Add stuff on report

main.tex

@@ -31,23 +31,72 @@
 
 \section{Introduction}
 
-\section{General architecture of the tool-set}
-   Big pipeline + pool + blabla\medskip
+\section{General architecture of the tool}
+
+The testing tool is aimed to be modular: it is made of independents blocks that
+are interfaced trough a scheduler. More precisely a master wrapper is written
+in Python that calls a first layer which performs the chosen heuristic. This
+layer is written in C++ for performances. The given discrepancy algorithm 
+--- written in C --- is called when evaluations of a state is needed.
+The wrapper dispatch the computations on the multi-core architecture of 
+modern computers\footnote{for us, between 2 and 4 physical cores and 4 or 8 
+virtual cores}. This basic architecture is described in figure~\ref{main_flow}.
+Experiments were conducted on two machines: 
+\begin{itemize}
+  \item 2.4 GHz Intel Dual Core i5 hyper-threaded to 2.8GHz, 8 Go 1600 MHz DDR3.
+  \item 2.8 GHz Intel Quad Core i7 hyper-threaded to 3.1GHz, 8 Go 1600 MHz DDR3.
+\end{itemize}
+
+\begin{figure}
+  \label{main_flow}
+\includegraphics[scale=0.6]{main_flow.pdf}
+\caption{Tool overview}
+\end{figure}
+
+On these machines, some basic profiling has make clear that 
+the main bottleneck of the computations is hiding in the \emph{computation
+of the discrepency}. The chosen algorithm and implantation of this 
+cost function is the DEM-algorithm of \emph{Magnus Wahlstr\o m}.\medskip
+
+All the experiments has been conducted on dimension 2,3,4 
+--- with a fixed Halton basis 7, 13, 29, 3 ---. Some minor tests have
+been made in order to discuss the dependency of the discrepency and 
+efficiency of the heuristics with regards to the values chosen for the
+prime base. The average results remains roughly identical when taking 
+changing these primes and taking them in the range [2, 100]. For such
+a reason we decided to pursue the full computations with a fixed 
+basis.
 
-   Un truc bien ca serait d'avoir un truc modulaire (genre les heuristiques 
-   sont des "blocs" et l'outil appelle un bloc quand il veut faire son test
-   en l'appelant en multicore" 
 
-   (c'est stupide mais ca fera un beau schéma!)
+\subsection{Algorithmic insights}
 
+To perform an experiment we made up a 
+loop above the main algorithm which calls the chosen heuristic multiple 
+times in order to smooth up the results and obtain more exploitable datas.
+Then an arithmetic mean of the result is performed on the values. In addition
+extremal values are also given in order to construct error bands graphs.
+
+A flowchart of the conduct of one experiment is described in the 
+flowchart~\ref{insight_flow}. The number of iteration of the heuristic is 
+I and the number of full restart is N. Th efunction Heuristic() correspond to
+a single step of the chosen heursitic. We now present an in-depth view of
+the implemented heuristics.
+
+\begin{figure}
+ \begin{mdframed}
+  \label{insight_flow}
+\includegraphics[scale=0.4]{insight.pdf}
+\caption{Flowchart of a single experiment}
+\end{mdframed}
+\end{figure}
 
 
-\section{Heuristics devlopped}
+\section{Heuristics developed}
 
 \subsection{Fully random search (Test case)}
  The first heuristic implemented is rge random search. We generates
  random sets of Halton points and select the best set with regard to its
- discrepancy iteratively. The process is wrapped up in the following 
+ discrepancy iteratively. The process is wrapped up in the 
  flowchart~\ref{rand_flow}. In order to generate at each step a random 
  permutation, we transform it directly from the previous one.
   More precisely the permutation is a singleton object which have method 
@@ -107,60 +156,98 @@ the naive implementation.
 
 \subsubsection{Results and stability}
 
-We first want to analyse the dependence of the results on the number of 
-iterations of the algorithm. To perform such an experiment we made up a 
-wrapper above the main algorithm which calls the random search on 
-different number of iterations. To smooth up the results and obtain
-more exploitable datas, we also perform an 
-arithmetic mean of fifteen searches for each expermiement. The flowchart of
-the conducts of this experiment is descibed in the figure:
+We first want to analyze the dependence of the results on the number of 
+iterations of the heuristic, in order to discuss its stability. 
+The results are compiled in the figures~\ref{rand_iter2}\ref{rand_iter3}.
+As expected from a fully random search, the error bands are very large for 
+low number of iterations ($15\%$ of the value for 200 iterations) and tends
+to shrink with a bigger number of iterations (arround $5\%$ for 1600 iterations).
+The average results are quite stable, they decrease progressively with 
+the growing number of iterations, but seems to get to a limits after 1000 iterations. This value acts as a threshold for the interesting number of iterations.
+As such interesting results can be conducted with \emph{only} 1000 iterations, 
+without alterating too much the quality of the set with regards to its
+discrepency and this heuristic.
+
+\begin{figure}
+  \label{rand_iter2}
+\includegraphics[scale=0.3]{Results/random_iter.png}
+\caption{Dependence on iterations number: D=2}
+\end{figure}
+\begin{figure}
+  \label{rand_iter3}
+\includegraphics[scale=0.3]{Results/random_iter_3.png}
+\caption{Dependence on iterations number: D=3}
+\end{figure}
+
 
+
+\subsection{Evolutionary heuristic: Simulated annealing and local search}
 \begin{figure}
  \begin{mdframed}
   \label{rand_flow}
-\includegraphics[scale=0.4]{flow_rand_2.pdf}
-\caption{Flowchart of the random search experiement for dependence on iterations}
+\includegraphics[scale=0.4]{flow_recuit.pdf}
+\caption{Flowchart of the simulated annealing local search heuristic}
 \end{mdframed}
 \end{figure}
 
-The results are compiled in the figure
+\subsubsection{Dependence on the temperature}
+
+\begin{figure}
+  \label{rand_flow}
+\includegraphics[scale=0.3]{Results/resu_temp3.png}
+\caption{Dependence on iterations number: D=3}
+\end{figure}
+
 \begin{figure}
   \label{rand_flow}
-\includegraphics[scale=0.6]{rand_res.png}
-\caption{Dependence on iterations number: D=2}
+\includegraphics[scale=0.3]{Results/resu_temp3_zoom.png}
+\caption{Dependence on iterations number: D=3}
+\end{figure}
+\begin{figure}
+  \label{rand_flow}
+\includegraphics[scale=0.3]{Results/resu_2_temp.png}
+\caption{Dependence on iterations number: D=3}
 \end{figure}
 
-The same experiment has been conducted on dimension 4 -- with Halton basis
-7, 13, 29, 3 ---:
+\subsubsection{Stability with regards to the number of iterations}
+
 \begin{figure}
   \label{rand_flow}
-\includegraphics[scale=0.6]{rand_search_4.png}
-\caption{Dependence on iterations number: D=4}
+\includegraphics[scale=0.3]{Results/sa_iter.png}
+\caption{Dependence on iterations number: D=3}
 \end{figure}
 
+\subsection{Genetic (5+5) search}
+\begin{figure}
+ \begin{mdframed}
+  \label{rand_flow}
+\includegraphics[scale=0.4]{flow_recuit.pdf}
+\caption{Flowchart of the simulated annealing local search heuristic}
+\end{mdframed}
+\end{figure}
 
-The same experiment has been conducted on dimension 6 -- with Halton basis
-7, 13, 29, 3, 11, 31---:
 \begin{figure}
   \label{rand_flow}
-\includegraphics[scale=0.6]{rand_search_4.png}
-\caption{Dependence on iterations number D=6}
+\includegraphics[scale=0.3]{Results/res_gen_2.png}
+\caption{Dependence on iterations number: D=3}
 \end{figure}
 
-Based on the same wrapper, we also experiments the dependence on the number
-of points, in dimension 2, 4, 6.
 \begin{figure}
   \label{rand_flow}
-\includegraphics[scale=0.6]{low_discr_rand.png}
-\caption{Dependence on iterations number}
+\includegraphics[scale=0.3]{Results/res_gen_2_zoom.png}
+\caption{Dependence on iterations number: D=3}
+\end{figure}
+\begin{figure}
+  \label{rand_flow}
+\includegraphics[scale=0.3]{Results/res_gen_3_zoom.png}
+\caption{Dependence on iterations number: D=3}
 \end{figure}
 
-\subsection{Local Search}
-  Blabla+Pipeline
-\subsection{Evolutionary heuristics}
-  Blabla+Pipeline
-\subsection{Genetic heuristics}
-  Blabla+Pipeline
+\begin{figure}
+  \label{rand_flow}
+\includegraphics[scale=0.3]{Results/res_gen_4_zoom.png}
+\caption{Dependence on iterations number: D=3}
+\end{figure}
 
 \section{Results}
 

main.tex.bak

@@ -0,0 +1,227 @@
+% ------------------------------------------------------------------------
+\documentclass{llncs}
+\input{prelude}
+\begin{document} 
+\title{Star Discrepancies for generalized Halton points}
+% \titlerunning{}  % abbreviated title (for running head)
+%                                     also used for the TOC unless
+%                                     \toctitle is used
+
+\author{Thomas Espitau\inst{1} \and Olivier Marty\inst{1}} 
+%
+% \authorrunning{} % abbreviated author list (for running head)
+%
+%%%% list of authors for the TOC (use if author list has to be modified)
+% \tocauthor{}
+%
+\institute{
+  $\mbox{}^1$ ENS Cachan \qquad
+}
+
+\maketitle
+
+\makeatletter
+
+\makeatother
+
+
+\begin{abstract}
+\end{abstract}
+
+
+\section{Introduction}
+
+\section{General architecture of the tool}
+
+The testing tool is aimed to be modular: it is made of independant blocks that
+are interfaced trough a scheduler. More precisely a master wrapper is written
+in Python that calls a first layer which performs the chosen heuristic. This
+layer is written in C++ for performences. The given discrepancy algorithm 
+--- written in C --- is called when evaluations of a state is needed.
+The wrapper dispatch the computations on the multi-core architecture of 
+modern computers\footnote{for us, between 2 and 4 physical cores and 4 or 8 
+virtual cores}. This basic architecture is described in figure~\ref{main_flow}.
+Experiments were conducted on two machines: 
+\begin{itemize}
+  \item 2.4 GHz Intel Dual Core i5 hyperthreaded to 2.8GHz, 8 Go 1600 MHz DDR3.
+  \item 2.8 GHz Intel Quad Core i7 hyperthreaded to 3.1GHz, 8 Go 1600 MHz DDR3.
+\end{itemize}
+
+\begin{figure}
+  \label{main_flow}
+\includegraphics[scale=0.6]{main_flow.pdf}
+\caption{Tool overview}
+\end{figure}
+
+
+\subsection{Algorithmics insights}
+
+\begin{figure}
+ \begin{mdframed}
+  \label{rand_flow}
+\includegraphics[scale=0.4]{flow_rand_2.pdf}
+\caption{Flowchart of the experiement for dependence on iterations}
+\end{mdframed}
+\end{figure}
+
+
+\section{Heuristics devlopped}
+
+\subsection{Fully random search (Test case)}
+ The first heuristic implemented is rge random search. We generates
+ random sets of Halton points and select the best set with regard to its
+ discrepancy iteratively. The process is wrapped up in the following 
+ flowchart~\ref{rand_flow}. In order to generate at each step a random 
+ permutation, we transform it directly from the previous one.
+  More precisely the permutation is a singleton object which have method 
+  random, built on the Knuth Fisher Yates shuffle. This algorithm allows
+  us to generate an uniformly chosen  permutation at each step. We recall 
+  this fact and detail the algorithm in the following section.
+\begin{figure}
+ \begin{mdframed}
+  \label{rand_flow}
+\includegraphics[scale=0.4]{flow_rand.pdf}
+\caption{Flowchart of the random search}
+\end{mdframed}
+\end{figure}
+
+
+  \subsubsection{The Knuth-Fisher-Yates shuffle}
+
+The Fisher–Yates shuffle is an algorithm for generating a random permutation 
+of a finite sets. The Fisher–Yates shuffle is unbiased, so that every 
+permutation is equally likely. We present here the Durstenfeld variant of 
+the algorithm, presented by Knuth in \emph{The Art of Computer programming}.
+The algorithm's time complexity is here $O(n)$, compared to $O(n^2)$ of 
+the naive implementation.
+
+\begin{algorithm}[H]
+  \SetAlgoLined
+  \SetKwFunction{Rand}{Rand}
+  \SetKwFunction{Swap}{Swap}
+  \KwData{A table T[1..n]}
+  \KwResult{Same table T, shuffled}
+  \For{$i\leftarrow 1$ \KwTo $n-1$}{
+     $j \leftarrow$ \Rand{$[1,n-i]$}\;
+     \Swap{$T[i], T[i+j]$}\;
+    }
+  \caption{KFY algorithm}
+\end{algorithm}
+
+
+\begin{lemma}
+  The resulting permutation of KFY is unbiased.
+\end{lemma}
+\begin{proof}
+  Let consider the set $[1,\ldots n]$ as the vertices of a random graph 
+  constructed as the trace of the execution of the algorithm: 
+  an edge $(i,j)$ exists in the graph if and only if the swap of $T[i]$ and
+  $T[j]$ had been executed. This graph encodes the permutation represented by
+  $T$. To be able to encode any permutation the considered graph must be 
+  connected --- in order to allow any pairs of points to be swapped ---.
+  Since by construction every points is reached by an edge, and that there 
+  exists exactly $n-1$ edges, we can conclude directly that any permutation can
+  be reached by the algorithm. Since the probability of getting a fixed graph 
+  of $n-1$ edges with every edges of degree at least one is $n!^{-1}$, the 
+  algorithm is thus unbiased.
+
+\end{proof}
+
+
+\subsubsection{Results and stability}
+
+We first want to analyse the dependence of the results on the number of 
+iterations of the algorithm. To perform such an experiment we made up a 
+wrapper above the main algorithm which calls the random search on 
+different number of iterations. To smooth up the results and obtain
+more exploitable datas, we also perform an 
+arithmetic mean of fifteen searches for each experiments. The flowchart of
+the conducts of this experiment is desibed in the figure:
+
+The results are compiled in the figure
+\begin{figure}
+  \label{rand_flow}
+\includegraphics[scale=0.3]{Results/random_iter.png}
+\caption{Dependence on iterations number: D=2}
+\end{figure}
+
+The sae experiment has been conducted on dimension 4 -- with Halton basis
+7, 13, 29, 3 ---:
+\begin{figure}
+  \label{rand_flow}
+\includegraphics[scale=0.3]{Results/random_iter_3.png}
+\caption{Dependence on iterations number: D=3}
+\end{figure}
+
+
+
+\subsection{Evolutionary heuristic: Simmulated annealing and local search}
+\begin{figure}
+ \begin{mdframed}
+  \label{rand_flow}
+\includegraphics[scale=0.4]{flow_recuit.pdf}
+\caption{Flowchart of the simulated annealing local search heuristic}
+\end{mdframed}
+\end{figure}
+
+\begin{figure}
+  \label{rand_flow}
+\includegraphics[scale=0.3]{Results/resu_temp3.png}
+\caption{Dependence on iterations number: D=3}
+\end{figure}
+
+\begin{figure}
+  \label{rand_flow}
+\includegraphics[scale=0.3]{Results/resu_temp3_zoom.png}
+\caption{Dependence on iterations number: D=3}
+\end{figure}
+\begin{figure}
+  \label{rand_flow}
+\includegraphics[scale=0.3]{Results/resu_2_temp.png}
+\caption{Dependence on iterations number: D=3}
+\end{figure}
+
+\begin{figure}
+  \label{rand_flow}
+\includegraphics[scale=0.3]{Results/sa_iter.png}
+\caption{Dependence on iterations number: D=3}
+\end{figure}
+
+\subsection{Genetic (5+5) search}
+\begin{figure}
+ \begin{mdframed}
+  \label{rand_flow}
+\includegraphics[scale=0.4]{flow_recuit.pdf}
+\caption{Flowchart of the simulated annealing local search heuristic}
+\end{mdframed}
+\end{figure}
+
+\begin{figure}
+  \label{rand_flow}
+\includegraphics[scale=0.3]{Results/res_gen_2.png}
+\caption{Dependence on iterations number: D=3}
+\end{figure}
+
+\begin{figure}
+  \label{rand_flow}
+\includegraphics[scale=0.3]{Results/res_gen_2_zoom.png}
+\caption{Dependence on iterations number: D=3}
+\end{figure}
+\begin{figure}
+  \label{rand_flow}
+\includegraphics[scale=0.3]{Results/res_gen_3_zoom.png}
+\caption{Dependence on iterations number: D=3}
+\end{figure}
+
+\begin{figure}
+  \label{rand_flow}
+\includegraphics[scale=0.3]{Results/res_gen_4_zoom.png}
+\caption{Dependence on iterations number: D=3}
+\end{figure}
+
+\section{Results}
+
+
+\section{Conclusion}
+
+\end{document}

src/ploter.py

@@ -13,12 +13,10 @@ def errorfill(x, y, yerr, color=None, alpha_fill=0.3, ax=None):
 
 
 
-from out_gen_4 import *
+from res import *
 
 errorfill(axis[0], resu[0], resu_bars[0])
 errorfill(axis[0], resu[1], resu_bars[1])
-errorfill(axis[0], resu[2], resu_bars[2])
-errorfill(axis[0], resu[3], resu_bars[3])
-errorfill(axis[0], resu[4], resu_bars[4])
+errorfill(axis[2], resu[2], resu_bars[2])
 
 plt.show()

src/random_search

@@ -1,585 +1,180 @@
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+0.078717	0.745562	0.442331	0.090535
+0.793003	0.899408	0.545779	0.423868
+0.935860	0.514793	0.683710	0.757202
+0.650146	0.437870	0.200951	0.201646
+0.507289	0.065089	0.649227	0.534979
+0.221574	0.218935	0.580262	0.868313
+0.364431	0.988166	0.511296	0.312757
+0.037901	0.834320	0.752675	0.646091
+0.752187	0.142012	0.166468	0.979424
+0.895044	0.680473	0.235434	0.028807
+0.609329	0.295858	0.407848	0.362140
+0.466472	0.603550	0.925089	0.695473
+0.180758	0.372781	0.614744	0.139918
+0.323615	0.757396	0.476813	0.473251
+0.058309	0.911243	0.890606	0.806584
+0.772595	0.526627	0.338882	0.251029
+0.915452	0.449704	0.063020	0.584362
+0.629738	0.035503	0.787158	0.917695
+0.486880	0.189349	0.959572	0.065844
+0.201166	0.958580	0.023781	0.399177
+0.344023	0.804734	0.265161	0.732510
+0.011662	0.112426	0.368609	0.176955
+0.725948	0.650888	0.989298	0.510288
+0.868805	0.266272	0.851367	0.843621
+0.583090	0.573964	0.713436	0.288066
+0.440233	0.343195	0.092747	0.621399
+0.154519	0.727811	0.816885	0.954733
+0.297376	0.881657	0.299643	0.102881
+0.113703	0.497041	0.127229	0.436214
+0.827988	0.420118	0.437574	0.769547
+0.970845	0.029586	0.541023	0.213992
+0.685131	0.183432	0.678954	0.547325
+0.542274	0.952663	0.196195	0.880658
+0.256560	0.798817	0.644471	0.325103
+0.399417	0.106509	0.575505	0.658436
+0.134111	0.644970	0.506540	0.991770
+0.848397	0.260355	0.747919	0.008230
+0.991254	0.568047	0.161712	0.341564
+0.705539	0.337278	0.230678	0.674897
+0.562682	0.721893	0.403092	0.119342
+0.276968	0.875740	0.920333	0.452675
+0.419825	0.491124	0.609988	0.786008
+0.093294	0.414201	0.472057	0.230453
+0.807580	0.000910	0.885850	0.563786
+0.950437	0.154756	0.334126	0.897119
+0.664723	0.923987	0.058264	0.045267
+0.521866	0.770141	0.782402	0.378601
+0.236152	0.077833	0.954816	0.711934
+0.379009	0.616295	0.002378	0.156379
+0.072886	0.231680	0.243757	0.489712
+0.787172	0.539372	0.347206	0.823045
+0.930029	0.308603	0.967895	0.267490
+0.644315	0.693218	0.829964	0.600823
+0.501458	0.847064	0.692033	0.934156
+0.215743	0.462449	0.071344	0.082305

src/res.py

@@ -0,0 +1,15 @@
+axis = {}
+resu = {}
+resu_bars = {}
+
+axis[0] = [10, 35, 60, 85, 110, 135, 160, 185, 210, 235, 260, 285, 310, 335, 360, 385, 410, 435, 460, 485, 510, 535, 560, 585]
+
+resu[0] = [0.15759425, 0.0711236125, 0.05134156249999999, 0.0390685875, 0.0331956375, 0.0288086875, 0.024430225, 0.021376725, 0.02072995, 0.019567849999999998, 0.017386600000000002, 0.015916175, 0.0148845375, 0.01337815, 0.0131273, 0.013137049999999997, 0.013144300000000001, 0.012171425, 0.0113505125, 0.010869475, 0.010356049999999999, 0.010085835, 0.009914015, 0.009272236250000001]
+resu_bars[0] = [[0.150549, 0.0677936, 0.0499395, 0.0378914, 0.0303172, 0.0277087, 0.023808, 0.020197, 0.0203114, 0.0190932, 0.0159221, 0.0154047, 0.0142995, 0.012701, 0.012736, 0.0125738, 0.0125413, 0.0119133, 0.0108696, 0.0100868, 0.0100911, 0.00973054, 0.00944531, 0.00891835], [0.163736, 0.0755705, 0.0535663, 0.0399611, 0.0342608, 0.0295125, 0.0249018, 0.022085, 0.0210025, 0.020665, 0.0179264, 0.0168273, 0.0154222, 0.0139772, 0.0135822, 0.0139057, 0.0136913, 0.012347, 0.0121063, 0.0114255, 0.0106159, 0.0104747, 0.0102338, 0.009492]]
+
+resu[1] = [0.14346549999999997, 0.063527975, 0.0449627875, 0.0354477875, 0.03032755, 0.026171999999999997, 0.021464425, 0.018726562500000002, 0.018666375, 0.0175885375, 0.0160137625, 0.014683637499999999, 0.013641075, 0.0121541625, 0.0118645625, 0.011869374999999998, 0.0119366375, 0.011047575, 0.0107343, 0.0101043725, 0.009634827500000002, 0.00951271125, 0.0093187775, 0.008447855]
+resu_bars[1] = [[0.135949, 0.0596545, 0.0410539, 0.0333211, 0.0292958, 0.025134, 0.0205975, 0.0174456, 0.0179174, 0.0161364, 0.0155649, 0.0140873, 0.0130282, 0.0117326, 0.0114335, 0.0112842, 0.0115576, 0.0105935, 0.0103573, 0.00972104, 0.00913606, 0.00923783, 0.00905022, 0.00816088], [0.152277, 0.0666826, 0.0472668, 0.0371538, 0.031289, 0.0273385, 0.0232342, 0.0202973, 0.0192605, 0.0190214, 0.0166344, 0.015149, 0.0141033, 0.0128459, 0.0121629, 0.0123784, 0.0125261, 0.0115158, 0.0112177, 0.010299, 0.00996569, 0.00970477, 0.00948486, 0.0088026]]
+
+axis[2] = [10, 35, 60, 85, 110, 135, 160, 185, 210, 235, 260, 285, 310, 335, 360, 385, 410, 435, 460, 485]
+resu[2] = [0.14120353333333335, 0.06548479333333333, 0.046063273333333335, 0.03528975333333333, 0.03027759333333333, 0.025914746666666665, 0.02187073333333334, 0.018703586666666664, 0.018072253333333333, 0.017285793333333337, 0.015746586666666666, 0.014146433333333331, 0.01340750666666667, 0.012070553333333333, 0.011922293333333332, 0.011630399999999997, 0.011643953333333335, 0.010854913333333332, 0.010259162666666665, 0.009978745999999998]
+resu_bars[2] = [[0.135949, 0.0608622, 0.0437328, 0.034098, 0.0291103, 0.0233957, 0.0206287, 0.0178056, 0.0161065, 0.0161443, 0.0150766, 0.0132946, 0.012501, 0.0113278, 0.011086, 0.0107817, 0.0106378, 0.0103302, 0.00955232, 0.00960693], [0.153533, 0.0715367, 0.0481157, 0.0377313, 0.0323401, 0.026981, 0.023421, 0.0198411, 0.019686, 0.0185712, 0.016576, 0.0150745, 0.014124, 0.0132404, 0.0124026, 0.012511, 0.0120181, 0.0114783, 0.0108302, 0.0101833]]

src/res3

@@ -1 +1 @@
-0.0370089
+0.0332504

src/res4

@@ -1 +1 @@
-0.0406788
+0.0486242

src/res_genetic_2

@@ -0,0 +1 @@
+0.00979823

src/res_genetic_3

@@ -0,0 +1 @@
+0.0331964

src/res_genetic_4

@@ -0,0 +1 @@
+0.0473771

src/res_genetic_i22

@@ -0,0 +1 @@
+0.0192831

src/wrapper_final.py

@@ -17,7 +17,7 @@ def generate(dim, l, la = 0.992, init = 0.01, it = 1000):
     resu_bars_mini = []
     resu_bars_maxi = []
     NB_IT = 8
-    for i in range(10, 600, 25):
+    for i in range(80, 200, 25):
         results[i] = []
         axis.append(i)
         mini  =10
@@ -47,8 +47,8 @@ def gen_generate(dim, l, mu = 5, la = 5, c = 0.5):
     resu = []
     resu_bars_mini = []
     resu_bars_maxi = []
-    NB_IT = 15
-    for i in range(10, 600, 25):
+    NB_IT = 8
+    for i in range(80, 200, 25):
         results[i] = []
         axis.append(i)
         mini  =10
@@ -72,11 +72,10 @@ def gen_generate(dim, l, mu = 5, la = 5, c = 0.5):
 
     errorfill(axis, resu, resu_bars)
 
-dim = 3
+dim = 4
 generate(dim, 1, 0.99, 0.001, 1000)
 generate(dim, 2, 0.99, 0.001, 1000)
-gen_generate(dim, 3, 5, 5, 0.9)
+gen_generate(dim, 3, 5, 5, 0.6)
 
-errorfill(axis, resu, resu_bars)
 
 plt.show()

src/wrapper_final3.py

@@ -72,7 +72,7 @@ def gen_generate(dim, l, mu = 5, la = 5, c = 0.5):
 
     errorfill(axis, resu, resu_bars)
 
-dim = 3
+dim = 4
 generate(dim, 1, 0.99, 0.001, 1000)
 generate(dim, 2, 0.99, 0.001, 1000)
 gen_generate(dim, 3, 5, 5, 0.9)

src/wrapper_genetic_iter.py

@@ -0,0 +1,54 @@
+import os
+import matplotlib.pyplot as plt
+results = {}
+
+def errorfill(x, y, yerr, color=None, alpha_fill=0.3, ax=None):
+    ax = ax if ax is not None else plt.gca()
+    if color is None:
+        color = ax._get_lines.color_cycle.next()
+    ymin, ymax = yerr
+    ax.plot(x, y, color=color)
+    ax.fill_between(x, ymax, ymin, color=color, alpha=alpha_fill)
+
+
+
+def gen_generate(dim, l, mu = 5, la = 5, c = 0.5, iter=1000):
+    axis = []
+    resu = []
+    resu_bars_mini = []
+    resu_bars_maxi = []
+    NB_IT = 8
+    for i in range(80, 200, 25):
+        results[i] = []
+        axis.append(i)
+        mini  =10
+        maxi = 0
+        for j in range(NB_IT):
+            os.system('./main '+str(l)+" "+str(i)+ " " + str(dim) + " "+str(iter)+ " "+ str(mu)+ " "+ str(la)+ " " + str(c) + " > res_genetic_i2"+str(dim))
+            r = open("res_genetic_i2"+str(dim), "r")
+            res = float(r.readline())
+            mini = min(res, mini)
+            maxi = max(res, maxi)
+            results[i].append(res)
+        resu.append(sum(results[i])/NB_IT)
+        resu_bars_mini.append(mini)
+        resu_bars_maxi.append(maxi)
+
+    resu_bars = [resu_bars_mini, resu_bars_maxi]
+    print results
+    print axis
+    print resu
+    print resu_bars
+
+    errorfill(axis, resu, resu_bars)
+
+dim = 2
+gen_generate(dim, 3, 5, 5, 0.5, 200)
+gen_generate(dim, 3, 5, 5, 0.5, 600)
+gen_generate(dim, 3, 5, 5, 0.5,1000)
+gen_generate(dim, 3, 5, 5, 0.5, 1200)
+gen_generate(dim, 3, 5, 5, 0.5, 1400)
+
+errorfill(axis, resu, resu_bars)
+
+plt.show()