From: Benjamin Auder <benjamin.auder@somewhere>
Date: Thu, 4 May 2017 12:45:54 +0000 (+0200)
Subject: update documention, fix package to compete with 'method Bruno'
X-Git-Url: https://git.auder.net/variants/Chakart/css/assets/doc/html/up.jpg?a=commitdiff_plain;h=4f3fdbb8e2ac4bd57a4e27539a58ef0e7ec2304c;p=talweg.git

update documention, fix package to compete with 'method Bruno'
---

diff --git a/Etude_En_Cours_R_E_bis.R b/Etude_En_Cours_R_E_bis.R
index 1fe1387..56240d0 100644
--- a/Etude_En_Cours_R_E_bis.R
+++ b/Etude_En_Cours_R_E_bis.R
@@ -107,11 +107,11 @@ Kvois = NULL
 #	w = w/sum(w)
 #	W = w %o% rep(1,48)
 
-print("Voisins + ij")
-print(sort(D)[1:nbvois])
-print(dateJPrev)
-print(rownames(data)[ind])
-print(data[ind, 1:48])
+#print("Voisins + ij")
+#print(sort(D)[1:nbvois])
+#print(dateJPrev)
+#print(rownames(data)[sort(ind)])
+#print(data[sort(ind), 1:48])
 
 	JourMoy = apply(data[ind, 1:48], 2, mean)
 	#JourMoy = apply(W*data[ind, 1:48], 2, sum)
diff --git a/pkg/R/Data.R b/pkg/R/Data.R
index 39e34de..d35c88a 100644
--- a/pkg/R/Data.R
+++ b/pkg/R/Data.R
@@ -1,6 +1,6 @@
 #' Data
 #'
-#' Data encapsulation as a list (days) of lists (components).
+#' Data encapsulation, in the form of a few lists (time-series + exogenous variables).
 #'
 #' The private field .tv is a list where each cell contains the hourly variables for a
 #' period of time of 24 hours, from 1am to next midnight. The other lists contain
diff --git a/pkg/R/F_Average.R b/pkg/R/F_Average.R
index bee1974..4d395ac 100644
--- a/pkg/R/F_Average.R
+++ b/pkg/R/F_Average.R
@@ -3,9 +3,9 @@
 #' Pointwise average of all the series of the same day of week in the past.
 #'
 #' For example, if the current day (argument "today") is a tuesday, then all series
-#' corresponding to wednesdays in the past (until the beginning or memory limit) are
-#' averaged to provide a smooth prediction. This forecast will most of the time be wrong,
-#' but will also look plausible enough.
+#' corresponding to tuesday in the past (until the beginning or memory limit) -- and in
+#' the future if 'opera' is FALSE -- are averaged to provide a smooth prediction. This
+#' forecast will most of the time be wrong, but will also look plausible enough.
 #'
 #' @usage # AverageForecaster$new(pjump)
 #'
diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index d63c177..9d1e3fb 100644
--- a/pkg/R/F_Neighbors.R
+++ b/pkg/R/F_Neighbors.R
@@ -1,27 +1,23 @@
 #' Neighbors Forecaster
 #'
-#' Predict next serie as a weighted combination of "futures of the past" days,
-#' where days in the past are chosen and weighted according to some similarity measures.
+#' Predict next serie as a weighted combination of curves observed on "similar" days in
+#' the past (and future if 'opera'=FALSE); the nature of the similarity is controlled by
+#' the options 'simtype' and 'local' (see below).
 #'
-#' The main method is \code{predictShape()}, taking arguments data, today, memory,
-#' predict_from, horizon respectively for the dataset (object output of
-#' \code{getData()}), the current index, the data depth (in days), the first predicted
-#' hour and the last predicted hour.
-#' In addition, optional arguments can be passed:
+#' Optional arguments:
 #' \itemize{
-#'   \item local : TRUE (default) to constrain neighbors to be "same days within same
-#'     season"
-#'   \item simtype : 'endo' for a similarity based on the series only,<cr>
+#'   \item local: TRUE (default) to constrain neighbors to be "same days in same season"
+#'   \item simtype: 'endo' for a similarity based on the series only,<cr>
 #'             'exo' for a similarity based on exogenous variables only,<cr>
 #'             'mix' for the product of 'endo' and 'exo',<cr>
 #'             'none' (default) to apply a simple average: no computed weights
-#'   \item window : A window for similarities computations; override cross-validation
+#'   \item window: A window for similarities computations; override cross-validation
 #'     window estimation.
 #' }
 #' The method is summarized as follows:
 #' \enumerate{
-#'   \item Determine N (=20) recent days without missing values, and followed by a
-#'     tomorrow also without missing values.
+#'   \item Determine N (=20) recent days without missing values, and preceded by a
+#'     curve also without missing values.
 #'   \item Optimize the window parameters (if relevant) on the N chosen days.
 #'   \item Considering the optimized window, compute the neighbors (with locality
 #'     constraint or not), compute their similarities -- using a gaussian kernel if
@@ -152,12 +148,6 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 					distances2 <- .computeDistsEndo(data, today, tdays, predict_from)
 					ordering <- order(distances2)
 					tdays <- tdays[ ordering[1:max_neighbs] ]
-
-					print("VVVVV")
-					print(sort(distances2)[1:max_neighbs])
-					print(integerIndexToDate(today,data))
-					print(lapply(tdays,function(i) integerIndexToDate(i,data)))
-					print(rbind(data$getSeries(tdays-1), data$getSeries(tdays)))
 				}
 			}
 			else
diff --git a/pkg/R/F_Zero.R b/pkg/R/F_Zero.R
index 4e1ae11..21e6e00 100644
--- a/pkg/R/F_Zero.R
+++ b/pkg/R/F_Zero.R
@@ -1,7 +1,6 @@
 #' Zero Forecaster
 #'
-#' Flat prediction: always forecast a serie of zeros.
-#' This serie is then adjusted using the ".pjump" function (see \code{Forecaster} class).
+#' Flat prediction: always forecast a serie of zeros (adjusted by 'pjump' function).
 #'
 #' @usage # ZeroForecaster$new(pjump)
 #'
diff --git a/pkg/R/Forecast.R b/pkg/R/Forecast.R
index a983046..ff7fb8d 100644
--- a/pkg/R/Forecast.R
+++ b/pkg/R/Forecast.R
@@ -3,7 +3,7 @@
 #' Forecast encapsulation as a list (days where prediction occur) of lists (components).
 #'
 #' The private field .pred is a list where each cell contains the predicted variables for
-#' a period of time of H<=24 hours, from hour P until P+H-1, where P == predict_from.
+#' a period of time of H-P+1<=24 hours, from hour P until H, where P == predict_from.
 #' \code{forecast$getForecast(i)} output forecasts for
 #' \code{data$getSerie(forecast$getIndexInData(i))}.
 #'
diff --git a/pkg/R/Forecaster.R b/pkg/R/Forecaster.R
index 5258760..2b259fc 100644
--- a/pkg/R/Forecaster.R
+++ b/pkg/R/Forecaster.R
@@ -6,8 +6,9 @@
 #' example "Neighbors" method stores informations about the considered neighborhood for
 #' the current prediction task) and one main function: \code{predictSerie()}. This last
 #' function (by default) calls \code{predictShape()} to get a forecast of a centered
-#' serie, and then calls the "jump prediction" function -- see "field" section -- to
-#' adjust it based on the last observed values.
+#' serie, and then calls the "jump prediction" function if it's provided -- see "field"
+#' section -- to adjust it based on the last observed values. The main method in derived
+#' forecasters is \code{predictShape()}; see 'Methods' section.
 #'
 #' @usage # Forecaster$new(pjump) #warning: predictShape() is unimplemented
 #'
@@ -15,25 +16,27 @@
 #' @field .pjump Function: how to predict the jump at day interface? The arguments of
 #'   this function are -- in this order:
 #'   \itemize{
-#'     \item data : object output of \code{getData()},
-#'     \item today : index (integer or date) of the last known day in data,
-#'     \item memory : number of days to use in the past (including today),
-#'     \item horizon : number of time steps to predict,
-#'     \item params : optimized parameters in the main method \code{predictShape()},
-#'     \item ... : additional arguments.
+#'     \item data: object output of \code{getData()},
+#'     \item today: index of the current day in data (known until predict_from-1),
+#'     \item memory: number of days to use in the past (including today),
+#'     \item predict_from: first time step to predict (in [1,24])
+#'     \item horizon: last time step to predict (in [predict_from,24]),
+#'     \item params: optimized parameters in the main method \code{predictShape()},
+#'     \item ...: additional arguments.
 #'   }
 #'   .pjump returns an estimation of the jump after the last observed value.
 #'
 #' @section Methods:
 #' \describe{
 #' \item{\code{initialize(data, pjump)}}{
-#'   Initialize a Forecaster object with a Data object and a jump prediction function.}
-#' \item{\code{predictSerie(today,memory,horizon,...)}}{
-#'   Predict a new serie of \code{horizon} values at day index \code{today} using
-#'   \code{memory} days in the past.}
-#' \item{\code{predictShape(today,memory,horizon,...)}}{
-#'   Predict a new shape of \code{horizon} values at day index \code{today} using
+#'   Initialize a Forecaster object with a Data object and a jump prediction function,
+#'   or NULL if \code{predictShape()} returns an adjusted curve.}
+#' \item{\code{predictSerie(data,today,memory,predict_from,horizon,...)}}{
+#'   Predict the next curve (at index today) from predict_from to horizon (hours), using
 #'   \code{memory} days in the past.}
+#' \item{\code{predictShape(data,today,memory,predict_from,horizon,...)}}{
+#'   Predict the shape of the next curve (at index today) from predict_from to horizon
+#'   (hours), using \code{memory} days in the past.}
 #' \item{\code{getParameters()}}{
 #'   Return (internal) parameters.}
 #' }
@@ -55,15 +58,20 @@ Forecaster = R6::R6Class("Forecaster",
 		predictSerie = function(data, today, memory, predict_from, horizon, ...)
 		{
 			# Parameters (potentially) computed during shape prediction stage
-			predicted_shape = self$predictShape(data,today,memory,predict_from,horizon,...)
-			predicted_delta = private$.pjump(data, today, memory, predict_from, horizon,
-				private$.params, ...)
+			predicted_shape <- self$predictShape(data,today,memory,predict_from,horizon,...)
+			predicted_delta <-
+				if (is.null(private$.pjump))
+					NULL
+				else
+					private$.pjump(data,today,memory,predict_from,horizon,private$.params,...)
 
-			# Predicted shape is aligned on the end of current day + jump
+			# Predicted shape is aligned on the end of current day + jump (if jump!=NULL)
 			c( data$getSerie(today)[if (predict_from>=2) 1:(predict_from-1) else c()],
-				predicted_shape - predicted_shape[1] + predicted_delta +
-					ifelse(predict_from>=2,
-						data$getSerie(today)[predict_from-1], tail(data$getSerie(today-1),1)) )
+				predicted_shape + ifelse( is.null(private$.pjump),
+					0,
+					predicted_delta - predicted_shape[1] +
+						ifelse(predict_from>=2,
+							data$getSerie(today)[predict_from-1], tail(data$getSerie(today-1),1)) ) )
 		},
 		predictShape = function(data, today, memory, predict_from, horizon, ...)
 			NULL #empty default implementation: to implement in inherited classes
diff --git a/pkg/R/computeError.R b/pkg/R/computeError.R
index c3bc4f3..db5783e 100644
--- a/pkg/R/computeError.R
+++ b/pkg/R/computeError.R
@@ -4,8 +4,10 @@
 #'
 #' @param data Object of class \code{Data} output of \code{getData}
 #' @param pred Object of class \code{Forecast} output of \code{computeForecast}
-#' @param horizon Horizon where to compute the error
-#'   (<= horizon used in \code{computeForecast})
+#' @param predict_from First time step to consider (>= predict_from used in
+#'   \code{computeForecast()})
+#' @param horizon Horizon where to compute the error (<= horizon used in
+#'   \code{computeForecast})
 #'
 #' @return A list (abs,MAPE) of lists (day,indices). The "indices" slots contain series
 #'   of size L where L is the number of predicted days; i-th value is the averaged error
diff --git a/pkg/R/computeForecast.R b/pkg/R/computeForecast.R
index ef46dd3..c778d66 100644
--- a/pkg/R/computeForecast.R
+++ b/pkg/R/computeForecast.R
@@ -1,28 +1,31 @@
 #' Compute forecast
 #'
-#' Predict time-series curves ("tomorrows") at the selected days indices ("todays").
-#' This function just runs a loop over all requested indices, and stores the individual
-#' forecasts into a list which is then turned into a Forecast object.
+#' Predict time-series curves ("today" from predict_from to horizon) at the selected days
+#' indices ("today" from 1am to predict_from-1). This function just runs a loop over all
+#' requested indices, and stores the individual forecasts into a Forecast object.
 #'
 #' @param data Object of class Data, output of \code{getData()}.
 #' @param indices Indices where to forecast (the day after); integers relative to the
 #'   beginning of data, or (convertible to) Date objects.
 #' @param forecaster Name of the main forecaster; more details: ?F_<forecastername>
-#' \itemize{
-#'   \item Persistence : use last (similar, next) day
-#'   \item Neighbors : weighted tomorrows of similar days
-#'   \item Average : average tomorrow of all same day-in-week
-#'   \item Zero : just output 0 (benchmarking purpose)
-#' }
+#'   \itemize{
+#'     \item Persistence : use last (similar) day
+#'     \item Neighbors : weighted similar days
+#'     \item Average : average curve of all same day-in-week
+#'     \item Zero : just output 0 (benchmarking purpose)
+#'   }
 #' @param pjump Function to predict the jump at the interface between two days;
 #'   more details: ?J_<functionname>
-#' \itemize{
-#'   \item Persistence : use last (similar, next) day
-#'   \item Neighbors: re-use the weights from F_Neighbors
-#'   \item Zero: just output 0 (no adjustment)
-#' }
+#'   \itemize{
+#'     \item Persistence : use last (similar) day
+#'     \item Neighbors: re-use the weights from F_Neighbors
+#'     \item Zero: just output 0 (no adjustment)
+#'   }
+#'   If pjump=NULL, then no adjustment is performed (output of \code{predictShape()} is
+#'   used directly).
+#' @param predict_from First time step to predict.
 #' @param memory Data depth (in days) to be used for prediction.
-#' @param horizon Number of time steps to predict.
+#' @param horizon Last time step to predict.
 #' @param ncores Number of cores for parallel execution (1 to disable).
 #' @param ... Additional parameters for the forecasting models.
 #'
@@ -31,23 +34,30 @@
 #' @examples
 #' ts_data <- system.file("extdata","pm10_mesures_H_loc.csv",package="talweg")
 #' exo_data <- system.file("extdata","meteo_extra_noNAs.csv",package="talweg")
-#' data <- getData(ts_data, exo_data, input_tz="GMT", working_tz="GMT",
-#'   predict_at=7, limit=200)
+#' data <- getData(ts_data, exo_data, limit=200)
 #' pred <- computeForecast(data, 100:130, "Persistence", "Zero",
-#'   memory=50, horizon=12, ncores=1)
-#' \dontrun{#Sketch for real-time mode:
+#'   predict_from=8, memory=50, horizon=12, ncores=1)
+#' \dontrun{
+#' #Sketch for real-time mode:
 #' data <- Data$new()
 #' forecaster <- MyForecaster$new(myJumpPredictFunc)
 #' repeat {
-#'   # In the morning 7am+ or afternoon 1pm+:
+#'   # As soon as daily predictions are available:
 #'   data$append(
-#'     times_from_H+1_yersteday_to_Hnow,
-#'     PM10_values_of_last_24h,
-#'     exogenous_measures_of_last_24h,
-#'     exogenous_predictions_for_next_24h)
+#'     level_hat=predicted_level,
+#'     exo_hat=predicted_exogenous)
+#'   # When a day ends:
+#'   data$append(
+#'     level=observed_level,
+#'     exo=observed_exogenous)
+#'   # And, at every hour:
+#'   data$append(
+#'     time=current_hour,
+#'     value=current_PM10)
+#'   # Finally, a bit before predict_from hour:
 #'   pred <- forecaster$predictSerie(data, data$getSize(), ...)
 #'   #do_something_with_pred
-#' }}
+#' } }
 #' @export
 computeForecast = function(data, indices, forecaster, pjump, predict_from,
 	memory=Inf, horizon=length(data$getSerie(1)), ncores=3, ...)
@@ -64,14 +74,18 @@ computeForecast = function(data, indices, forecaster, pjump, predict_from,
 	integer_indices = sapply(indices, function(i) dateIndexToInteger(i,data))
 	if (any(integer_indices<=0 | integer_indices>data$getSize()))
 		stop("Indices out of range")
-	if (!is.character(forecaster) || !is.character(pjump))
-		stop("forecaster (name) and pjump (function) should be of class character")
+	if (!is.character(forecaster))
+		stop("forecaster (name): character")
+	if (!is.null(pjump) && !is.character(pjump))
+		stop("pjump (function): character or NULL")
 
 	pred = Forecast$new( sapply(indices, function(i) integerIndexToDate(i,data)) )
 	forecaster_class_name = getFromNamespace(
 		paste(forecaster,"Forecaster",sep=""), "talweg")
-	forecaster = forecaster_class_name$new( #.pjump =
-		getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg"))
+
+	if (!is.null(pjump))
+		pjump <- getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg")
+	forecaster = forecaster_class_name$new(pjump)
 
 	computeOneForecast <- function(i)
 	{
diff --git a/pkg/R/plot.R b/pkg/R/plot.R
index 465b606..2488d81 100644
--- a/pkg/R/plot.R
+++ b/pkg/R/plot.R
@@ -11,13 +11,7 @@ plotCurves <- function(data, indices=seq_len(data$getSize()))
 	series = data$getSeries(indices)
 	yrange = quantile(series, probs=c(0.025,0.975), na.rm=TRUE)
 	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
-	for (i in seq_along(indices))
-	{
-		plot(series[,i], type="l", ylim=yrange,
-			xlab=ifelse(i==1,"Time (hours)",""), ylab=ifelse(i==1,"PM10",""))
-		if (i < length(indices))
-			par(new=TRUE)
-	}
+	matplot(series, type="l", ylim=yrange, xlab="Time (hours)", ylab="PM10")
 }
 
 #' Plot error