gambergeance
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Projet.Rmd
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Projet.Rmd
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---
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title: "Projet"
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output: html_document
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output:
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pdf_document: default
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html_document: default
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date: "2024-12-04"
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---
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@ -12,6 +14,8 @@ library(reshape2)
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library(corrplot)
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library(FactoMineR)
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library(factoextra)
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library(cluster)
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library(mclust)
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```
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```{r}
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@ -19,6 +23,7 @@ T = read.table("DataProjet3MIC-2425.txt",header=TRUE,sep=";")
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T$ExpT1 = as.factor(T$ExpT1)
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T$ExpT2 = as.factor(T$ExpT2)
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T$ExpT3 = as.factor(T$ExpT3)
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#centrer T
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head(T)
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summary(T)
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str(T)
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@ -281,9 +286,7 @@ Contexte : les relevés aux heures sont décrits par les gènes ( les gènes son
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- les genes proches d'un axe sont très représentés par celui-ci
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- les genes dont l'angle entre eux est petit sont corrélés entre eux
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#### Dire : Ce coté on voit bien qu'on est plus ce type de gènes et vers le haut c'est plutot ce type là etc...
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## ON EST SENSE VOIR UN TRUC IMPORTANT D'APRES LA PROF MAIS JE VOIS RIEN
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### interprétation globale du couple de graphes
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## Interprétation globale du couple de graphes
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On voit que les genes se polarisent principalement sur l'axe 1 dans un sens ou l'autre. Les flèches sont d'une longueur presque du rayon du cercle, indiquant une participation très forte des genes dans la variance expliquée par ces dimensions.
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Il n'y a pas de tendance particulière sur la direction selon l'axe 2 des flèches : Dans chaque "polarité" de fleches selon l'axe 1, il y a des fleches dont la direction est negative d'autres positive selon l'axe 2.
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@ -291,7 +294,10 @@ Il n'y a pas de tendance particulière sur la direction selon l'axe 2 des flèch
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Le traitement 1 est entièrement groupé sur des valeurs très negatives de l'axe 1. On remarque dans ce groupement la présence des T3 et T4 à la première heure de relevés d'expression des gènes.
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axe 1: force du changement d'expression : si on sur-exprime/sous-exprime de beaucoup ( genre 0=>5) ou pas, polarisation ?
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## ON EST SENSE VOIR UN TRUC IMPORTANT D'APRES LA PROF MAIS JE VOIS RIEN
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### Les axes générés par l'ACP sont purement virtuels, mais on peut y préter un sens plus ou moins défini selon les variables qui y sont corrélées
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axe 1: force du changement d'expression des gènes : si on sur-exprime/sous-exprime de beaucoup ( genre 0=>5) ou pas, polarisation ?
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axe 2 : vers le haut => ça va changer d'expression bientot, vers le bas => ça se stabilise ( donc les genes pointant vers le haut changent d'expression, et ceux vers le bas ont tendance à rester plutot pareil )
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@ -302,10 +308,6 @@ axe 2 : vers le haut => ça va changer d'expression bientot, vers le bas => ça
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En ayant en tête les histogrammes de l'analyse descriptive, on pourrait y voir un axe représentant l'expression : les valeurs négatives portent les gènes dont l'expression relative est.
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En regardant le graphe des individus (résultats aux heures de relevés), on a effectivement les heures groupées à des valeurs negatives de l'axe 1 correspondant aux relevés du traitement 1 qui, souvenons-nous toujours des histogrammes, ne change l'expression relative que de très peu de gènes.
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Pour l'interprétation du second axe, les gènes semblent y être positivement et negativement corrélés quel que soit leur correlation avec l'axe 1.
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@ -318,11 +320,91 @@ En regardant les individus, on observe que plus l'heure est tardive, plus ils te
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### afin de visualiser les corrélations des variables intiales avec toutes les méta-variables (pas trs utile on a déjà le cercle)
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```{r fig.height=18}
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# que 50 gènes sinon c'est le bordel
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corrplot(res_pca$var$cor[1:50,],method="ellipse", type="lower", bg = "lightgrey")
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```
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MAIS C'EST INBUVABLE
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### plus ou moins inutile ici de regarder d'autres plans
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```{r}
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fviz_pca_ind(res_pca,axes = c(1,3),geom=c("point"))
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fviz_pca_ind(res_pca,axes = c(1,4),geom=c("point"))
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fviz_pca_ind(res_pca,axes = c(1,5),geom=c("point"))
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```
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## Clustering
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Nous avons choisi d'effectuer un clustering de l'ACP réalisée plus haut grâce à la méthode des kmeans. Car à l'oeil nu, on dénote déjà très précisément 3 ou 4 clusters donc il sera aisé de trouver le nombre de classes, car normalement il sera de 3 ou 4.
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```{r}
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#centrage et réduction des données
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s = scale(donnees_transposees)
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Kmax<-15
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reskmeanscl<-matrix(0,nrow=nrow(s),ncol=Kmax-1)
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Iintra<-NULL
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for (k in 2:Kmax){
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resaux<-kmeans(s,k)
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reskmeanscl[,k-1]<-resaux$cluster
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Iintra<-c(Iintra,resaux$tot.withinss)
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}
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df<-data.frame(K=2:15,Iintra=Iintra)
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ggplot(df,aes(x=K,y=Iintra))+
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geom_line()+
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geom_point()+
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xlab("Nombre de classes")+
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ylab("Inertie intraclasse")
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```
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On voit un coude à 4 clusters ?
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```{r}
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Silhou<-NULL
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for (k in 2:Kmax){
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aux<-silhouette(reskmeanscl[,k-1], daisy(s))
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Silhou<-c(Silhou,mean(aux[,3]))
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}
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df<-data.frame(K=2:Kmax,Silhouette=Silhou)
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ggplot(df,aes(x=K,y=Silhouette))+
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geom_point()+
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geom_line()+theme(legend.position = "bottom")
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aux<-silhouette(reskmeanscl[,3-1], daisy(s))
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fviz_silhouette(aux)+
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theme(plot.title = element_text(size =9))
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rm(df,Silhou,aux)
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```
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Silhouette fait un pic à 5 et pas 4 :/
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### visualisation du clustering
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```{r}
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#graphes des clusters
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res_kmeans = kmeans(s,3)
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fviz_cluster(res_kmeans,data=donnees_transposees,
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ellipse.type="norm",labelsize=8,
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geom=c("point"))+ggtitle("")
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table(wine$Type,wine$Qualite)
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adjustedRandIndex(wine$Type,reskmeans$cluster) # fonction de similarité, on a 0,35 avec Type, ce n'est donc pas fou non plus
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adjustedRandIndex(wine$Qualite,reskmeans$cluster) # on voit que la similarité avec la qualité donne 0 => cela n'a rien à voir
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clust<-paste("Cl-K",res_kmeans$cluster,sep="")
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Tab<-melt(table(clust,donnees_transposees[,1]))
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ggplot(Tab,aes(y=value,axis1=clust,axis2=Var2))+
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geom_alluvium(aes(fill=clust))+
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geom_stratum(width = 1/12)+
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geom_text(stat = "stratum", aes(label = after_stat(stratum)))+
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theme(legend.position = "none")
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chordDiagram(table(clust,donnees_transposees[,2]))
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```
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This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023/Debian) (preloaded format=pdflatex 2024.12.28) 28 DEC 2024 12:03
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entering extended mode
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restricted \write18 enabled.
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Package xcolor Info: Model `cmy' substituted by `cmy0' on input line 1350.
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Package xcolor Info: Model `hsb' substituted by `rgb' on input line 1354.
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Package xcolor Info: Model `RGB' extended on input line 1366.
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Package xcolor Info: Model `HTML' substituted by `rgb' on input line 1368.
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Package xcolor Info: Model `Hsb' substituted by `hsb' on input line 1369.
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Package xcolor Info: Model `tHsb' substituted by `hsb' on input line 1370.
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Package xcolor Info: Model `HSB' substituted by `hsb' on input line 1371.
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Package xcolor Info: Model `Gray' substituted by `gray' on input line 1372.
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Package xcolor Info: Model `wave' substituted by `hsb' on input line 1373.
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) (/usr/share/texlive/texmf-dist/tex/latex/geometry/geometry.sty
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Package: geometry 2020/01/02 v5.9 Page Geometry
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(/usr/share/texlive/texmf-dist/tex/generic/iftex/ifvtex.sty
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Package: ifvtex 2019/10/25 v1.7 ifvtex legacy package. Use iftex instead.
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)
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\Gm@cnth=\count276
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\Gm@cntv=\count277
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\c@Gm@tempcnt=\count278
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\Gm@bindingoffset=\dimen151
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\Gm@wd@mp=\dimen152
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\Gm@odd@mp=\dimen153
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\Gm@even@mp=\dimen154
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\Gm@layoutwidth=\dimen155
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\Gm@layoutheight=\dimen156
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\Gm@layouthoffset=\dimen157
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\Gm@layoutvoffset=\dimen158
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\Gm@dimlist=\toks27
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) (/usr/share/texlive/texmf-dist/tex/latex/fancyvrb/fancyvrb.sty
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Package: fancyvrb 2024/01/20 4.5c verbatim text (tvz,hv)
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\FV@CodeLineNo=\count279
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\FV@InFile=\read2
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\FV@TabBox=\box54
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\c@FancyVerbLine=\count280
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\FV@StepNumber=\count281
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\FV@OutFile=\write3
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)
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! LaTeX Error: File `framed.sty' not found.
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Type X to quit or <RETURN> to proceed,
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or enter new name. (Default extension: sty)
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Enter file name:
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! Emergency stop.
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<read *>
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l.47 \definecolor
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{shadecolor}{RGB}{248,248,248}^^M
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Here is how much of TeX's memory you used:
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5738 strings out of 476182
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84709 string characters out of 5795594
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1922975 words of memory out of 5000000
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27732 multiletter control sequences out of 15000+600000
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558832 words of font info for 37 fonts, out of 8000000 for 9000
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14 hyphenation exceptions out of 8191
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56i,0n,65p,511b,102s stack positions out of 10000i,1000n,20000p,200000b,200000s
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! ==> Fatal error occurred, no output PDF file produced!
|
1311
Projet.tex
Normal file
1311
Projet.tex
Normal file
File diff suppressed because it is too large
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Reference in a new issue