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e0b8071fbf656643262744821e9c282c
mooc-rr
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701fb1c8
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701fb1c8
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Apr 01, 2020
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e0b8071fbf656643262744821e9c282c
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Update 4 toy_document_fr.Rmd
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module2/exo1/toy_document_fr.Rmd
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701fb1c8
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@@ -15,8 +15,19 @@ pi
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@@ -15,8 +15,19 @@ pi
```
```
## En utilisant la méthode des aiguilles de Buffon ```
## En utilisant la 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__ :
Mais calculé avec la __méthode__ des [aiguilles de Buffon](https://fr.wikipedia.org/wiki/Aiguille_de_Buffon), on obtiendrait comme __approximation__ :
```{r```{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 $\pi$ en comptant combien de fois, en moyenne, $X^2 + Y^2$ est inférieur à 1 :
```{r}
```{r}
set.seed(42) ```{r pressure, echo=FALSE}
4*mean(df$Accept)
```
set.seed(42)
N = 100000
N = 100000
x = runif(N)
x = runif(N)
theta = pi/2*runif(N)
theta = pi/2*runif(N)
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