% Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/tSNE_class.R

 \name{tSNE}

 \alias{tSNE}

 \title{tSNE}

 \usage{

 tSNE(
   dims = 2,
   perplexity = 30,
   max_iter = 100,
   theta = 0.5,
   check_duplicates = FALSE,
   init = NULL,
   eta = 200,
   ...
 )

 }

 \arguments{

 \item{dims}{(numeric) The number of tSNE dimensions computed. The default is \code{2}.\cr}

 \item{perplexity}{(numeric) Perplexity parameter. The default is \code{30}.\cr}

 \item{max_iter}{(numeric) The maximum number of tSNE iterations. The default is \code{100}.\cr}

 \item{theta}{(numeric) Speed/accuracy trade-off. A value of 0 gives an exact tSNE. The default is \code{0.5}.\cr}

 \item{check_duplicates}{(logical) Check for duplicates. Allowed values are limited to the following: \itemize{ \item{\code{"TRUE"}: Checks for the presence of exact duplicate samples.}\item{\code{"FALSE"}: Does not check for exact duplicate samples.}} The default is \code{FALSE}.\cr}

 \item{init}{(NULL, data.frame, DatasetExperiment) A set of coordinates for initialising the tSNE algorithm. NULL uses random initialisation. The default is \code{NULL}.}

 \item{eta}{(numeric) The learning rate parameter. The default is \code{200}.\cr}

 \item{...}{Additional slots and values passed to \code{struct_class}.}

 }

 \value{

 A  \code{tSNE} object with the following \code{output} slots:
 \tabular{ll}{
 \code{Y} \tab          (DatasetExperiment)  \cr
 }

 }

 \description{

 t-Distributed Stochastic Neighbor Embedding.

 }
 \details{

 This object makes use of functionality from the following packages:\itemize{  \item{\code{Rtsne}}}

 }

 \section{Inheritance}{
 
 A \code{tSNE} object inherits the following \code{struct} classes: \cr\cr

 \verb{[tSNE]} >> \verb{[model]} >> \verb{[struct_class]}

 }
 

 \examples{

 M = tSNE(
       dims = 2,
       perplexity = 30,
       max_iter = 1000,
       theta = 0.5,
       check_duplicates = FALSE,
       init = NULL,
       eta = 200)
 

 M = tSNE()
 
 }

 \references{

 Krijthe JH (2015). \emph{Rtsne: T-Distributed Stochastic Neighbor Embedding
 using Barnes-Hut Implementation}. R package version 0.17,
 \url{https://github.com/jkrijthe/Rtsne}.
 

 van der Maaten L, Hinton G (2008). "Visualizing High-Dimensional Data
 Using t-SNE." \emph{Journal of Machine Learning Research}, \emph{9}, 2579-2605.

 van der Maaten L (2014). "Accelerating t-SNE using Tree-Based
 Algorithms." \emph{Journal of Machine Learning Research}, \emph{15}, 3221-3245.

 }