X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=.Rhistory;fp=.Rhistory;h=0000000000000000000000000000000000000000;hb=53fa233d8fbeaf4d51a4874ba69d8472d01d04ba;hp=6616dbe0fcc95e8cd68c1b6e45c962ab88f4ce4d;hpb=e54d1bb9c8788781c0a2bd911bb14a087a98e7bf;p=valse.git

diff --git a/.Rhistory b/.Rhistory
deleted file mode 100644
index 6616dbe..0000000
--- a/.Rhistory
+++ /dev/null
@@ -1,512 +0,0 @@
-lines(1:15, rep(0,15), col='red')
-N       = 300
-mugamma = c(1,2,3)
-Gamma   = matrix(c(5,-0.02,0.01,-0.02,1,0.1,0.01,0.1,1),3,3)
-s2      = 0.01
-Si      = getbasismatrix(seq(0,1,length=15),create.fourier.basis(nbasis = 3))
-gammai  = t(sapply(1:N,FUN = function(i){
-rmvnorm(1,mugamma,Gamma)
-}))
-Xdata   = t(sapply(1:N,FUN = function(i){
-as.vector(Si%*%gammai[i,])+rnorm(15,0,s2)
-}))
-matplot(t(Xdata),type="l")
-#beta = c(.1,.5,-.3)
-beta = c(1,-3.5,4.5,-2.5)
-pydata = sapply(1:N,FUN=function(i){
-a = sum(beta*c(1,gammai[i,]))
-return(exp(a)/(1+exp(a)))
-})
-U     = runif(length(pydata))
-ydata = as.numeric(U<pydata)
-plot(sort(pydata),col=ydata[order(pydata)]+2)
-wi = Si %*% beta[-1]
-plot(wi, type='l')
-lines(1:15,rep(0,15))
-plot( apply(t(Xdata[ydata==1,]),1,mean)-apply(t(Xdata[ydata==0,]),1,mean),typ="l",lty=1,col="green")
-for (ite in 1:30){
-print(ite)
-Xdata   = t(sapply(1:N,FUN = function(i){
-as.vector(Si%*%gammai[i,])+rnorm(15,0,s2)
-}))
-#beta = c(.1,.5,-.3)
-beta = c(1,-3.5,4.5,-2.5)
-pydata = sapply(1:N,FUN=function(i){
-a = sum(beta*c(1,gammai[i,]))
-return(exp(a)/(1+exp(a)))
-})
-U     = runif(length(pydata))
-ydata = as.numeric(U<pydata)
-lines( apply(t(Xdata[ydata==1,]),1,mean)-apply(t(Xdata[ydata==0,]),1,mean),col="green")
-}
-lines(1:15, rep(0,15), col='red')
-matplot(Xdata)
-matplot(Xdata,type='l')
-matplot(t(Xdata),type='l')
-matplot(apply(Xdata, 2, mean),type='l')
-mean(apply(Xdata, 2, mean))
-N       = 3000
-mugamma = c(1,2,3)
-Gamma   = matrix(c(5,-0.02,0.01,-0.02,1,0.1,0.01,0.1,1),3,3)
-s2      = 0.01
-Si      = getbasismatrix(seq(0,1,length=15),create.fourier.basis(nbasis = 3))
-gammai  = t(sapply(1:N,FUN = function(i){
-rmvnorm(1,mugamma,Gamma)
-}))
-Xdata   = t(sapply(1:N,FUN = function(i){
-as.vector(Si%*%gammai[i,])+rnorm(15,0,s2)
-}))
-matplot(t(Xdata),type="l")
-#beta = c(.1,.5,-.3)
-beta = c(1,-3.5,4.5,-2.5)
-pydata = sapply(1:N,FUN=function(i){
-a = sum(beta*c(1,gammai[i,]))
-return(exp(a)/(1+exp(a)))
-})
-U     = runif(length(pydata))
-ydata = as.numeric(U<pydata)
-plot(sort(pydata),col=ydata[order(pydata)]+2)
-wi = Si %*% beta[-1]
-plot(wi, type='l')
-lines(1:15,rep(0,15))
-plot( apply(t(Xdata[ydata==1,]),1,mean)-apply(t(Xdata[ydata==0,]),1,mean),typ="l",lty=1,col="green")
-for (ite in 1:30){
-print(ite)
-Xdata   = t(sapply(1:N,FUN = function(i){
-as.vector(Si%*%gammai[i,])+rnorm(15,0,s2)
-}))
-#beta = c(.1,.5,-.3)
-beta = c(1,-3.5,4.5,-2.5)
-pydata = sapply(1:N,FUN=function(i){
-a = sum(beta*c(1,gammai[i,]))
-return(exp(a)/(1+exp(a)))
-})
-U     = runif(length(pydata))
-ydata = as.numeric(U<pydata)
-lines( apply(t(Xdata[ydata==1,]),1,mean)-apply(t(Xdata[ydata==0,]),1,mean),col="green")
-}
-lines(1:15, rep(0,15), col='red')
-Gamma
-Gamma   = matrix(c(5,-0.02,0.01,-0.02,5,0.1,0.01,0.1,5),3,3)
-s2      = 0.01
-Si      = getbasismatrix(seq(0,1,length=15),create.fourier.basis(nbasis = 3))
-gammai  = t(sapply(1:N,FUN = function(i){
-rmvnorm(1,mugamma,Gamma)
-}))
-Xdata   = t(sapply(1:N,FUN = function(i){
-as.vector(Si%*%gammai[i,])+rnorm(15,0,s2)
-}))
-matplot(t(Xdata),type="l")
-#beta = c(.1,.5,-.3)
-plot(sort(sapply(1:N,FUN=function(i) 1/(1+exp((-1)*(resEM$betah[1,ITfinal]+sum(resEM$betah[-1,ITfinal]*resEM$gammahat[i,]))))) ))
-plot(wi, type='l')
-wi = Si %*% beta[-1]
-plot(wi, type='l')
-lines(1:15,rep(0,15))
-plot( apply(t(Xdata[ydata==1,]),1,mean)-apply(t(Xdata[ydata==0,]),1,mean),typ="l",lty=1,col="green")
-for (ite in 1:30){
-print(ite)
-Xdata   = t(sapply(1:N,FUN = function(i){
-as.vector(Si%*%gammai[i,])+rnorm(15,0,s2)
-}))
-#beta = c(.1,.5,-.3)
-beta = c(1,-3.5,4.5,-2.5)
-pydata = sapply(1:N,FUN=function(i){
-a = sum(beta*c(1,gammai[i,]))
-return(exp(a)/(1+exp(a)))
-})
-U     = runif(length(pydata))
-ydata = as.numeric(U<pydata)
-lines( apply(t(Xdata[ydata==1,]),1,mean)-apply(t(Xdata[ydata==0,]),1,mean),col="green")
-}
-lines(1:15, rep(0,15), col='red')
-N       = 200
-M       = 100
-mugamma = c(1,2,3,2,4,6)
-LGamma  = matrix(0,nrow=6,ncol=6)
-LGamma[lower.tri(LGamma,diag=FALSE)] = rnorm(6*5/2,0,0.05)
-LGamma  = LGamma + diag(runif(6,0.2,1))
-Gamma   = LGamma%*%t(LGamma)
-s2      = 0.2
-freq      = seq(0,1,length=M)
-intervals = list(12:25,67:89)
-Si        = as.matrix(bdiag(getbasismatrix(freq[12:25],create.fourier.basis(nbasis = 3)), getbasismatrix(freq[67:89],create.fourier.basis(nbasis = 3))))
-gammai  = t(sapply(1:N,FUN = function(i){
-rmvnorm(1,mugamma,Gamma)
-}))
-Xdata   = t(sapply(1:N,FUN = function(i){
-as.vector(Si%*%gammai[i,])+rnorm(nrow(Si),0,s2)
-}))
-xdata = matrix(0,nrow=N,ncol=M)
-xdata[,intervals[[1]] ] = Xdata[,1:14]
-xdata[,intervals[[2]] ] = Xdata[,15:37]
-beta = c(2,0.5,-2,1,0.2,1,-1)
-pydata = sapply(1:N,FUN=function(i){
-a = sum(beta*c(1,gammai[i,]))
-return(exp(a)/(1+exp(a)))
-})
-U     = runif(length(pydata))
-ydata = as.numeric(U<pydata)
-plot(sort(pydata),col=ydata[order(pydata)]+1)
-nBasis    = c(3,3)
-L   = 20
-eer = rep(0,L)
-for (ell in 1:L){
-kapp      = sample(1:200,0.8*200,replace=FALSE)
-xdata.app = xdata[kapp,]
-ydata.app = ydata[kapp]
-ktest      = setdiff(1:200,kapp)
-xdata.test = xdata[ktest,]
-ydata.test = ydata[ktest]
-resEM    = EM.Bands.function(ydata,xdata,freq,intervals,nBasis,MCit=1000,25,eps=10^-8,keep=TRUE)
-Yres     = Y.predB(xdata.test,freq,intervals,resEM$muh,resEM$Gammah,resEM$s2h,resEM$betah,nBasis=c(3,3))
-eer[ell] = mean(Yres$Ypred==ydata.test)
-cat("\n", "ell =", ell)
-}
-source('~/Dropbox/projet spectres/algoEM/EMalgorithmBandes.R')
-for (ell in 1:L){
-kapp      = sample(1:200,0.8*200,replace=FALSE)
-xdata.app = xdata[kapp,]
-ydata.app = ydata[kapp]
-ktest      = setdiff(1:200,kapp)
-xdata.test = xdata[ktest,]
-ydata.test = ydata[ktest]
-resEM    = EM.Bands.function(ydata,xdata,freq,intervals,nBasis,MCit=1000,25,eps=10^-8,keep=TRUE)
-Yres     = Y.predB(xdata.test,freq,intervals,resEM$muh,resEM$Gammah,resEM$s2h,resEM$betah,nBasis=c(3,3))
-eer[ell] = mean(Yres$Ypred==ydata.test)
-cat("\n", "ell =", ell)
-}
-N       = 3000
-mugamma = c(1,2,3)
-Gamma   = matrix(c(5,-0.02,0.01,-0.02,5,0.1,0.01,0.1,5),3,3)
-s2      = 0.01
-Si      = getbasismatrix(seq(0,1,length=15),create.fourier.basis(nbasis = 3))
-gammai  = t(sapply(1:N,FUN = function(i){
-rmvnorm(1,mugamma,Gamma)
-}))
-Xdata   = t(sapply(1:N,FUN = function(i){
-as.vector(Si%*%gammai[i,])+rnorm(15,0,s2)
-}))
-matplot(t(Xdata),type="l")
-#beta = c(.1,.5,-.3)
-beta = c(1,-3.5,4.5,-2.5)
-pydata = sapply(1:N,FUN=function(i){
-a = sum(beta*c(1,gammai[i,]))
-return(exp(a)/(1+exp(a)))
-})
-U     = runif(length(pydata))
-ydata = as.numeric(U<pydata)
-plot(sort(pydata),col=ydata[order(pydata)]+2)
-wi = Si %*% beta[-1]
-plot(wi, type='l')
-lines(1:15,rep(0,15))
-plot( apply(t(Xdata[ydata==1,]),1,mean)-apply(t(Xdata[ydata==0,]),1,mean),typ="l",lty=1,col="green")
-for (ite in 1:30){
-print(ite)
-Xdata   = t(sapply(1:N,FUN = function(i){
-as.vector(Si%*%gammai[i,])+rnorm(15,0,s2)
-}))
-#beta = c(.1,.5,-.3)
-beta = c(1,-3.5,4.5,-2.5)
-pydata = sapply(1:N,FUN=function(i){
-a = sum(beta*c(1,gammai[i,]))
-return(exp(a)/(1+exp(a)))
-})
-U     = runif(length(pydata))
-ydata = as.numeric(U<pydata)
-lines( apply(t(Xdata[ydata==1,]),1,mean)-apply(t(Xdata[ydata==0,]),1,mean),col="green")
-}
-lines(1:15, rep(0,15), col='red')
-N       = 3000
-mugamma = c(1,2,3)
-Gamma   = matrix(c(5,-0.02,0.01,-0.02,1,0.1,0.01,0.1,5),3,3)
-s2      = 0.01
-Si      = getbasismatrix(seq(0,1,length=15),create.fourier.basis(nbasis = 3))
-gammai  = t(sapply(1:N,FUN = function(i){
-rmvnorm(1,mugamma,Gamma)
-}))
-Xdata   = t(sapply(1:N,FUN = function(i){
-as.vector(Si%*%gammai[i,])+rnorm(15,0,s2)
-}))
-matplot(t(Xdata),type="l")
-#beta = c(.1,.5,-.3)
-beta = c(1,-3.5,4.5,-2.5)
-pydata = sapply(1:N,FUN=function(i){
-a = sum(beta*c(1,gammai[i,]))
-return(exp(a)/(1+exp(a)))
-})
-U     = runif(length(pydata))
-ydata = as.numeric(U<pydata)
-plot(sort(pydata),col=ydata[order(pydata)]+2)
-# ydata = ydata.noise
-wi = Si %*% beta[-1]
-plot(wi, type='l')
-lines(1:15,rep(0,15))
-plot( apply(t(Xdata[ydata==1,]),1,mean)-apply(t(Xdata[ydata==0,]),1,mean),typ="l",lty=1,col="green")
-for (ite in 1:30){
-print(ite)
-Xdata   = t(sapply(1:N,FUN = function(i){
-as.vector(Si%*%gammai[i,])+rnorm(15,0,s2)
-}))
-#beta = c(.1,.5,-.3)
-beta = c(1,-3.5,4.5,-2.5)
-pydata = sapply(1:N,FUN=function(i){
-a = sum(beta*c(1,gammai[i,]))
-return(exp(a)/(1+exp(a)))
-})
-U     = runif(length(pydata))
-ydata = as.numeric(U<pydata)
-lines( apply(t(Xdata[ydata==1,]),1,mean)-apply(t(Xdata[ydata==0,]),1,mean),col="green")
-}
-lines(1:15, rep(0,15), col='red')
-matplot(t(Xdata[ydata==1,]),typ="l",lty=1,col="red")
-matplot(t(Xdata[ydata==0,]),typ="l",lty=1,col="black",add=TRUE)
-N       = 200
-M       = 100
-mugamma = c(1,2,3,2,4,6)
-LGamma  = matrix(0,nrow=6,ncol=6)
-LGamma[lower.tri(LGamma,diag=FALSE)] = rnorm(6*5/2,0,0.05)
-LGamma  = LGamma + diag(runif(6,0.2,1))
-Gamma   = LGamma%*%t(LGamma)
-s2      = 0.2
-freq      = seq(0,1,length=M)
-intervals = list(12:25,67:89)
-Si        = as.matrix(bdiag(getbasismatrix(freq[12:25],create.fourier.basis(nbasis = 3)), getbasismatrix(freq[67:89],create.fourier.basis(nbasis = 3))))
-gammai  = t(sapply(1:N,FUN = function(i){
-rmvnorm(1,mugamma,Gamma)
-}))
-Xdata   = t(sapply(1:N,FUN = function(i){
-as.vector(Si%*%gammai[i,])+rnorm(nrow(Si),0,s2)
-}))
-xdata = matrix(0,nrow=N,ncol=M)
-xdata[,intervals[[1]] ] = Xdata[,1:14]
-xdata[,intervals[[2]] ] = Xdata[,15:37]
-beta = c(2,0.5,-2,1,0.2,1,-1)
-pydata = sapply(1:N,FUN=function(i){
-a = sum(beta*c(1,gammai[i,]))
-return(exp(a)/(1+exp(a)))
-})
-U     = runif(length(pydata))
-ydata = as.numeric(U<pydata)
-plot(sort(pydata),col=ydata[order(pydata)]+1)
-nBasis    = c(3,3)
-resEM = EM.Bands.function(ydata,xdata,freq,intervals,nBasis,MCit=500,100,eps=10^-8,keep=TRUE)
-source('~/Dropbox/projet spectres/algoEM/EMalgorithmBandes.R')
-resEM = EM.Bands.function(ydata,xdata,freq,intervals,nBasis,MCit=500,100,eps=10^-8,keep=TRUE)
-L   = 20
-eer = rep(0,L)
-for (ell in 1:L){
-kapp      = sample(1:200,0.8*200,replace=FALSE)
-xdata.app = xdata[kapp,]
-ydata.app = ydata[kapp]
-ktest      = setdiff(1:200,kapp)
-xdata.test = xdata[ktest,]
-ydata.test = ydata[ktest]
-resEM    = EM.Bands.function(ydata,xdata,freq,intervals,nBasis,MCit=1000,25,eps=10^-8,keep=FALSE)
-Yres     = Y.predB(xdata.test,freq,intervals,resEM$muh,resEM$Gammah,resEM$s2h,resEM$betah,nBasis=c(3,3))
-eer[ell] = mean(Yres$Ypred==ydata.test)
-cat("\n", "ell =", ell)
-}
-source('~/Dropbox/projet spectres/algoEM/EMalgorithmBandes.R')
-for (ell in 1:L){
-kapp      = sample(1:200,0.8*200,replace=FALSE)
-xdata.app = xdata[kapp,]
-ydata.app = ydata[kapp]
-ktest      = setdiff(1:200,kapp)
-xdata.test = xdata[ktest,]
-ydata.test = ydata[ktest]
-resEM    = EM.Bands.function(ydata,xdata,freq,intervals,nBasis,MCit=1000,25,eps=10^-8,keep=FALSE)
-Yres     = Y.predB(xdata.test,freq,intervals,resEM$muh,resEM$Gammah,resEM$s2h,resEM$betah,nBasis=c(3,3))
-eer[ell] = mean(Yres$Ypred==ydata.test)
-cat("\n", "ell =", ell)
-}
-eer
-mean(eer)
-source('~/Dropbox/projet spectres/cluster/EMLiqArtBandeHugues.R')
-library(fda)
-library(Matrix)
-library(mvtnorm)
-source('EMalgorithm.R')
-source('EMLiqArtBandeHugues.R')
-for (nVC in 1:100){
-EMLiqArtBandeHugues(nVC)
-}
-setwd("~/Dropbox/projet spectres/cluster")
-library(fda)
-library(Matrix)
-library(mvtnorm)
-source('EMalgorithm.R')
-source('EMLiqArtBandeHugues.R')
-for (nVC in 1:100){
-EMLiqArtBandeHugues(nVC)
-}
-sims= c(63:67,69,71:74,76:85,88:90,93:100)
-#simulation_settings=expand.grid(c(1,2,3,4,5),c(1,2),c(600),c(300))
-#for (i in 1:nrow(simulation_settings)){
-aa=cbind(sims,rep(9, each =length(sims)))
-colnames(aa)=c("isim","model")
-write.table(aa,file=paste("SimulationSettings9.txt",sep=""),row.names=FALSE,sep=",")
-setwd("~/Dropbox/WarpMixedModel/warpingMixedModel/SuperComputer")
-write.table(aa,file=paste("SimulationSettings9.txt",sep=""),row.names=FALSE,sep=",")
-sims= 1:100
-colnames(sims) = "isim"
-sims= 1:100
-#simulation_settings=expand.grid(c(1,2,3,4,5),c(1,2),c(600),c(300))
-#for (i in 1:nrow(simulation_settings)){
-aa=cbind(sims,rep(10, each =length(sims)))
-colnames(aa)=c("isim","model")
-write.table(aa,file=paste("SimulationSettings10.txt",sep=""),row.names=FALSE,sep=",")
-load("~/valse/data/data.RData")
-load("~/valse/data/data.RData")
-X
-Y
-library("devtools", lib.loc="~/R/x86_64-pc-linux-gnu-library/3.3")
-library(roxygen2)
-setwd("~/valse")
-document()
-document()
-setwd("~/")
-install("valse")
-c(9.75,
-20,
-11.75,
-11,
-8.25,
-12.5,
-11,
-18,
-7,
-12.75,
-13,
-14.75,
-4.75,
-11,
-20,
-13.5,
-8,
-13.25,
-6,
-17.5,
-13.25,
-8.5,
-9.5,
-16,
-8,
-9.5,
-10.25,
-13.75,
-9,
-14.75,
-12.5,
-19.5,
-17.5,
-7,
-11.5,
-4,
-7.5,
-13.25,
-10.5
-)
-notes = c(9.75,
-20,
-11.75,
-11,
-8.25,
-12.5,
-11,
-18,
-7,
-12.75,
-13,
-14.75,
-4.75,
-11,
-20,
-13.5,
-8,
-13.25,
-6,
-17.5,
-13.25,
-8.5,
-9.5,
-16,
-8,
-9.5,
-10.25,
-13.75,
-9,
-14.75,
-12.5,
-19.5,
-17.5,
-7,
-11.5,
-4,
-7.5,
-13.25,
-10.5
-)
-hist(notes)
-hist(notes, nclass = 20)
-notesBis = c(7.375,19.375,10.125,9,6,12.25,11,18.75,10.5,11.5,15.5,13,2.375,12.75,17.75,15.375,8.125,16,8.5,14.5,11.625,11.25,9.625,14,
-6.5,11.375,11.875,16.25,10.125,12,6.25,9.75,8.75,3.5,0,0,0,5.75,2,3.75,6.625,5.25)
-hist(notesBis)
-notes = c(7,
-19,
-10,
-9,
-6,
-12,
-11,
-19,
-11,
-12,
-16,
-13,
-2,
-13,
-18,
-15,
-8,
-16,
-9,
-15,
-12,
-11,
-10,
-14,
-7,
-11,
-12,
-16,
-13,
-15,
-14,
-19,
-14,
-4,  9,
-8,
-7,
-8,
-8
-)
-hist(notes)
-shiny::runApp('Dropbox/cranview-master')
-packages = 'shock'
-min_date = Sys.Date() - 1
-for (pkg in packages)
-{
-# api data for package. we want the initial release - the first element of the "timeline"
-pkg_data = httr::GET(paste0("http://crandb.r-pkg.org/", pkg, "/all"))
-pkg_data = httr::content(pkg_data)
-initial_release = pkg_data$timeline[[1]]
-min_date = min(min_date, as.Date(initial_release))
-}
-min_date
-load("~/valse/data/data.RData")
-k = 2
-init = initSmallEM(k, X, Y)
-setwd("~/valse")
-library("devtools", lib.loc="~/R/x86_64-pc-linux-gnu-library/3.3")
-library("roxygen2", lib.loc="~/R/x86_64-pc-linux-gnu-library/3.3")