Commit f6bc01a9 authored by Meiling WU's avatar Meiling WU

Update toy_document_orgmode_R_fr

parent 586c8d00
---
title: "À propos du calcul de pi"
auteur : EvaC
title: "On the computation of pi"
auteur : Meiling WU
output: html_notebook
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
#+HTML_HEAD: <link rel="stylesheet" type="text/css" href="http://www.pirilampo.org/styles/readtheorg/css/htmlize.css"/>
#+HTML_HEAD: <link rel="stylesheet" type="text/css" href="http://www.pirilampo.org/styles/readtheorg/css/readtheorg.css"/>
#+HTML_HEAD: <script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.3/jquery.min.js"></script>
#+HTML_HEAD: <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.4/js/bootstrap.min.js"></script>
#+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/lib/js/jquery.stickytableheaders.js"></script>
#+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/readtheorg/js/readtheorg.js"></script>
## En demandant à la lib maths
Mon ordinateur m'indique que $\pi$ vaut *approximativement*
#+PROPERTY: header-args :session :exports both
```{r cars}
pi
*3.141592653589793*
* Asking the maths library
My computer tells me that $\pi$ is /approximatively/
```
#+begin_src R :results output :session *R* :exports both
pi
#+end_src
## En utilisant la méthode des aiguilles de Buffon
Mais calculé avec le __méthode__ des [aiguilles de Buffon](https://fr.wikipedia.org/wiki/Aiguille_de_Buffon), on obtiendrait comme __approximation__ :
#+RESULTS:
: [1] 3.141593
```{r}
* Buffon's needle
Applying the method of [[https://en.wikipedia.org/wiki/Buffon%2527s_needle_problem][Buffon's needle]], we get the *approximation*
#+begin_src R :results output :session *R* :exports both
set.seed(42)
N = 100000
x = runif(N)
theta = pi/2*runif(N)
2/(mean(x+sin(theta)>1))
_3.12891113892_
```
#+end_src
#+RESULTS:
: [1] 3.14327
# Avec un argument “fréquentiel” de surface
Sinon, une méthode plus simple à comprendre et ne faisant pas intervenir d’appel à la fonction sinus se base sur le fait que si
$X \sim U(0,1)$ et
$Y \sim U(0,1)$ alors
$P[X^{2} + Y^{2} \leq 1]= \pi /4$
(voir [méthode de Monte Carlo sur Wikipedia](https://fr.wikipedia.org/wiki/M%C3%A9thode_de_Monte-Carlo#D%C3%A9termination_de_la_valeur_de_%CF%80). Le code suivant illustre ce fait:
* Using a surface fraction argument
A method that is easier to understand and does not make use of the $\sin$ function is based on the fact that if $X\sim U(0,1)$ and $Y\sim U(0,1)$, then $P[X^2+Y^2\leq 1] = \pi/4$ (see [[https://en.wikipedia.org/wiki/Monte_Carlo_method]["Monte Carlo method" on Wikipedia]]). The following code uses this approach:
```{r}
#+begin_src R :results output graphics :file figure_pi_mc1.png :exports both :width 600 :height 400 :session *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:
#+end_src
#+RESULTS:
[[file:figure_pi_mc1.png]]
```{r }
It is then straightforward to obtain a (not really good) approximation to $\pi$ by counting how many times, on average, $X^2 + Y^2$ is smaller than 1:
#+begin_src R :results output :session *R* :exports both
4*mean(df$Accept)
3.1120000000000001
```
#+end_src
#+RESULTS:
: [1] 3.156
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