Torvaney

library(dplyr)
library(ggplot2)

length_army <- 60

army_front <- function(x) (length_army * x) + length_army
army_back <- function(x) (length_army * x)

velocity_king <- length_army + length_army * sqrt(2)

Torvaney

data {
    int<lower=0> N; // N games
    int<lower=0> P; // P teams

    // Each team is referred to by an integer that acts as an index for the ratings vector.
    int team1[N]; // Indicator arrays for team 1
    int team2[N]; // Indicator arrays for team 1
    int results[N]; // Results. 1 if home win, 2 if draw, 3 if away win.

    real<lower=0> nu_sigma;

Torvaney

library(tidyverse)
library(gganimate)
library(ggforce)
library(tweenr)

# Making up data
n_balls <- 20
d <- tibble(x = rnorm(n_balls),
            y = rnorm(n_balls),
            time = sample(100, n_balls),

Torvaney

"""
A python script to test strategies for the for the riddle described on TED-ed's
youtube channel: www.youtube.com/watch?v=dh4nEuhZBgg

Your interstellar police squad has tracked a group of criminals to a cluster of
seven planets. Now you must apprehend them before their reinforcements arrive.
Of course, the fugitives won’t just stay put – they’ll try to dodge you by
moving from planet to planet. Can you devise a sequence for searching the
planets that’s guaranteed to catch them in ten warps or less?
Edwin F. Meyer shows how.

Torvaney

library(tidyverse)
library(tidytext)

# Load data ----

# Slightly different to https://juliasilge.com/blog/word-vectors-take-two/
#  just because I have this data locally

austen_text <- janeaustenr::northangerabbey %>%
  as_tibble() %>%

Torvaney

library(tidyverse)

stirling_formula <- function(n) sqrt(2*pi*n)*(n / exp(1))^n

x <- 1:10

breaks_log10 <- 1:10 %>% 
  map_dbl(~ 10^.x) %>% 
  map(~ .x*(1:10)) %>% 
  unlist()

Torvaney

log <- function(f, ...) {
  function_name <- as.character(substitute(f))
  
  dots <- list(...)
  args <- paste0(dots, collapse = ",")
  
  function_result <- f(...)
  print(stringr::str_glue("{function_name}({args}) -> {function_result}"))
  
  function_result

Torvaney

library(tidyverse)
library(rgdal)
library(plotKML)

#Set input and output directories
os50_contours_dir <- here::here("~/Downloads/contours/")
output_dir <- here::here("~/Downloads/kml/")

#Load all shapefiles in input directory
shape_filenames <- list.files(os50_contours_dir, "*line.shp")

Torvaney

# CF: www.coursera.org/learn/ml-clustering-and-retrieval/lecture/aZCHX/optional-improving-efficiency-through-multiple-tables
tibble(x = seq(0, 0.20, 0.001)) %>% 
  crossing(h = 1:4) %>% 
  mutate(single = 1 - (1-x)^h -h*x*(1-x)^(h-1),
         multiple = (1 - (1-x)^h)^(h+1),
         n_lines = h + 1) %>% 
  gather(key, value, -x, -h, -n_lines) %>% 
  ggplot(aes(x = x, y = value, 
             colour = key, group = key)) +
  geom_path() +

Torvaney

tau <- function(hg, ag, home_rates, away_rates, rho) {
  case_when(
    (hg == 0) && (ag == 0) ~ 1 - home_rates * away_rates * rho,
    (hg == 0) && (ag == 1) ~ 1 + home_rates * rho,
    (hg == 1) && (ag == 0) ~ 1 + away_rates * rho,
    (hg == 1) && (ag == 1) ~ 1 - rho,
    TRUE ~ 1
  )
}