days_in=tdays_cut, operational=opera)
nb_neighbs <- round( window[length(window)] )
# TODO: 10 == magic number
- tdays <- .getConstrainedNeighbs(today, data, tdays, min_neighbs=nb_neighbs)
+ tdays <- .getConstrainedNeighbs(today, data, tdays, nb_neighbs, opera)
if (length(tdays) == 1)
{
if (final_call)
if (simtype == "exo" || simtype == "mix")
{
- distances2 <- .computeDistsExo(data, today, tdays)
+ distances2 <- .computeDistsExo(data, today, tdays, opera)
# Compute exogen similarities using the given window
window_exo = ifelse(simtype=="mix", window[2], window[1])
# @param min_neighbs Minimum number of points in a neighborhood
# @param max_neighbs Maximum number of points in a neighborhood
#
-.getConstrainedNeighbs = function(today, data, tdays, min_neighbs=10)
+.getConstrainedNeighbs = function(today, data, tdays, min_neighbs, opera)
{
- levelToday = data$getLevelHat(today)
+ levelToday = ifelse(opera, tail(data$getLevelHat(today),1), data$getLevel(today))
distances = sapply( tdays, function(i) abs(data$getLevel(i) - levelToday) )
#TODO: 1, +1, +3 : magic numbers
dist_thresh = 1
})
}
-.computeDistsExo <- function(data, today, tdays)
+.computeDistsExo <- function(data, today, tdays, opera)
{
M = matrix( ncol=1+length(tdays), nrow=1+length(data$getExo(1)) )
- M[,1] = c( data$getLevelHat(today), as.double(data$getExoHat(today)) )
+ if (opera)
+ M[,1] = c( tail(data$getLevelHat(today),1), as.double(data$getExoHat(today)) )
+ else
+ M[,1] = c( data$getLevel(today), as.double(data$getExo(today)) )
for (i in seq_along(tdays))
M[,i+1] = c( data$getLevel(tdays[i]), as.double(data$getExo(tdays[i])) )
# Parameters (potentially) computed during shape prediction stage
predicted_shape <- self$predictShape(data,today,memory,predict_from,horizon,...)
- if (is.na(predicted_shape))
+ if (is.na(predicted_shape[1]))
return (NA)
predicted_delta <- private$.pjump(data, today, memory, predict_from,
#'
#' @aliases J_Zero
#'
-getZeroJumpPredict = function(data, today, memory, horizon, params, ...)
+getZeroJumpPredict = function(data, today, memory, predict_from, horizon, params, ...)
{
list(...)$first_pred - ifelse( predict_from >= 2,
data$getSerie(today)[predict_from-1], tail(data$getSerie(today-1),1) )
{
time = c(time, ts_df[line,1])
serie = c(serie, ts_df[line,2])
- level_hat = c(level_hat, #in case of data file is incomplete...
+ level_hat = c(level_hat, #if data file is incomplete...
ifelse(ncol(ts_df) > 2, ts_df[line,3], mean(serie,na.rm=TRUE)))
line = line + 1
};