X-Git-Url: https://git.auder.net/?a=blobdiff_plain;ds=sidebyside;f=vignettes%2Freport.Rmd;h=a67223be38376248d280b6b5911ec596f2627613;hb=cff1083b37d40af662fab47bbc698a63a001de1b;hp=41497177de20a8c05c9e2bafbbe7bb51cd3dd9c6;hpb=3d5b50604d7e63fa0a0d6c37d34f6a4595bcfd34;p=morpheus.git

diff --git a/vignettes/report.Rmd b/vignettes/report.Rmd
index 4149717..a67223b 100644
--- a/vignettes/report.Rmd
+++ b/vignettes/report.Rmd
@@ -14,149 +14,22 @@ knitr::opts_chunk$set(echo = TRUE, include = TRUE,
 ```
 
 0) Tell that we try to learn classification parameters in a non-EM way, using algebric manipulations.
-1) Model.
-2) Algorithm (as in article)
-3) Experiments: show package usage
-
-# Expériences
-
-```{r, results="show", include=TRUE, echo=TRUE}
-library(Rmixmod) #to get clustering probability matrix
-library(ClusVis)
-library(FactoMineR) #for PCA
 
-plotPCA <- function(prob)
-{
-  par(mfrow=c(2,2), mar=c(4,4,2,2), mgp=c(2,1,0))
-  partition <- apply(prob, 1, which.max)
-  n <- nrow(prob)
-  K <- ncol(prob)
-  palette <- rainbow(K, s=.5)
-  cols <- palette[partition]
-  tmp <- PCA(rbind(prob, diag(K)), ind.sup=(n+1):(n+K), scale.unit=F, graph=F)
-  scores <- tmp$ind$coord[,1:3] #samples coords, by rows
-  ctrs <- tmp$ind.sup$coord #projections of indicator vectors (by cols)
-  for (i in 1:2)
-  {
-    for (j in (i+1):3)
-    {
-      absc <- scores[,i]
-      ords <- scores[,j]
-      xrange <- range(absc)
-      yrange <- range(ords)
-      plot(absc, ords, col=c(cols,rep(colors()[215],K),rep(1,K)),
-        pch=c(rep("o",n),rep(as.character(1:K),2)),
-        xlim=xrange, ylim=yrange,
-        xlab=paste0("Dim ", i, " (", round(tmp$eig[i,2],2), "%)"),
-        ylab=paste0("Dim ", j, " (", round(tmp$eig[j,2],2), "%)"))
-      ctrsavg <- t(apply(as.matrix(palette), 1,
-        function(cl) c(mean(absc[cols==cl]), mean(ords[cols==cl]))))
-			text(ctrsavg[,1], ctrsavg[,2], as.character(1:K), col=colors()[215])
-			text(ctrs[,i], ctrs[,j], as.character(1:K), col=1)
-			title(paste0("PCA ", i, "-", j, " / K=",K))
-    }
-  }
-  # TODO:
-  plot(0, xaxt="n", yaxt="n", xlab="", ylab="", col="white", bty="n")
-}
+*morpheus* is a contributed R package which attempts to find the parameters of a mixture of logistic classifiers.
+When the data under study come from several groups that have different characteristics, using mixture models is a very popular way to handle heterogeneity.
+Thus, many algorithms were developed to deal with various mixtures models. Most of them use likelihood methods or Bayesian methods that are likelihood dependent.
+*flexmix* is an R package which implements these kinds of algorithms.
 
-plotClvz <- function(xem, alt=FALSE)
-{
-  par(mfrow=c(2,2), mar=c(4,4,2,2), mgp=c(2,1,0))
-  if (alt) {
-    resvisu <- clusvis(log(xem@bestResult@proba), xem@bestResult@parameters@proportions)
-  } else {
-    resvisu <- clusvisMixmod(xem)
-  }
-  plotDensityClusVisu(resvisu, positionlegend=NULL)
-  plotDensityClusVisu(resvisu, add.obs=TRUE, positionlegend=NULL)
-  # TODO:
-  plot(0, xaxt="n", yaxt="n", xlab="", ylab="", col="white", bty="n")
-  plot(0, xaxt="n", yaxt="n", xlab="", ylab="", col="white", bty="n")
-}
+However, one problem of such methods is that they can converge to local maxima, so several starting points must be explored.
+Recently, spectral methods were developed to bypass EM algorithms and they were proved able to recover the directions of the regression parameter
+in models with known link function and random covariates (see [9]).
+Our package extends such moment methods using least squares to get estimators of the whole parameters (with theoretical garantees, see [XX]).
+Currently it can handle only binary output $-$ which is a common case.
 
-grlplot <- function(x, K, alt=FALSE) #x: data, K: nb classes
-{
-  xem <- mixmodCluster(x, K, strategy=mixmodStrategy(nbTryInInit=500,nbTry=25))
-  plotPCA(xem@results[[1]]@proba)
-  plotClvz(xem, alt)
-}
-```
+1) Model.
 
-## Iris data
+TODO: retrouver mon texte initial + article.
 
-```{r, results="show", include=TRUE, echo=TRUE}
-data(iris)
-x <- iris[,-5] #remove class info
-for (i in 3:5)
-{
-  print(paste("Resultats en", i, "classes"))
-  grlplot(x, i)
-}
-```
+2) Algorithm (as in article)
 
-### finance dataset (from Rmixmod package)
-#
-#This dataset has two categorical attributes (the year and financial status), and four continuous ones.
-#
-#Warnings, some probabilities of classification are exactly equal to zero then we cannot use ClusVis
-#
-#```{r, results="show", include=TRUE, echo=TRUE}
-#data(finance)
-#x <- finance[,-2]
-#for (i in 3:5)
-#{
-#  print(paste("Resultats en", i, "classes"))
-#  grlplot(x, i, TRUE)
-#}
-#```
-#
-### "Cathy dataset" (12 clusters)
-#
-#Warnings, some probabilities of classification are exactly equal to zero then we cannot use ClusVis
-#
-#```{r, results="hide", include=TRUE, echo=TRUE}
-#cathy12 <- as.matrix(read.table("data/probapostCatdbBlocAtrazine-K12.txt"))
-#resvisu <- clusvis(log(cathy12), prop = colMeans(cathy12))
-#par(mfrow=c(2,2), mar=c(4,4,2,2), mgp=c(2,1,0))
-#plotDensityClusVisu(resvisu, positionlegend = NULL)
-#plotDensityClusVisu(resvisu,  add.obs = TRUE, positionlegend = NULL)
-#plotPCA(cathy12)
-#```
-#
-### Pima indian diabete
-#
-#[Source.](https://gist.github.com/ktisha/c21e73a1bd1700294ef790c56c8aec1f)
-#
-#```{r, results="show", include=TRUE, echo=TRUE}
-#load("data/pimaData.rda")
-#for (i in 3:5)
-#{
-#  print(paste("Resultats en", i, "classes"))
-#  grlplot(x, i)
-#}
-#```
-#
-### Breast cancer
-#
-#[Source.](http://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+\%28diagnostic\%29)
-#
-#```{r, results="show", include=TRUE, echo=TRUE}
-#load("data/wdbc.rda")
-#for (i in 3:5)
-#{
-#  print(paste("Resultats en", i, "classes"))
-#  grlplot(x, i)
-#}
-#```
-#
-### House-votes
-#
-#```{r, results="show", include=TRUE, echo=TRUE}
-#load("data/house-votes.rda")
-#for (i in 3:5)
-#{
-#  print(paste("Resultats en", i, "classes"))
-#  grlplot(x, i)
-#}
-#```
+3) Experiments: show package usage