test auto-indenter

[valse.git] / README.md

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-# model SELECTion
+# VAriable seLection with mixtureS of modEls
 
-This code is the applied part of the PhD thesis of [Emilie Devijver](http://www.math.u-psud.fr/~devijver/).
+This code is the applied part of the PhD thesis of [Benjamin Gohehry](http://www.math.u-psud.fr/~goehry/).
 
 ## Description
 
-The function selmix delivers a multivariate Gaussian mixture in regression model collection. 
-According to the parameter estimation, we can compute classical model selection criterion, as BIC or AIC, or slope heuristic, using the CAPUSHE package. 
-The methodology used is described in 'Model-Based Clustering for High-Dimensional Data. Application to Functional Data.', 
-available at [this location](https://hal.archives-ouvertes.fr/hal-01060063)
+TODO : see R package
 
-## Arguments
+Trouver un jeu de données (+) intéressant (que les autres) ?
+Ajouter toy datasets pour les tests (ou piocher dans MASS ?)
 
-Regressors, denoted by X (of size n x p) and responses, denoted by Y (of size n x q) are must-have arguments. 
-
-Optionally, we could add
-
-* gamma: weight power in the Lasso penalty (according to Stadler et al., $\gamma \in \{0,1/2,1\}$;
-* mini: the minimum number of iterations;
-* maxi: the maximum number of iterations;
-* tau: the threshold for stopping EM algorithm;
-* kmin and kmax: the bounds of interesting number of components,
-* rangmin and rangmax: the bounds of interesting rank values.
-
-## Usage
-
-       objet = selmix(X,Y)
-       objet.run(index)
-
-For index=1, it computes the Lasso-MLE procedure.
-For index=2, it computes the Lasso-Rank procedure.
-
-/!\ Be careful to the current path /!\
-
-## Values
-
-* phiInit, rhoInit, piInit, gamInit: the initialization of the matrices phi, rho, pi and gamma,
-* gridLambda: grid of regularization parameters used to select relevant variables (if kmax-kmin=0, it is, if not, it is the last grid of regularization parameters)
-* A1,A2: indices of variables selected or not selected (matrices of size (p+1) x q x size(gridLambda))
-* Phi,Rho,Pi: estimations of each parameter thanks to the procedure LassoMLE if compute index=1, and thanks to the procedure LassoRank if computed index=2.
-
-
-## Example
-
-       n=10;
-       p=10;
-       q=5;
-       X=randn(n,p);
-       Y=randn(n,q);
-
-       objet=selmix(X,Y);
-       objet.run(1);
-       objet.run(2);
+ED : j'ai simulé un truc basique via 'generateXYdefault(10,5,6,2)'