yl=as.character(classe$clsuter)
yl=sprintf("%f4.2",densites)
text(acp$li, labels=yl)
yl=sprintf("%4.2f",densites)
text(acp$li, labels=yl)
plot(acp$li[,1],acp$li[,2],type='n')
yl=as.character(classe$cluster)
yl=sprintf("%4.2f",densites)
text(acp$li, labels=yl)
yl=sprintf("%4.2f",densites)
yl=sprintf("%f4.2",densites)
densites = as.matrix(densites)
yl=sprintf("%f4.2",densites)
plot(acp$li[,1],acp$li[,2],type='n')
text(acp$li, labels=yl)
plot(acp$li[,1],acp$li[,2],type='n')
yl=sprintf("%4.2f",densites)
text(acp$li, labels=yl)
library(MASS)
lda <- lda(acp$li,type)
compteur=0
for(i in 1:100){
p=predict(lda,acp$li[i,])$class
if(p==type[i]){
compteur=compteur+1
}
}
compteur
ldafw <- lda(acp$li,type)
type=classe$cluster
ldafw <- lda(acp$li,type)
compteur=0
for(i in 1:100){
p=predict(ldafw,acp$li[i,])$class
if(p==type[i]){
compteur=compteur+1
}
}
compteur
lqda<-qda(acp$li,type)
compteur=0
for(i in 1:100){
p=predict(lqda,acp$li[i,])$class
if(p==type[i]){
compteur=compteur+1
}
}
compteur
library(ade4)
rep="/Users/Francois/Documents/Cours/AD/FW_TPnote/Fichiers_cours/"
fic="Xcal_5.txt"
nomX=paste(rep,fic,sep="")
Xcal_5 <- read.table(nomX,header = FALSE)
# lecture des densités
fic="ycal_5.txt"
nomY=paste(rep,fic,sep="")
ycal_5 <-read.table(nomY,header=FALSE)
mat_x <- as.matrix(Xcal_5)
acp1 <- dudi.pca(Xcal_5, center=FALSE,scal = FALSE, scannf = FALSE, nf=3)
plot(acp1$li[,1],acp1$li[,2])
s.label(acp1$li, xax=1, yax=2)
plot(acp1$li[,1],acp1$li[,2])
summary(acp1)
plot(Xcal_5[1], type='l')
plot(Xcal_5[1,], type='l')
plot(Xcal_5[1,], type="l")
plot(mat_x[1,], type="l")
acp1 <- dudi.pca(mat_x, center=FALSE,scal = FALSE, scannf = FALSE, nf=3)
plot(acp1$li[,1],acp1$li[,2])
s.label(acp1$li, xax=1, yax=2)
acp1 <- dudi.pca(mat_x, center=FALSE,scal = FALSE, scannf = FALSE, nf=3)
plot(acp1$li[,1],acp1$li[,2])
yl=as.character(seq(1:100))
text(acp1$li,labels=yl)
plot(acp1$li[,1],acp1$li[,2],type="n")
text(acp1$li,labels=yl)
s.label(acp1$li, xax=1, yax=2)
rep="/Users/Francois/Documents/Cours/AD/FW_TPnote/Fichiers_cours/"
fic="Xcal_5.txt"
nomX=paste(rep,fic,sep="")
var <- read.table(nomX,header = FALSE)
# lecture des densités
fic="ycal_5.txt"
nomY=paste(rep,fic,sep="")
densite <-read.table(nomY,header=FALSE)
mat<-as.matrix(var)
plot(mat[1,],type="l")
library(ade4)
acp<-dudi.pca(mat)
plot(acp$li[,1],acp$li[,2])
s.label(acp$li)
class=kmeans(acp$li,2)
dens_m = as.matrix(ycal)
y_cal <-read.table(nomY,header=FALSE)
ycal <-read.table(nomY,header=FALSE)
rm(y_cal)
dens_m = as.matrix(ycal)
ylab = sprintf("%4.2f",dens_m)
plot(acp$li[,1],acp$li[,2])
text(acp$li,labels = ylab)
rep="/Users/Francois/Documents/Cours/AD/FW_TPnote/Fichiers_cours/"
fic="Xcal_1.txt"
nomX=paste(rep,fic,sep="")
spectre <- read.table(nomX,header = FALSE)
# lecture des densités
fic="ycal_1.txt"
nomY=paste(rep,fic,sep="")
densite <-read.table(nomY,header=FALSE)
spectre=as.matrix(spectre)
densite=as.matrix(densite)
plot(spectre[1,])
acp_spectre=dudi.pca(spectre[,], scale=FALSE, center=FALSE,scannf=FALSE, nf=3)
plot(acp_spectre$li[,1],acp_spectre$li[,2],type='n')
yl=sprintf("4.2f","y")
text(yl)
plot(acp_spectre$li[,1],acp_spectre$li[,2],type='o')
plot(acp_spectre$li[,1],acp_spectre$li[,2])
acp_spectre$eig
rep="/Users/Francois/Documents/Cours/AD/FW_TPnote/Fichiers_cours/"
fic="Xcal_8.txt"
nomX=paste(rep,fic,sep="")
x <- read.table(nomX,header = FALSE)
# lecture des densités
fic="ycal_8.txt"
nomY=paste(rep,fic,sep="")
y <-read.table(nomY,header=FALSE)
x = as.matrix(x)
y = as.matrix(y)
plot(x[1,], type = 'l', xlab = 'num?ro du nombre d\'onde',main = '1er spectre')
acp = dudi.pca(x, center = FALSE, scale = FALSE, scannf = FALSE, nf = 3)
plot(acp$li[,1], acp$li[,2])
plot(acp$li)
dx = dist(x) # matrice des distances
plot(hclust(dx, "average")) # dendogramme
fac = cutree(hclust(dx, "average"), k = 2)
couleur = c("red","blue")
s.class(acp$li, fac = as.factor(fac), col = couleur)
plot(acp$li[,1], acp$li[,2], type = 'n', main = 'Densit?s')
ylabel = sprintf("%4.2f",y)
text(acp$li[,1], acp$li[,2], ylabel)
lda = lda(acp$li, fac)
nb_vraie = 0
p = rep(0,100)
c = rep(0,100)
for(i in 1:100){ # length(y) = 100
p[i] = predict(lda, acp$li[i,])$class
if(p == fac[i]){
col[i] = 1
nb_vraie = nb_vraie + 1
}
}
prop = nb_vraie/length(fac)
nb_vraie = 0
p = rep(0,100)
c = rep(0,100)
i
p[i] = predict(lda, acp$li[i,])$class
for(i in 1:100){ # length(y) = 100
p[i] = predict(lda, acp$li[i,])$class
if(p == fac[i]){
col[i] = 1
nb_vraie = nb_vraie + 1
}
}
p = rep(0,100)
c = rep(0,100)
for(i in 1:100){ # length(y) = 100
p[i] = predict(lda, acp$li[i,])$class
if(p[i] == fac[i]){
c[i] = 1
nb_vraie = nb_vraie + 1
}
}
prop = nb_vraie/length(fac)
plot(acp$li[,1], acp$li[,2], type = 'n', main = 'Densit?s')
clabel = sprintf("%4.2f",fac)
text(acp$li[,1], acp$li[,2], clabel)
qda = qda(acp$li, fac)
nb_vraie = 0
for(i in 1:100){
p = predict(qda, acp$li[i,])$class
if(p == fac[i]){
nb_vraie = nb_vraie + 1
}
}
prop = nb_vraie/length(fac)
dy = dist(y) # matrice des distances
plot(hclust(dy, "average")) # dendogramme
fac2 = cutree(hclust(dy, "average"), k = 2)
lda2 = lda(acp$li, fac2)
nb_vraie = 0
p = rep(0,100)
c = rep(0,100)
for(i in 1:100){
p[i] = predict(lda2, acp$li[i,])$class
if(p == fac[i]){
c[i] = 1
nb_vraie = nb_vraie + 1
}
}
prop = nb_vraie/length(fac2)
nb_vraie = 0
p = rep(0,100)
c = rep(0,100)
for(i in 1:100){
p[i] = predict(lda2, acp$li[i,])$class
if(p[i] == fac[i]){
c[i] = 1
nb_vraie = nb_vraie + 1
}
}
prop = nb_vraie/length(fac2)
prop
rep="/Users/Francois/Documents/Cours/AD/FW_TPnote/Fichiers_cours/"
fic="Xcal_5.txt"
nomX=paste(rep,fic,sep="")
xcal <- read.table(nomX,header = FALSE)
# lecture des densités
fic="ycal_5.txt"
nomY=paste(rep,fic,sep="")
ycal <-read.table(nomY,header=FALSE)
xcal=as.matrix(xcal)
ycal=as.matrix(ycal)
plot(xcal[,1],type='l')
acp=dudi.pca(xcal,center=FALSE,scale=FALSE,scannf=FALSE,nf=3)
s.label(acp$li)
plot(acp$li)
km=kmeans(acp$li,2)
yl=as.character(km$cluster)
text(acp$li[,1],acp$li[,2],yl)
vp=prcomp(xcal)
plot(vp,col=rainbow(8))
plot(acp$li[,1],acp$li[,2],type="n")
yl=as.character(km$cluster)
text(acp$li[,1],acp$li[,2],yl)
plot(acp$li[,1],acp$li[,2],type='n')
yl=sprintf("%4.2f",ycal)
text(acp$li[,1],acp$li[,2],yl)
lda1=lda(acp$li,km$cluster)
err=0
p=predict(lda1)
compteur=0
for (i in 1:length(p$class))
{
p=predict(lda1, acp$li[i,1:3])$class
if (km$cluster[i]==p)
{compteur=compteur+1;}
else {err=i}
}
compteur
ratio=(compteur/length(type))*100
ratio
err
err=0
compteur=0
for (i in 1:length(p$class))
{
p=predict(lda1, acp$li[i,1:3])$class
if (km$cluster[i]==p)
{compteur=compteur+1;}
else {err=i}
}
p=predict(lda1, acp$li[i,1:3])
p=predict(lda1, acp$li[i,1:3])$class
err=0
compteur=0
for (i in 1:100)
{
p=predict(lda1, acp$li[i,1:3])$class
if (km$cluster[i]==p)
{compteur=compteur+1;}
else {err=i}
}
compteur
rep="/Users/Francois/Documents/Cours/AD/FW_TPnote/Fichiers_cours/"
fic="Xcal_4.txt"
nomX=paste(rep,fic,sep="")
donnees <- read.table(nomX,header = FALSE)
# lecture des densités
fic="ycal_4.txt"
nomY=paste(rep,fic,sep="")
densites <-read.table(nomY,header=FALSE)
rm
donnees=as.matrix(donnees)
densites=as.matrix(densites)
acp=dudi.pca(donnees,center=FALSE,scale=FALSE,nf=3)
s.label(acp$li)
d=donnees[-74,] #puis on refait une acp
acp=dudi.pca(d,center=FALSE,scale=FALSE,nf=3)
d=donnees[-74,] #puis on refait une acp
acp=dudi.pca(d,center=FALSE,scale=FALSE,nf=3)
s.label(acp$li)
partition = kmeans(acp$li,2)
plot(acp$li[,1],acp$li[,2],type="n")
yl=as.character(partition$cluster)
text(acp$li,labels = yl)
plot(acp$li[,1],acp$li[,2],type="n")
yl=sprintf("%4.2f",densites)
text(acp$li,labels = yl)
library(ade4)
library(MASS)
rep="/Users/Francois/Documents/Cours/AD/FW_TPnote/Fichiers_cours/"
fic="Xcal_10.txt"
nomX=paste(rep,fic,sep="")
spectre <- read.table(nomX,header = FALSE)
# lecture des densités
fic="ycal_10.txt"
nomY=paste(rep,fic,sep="")
densite <-read.table(nomY,header=FALSE)
# transformation en matrice
spectre1=as.matrix(spectre)
# lecture des densités
#densite <- read.table("ycal_10.txt")
densite1=as.matrix(densite)
plot(spectre1[12,], main= "1er spectre")
spectre.acp <- dudi.pca(spectre, scale=FALSE, center=FALSE)
s.label(spectre)
s.label(spectre.acp)
s.label(spectre.acp$li, xax=1, yax=2)
spectre2=spectre[-16,] # on enleve la 15e valeur
spectre2.acp <- dudi.pca(spectre2, scale=FALSE, center=FALSE)
s.label(spectre2.acp$li, xax=1, yax=2)
plot(spectre2.acp$li)
plot(spectre2.acp$li[,1], spectre2.acp$li[,2], type="n")
ylabel= sprintf("%4.2f", densite1)
text(ylabel)
text(spectre2.acp$li[,1], spectre2.acp$li[,2],ylabel)
kspectre=kmeans(spectre2, centers=2)
fac=as.factor(kspectre$cluster)
dis<-discrimin(spectre2.acp, fac)
kspectre=kmeans(spectre2, centers=2)
fac=kspectre$cluster
lda<-lda(spectre2.acp$li, fac)
plot(lda)
rm(list=ls())
library(MASS)
library(ade4)
rep="/Users/Francois/Documents/Cours/AD/FW_TPnote/Fichiers_cours/"
fic="Xcal_6.txt"
nomX=paste(rep,fic,sep="")
Xcal_6 <- read.table(nomX,header = FALSE)
# lecture des densités
fic="ycal_6.txt"
nomY=paste(rep,fic,sep="")
ycal_6 <-read.table(nomY,header=FALSE)
matriceX = as.matrix(Xcal_6)
matriceY = as.matrix(ycal_6)
plot(matriceX[1,], type='l')
acp = dudi.pca(matriceX, scannf=FALSE, nf=3)
plot(acp$li)
h0 = hclust(dist(acp$li), "average")
plot(h0, main='Dendogramme')
fac = cutree(h0, k=2)
parti = as.factor(cutree(h0, k = 2))
s.class(acp$li, parti, sub = "average", csub = 2, col=c('blue','red'))
plot(acp$li[,1], acp$li[,2], type='n')
ylabel = sprintf("%4.2f", matriceY)
text(acp$li[,1], acp$li[,2], ylabel)
lda=lda(acp$li, as.character(fac))
nb = 0
for (i in 1:100){
p = predict(lda, acp$li[i,])$class
if(p == fac[i]){
nb = nb+1
}
}
prop = nb/length(fac)
rep="/Users/Francois/Documents/Cours/AD/FW_TPnote/Fichiers_cours/"
fic="Xcal_4.txt"
nomX=paste(rep,fic,sep="")
Xcal_4 <- read.table(nomX,header = FALSE)
rep="/Users/Francois/Documents/Cours/AD/FW_TPnote/Fichiers_cours/"
fic="Xcal_4.txt"
nomX=paste(rep,fic,sep="")
Xcal_4 <- read.table(nomX,header = FALSE)
plot(Xcal_4[1,],type='l')
plot(Xcal_4[1,],type="l")
Xcal_4=as.matrix(Xcal_4)
plot(Xcal_4[1,],type="l")
library(ade4)
library(MASS)
acp=dudi.pca(Xcal_4,scannf=FALSE,nf=3,center=FALSE,scale=FALSE)
biplot(acp)
acp$eig[1:3]
acp=dudi.pca(Xcal_4,nf=3,center=FALSE,scale=FALSE)
summary(acp)
plot(acp$li)
plot(acp$li[,1],acp$li[,2])
s.label(acp$li,xax=1,yax=2)
library(ade4)
library(MASS)
rep="/Users/Francois/Documents/Cours/AD/FW_TPnote/Fichiers_cours/"
fic="Xcal_7.txt"
nomX=paste(rep,fic,sep="")
x <- read.table(nomX,header = FALSE)
# lecture des densités
fic="ycal_7.txt"
nomY=paste(rep,fic,sep="")
y <-read.table(nomY,header=FALSE)
matx = as.matrix(x)
maty = as.matrix(y)
plot(matx[1,], type = 'l',ylab= ' ', xlab = "n? du nombre d onde", main = "1er spectre")
acp=dudi.pca(matx, scannf=F, nf=3, scale=FALSE, center=FALSE)
plot(acp$li[,1], acp$li[,2])
plot(acp$li[,1], acp$li[,3])
plot(acp$li[,2], acp$li[,3])
s.label(acp$li)
matx = matx[-47,] #On retire la ligne 47
acp=dudi.pca(matx, scannf=F, nf=3, scale=FALSE, center=FALSE)
plot(acp$li)
s.label(acp$li)
dx = dist(matx)
plot(hclust(dx,"average"))
fac = cutree(hclust(dx,"average"), k=2)
col = c("blue", "red")
s.class(acp$li,fac=as.factor(fac), col = col)
plot(acp$li[,1], acp$li[,2], type = 'n')
ylabel = sprintf("%4.2f",maty)
text(acp$li[,1], acp$li[,2], ylabel)
lda = lda(acp$li, as.character(fac))
nb = 0
for (i in 1:100){
p = predict(lda, acp$li[i,])$class
if(p == fac[i]){
nb = nb+1
}
}
prop = nb/length(fac)
nb = 0
for (i in 1:100){
p = predict(lda, acp$li[i,])$class
if(p == fac[i]){
nb = nb+1
}
}
dim(acp$li)
nb = 0
for (i in 1:99){
p = predict(lda, acp$li[i,])$class
if(p == fac[i]){
nb = nb+1
}
}
prop = nb/length(fac)
library(fpc)
setwd("~/Documents/AVERBUCH/pgm/WORK")
install.packages("MonoPoly")
help(MonoPoly)
library(MonoPoly)
help(MonoPoly)
beta <- c(1,2,1)
x <- 0:10
evalPol(x, beta)
str(evalPol(x, beta))
monpol(y~x, w0)
w0
plot(x,y)
plot(w0.x,w0.y)
w0
plot(w0$x,w0$y)
FW=monpol(y~x, w0)
FW
FW=monpol(y~x, w0,degree=4)
FW=monpol(y~x, w0,degree=5)
w0$y
FW=monpol(y~x, w0,degree=3)
FW
FW=monpol(y~x, w0,degree=5)
FW
fitted(FW)
yhat=fitted(FW)
(w0$y-yhat)^2
sqrt(1/21*sum((w0$y-yhat)^2))
plot(w0$x,yhat)
FW=monpol(y~x, w1,degree=5)
FW=monpol(y~x, w2,degree=5)
yhat=fitted(FW)
plot(w2$x,yhat)
x=c(0.0759666916908419
0.123318934835166
0.183907788282417
0.239916153553658
0.239952525664903
0.401808033751942
0.417267069084370)
x=c(0.0759666916908419,
0.123318934835166,
0.183907788282417,
0.239916153553658,
0.239952525664903,
0.401808033751942,
0.417267069084370)
x
y=c(0.0759666916908419,
0.123318934835166,
0.183907788282417,
0.239916153553658,
0.239952525664903,
0.401808033751942,
0.417267069084370)
xy=data.frame(x=x,y,y)
xy=data.frame(x=x,y=y)
xy
FW=monpol(y~x, xy,degree=3)
FW
yhat=fitted(FW)
sqrt(1/length(xy)*sum((w0$y-yhat)^2))
yhat
length(xy)
sqrt(1/7*sum((xy$y-yhat)^2))
sqrt(1/7*sum((xy$y-yhat)^2))
plot(xy$x,yhat)
