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6f1fc9e12f6e7e94b21490a9d2cd42dd
mooc-rr
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efe84f02
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efe84f02
authored
Oct 29, 2024
by
6f1fc9e12f6e7e94b21490a9d2cd42dd
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Update toy_document_fr.Rmd
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module2/exo1/toy_document_fr.Rmd
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efe84f02
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@@ -5,9 +5,7 @@ _25 juin 2018_
## En demandant à la lib maths
Mon ordinateur m’indique que π
vaut approximativement
Mon ordinateur m'indique que π vaut approximativement
pi
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@@ -15,13 +13,15 @@ pi
## En utilisant la méthode des aiguilles de Buffon ##
Mais calculé avec la méthode des
aiguilles 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 :
```
'set.seed(42)
N = 100000
x = runif(N)
theta = pi/2*runif(N)
2/(mean(x+sin(theta)>1))'
```
## [1] 3.14327
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@@ -32,12 +32,14 @@ et Y∼U(0,1) alors P[X2+Y2≤1]=π/4
(voir méthode de Monte Carlo sur Wikipedia). Le code suivant illustre ce fait:
```
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
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