[aggexp.git] / R / m_MLPoly.R

#' @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)
		}
	)
)
Commit	Line	Data
	1	#' @include b_LinearAlgorithm.R
	2
	3	#' @title MLpoly Algorithm
	4	#'
	5	#' @description MLpoly Algorithm.
	6	#' Inherits \code{\link{LinearAlgorithm}}
	7	#'
	8	#' @field alpha Importance of weights redistribution, in [0,1]. Default: 0
	9	#' @field grad Whether to use or not the (sub)gradient trick. Default: FALSE
	10	#'
	11	MLpoly = setRefClass(
	12	Class = "MLpoly",
	13
	14	fields = c(
	15	alpha = "numeric",
	16	grad = "logical"
	17	),
	18
	19	contains = "LinearAlgorithm",
	20
	21	methods = list(
	22	initialize = function(...)
	23	{
	24	callSuper(...)
	25	if (length(alpha) == 0 \|\| alpha < 0. \|\| alpha > 1.)
	26	alpha <<- 0. #no redistribution
	27	if (length(grad) == 0)
	28	grad <<- FALSE
	29	},
	30	predict_noNA = function(XY, x)
	31	{
	32	K = ncol(XY) - 1
	33	if (K == 1)
	34	{
	35	#shortcut: nothing to combine
	36	finalWeight = 1.
	37	}
	38
	39	else
	40	{
	41	X = XY[,names(XY) != "Measure"]
	42	Y = XY[,"Measure"]
	43	finalWeight = .C("ml_predict_noNA", X = as.double(t(X)), Y = as.double(Y), n = as.integer(nrow(XY)),
	44	K = as.integer(K), alpha=as.double(alpha), grad = as.integer(grad), weight=double(K))$weight
	45	}
	46
	47	appendWeight(finalWeight)
	48	return (matricize(x) %*% finalWeight)
	49	}
	50	)
	51	)