Tony AntoniosBarotsis
📚
AntoniosBarotsis
/ script.js
Last active
November 26, 2025 16:04
Adds an `export` button to opened `race.watch` events for exporting an `ics` file
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| // ==UserScript== | |
| // @name Raceday.watch calendar event exporter | |
| // @namespace http://tampermonkey.net/ | |
| // @version 2025-11-11 | |
| // @description try to take over the world! | |
| // @author You | |
| // @match https://raceday.watch/ | |
| // @icon https://www.google.com/s2/favicons?sz=64&domain=raceday.watch | |
| // @grant none | |
| // ==/UserScript== |
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| tab_spaces = 2 | |
| edition ="2021" |
AntoniosBarotsis
/ rss2email_msrv.json
Last active
March 25, 2025 16:22
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| {"label":"MSRV","message":"1.78.0","schemaVersion":1,"color":"32ca55","labelColor":"353d46"} |
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| # add the below section to `.cargo/config.toml` | |
| [target.'cfg(all())'] | |
| rustflags = [ | |
| # BEGIN - Embark standard lints v6 for Rust 1.55+ | |
| # do not change or add/remove here, but one can add exceptions after this section | |
| # for more info see: <https://github.com/EmbarkStudios/rust-ecosystem/issues/59> | |
| "-Dunsafe_code", | |
| "-Wclippy::all", | |
| "-Wclippy::await_holding_lock", |
AntoniosBarotsis
/ findWifiPass.ps1
Created
October 10, 2021 18:15
Retrieve the password of a known network
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| netsh wlan show profile [NETWORK NAME] key=clear | findstr "Key Content" |
AntoniosBarotsis
/ kmeans_elbow.R
Created
August 3, 2020 15:48
Using the elbow method to find the optimal amount of clusters in a given data set
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| set.seed(1234) | |
| wcss <- vector() | |
| for(i in 1:10) { | |
| wcss[i] = sum(kmeans(X, i)$withinss) | |
| } | |
| plot(1:10, wcss, type = 'b', | |
| main = paste('Clusters of <data>'), | |
| xlab = 'Number of Clusters', |
AntoniosBarotsis
/ backward_elimination.R
Last active
July 30, 2020 15:59
Backward Elimination algorithm implementation in R for Multiple Linear Regression (MLR)
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| # Automatic backward elimination | |
| backwardElimination <- function(x, sl) { | |
| # Number of features | |
| numVars = length(x) | |
| for (i in c(1:numVars)){ | |
| # Train regressor on all (remaining) features | |
| regressor = lm(formula = Profit ~ ., data = x) | |
| # Query all the p-values and extract the largest one | |
| maxVar = max(coef(summary(regressor))[c(2:numVars), "Pr(>|t|)"]) | |
| # If that value is greater than the significance level (SL) then drop the feature |