Singular Value decomposition for LazyMatrix.

Performs lazy SVD using irlba for partial Singular value decomposition on sparse matrices.

Usage

# S4 method for class 'LazyMatrix'
svd(x, nu = min(n, p), nv = min(n, p))

Arguments

x

A LazyMatrix object.

nu

number of left singular vectors to estimate (defaults to nv).

nv

number of right singular vectors to estimate.

Value

A list with entries:

d

max(nu, nv) approximate singular values

u

nu approximate left singular vectors (only when right_only=FALSE)

v

nv approximate right singular vectors

iter

The number of Lanczos iterations carried out

mprod

The total number of matrix vector products carried out

Examples

set.seed(123)
mat_a <- matrix(rnorm(500), nrow = 50, ncol = 10)
lazy_a <-LazyMatrix(mat_a, scale = "sd", location = "mean")
S <- svd(lazy_a)
#> Warning: You're computing too large a percentage of total singular values, use a standard svd instead.
# Receive singular values with
S$d
#> [1] 9.132746 8.248143 7.827175 7.606301 7.062690