 sparse Matrix of class "dgCMatrix" a b c d e f a . 4 2 5 . . b 1 . 3 1 . . c 2 5 . 2 . . d 3 1 4 . 5 2 e . . . 3 . 3 f . . . 4 1 .
To iterate over all values in a column of this matrix we can do:
// [[Rcpp::depends(RcppEigen)]] #include <RcppEigen.h> using namespace Rcpp; typedef Eigen::MappedSparseMatrix<double> MSpMat; // [[Rcpp::export]] void mat_loop1 (MSpMat X, int j){ Rcout << "Standard looping over a sparse matrix" << std::endl; for (int i=0; i<X.rows(); ++i){ Rcout << " i,j=" << i << "," << j << " value=" << X.coeff(i,j) << std::endl; } } mat_loop1( M1, 1 ); Standard looping over a sparse matrix i,j=0,1 value=4 i,j=1,1 value=0 i,j=2,1 value=5 i,j=3,1 value=1 i,j=4,1 value=0 i,j=5,1 value=0
To iterate over only the non-zero elements in a column we can do:
// [[Rcpp::depends(RcppEigen)]] #include <RcppEigen.h> using namespace Rcpp; typedef Eigen::MappedSparseMatrix<double> MSpMat; typedef MSpMat::InnerIterator InIterMat; typedef Eigen::SparseVector<double> SpVec; typedef SpVec::InnerIterator InIterVec; // [[Rcpp::export]] void mat_loop2 (MSpMat X, int j){ Rcout << "Standard looping over a sparse matrix" << std::endl; for (InIterMat i_(X, j); i_; ++i_){ Rcout << " i,j=" << i_.index() << "," << j << " value=" << i_.value() << std::endl; } } mat_loop2( M1, 2 ); Standard looping over a sparse matrix i,j=0,2 value=2 i,j=1,2 value=3 i,j=3,2 value=4
Example from graph theory
A graph with nodes V={1,2,âŚn} can be reprsented by an adjacency matrix, say A, with the following semantics: A is n x n. The entry A(i,j) is non-zero if and only if there is an edge from node i to node j. If also A(j,i) is non-zero then the edge between i and j is undirected. Hence if A is symmetric then all edges are undirected, and the corresponding graph is undirected. In the following we focus on undirected graphs. If there is an edge between i and j we say that i and j are neighbours. A subset U of the nodes is complete if all pairs of nodes in U are neighbours. Here we shall implement a function which for a sparse matrix representation of and undirected graph will determine if a given set of nodes is complete.
M1 <- new("dgCMatrix" , i = c(1L, 2L, 3L, 0L, 2L, 3L, 0L, 1L, 3L, 0L, 1L, 2L, 4L, 5L, 3L, 5L, 3L, 4L) , p = c(0L, 3L, 6L, 9L, 14L, 16L, 18L) , Dim = c(6L, 6L) , Dimnames = list(c("a", "b", "c", "d", "e", "f"), c("a", "b", "c", "d", "e", "f")) , x = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) , factors = list() ); M1 6 x 6 sparse Matrix of class "dgCMatrix" a b c d e f a . 1 1 1 . . b 1 . 1 1 . . c 1 1 . 1 . . d 1 1 1 . 1 1 e . . . 1 . 1 f . . . 1 1 .
Define two subsets of nodes
idx1 <- c(2L, 3L) idx2 <- c(3L, 4L, 5L)
With an extensive use of sparse matrix and vector iterators we can solve the task as follows:
// [[Rcpp::depends(RcppEigen)]] #include <RcppEigen.h> using namespace Rcpp; typedef Eigen::MappedSparseMatrix<double> MSpMat; typedef MSpMat::InnerIterator InIterMat; typedef Eigen::SparseVector<double> SpVec; typedef SpVec::InnerIterator InIterVec; bool do_is_complete2 (const MSpMat X, SpVec sidx){ int n = X.cols(); if (X.rows() != n) throw std::invalid_argument("Sparse matrix X must be square"); for (InIterVec ii_(sidx); ii_; ++ii_){ int i0 = ii_.value() - 1; //Rcpp::Rcout << "i0 = " << i0 << std::endl; InIterMat it(X, i0); // iterator of the i0-column for (InIterVec kk_(sidx); kk_; ++kk_){ int k0 = kk_.value() - 1; //Rcpp::Rcout << " k0 = " << k0 << ", it.row ="; if (k0 == i0) continue; bool foundit = false; for (; it; ++it) { //Rcpp::Rcout << " " << it.row(); if (it.row() == k0) { foundit = true; ++it; break; } if (it.row() > k0) return false; } if (!foundit) return false; //Rcpp::Rcout << std::endl; } } return true; } //[[Rcpp::export]] bool is_complete2 (const MSpMat X, const Eigen::VectorXi idx){ SpVec sidx = idx.sparseView(); return do_is_complete2( X, sidx ); } c( is_complete2( M1, idx1 ), is_complete2( M1, idx2 ) ) [1] TRUE FALSE
For comparison we implement the same function using the .coeff() method for looking up values in the adjacency matrix directly:
// [[Rcpp::depends(RcppEigen)]] #include <RcppEigen.h> using namespace Rcpp; typedef Eigen::MappedSparseMatrix<double> MSpMat; typedef MSpMat::InnerIterator InIterMat; typedef Eigen::SparseVector<double> SpVec; typedef SpVec::InnerIterator InIterVec; bool do_is_complete0 (const MSpMat X, SpVec sidx ){ int n = X.cols(); if (X.rows() != n) throw std::invalid_argument("Sparse matrix X must be square"); int i2, j2; for (InIterVec i_(sidx); i_; ++i_){ i2 = i_.value() - 1; for (InIterVec j_(sidx); j_; ++j_){ j2 = j_.value() - 1 ; if (i2>j2) if (X.coeff( i2, j2)==0) return false; } } return true; } //[[Rcpp::export]] bool is_complete0 (const MSpMat X, const Eigen::VectorXi idx){ SpVec sidx = idx.sparseView(); return do_is_complete0( X, sidx ); } c( is_complete0( M1, idx1 ), is_complete0( M1, idx2 ) ) [1] TRUE FALSE
NOTICE: For large sets U (and hence for large graphs) the first implementation is considerably faster than the second.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.