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00687499e7b2d1fa9ae9497579bfab98
mooc-rr
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006ba43a
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006ba43a
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Nov 03, 2023
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00687499e7b2d1fa9ae9497579bfab98
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module2/exo1/toy_document_fr.Rmd
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@@ -12,8 +12,30 @@ knitr::opts_chunk$set(echo = TRUE)
## Calcul de Pi
```{r Pi}
pi
```
## Méthode des aiguilles de Buffon
Mais calculé avec la __méthode__ des [aiguilles de Buffon](https://fr.wikipedia.org/wiki/Aiguille_de_Buffon), on obtiendrait comme approximation :
```{r}
set.seed(42)
N = 100000
x = runif(N)
theta = pi/2*runif(N)
2/(mean(x+sin(theta)>1))
```
## Avec un argument “fréquentiel” de surface
```{r}
set.seed(42)
N = 1000
df = data.frame(X = runif(N), Y = runif(N))
df$Accept = (df$X**2 + df$Y**2 <=1)
library(ggplot2)
ggplot(df, aes(x=X,y=Y,color=Accept)) + geom_point(alpha=.2) + coord_fixed() + theme_bw()
```
# Il est alors aisé d’obtenir une approximation (pas terrible) de π en comptant combien de fois, en moyenne, X2+Y2 est inférieur à 1:
```{r}
4*mean(df$Accept)
```
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