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[talweg.git]
/
pkg
/
R
/
F_Neighbors.R
diff --git
a/pkg/R/F_Neighbors.R
b/pkg/R/F_Neighbors.R
index
7a3fbe5
..
a3d44a3
100644
(file)
--- a/
pkg/R/F_Neighbors.R
+++ b/
pkg/R/F_Neighbors.R
@@
-4,24
+4,29
@@
#'
#' Predict tomorrow as a weighted combination of "futures of the past" days.
#' Inherits \code{\link{Forecaster}}
#'
#' Predict tomorrow as a weighted combination of "futures of the past" days.
#' Inherits \code{\link{Forecaster}}
+#'
NeighborsForecaster = R6::R6Class("NeighborsForecaster",
inherit = Forecaster,
public = list(
NeighborsForecaster = R6::R6Class("NeighborsForecaster",
inherit = Forecaster,
public = list(
- predictShape = function(today, memory, horizon, ...)
+ predictShape = function(
data,
today, memory, horizon, ...)
{
# (re)initialize computed parameters
private$.params <- list("weights"=NA, "indices"=NA, "window"=NA)
{
# (re)initialize computed parameters
private$.params <- list("weights"=NA, "indices"=NA, "window"=NA)
+ # Do not forecast on days with NAs (TODO: softer condition...)
+ if (any(is.na(data$getCenteredSerie(today))))
+ return (NA)
+
# Determine indices of no-NAs days followed by no-NAs tomorrows
# Determine indices of no-NAs days followed by no-NAs tomorrows
- fdays =
private$.data$getCoupleDays(
max(today-memory,1), today-1)
+ fdays =
getNoNA2(data,
max(today-memory,1), today-1)
# Get optional args
simtype = ifelse(hasArg("simtype"), list(...)$simtype, "mix") #or "endo", or "exo"
kernel = ifelse(hasArg("kernel"), list(...)$kernel, "Gauss") #or "Epan"
if (hasArg(h_window))
{
# Get optional args
simtype = ifelse(hasArg("simtype"), list(...)$simtype, "mix") #or "endo", or "exo"
kernel = ifelse(hasArg("kernel"), list(...)$kernel, "Gauss") #or "Epan"
if (hasArg(h_window))
{
- return ( private$.predictShapeAux(
+ return ( private$.predictShapeAux(
data,
fdays, today, horizon, list(...)$h_window, kernel, simtype, TRUE) )
}
fdays, today, horizon, list(...)$h_window, kernel, simtype, TRUE) )
}
@@
-36,12
+41,13
@@
NeighborsForecaster = R6::R6Class("NeighborsForecaster",
for (i in intersect(fdays,sdays))
{
# mix_strategy is never used here (simtype != "mix"), therefore left blank
for (i in intersect(fdays,sdays))
{
# mix_strategy is never used here (simtype != "mix"), therefore left blank
- prediction = private$.predictShapeAux(fdays, i, horizon, h, kernel, simtype, FALSE)
+ prediction = private$.predictShapeAux(data,
+ fdays, i, horizon, h, kernel, simtype, FALSE)
if (!is.na(prediction[1]))
{
nb_jours = nb_jours + 1
error = error +
if (!is.na(prediction[1]))
{
nb_jours = nb_jours + 1
error = error +
- mean((
private$.
data$getCenteredSerie(i+1)[1:horizon] - prediction)^2)
+ mean((data$getCenteredSerie(i+1)[1:horizon] - prediction)^2)
}
}
return (error / nb_jours)
}
}
return (error / nb_jours)
@@
-60,33
+66,31
@@
NeighborsForecaster = R6::R6Class("NeighborsForecaster",
if (simtype == "endo")
{
if (simtype == "endo")
{
- return (private$.predictShapeAux(
+ return (private$.predictShapeAux(
data,
fdays, today, horizon, h_best_endo, kernel, "endo", TRUE))
}
if (simtype == "exo")
{
fdays, today, horizon, h_best_endo, kernel, "endo", TRUE))
}
if (simtype == "exo")
{
- return (private$.predictShapeAux(
+ return (private$.predictShapeAux(
data,
fdays, today, horizon, h_best_exo, kernel, "exo", TRUE))
}
if (simtype == "mix")
{
h_best_mix = c(h_best_endo,h_best_exo)
fdays, today, horizon, h_best_exo, kernel, "exo", TRUE))
}
if (simtype == "mix")
{
h_best_mix = c(h_best_endo,h_best_exo)
- return(private$.predictShapeAux(
+ return(private$.predictShapeAux(
data,
fdays, today, horizon, h_best_mix, kernel, "mix", TRUE))
}
}
),
private = list(
# Precondition: "today" is full (no NAs)
fdays, today, horizon, h_best_mix, kernel, "mix", TRUE))
}
}
),
private = list(
# Precondition: "today" is full (no NAs)
- .predictShapeAux = function(fdays, today, horizon, h, kernel, simtype, final_call)
+ .predictShapeAux = function(
data,
fdays, today, horizon, h, kernel, simtype, final_call)
{
fdays = fdays[ fdays < today ]
# TODO: 3 = magic number
if (length(fdays) < 3)
return (NA)
{
fdays = fdays[ fdays < today ]
# TODO: 3 = magic number
if (length(fdays) < 3)
return (NA)
- data = private$.data #shorthand
-
if (simtype != "exo")
{
h_endo = ifelse(simtype=="mix", h[1], h)
if (simtype != "exo")
{
h_endo = ifelse(simtype=="mix", h[1], h)
@@
-97,17
+101,22
@@
NeighborsForecaster = R6::R6Class("NeighborsForecaster",
{
delta = data$getCenteredSerie(today) - data$getCenteredSerie(fdays[i])
# Require at least half of non-NA common values to compute the distance
{
delta = data$getCenteredSerie(today) - data$getCenteredSerie(fdays[i])
# Require at least half of non-NA common values to compute the distance
- if (
sum(is.na(delta)) <= 0) #length(delta)/2
)
- distances2[i] = mean(delta^2)
#, na.rm=TRUE)
+ if (
!any( is.na(delta) )
)
+ distances2[i] = mean(delta^2)
}
sd_dist = sd(distances2)
if (sd_dist < .Machine$double.eps)
}
sd_dist = sd(distances2)
if (sd_dist < .Machine$double.eps)
+ {
+ warning("All computed distances are very close: stdev too small")
sd_dist = 1 #mostly for tests... FIXME:
sd_dist = 1 #mostly for tests... FIXME:
+ }
simils_endo =
if (kernel=="Gauss")
exp(-distances2/(sd_dist*h_endo^2))
simils_endo =
if (kernel=="Gauss")
exp(-distances2/(sd_dist*h_endo^2))
- else { #Epanechnikov
+ else
+ {
+ # Epanechnikov
u = 1 - distances2/(sd_dist*h_endo^2)
u[abs(u)>1] = 0.
u
u = 1 - distances2/(sd_dist*h_endo^2)
u[abs(u)>1] = 0.
u
@@
-135,10
+144,17
@@
NeighborsForecaster = R6::R6Class("NeighborsForecaster",
}
sd_dist = sd(distances2)
}
sd_dist = sd(distances2)
+ if (sd_dist < .Machine$double.eps)
+ {
+ warning("All computed distances are very close: stdev too small")
+ sd_dist = 1 #mostly for tests... FIXME:
+ }
simils_exo =
if (kernel=="Gauss")
exp(-distances2/(sd_dist*h_exo^2))
simils_exo =
if (kernel=="Gauss")
exp(-distances2/(sd_dist*h_exo^2))
- else { #Epanechnikov
+ else
+ {
+ # Epanechnikov
u = 1 - distances2/(sd_dist*h_exo^2)
u[abs(u)>1] = 0.
u
u = 1 - distances2/(sd_dist*h_exo^2)
u[abs(u)>1] = 0.
u
@@
-163,13
+179,12
@@
NeighborsForecaster = R6::R6Class("NeighborsForecaster",
private$.params$weights <- similarities
private$.params$indices <- fdays
private$.params$window <-
private$.params$weights <- similarities
private$.params$indices <- fdays
private$.params$window <-
- if (simtype=="endo")
{
+ if (simtype=="endo")
h_endo
h_endo
- } else if (simtype=="exo") {
+ else if (simtype=="exo")
h_exo
h_exo
-
} else {
#mix
+
else
#mix
c(h_endo,h_exo)
c(h_endo,h_exo)
- }
}
return (prediction)
}
return (prediction)