bigstatsr (version 1.6.1)

big_cprodMat: Cross-product with a matrix

Description

Cross-product between a Filebacked Big Matrix and a matrix.

Usage

big_cprodMat(
  X,
  A.row,
  ind.row = rows_along(X),
  ind.col = cols_along(X),
  ncores = 1,
  block.size = block_size(nrow(X), ncores),
  center = NULL,
  scale = NULL
)
# S4 method for FBM,matrix
crossprod(x, y)
# S4 method for FBM,matrix
tcrossprod(x, y)
# S4 method for matrix,FBM
crossprod(x, y)
# S4 method for matrix,FBM
tcrossprod(x, y)

Value

\(X^T \cdot A\).

Arguments

X

An object of class FBM.

A.row

A matrix with length(ind.row) rows.

ind.row

An optional vector of the row indices that are used. If not specified, all rows are used. Don't use negative indices.

ind.col

An optional vector of the column indices that are used. If not specified, all columns are used. Don't use negative indices.

ncores

Number of cores used. Default doesn't use parallelism. You may use nb_cores.

block.size

Maximum number of columns read at once. Default uses block_size.

center

Vector of same length of ind.col to subtract from columns of X.

scale

Vector of same length of ind.col to divide from columns of X.

x

A 'double' FBM or a matrix.

y

A 'double' FBM or a matrix.

Matrix parallelization

Large matrix computations are made block-wise and won't be parallelized in order to not have to reduce the size of these blocks. Instead, you can use the MKL or OpenBLAS in order to accelerate these block matrix computations. You can control the number of cores used by these optimized matrix libraries with bigparallelr::set_blas_ncores().

Examples

X <- big_attachExtdata()
n <- nrow(X)
m <- ncol(X)
A <- matrix(0, n, 10); A[] <- rnorm(length(A))

test <- big_cprodMat(X, A)
true <- crossprod(X[], A)
all.equal(test, true)

X2 <- big_copy(X, type = "double")
all.equal(crossprod(X2, A), true)

# subsetting
ind.row <- sample(n, n/2)
ind.col <- sample(m, m/2)

tryCatch(test2 <- big_cprodMat(X, A, ind.row, ind.col),
         error = function(e) print(e))
# returns an error. You need to use the subset of A:
test2 <- big_cprodMat(X, A[ind.row, ], ind.row, ind.col)
true2 <- crossprod(X[ind.row, ind.col], A[ind.row, ])
all.equal(test2, true2)