---
title: "Basic usage"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Basic usage}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

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

```{r setup}
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:

```{r finalloss}
print(results$convergence[length(results$convergence)])
```

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

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

.
