%\VignetteIndexEntry{Learning With MixAll}
%\VignetteKeywords{Rcpp, C++, STK++, Learning, Missing Values}
%\VignettePackage{MixAll}

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\SweaveOpts{concordance=TRUE}

<<prelim,echo=FALSE,print=FALSE>>=
library(MixAll)
MixAll.version <- packageDescription("MixAll")$Version
MixAll.date <- packageDescription("MixAll")$Date
set.seed(2)
@

\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage[english]{babel}
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\title{\pkg{MixAll}: Learning mixture models}
\Plaintitle{MixAll:  Learning mixture models with Mixed data and Missing Values}
\Shorttitle{MixAll: Learning data}

\Abstract{
  The MixALL package can also be used in order to learn mixture models when the
  labels class are known. This short vignette assume that you have already read
  the vignette "Clustering With MixAll" (\cite{CRAN:MixAll}).
}

\Keywords{\proglang{R}, \proglang{C++}, \proglang{STK++}, Learning, missing
values}
\Plainkeywords{R, C++, STK++, Clustering, missing values}


\Address{
  Serge Iovleff\\
  Univ. Lille 1, CNRS U.M.R. 8524, Inria Lille Nord Europe \\
  59655 Villeneuve d'Ascq Cedex, France \\
  E-mail: \email{Serge.Iovleff@stkpp.org} \\
  URL: \url{http://www.stkpp.org}\\
}


% Title Page
\author{Serge Iovleff\\University Lille 1}
\date{now.data}

\begin{document}

\SweaveOpts{concordance=FALSE}
\maketitle

%\tableofcontents

\section{Introduction}

It is possible to perform supervised learning with MixAll when the labels of the
individuals are known. Let us recall the notations defined in the
\cite{CRAN:MixAll} vignette. $\X$ denote an arbitrary measurable space,
$\Z=\{1,\ldots,K\}$ is the label set and
$(\bx,\bz)=\{({\bx}_1,{\bz}_1),...,({\bx}_n,\bz_n)\}$ represents $n$ independent
vectors in $\X\times\Z$ such that each $\Pr({\bz}_i=k) = p_k$ and such that
conditionnaly to ${\bz}_i=k$, ${\bx}_i$ arises from a probability distribution
with density
\begin{equation}
  h({\bx}_{i}| \blambda_{k},\balpha)
\end{equation}
parameterized by $\blambda_k$ and $\balpha$.

Given the matrix of obervation$\bx,$ and the vector of labels $\bz$, the learning methods will
estimate the unknown parameters $\blambda_k$ and $\balpha$.

\section{Learning with MixAll}
\label{sec:LearnWithMixAll}

Learning analysis can be performed with the functions
\begin{enumerate}
\item \code{learnDiagGaussian} for diagonal Gaussian mixture models,
\item \code{learnCategorical} for Categorical mixture models,
\item \code{learnPoisson} for Poisson mixture models,
\item \code{learnGamma} for gamma mixture models,
%\item \code{learnKernel} for kernel mixture models,
\item \code{learnMixedData} for MixedData mixture models.
\end{enumerate}

These functions have a common set of parameters with default values given
in the table~\ref{tab:learnCommon}.

\begin{table}[H]
\centering
\begin{tabular}{|p{0.3\textwidth}|p{0.65\textwidth}|}
\hline
Input Parameter & Description \\
\hline
\code{data} & A matrix (or a list of matrix for mixed data) with the data
to learn. \\
\hline
\code{labels} & A vector with the classes of each individuals. Values must
be between $1$ and $K$.\\
\hline
\code{models} & A \code{vector} with the models to adjust to each data set in
case of mixed data, or a set of models to try to adjust. Default is
\code{cluster*Names()} where '*' stands for DiagGaussian, Poisson, Gamma or
Categorical.\\
\hline
\code{prop} & A vector of size $K$ with the proportions of each class.
If \code{prop} is \code{NULL} then the proportions are computed using
the empirical distribution of the \code{labels}.\\
\hline
\code{algo} & A string defining the algorithm to use for the missing values.
Possible values \code{"impute"}, \code{"simul"}. \\
\hline
\code{nbIter} & maximal number of iteration to perform. Default value
is 100. Note that if there is no missing values, it should be 1.\\
\hline
\code{epsilon} &  threshold to use in order to stop the iterations
(not used by the \code{"simul"} algorithm). Default value 1e-08.\\
\hline
\code{criterion} & A string defining the model selection criterion to use.
The  best model is the one with the lowest criterion value.
Possible values: \code{"AIC"}, \code{"BIC"}, \code{"ICL"}. Default is
\code{"ICL"}.\\
\hline
\code{nbCore} & An integer defining the number of processor to use. Default
is 1, 0 for all cores. \\
\hline
\end{tabular}
\caption{List of common parameters of the learning functions.}
\label{tab:learnCommon}
\end{table}

The \code{learnKernel} function has two more arguments described in
table~\ref{tab:learnKernel}.
\begin{table}[H]
\centering
\begin{tabular}{|p{0.3\textwidth}|p{0.65\textwidth}|}
\hline
Input Parameter & Description \\
\hline
\code{kernelName} & A \code{string} defining the kernel to use. Use
a \code{"gaussian"} kernel by default. Possible values are \code{"gaussian"},
"polynomial" or "exponential".\\
\hline
\code{kernelParameters} & A vector with the kernel parameter value(s). Default
value is $1$.\\
\hline
\end{tabular}
\caption{List of all the specific parameters of the
\code{learnKernel} function.}
\label{tab:learnKernel}
\end{table}

\subsection{Learning Multivariate (diagonal) Gaussian Mixture Models}
\label{subsec:learnDiagGaussian}

Multivariate Gaussian mixture models (without correlations) can be learned using
the \code{learnDiagGaussian} function. We illustrate this function with the well
known geyser data set (\cite{azzalini1990},\cite{hardle1991}).
<<fig=TRUE>>=
data(iris);
x <- as.matrix(iris[,1:4]); z <- as.vector(iris[,5]); n <- nrow(x); p <- ncol(x);
indexes <- matrix(c(round(runif(5,1,n)), round(runif(5,1,p))), ncol=2);
cbind(indexes, x[indexes]) # display true values
x[indexes] <- NA;          # set them as missing
model <- learnDiagGaussian(data=x, labels = z, models = clusterDiagGaussianNames(prop = "equal"))
summary(model)
missingValues(model)
plot(model)
@

\subsection{Learning Multivariate categorical Mixture Models}
\label{subsec:learnCategorical}

Categorical (nominal) data can be learned using the \code{learnCategorical}
function.


We illustrate this function with the birds data set.
<<fig=TRUE>>=
data(birds)
## add 10 missing values
x <- as.matrix(birds[,2:5]); z <- as.vector(birds[,1]); n <- nrow(x); p <- ncol(x);
indexes <- matrix(c(round(runif(5,1,n)), round(runif(5,1,p))), ncol=2);
cbind(indexes, x[indexes]) # display true values
x[indexes] <- NA;          # set them as missing
model <- learnCategorical( data=x, labels=z
                         , models = clusterCategoricalNames(prop = "equal")
                         , algo="simul", nbIter = 2)
summary(model)
missingValues(model)
plot(model)
@


\subsection{Learning Multivariate Gamma Mixture Models}
\label{subsec:learnGamma}

Gamma data can be learned using the \code{learnGamma} function.

We illustrate this function with the iris data set.
<<fig=TRUE>>=
data(iris)
x <- as.matrix(iris[,1:4]); z <- as.vector(iris[,5]); n <- nrow(x); p <- ncol(x);
indexes <- matrix(c(round(runif(5,1,n)), round(runif(5,1,p))), ncol=2);
cbind(indexes, x[indexes]) # display true values
x[indexes] <- NA;          # set them as missing
model <- learnGamma( data=x, labels= z,
                    , models = clusterGammaNames(prop = "equal")
                    , algo = "simul", nbIter = 2, epsilon = 1e-08
                )
summary(model)
missingValues(model)
plot(model)
@

\subsection{Learning Multivariate Poisson Models}
\label{subsec:learnPoisson}

Poisson data (count data) can be learned using the \code{learnPoisson}
function.

We illustrate this function with the debTrivedi data set.
<<fig=TRUE>>=
data(DebTrivedi)
x <- DebTrivedi[, c(1, 6, 8, 15)]; z <- DebTrivedi$medicaid; n <- nrow(x); p <- ncol(x);
indexes <- matrix(c(round(runif(5,1,n)), round(runif(5,1,p))), ncol=2);
cbind(indexes, x[indexes]) # display true values
x[indexes] <- NA;          # set them as missing
model <- learnPoisson( data=x, labels=z
                     , models = clusterPoissonNames(prop = "equal")
                     , algo="simul", nbIter = 2, epsilon =  1e-08
)
summary(model)
missingValues(model)
plot(model)
@

%\subsection{Learning Kernel Mixture Models}
%\label{subsec:learnKernel}

%Data can be clustered using the \code{learnKernel} function.

%We illustrate this function with the bullsEye data set.
%<<fig=TRUE>>=
%data(bullsEye)
%x <- bullsEye[,1:2]; x = as.matrix(x); n <- nrow(x); p <- ncol(x);
%z <- bullsEye[,3];
%model <- learnKernel( data=x, labels=z
%                    , models = clusterKernelNames(prop = "equal")
%                    , dim = 50, kernelName = "gaussian", kernelParameters = 1.
%                    , algo="impute", nbIter = 1, epsilon =  1e-08
%                    )
%summary(model)
%plot(model)
%@

\subsection{Learning Mixed data sets}
\label{subsec:learnMixedData}

Mixed data sets can be learned using the \code{learnMixedData}
function. The original mixed data set has to be splited in multiple
homogeneous data sets and each one associated to a mixture model name.


We illustrate this function with the HeartDisease data set.

<<fig=TRUE>>=
data(HeartDisease.cat)
data(HeartDisease.cont)
data(HeartDisease.target)
ldata = list(HeartDisease.cat, HeartDisease.cont);
models = c("categorical_pk_pjk","gaussian_pk_sjk")
z<-HeartDisease.target[[1]];
model <- learnMixedData(ldata, models, z, algo="simul", nbIter=2)
summary(model)
missingValues(model)
plot(model)
@

\bibliography{MixAll}


\end{document}
