reorganize folder

[aggexp.git] / R / m_MLPoly.R

diff --git a/R/m_MLPoly.R b/R/m_MLPoly.R
deleted file mode 100644 (file)
index a19a2c9..0000000
--- a/R/m_MLPoly.R
+++ /dev/null
@@ -1,51 +0,0 @@
-#' @include b_LinearAlgorithm.R
-
-#' @title MLpoly Algorithm
-#'
-#' @description MLpoly Algorithm.
-#' Inherits \code{\link{LinearAlgorithm}}
-#'
-#' @field alpha Importance of weights redistribution, in [0,1]. Default: 0
-#' @field grad Whether to use or not the (sub)gradient trick. Default: FALSE
-#'
-MLpoly = setRefClass(
-       Class = "MLpoly",
-
-       fields = c(
-               alpha = "numeric",
-               grad = "logical"
-       ),
-
-       contains = "LinearAlgorithm",
-
-       methods = list(
-               initialize = function(...)
-               {
-                       callSuper(...)
-                       if (length(alpha) == 0 || alpha < 0. || alpha > 1.)
-                               alpha <<- 0. #no redistribution
-                       if (length(grad) == 0)
-                               grad <<- FALSE
-               },
-               predict_noNA = function(XY, x)
-               {
-                       K = ncol(XY) - 1
-                       if (K == 1)
-                       {
-                               #shortcut: nothing to combine
-                               finalWeight = 1.
-                       }
-
-                       else
-                       {
-                               X = XY[,names(XY) != "Measure"]
-                               Y = XY[,"Measure"]
-                               finalWeight = .C("ml_predict_noNA", X = as.double(t(X)), Y = as.double(Y), n = as.integer(nrow(XY)), 
-                                       K = as.integer(K), alpha=as.double(alpha), grad = as.integer(grad), weight=double(K))$weight
-                       }
-
-                       appendWeight(finalWeight)
-                       return (matricize(x) %*% finalWeight)
-               }
-       )
-)