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## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ---- eval = FALSE------------------------------------------------------------
# library(devtools)
# install_github("zdebruine/RcppML")
## -----------------------------------------------------------------------------
library(RcppML)
library(Matrix)
## -----------------------------------------------------------------------------
# construct a system of equations
X <- matrix(rnorm(2000),100,20)
btrue <- runif(20)
y <- X %*% btrue + rnorm(100)
a <- crossprod(X)
b <- crossprod(X, y)
# solve the system of equations
x <- RcppML::nnls(a, b)
# use only coordinate descent
x <- RcppML::nnls(a, b, fast_nnls = FALSE, cd_maxit = 1000, cd_tol = 1e-8)
## -----------------------------------------------------------------------------
# simulate a sparse matrix
A <- rsparsematrix(1000, 100, 0.1)
# simulate a linear factor model
w <- matrix(runif(1000 * 10), 1000, 10)
# project the model
h <- RcppML::project(A, w)
## -----------------------------------------------------------------------------
A <- rsparsematrix(100, 100, 0.1)
model <- RcppML::nmf(A, 10, verbose = F)
w <- model$w
d <- model$d
h <- model$h
model_tolerance <- tail(model$tol, 1)
## -----------------------------------------------------------------------------
A_sym <- as(crossprod(A), "dgCMatrix")
model <- RcppML::nmf(A_sym, 10, verbose = F)
## -----------------------------------------------------------------------------
RcppML::mse(A_sym, model$w, model$d, model$h)
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