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

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

```{r setup}
library(rwig) |> suppressPackageStartupMessages()
```

Let's say we have some documents as character vectors,
and we want to discover the underlying topics.
This is called "topic modeling", and Latent Dirichlet Allocation (LDA)
is probably the most famous among all of topic models.
Here, we consider the Wasserstein Dicionary Learning (WDL) model.

```{r}
# a very simple example
sentences <- c("this is a sentence", "this is another one", "yet another sentence")
wdl_fit <- wdl(sentences, specs = wdl_specs(
  wdl_control = list(num_topics = 2),
  word2vec_control = list(min_count = 1)
))

wdl_fit
```

We can see from the topics that they are vectors of the tokens (words)
with associated probabilities.
If you want to access the topics,
you can do this:

```{r}
wdl_fit$topics
```

Alternatively, you can also obtain the weights of the topics
used to re-construct the input data:

```{r}
wdl_fit$weights
```

## See Also

See also `vignette("specs")`.

## References

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003).
Latent dirichlet allocation.
_Journal of Machine Learning Research_, 3(Jan), 993–1022.

Peyré, G., & Cuturi, M. (2019). Computational Optimal Transport:
With Applications to Data Science.
_Foundations and Trends® in Machine Learning_, 11(5–6), 355–607.
https://doi.org/10.1561/2200000073

Schmitz, M. A., Heitz, M., Bonneel, N., Ngolè, F., Coeurjolly, D.,
Cuturi, M., Peyré, G., & Starck, J.-L. (2018).
Wasserstein dictionary learning:
Optimal transport-based unsupervised nonlinear dictionary learning.
_SIAM Journal on Imaging Sciences_, 11(1), 643–678.
https://doi.org/10.1137/17M1140431

Xie, F. (2025). Deriving the Gradients of Some Popular Optimal
Transport Algorithms (No. arXiv:2504.08722). _arXiv_.
https://doi.org/10.48550/arXiv.2504.08722