updated report

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`)

Sequences/main.py

@@ -145,8 +145,8 @@ if __name__ == "__main__":
  
     jobs = [  
         #[j_testVenDerMany,[]],
-        #[j_testVenDerManyD,[]],
-        [j_MonteCarlo,[]] 
+        [j_testVenDerManyD,[]],
+        #[j_MonteCarlo,[]] 
     ]
 
 

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@@ -913,36 +913,22 @@ Overfull \hbox (17.48393pt too wide) in paragraph at lines 90--97
 []\TU/lmr/m/n/12 Ce rendu fait suite au troisième cours de Simulation Informatique et Gestion d’Incertitudes
  []
 
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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}