Add t-umap images

vignettes/articles/tumap.Rmd

@@ -1,24 +1,64 @@
 ---
 title: "t-UMAP"
+resource_files:
+  - img/tumap/umap.png
+  - img/tumap/tumap.png
 ---
 
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+
 If you choose the UMAP curve parameters to be `a = 1` and `b = 1`, you get
 back the Cauchy distribution used in
 [t-Distributed Stochastic Neighbor Embedding](https://lvdmaaten.github.io/tsne/)
 and [LargeVis](https://arxiv.org/abs/1602.00370). This also happens to
-significantly simplify the gradient leading to a noticeable speed-up: for MNIST,
-I saw the optimization time drop from 66 seconds to 18 seconds. The trade off is
-that you will see larger, more spread-out clusters than with the typical UMAP
-settings (they're still more compact than you see in t-SNE, however). To try
-t-UMAP, use the `tumap` function:
-
-```R
-mnist_tumap <- tumap(mnist, n_neighbors = 15, verbose = TRUE)
+significantly simplify the gradient leading to a noticeable speed-up.
+
+For MNIST:
+
+```{r install and download, eval = FALSE}
+library(uwot)
+# install snedata package from github
+# pak::pkg_install("jlmelville/snedata")
+mnist <- snedata::download_mnist()
+```
+
+I saw the optimization time drop from 66 seconds with UMAP:
+
+```{r umap, eval = FALSE}
+mnist_umap <- umap(mnist, n_neighbors = 15)
+```
+
+
+```{r, echo = FALSE, out.width = "75%", fig.cap = "MNIST UMAP"}
+knitr::include_graphics("img/tumap/umap.png")
 ```
 
+to 18 seconds with t-UMAP:
+
+```{r tumap, eval = FALSE}
+mnist_tumap <- tumap(mnist, n_neighbors = 15)
+```
+
+```{r, echo = FALSE, out.width = "75%", fig.cap = "MNIST t-UMAP"}
+knitr::include_graphics("img/tumap/tumap.png")
+```
+
+You will still spend most of the time in the nearest neighbor search, so you
+will really see a difference in terms of total time with larger values of
+`n_epochs`. The trade off, as you can see, is that you will see larger, more
+spread-out clusters than with the typical UMAP settings (they're still more
+compact than you see in t-SNE, however). I think it's worth the trade-off.
+
 Note that using `umap(a = 1, b = 1)` doesn't use the simplified gradient, so
 you won't see any speed-up that way.
 
 Some examples comparing UMAP and t-UMAP are in the
 [examples](https://jlmelville.github.io/uwot/articles/umap-examples.html)
 article.
+
+