diff --git a/MonteCarlo/rapport/rendu.pdf b/MonteCarlo/rapport/rendu.pdf
new file mode 100644
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diff --git a/Normal/rapport/rendu.pdf b/Normal/rapport/rendu.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..d8beb9d1391f9b67874ded43f1144fd007577bee
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diff --git a/README.md b/README.md
index 7855347c378bf8da0cc32ce2bb9613138d464405..f5529a23d7769de22b6cef1f4325b4cd8519066e 100644
--- a/README.md
+++ b/README.md
@@ -2,3 +2,12 @@ Ce dossier contient les projets de SIGI (Simulations Informatiques et
Gestions des Incertitudes)
contact : enzo.decarvalhobittencourt@ensiie.eu
+
+dépendances (disponibles sur pip):
+ - numpy
+ - seaborn
+ - matplotlib
+
+Pour lancer les différents calculs :
+- Pour chaque projet, se placer dans le dossier et éxecuter le main.py
+(`python3 main.py`)
diff --git a/Sequences/main.py b/Sequences/main.py
index c61a0125dc790bff3a6a972229dd9f7801db951a..fb63bdb8e308da6f24f6bad287a40e18401a4571 100644
--- a/Sequences/main.py
+++ b/Sequences/main.py
@@ -145,8 +145,8 @@ if __name__ == "__main__":
jobs = [
#[j_testVenDerMany,[]],
- #[j_testVenDerManyD,[]],
- [j_MonteCarlo,[]]
+ [j_testVenDerManyD,[]],
+ #[j_MonteCarlo,[]]
]
diff --git a/Sequences/rapport/rendu.aux b/Sequences/rapport/rendu.aux
index 5c9a6a5aa0414603a1675ce596f68751b55cda09..5f97655908ddce07a9e28c14d30c553ab4fffad6 100644
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+++ b/Sequences/rapport/rendu.aux
@@ -17,16 +17,5 @@
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\abx@aux@read@bbl@mdfivesum{nobblfile}
-\gdef \@abspage@last{5}
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index 5032c7fd344e01cea97867c7dcb32b9240498913..2f2e23d578ea03de0fcede1b1c92bda7aab7f3e0 100644
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+++ b/Sequences/rapport/rendu.fls
@@ -1,4 +1,4 @@
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+PWD /home/jan-anso/ENSIIE/SIGI/Sequences/rapport
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diff --git a/Sequences/rapport/rendu.log b/Sequences/rapport/rendu.log
index 79968bda30cd86feb71680d5f0b707b4a8653c01..7ea7554650db9ab05648ab74c27004dd2878088f 100644
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[]\TU/lmr/m/n/12 Ce rendu fait suite au troisième cours de Simulation Informatique et Gestion d’Incertitudes
[]
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index 29050f7aabf7da2adf067be92bd2f78d179674d0..f16dc41d9ec69bef4b47737621a5de170de45999 100644
--- a/Sequences/rapport/rendu.tex
+++ b/Sequences/rapport/rendu.tex
@@ -80,7 +80,7 @@
\title{\center{
{\small Simulation Informatique et Gestion des Incertitudes\\}
- Méthode de Monte Carlo
+ Suite à discrépance faible
}}
\author{Enzo DE CARVALHO}
\date{}
@@ -92,71 +92,6 @@ d'Incertitudes sur la loi normale. Le but est ici de travailler sur la méthode
de Monte Carlo, mesurer son erreur et son intervalle de confiance.
Pour cela, nous utilisons ici \textit{Python}. Le code sur lequel ce rendu
s'appuie est mis à dispositions dans le dossier
-\texttt{MonteCarlo} du dêpot git sur
+\texttt{Sequence} du dêpot git sur
\hyperlink{https://git.iiens.net/de-carva2021/SIGI/}{\texttt{https://git.iiens.net/de-carva2021/SIGI/}}
-
-\section{Calcul d'intégrales}
-Le but est ici de calculer l'intégrale $J$ :
-$$ J = \int_{0.1}^{0.9} \frac{1}{2}e^{\arcsin(x)} \frac{x}{\sqrt{1-x^2}}dx $$
-\`A partir de différentes méthode numérique, et de comparer les résultats
-obtenus, et la convergence des méthodes.
-
-On comparera nos résultats avec une solution analytique, cacluler à l'aide de
-cette expression :
-{\center
-\texttt{ I = .25*(np.exp(np.arcsin(.9))*(.9 - np.sqrt(0.19))
-\\+ np.exp(np.arcsin(.1))*(np.sqrt(0.99) - 0.1))}\\
-}
-(\texttt{np} faisant ici référence au paquet
-\texttt{numpy} de python.)
-
-\subsection{Méthode de Quadrature}
-On opte pour une méthode des rectangles (intégrale de Riemann), puis pour une
-méthode des trapèzes. On trace ensuite l'évolution de l'erreur (différence)
-entre les intégrales obtenues de cette manière avec la solution I
-[\ref{fig:tra}], [\ref{fig:rec}].
-
-On note que ces méthodes converge de manière inverse.
-
-\begin{figure}
- \centering
- \includegraphics
- {figures/Error_evolution_of_jQuadrature_rect_by_number_of_points.png}
- \caption{\'Evolution de l'erreur par la méthode des rectangles}
- \label{fig:rec}
-\end{figure}
-
-\begin{figure}
- \centering
- \includegraphics
- {figures/Error_evolution_of_jQuadrature_trap_by_number_of_points.png}
- \caption{\'Evolution de l'erreur par la méthode des trapèzes}
- \label{fig:tra}
-\end{figure}
-
-
-\subsection{Méthode de MonteCarlo}
-Pour la méthode de MonteCarlo, on tire N valeurs aléatoires entre 0.1 et 0.9,
-sur lequels ensuite l'on calcule leurs images par la fonction à intégrer, et
-l'on multiplie la somme de ces résultats par $\frac{0.9 - 0.1}{N}$. L'on note
-que les résultats obtenus convergent beaucoup moins vite.
-\begin{figure}
- \centering
- \includegraphics
- {figures/Error_evolution_of_jMonteCarlo_by_number_of_points.png}
- \caption{\'Evolution de l'erreur par la méthode de Monte-Carlo}
- \label{fig:MC}
-\end{figure}
-
-Afin de mieux représenter l'évolution de l'erreur comise par la méthode de
-MonteCarlo, on réalise plusieurs tests, et on les affiches superposés
-(\ref{fig:lot}).
-
-\begin{figure}
- \centering
- \includegraphics{figures/Error evolution of jMonteCarlo_lot by number of points.png}
- \caption{Superpositions de 120 tests par la Méthode de MonteCarlo}
- \label{fig:lot}
-\end{figure}
-
\end{document}
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