[talweg.git] / pkg / R / J_Neighbors.R

#' getNeighborsJumpPredict
#'
#' Apply optimized weights on gaps observed on selected neighbors.
#' This jump prediction method can only be used in conjunction with the Neighbors
#' Forecaster, because it makes use of the optimized parameters to re-apply the weights
#' on the jumps observed at days interfaces of the past neighbors.
#'
#' @inheritParams computeForecast
#' @inheritParams getZeroJumpPredict
#'
#' @aliases J_Neighbors
#'
getNeighborsJumpPredict = function(data, today, memory, horizon, params, ...)
{
	first_day = max(1, today-memory)
	filter = (params$indices >= first_day)
	indices = params$indices[filter]
	weights = params$weights[filter]

	if (any(is.na(weights) | is.na(indices)))
		return (NA)

	gaps = sapply(indices, function(i) {
		head( data$getSerie(i+1), 1) - tail( data$getSerie(i), 1)
	})
	scal_product = weights * gaps
	norm_fact = sum( weights[!is.na(scal_product)] )
	sum(scal_product, na.rm=TRUE) / norm_fact
}
Commit	Line	Data
	1	#' getNeighborsJumpPredict
	2	#'
	3	#' Apply optimized weights on gaps observed on selected neighbors.
	4	#' This jump prediction method can only be used in conjunction with the Neighbors
	5	#' Forecaster, because it makes use of the optimized parameters to re-apply the weights
	6	#' on the jumps observed at days interfaces of the past neighbors.
	7	#'
	8	#' @inheritParams computeForecast
	9	#' @inheritParams getZeroJumpPredict
	10	#'
	11	#' @aliases J_Neighbors
	12	#'
	13	getNeighborsJumpPredict = function(data, today, memory, horizon, params, ...)
	14	{
	15	first_day = max(1, today-memory)
	16	filter = (params$indices >= first_day)
	17	indices = params$indices[filter]
	18	weights = params$weights[filter]
	19
	20	if (any(is.na(weights) \| is.na(indices)))
	21	return (NA)
	22
	23	gaps = sapply(indices, function(i) {
	24	head( data$getSerie(i+1), 1) - tail( data$getSerie(i), 1)
	25	})
	26	scal_product = weights * gaps
	27	norm_fact = sum( weights[!is.na(scal_product)] )
	28	sum(scal_product, na.rm=TRUE) / norm_fact
	29	}