[aggexp.git] / pkg / R / m_RidgeRegression.R

#' @include b_LinearAlgorithm.R

#' @title Ridge Regression Algorithm
#'
#' @description Ridge Regression Algorithm.
#' Inherits \code{\link{LinearAlgorithm}}
#'
#' @field lambda Value of lambda (let undefined for cross-validation). Default: undefined
#' @field lambdas Vector of "optimal" lambda values over time. TODO: remove for production
#'
RidgeRegression = setRefClass(
	Class = "RidgeRegression",

	fields = c(
		lambda = "numeric",
		lambdas = "numeric"
	),

	contains = "LinearAlgorithm",
	
	methods = list(
		predict_noNA = function(XY, x)
		{
			if (length(lambda) > 0 || nrow(XY) < 30) #TODO: magic number
			{
				#simple ridge regression with fixed lambda (not enough history for CV)
				X = matricize(XY[,names(XY) != "Measure"])
				Y = XY[,"Measure"]
				lambda_ = ifelse(length(lambda) > 0, lambda, LAMBDA)
				weight = ridgeSolve(X, Y, lambda_)
			}

			else
			{
				#enough data for cross-validations
				require(MASS, quietly=TRUE)
				gridLambda = seq(0.05,5.05,0.1)
				res_lmr = lm.ridge(Measure ~ . + 0, data=XY, lambda = gridLambda)
				lambda_ = res_lmr$lambda[which.min(res_lmr$GCV)]
				weight = as.matrix(coef(res_lmr))[which.min(res_lmr$GCV),]
			}

			lambdas <<- c(lambdas, lambda_)

			appendWeight(weight)
			return (matricize(x) %*% weight)
		}
	)
)
Commit	Line	Data
	1	#' @include b_LinearAlgorithm.R
	2
	3	#' @title Ridge Regression Algorithm
	4	#'
	5	#' @description Ridge Regression Algorithm.
	6	#' Inherits \code{\link{LinearAlgorithm}}
	7	#'
	8	#' @field lambda Value of lambda (let undefined for cross-validation). Default: undefined
	9	#' @field lambdas Vector of "optimal" lambda values over time. TODO: remove for production
	10	#'
	11	RidgeRegression = setRefClass(
	12	Class = "RidgeRegression",
	13
	14	fields = c(
	15	lambda = "numeric",
	16	lambdas = "numeric"
	17	),
	18
	19	contains = "LinearAlgorithm",
	20
	21	methods = list(
	22	predict_noNA = function(XY, x)
	23	{
	24	if (length(lambda) > 0 \|\| nrow(XY) < 30) #TODO: magic number
	25	{
	26	#simple ridge regression with fixed lambda (not enough history for CV)
	27	X = matricize(XY[,names(XY) != "Measure"])
	28	Y = XY[,"Measure"]
	29	lambda_ = ifelse(length(lambda) > 0, lambda, LAMBDA)
	30	weight = ridgeSolve(X, Y, lambda_)
	31	}
	32
	33	else
	34	{
	35	#enough data for cross-validations
	36	require(MASS, quietly=TRUE)
	37	gridLambda = seq(0.05,5.05,0.1)
	38	res_lmr = lm.ridge(Measure ~ . + 0, data=XY, lambda = gridLambda)
	39	lambda_ = res_lmr$lambda[which.min(res_lmr$GCV)]
	40	weight = as.matrix(coef(res_lmr))[which.min(res_lmr$GCV),]
	41	}
	42
	43	lambdas <<- c(lambdas, lambda_)
	44
	45	appendWeight(weight)
	46	return (matricize(x) %*% weight)
	47	}
	48	)
	49	)