User contributions for Brb
25 December 2025
- 09:0709:07, 25 December 2025 diff hist +110 Recipes →地瓜 Sweet potato
- 09:0409:04, 25 December 2025 diff hist +141 Health →眩暈 vertigo, inner ear problem
- 09:0309:03, 25 December 2025 diff hist +133 Raspberry →Bootloader current
- 09:0009:00, 25 December 2025 diff hist +305 Online tools →Gmail tricks
- 08:5808:58, 25 December 2025 diff hist −10 Mac →Check the privacy policies
- 08:5708:57, 25 December 2025 diff hist +134 Mac →Check the privacy policies
- 08:5508:55, 25 December 2025 diff hist +183 Browser →Librewolf
- 08:5308:53, 25 December 2025 diff hist +136 Hardware →HDMI & CEC
- 08:5208:52, 25 December 2025 diff hist +209 Rust →Install current
- 08:4608:46, 25 December 2025 diff hist +194 Docker Applications →Website analysis
- 08:3008:30, 25 December 2025 diff hist +150 Health →Vitamin B12 (Cobalamin/Cyanocobalamin)
- 08:1108:11, 25 December 2025 diff hist +150 Mac →Apple Server
23 December 2025
- 20:5820:58, 23 December 2025 diff hist +31 RetroPie →Key mapper current
- 20:4520:45, 23 December 2025 diff hist +319 RetroPie →Wii remote
- 12:5012:50, 23 December 2025 diff hist +328 Life →保險 Insurance
22 December 2025
- 20:4120:41, 22 December 2025 diff hist +69 Android →File manager/explorer
- 20:3820:38, 22 December 2025 diff hist +328 Android →Solid explorer
- 14:1814:18, 22 December 2025 diff hist +202 Statistics →UMAP current
- 10:1310:13, 22 December 2025 diff hist +468 Heatmap →ComplexHeatmap current
- 10:0810:08, 22 December 2025 diff hist +1,432 N File:Annotation legend param.png <syntaxhighlight lang='r'> library(RColorBrewer) library(ComplexHeatmap) set.seed(123) n <- 100 df <- data.frame( Subtype = sample(c("Hyperdiploid", "Ph-like", "DUX4", "Ph"), n, replace = TRUE), Sex = sample(c("Male", "Female"), n, replace = TRUE), Age_Group = sample(c("Childhood", "Adult"), n, replace = TRUE) ) # 1. Define distinct palettes # Subtype: Using "Set3" or "Paired" for many categories subtype_cols <- setNames( colorRampPalette(brewer.pal(12, "Paired"))(length(unique(df$S... current
- 09:3809:38, 22 December 2025 diff hist +179 Car →EV car current
- 09:3609:36, 22 December 2025 diff hist +174 Docker Applications →Bitwarden
- 09:3409:34, 22 December 2025 diff hist +193 Android →Weather
- 09:3209:32, 22 December 2025 diff hist +83 NAS →OpenCloud
- 09:3009:30, 22 December 2025 diff hist +298 Online tools →Sound
- 09:2709:27, 22 December 2025 diff hist +201 NAS →NextCloud
21 December 2025
- 13:4113:41, 21 December 2025 diff hist +221 NAS →Sharing
- 13:3813:38, 21 December 2025 diff hist +1,081 NAS →Sharing
- 12:1912:19, 21 December 2025 diff hist +319 NAS →Sharing
- 12:0312:03, 21 December 2025 diff hist +705 NAS →Upgrade
- 12:0212:02, 21 December 2025 diff hist +140 NAS →Where are shared folders
- 10:1610:16, 21 December 2025 diff hist +903 NAS →Update
20 December 2025
- 19:5119:51, 20 December 2025 diff hist +141 Tidymodels →tidyAML current
- 19:4919:49, 20 December 2025 diff hist +140 Self hosting →Turnkey distros/images/appliance
- 19:4819:48, 20 December 2025 diff hist +168 Ubuntu →Gparted current
- 19:4119:41, 20 December 2025 diff hist +168 Router →USB port current
- 19:3719:37, 20 December 2025 diff hist +321 Android →Media player
- 19:3519:35, 20 December 2025 diff hist +251 Android →Video Editor
- 19:3119:31, 20 December 2025 diff hist +184 Online tools →Podcast
- 19:2919:29, 20 December 2025 diff hist +269 Statistics →Box(Box, whisker & outlier)
- 19:2519:25, 20 December 2025 diff hist +176 Tidyverse →Examples current
- 19:2219:22, 20 December 2025 diff hist +325 Statistics →UMAP
- 19:1419:14, 20 December 2025 diff hist +377 PCA →Biplot current
- 13:1813:18, 20 December 2025 diff hist +344 NAS →TrueNAS
19 December 2025
- 15:3415:34, 19 December 2025 diff hist +62 Statistics →UMAP
- 15:3215:32, 19 December 2025 diff hist +233 Statistics →UMAP
- 14:1514:15, 19 December 2025 diff hist +569 Statistics →UMAP
- 13:3713:37, 19 December 2025 diff hist +119 Statistics →UMAP
- 13:3313:33, 19 December 2025 diff hist +1,471 N File:Uwot.png <syntaxhighlight lang='r'> # Prepare data library(uwot) library(ggplot2) library(dplyr) # training and test sets (as given) iris_train <- iris[c(1:10, 51:60), ] iris_test <- iris[100:110, ] # numeric feature matrices x_train <- as.matrix(iris_train[, 1:4]) x_test <- as.matrix(iris_test[, 1:4]) # Fit UMAP on training data (keep model) set.seed(123) umap_model <- umap( x_train, n_neighbors = 5, min_dist = 0.1, n_components = 2, ret_model = TRUE ) # training embedding train_embe... current
- 13:0613:06, 19 December 2025 diff hist +37 Statistics →UMAP
