Basic usage"

In nmfbin: Non-Negative Matrix Factorization for Binary Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

The main function nmfbin() operates on binary matrices like so:

library(nmfbin)

# Create a binary matrix for demonstration
X <- matrix(sample(c(0, 1), 100, replace = TRUE), ncol = 10)

# Perform Logistic NMF
results <- nmfbin(X, k = 3, optimizer = "mur", init = "nndsvd", loss_fun = "logloss", max_iter = 500)

We can retrieve the final loss value before convergence criteria were reached:

print(results$convergence[length(results$convergence)])

We can also easily plot the optimization process at every iteration:

plot(results$convergence,
     xlab = "Iteration",
     ylab = "Negative log-likelihood loss")

nmfbin documentation built on Sept. 21, 2023, 5:07 p.m.