Changed ones() to one()

.Rbuildignore

@@ -2,7 +2,7 @@
 ^\.Rproj\.user$
 ^make.R
 ^data/all
-^vignettes/tmp-output
+^tmp
 ^vignettes/make.R
 ^vignettes/shared-init.Rmd
 ^vignettes/cache

.gitignore

@@ -21,15 +21,5 @@ misc-R/*cache*
 vignettes/*cache*
 vignettes/*genfig*
 vignettes/*_files*
-vignettes/tmp-output/
-vignettes/setup-data_cache/
-vignettes/solar-forecasting_cache-rls/
-vignettes/building-heat-load-forecasting_cache/
-vignettes/onlineforecasting_pdf_source/onlineforecasting\.tex
-
-vignettes/onlineforecasting_pdf_source/*cache*
-vignettes/onlineforecasting_pdf_source/*genfig*
-vignettes/onlineforecasting_pdf_source/onlineforecasting-tikzDictionary
-vignettes/onlineforecasting_pdf_source/onlineforecasting.log
-vignettes/onlineforecasting_pdf_source/onlineforecasting.pdf
 
+tmp/

R/bspline.R

@@ -17,7 +17,7 @@
 #' 
 #' @param X data.frame (as part of data.list) with horizons as columns named \code{kxx} (i.e. one for each horizon)
 #' @param Boundary.knots The value is NA: then the boundaries are set to the range of each horizons (columns in X). See \code{?splines::bs}
-#' @param intercept Default value is TRUE: in an onlineforecast model there is no intercept per defauls (set by \code{ones()}. See \code{?splines::bs}
+#' @param intercept Default value is TRUE: in an onlineforecast model there is no intercept per defauls (set by \code{one()}. See \code{?splines::bs}
 #' @param df See \code{?splines::bs}
 #' @param knots See \code{?splines::bs}
 #' @param degree See \code{?splines::bs}

R/forecastmodel.R

@@ -133,7 +133,7 @@ forecastmodel <- R6::R6Class("forecastmodel", public = list(
 
         # Keep the prm
         self$prm <- prm
-        # Find if any opt parameters, first the ones with "__" hence for the inputs
+        # Find if any opt parameters, first the one with "__" hence for the inputs
         pinputs <- prm[grep("__",nams(prm))]
         # If none found for inputs, then the rest must be for regression
         if (length(pinputs) == 0 & length(prm) > 0) {

R/input_class.R

@@ -8,7 +8,7 @@ input_class <- R6::R6Class(
       state_L = list(),
       state_i = integer(1),
       ## The model in which it is included (reference to the R6 forecastmodel object), its needed here,
-      ##   since transformation functions (like AR, ones) need to access information about the model (like kseq)
+      ##   since transformation functions (like AR, one) need to access information about the model (like kseq)
       model = NA,
 
       ## methods

R/lm_fit.R

@@ -38,7 +38,7 @@
 #' model <- forecastmodel$new()
 #' model$output <- "y"
 #' model$add_inputs(Ta = "Ta",
-#'                  mu = "ones()")
+#'                  mu = "one()")
 #' model$add_regprm("rls_prm(lambda=0.99)")
 #'
 #' # Before fitting the model, define which points to include in the evaluation of the score function
@@ -79,7 +79,7 @@ lm_fit <- function(prm=NA, model, data, scorefun = NA, returnanalysis = TRUE, pr
     # - If scorefun is given, e.g. rmse() then the value of this is returned
 
     if(printout){
-        # Should here actually only print the ones that were found and changed?
+        # Should here actually only print the one that were found and changed?
         cat("----------------\n")
         if(is.na(prm[1])){
             cat("prm=NA, so current parameters are used.\n")

R/lm_optim.R

@@ -28,7 +28,7 @@
 #' # Define a simple model 
 #' model <- forecastmodel$new()
 #' model$add_inputs(Ta = "lp(Ta, a1=0.9)",
-#'                  mu = "ones()")
+#'                  mu = "one()")
 #' # Before fitting the model, define which points to include in the evaluation of the score function
 #' D$scoreperiod <- in_range("2010-12-20", D$t)
 #' # And the sequence of horizons to fit for

R/lm_predict.R

@@ -14,7 +14,7 @@
 #' D$y <- D$heatload
 #' # Define a model 
 #' model <- forecastmodel$new()
-#' model$add_inputs(Ta = "lp(Ta, a1=0.7)", mu = "ones()")
+#' model$add_inputs(Ta = "lp(Ta, a1=0.7)", mu = "one()")
 #'
 #' # Before fitting the model, define which points to include in the evaluation of the score function
 #' D$scoreperiod <- in_range("2010-12-20", D$t)

R/long_format.R

@@ -21,7 +21,7 @@
 #' # Define a model
 #' model <- forecastmodel$new()
 #' model$add_inputs(Ta = "Ta",
-#'                  mu = "ones()")
+#'                  mu = "one()")
 #' model$add_regprm("rls_prm(lambda=0.99)")
 #' model$kseq <- 1:6
 #' # Fit it

R/ones.R

@@ -2,7 +2,7 @@
 #library(devtools)
 #document()
 #load_all(as.package("../../onlineforecast"))
-#?ones
+#?one
 
 #' Returns a data.frame of ones which can be used in forecast model inputs
 #'
@@ -12,26 +12,26 @@
 #'
 #' @title Create ones for model input intercept
 #' @return A data.frame of ones
-#' @name ones
+#' @name one
 #' @examples
 #'
 #' # A model
 #' model <- forecastmodel$new()
 #' # Use the function in the input definition
-#' model$add_inputs(mu = "ones()")
+#' model$add_inputs(mu = "one()")
 #' # Set the forecast horizons
 #' model$kseq <- 1:4
-#' # During the transformation stage the ones will be generated for the horizons
+#' # During the transformation stage the one will be generated for the horizons
 #' model$transform_data(subset(Dbuilding, 1:7))
 #'
 #' @export
 
-ones <- function(){
+one <- function(){
     # To find kseq, get the model (remember it is call per reference, so don't change it without cloning)
     model <- get("self", parent.env(parent.frame(4)))
     # Get the data to find the all the names with k in data
     data <- get("data", parent.env(parent.frame()))
     n <- length(data$t)
-    # Generate the matrix of ones and return it as a data.frame
+    # Generate the matrix of one and return it as a data.frame
     as.data.frame(matrix(1, nrow=n, ncol=length(model$kseq), dimnames=list(NULL, pst("k",model$kseq))))
 }

R/plot_ts.R

@@ -466,7 +466,7 @@ plot_ts_series <- function(data, pattern, iplot = 1,
 #' model <- forecastmodel$new()
 #' model$output = "heatload"
 #' model$add_inputs(Ta = "Ta",
-#'                  mu = "ones()")
+#'                  mu = "one()")
 #' model$add_regprm("rls_prm(lambda=0.9)")
 #' model$kseq <- c(3,18)
 #' fit1 <- rls_fit(NA, model, D, returnanalysis = TRUE)

R/rls_fit.R

@@ -54,7 +54,7 @@
 #' model <- forecastmodel$new()
 #' model$output <- "y"
 #' model$add_inputs(Ta = "Ta",
-#' 		    mu = "ones()")
+#' 		    mu = "one()")
 #' model$add_regprm("rls_prm(lambda=0.99)")
 #'
 #' # Before fitting the model, define which points to include in the evaluation of the score function

R/rls_optim.R

@@ -27,7 +27,7 @@
 #' D$y <- D$heatload
 #' # Define a simple model 
 #' model <- forecastmodel$new()
-#' model$add_inputs(Ta = "Ta", mu = "ones()")
+#' model$add_inputs(Ta = "Ta", mu = "one()")
 #' model$add_regprm("rls_prm(lambda=0.99)")
 #'
 #' # Before fitting the model, define which points to include in the evaluation of the score function

R/rls_predict.R

@@ -13,7 +13,7 @@
 #' D$y <- D$heatload
 #' # Define a simple model 
 #' model <- forecastmodel$new()
-#' model$add_inputs(Ta = "Ta", mu = "ones()")
+#' model$add_inputs(Ta = "Ta", mu = "one()")
 #' model$add_regprm("rls_prm(lambda=0.99)")
 #'
 #' # Before fitting the model, define which points to include in the evaluation of the score function

R/rls_prm.R

@@ -18,7 +18,7 @@
 #' D$scoreperiod <- in_range("2010-12-20", D$t)
 #' # Define a simple model 
 #' model <- forecastmodel$new()
-#' model$add_inputs(Ta = "Ta", mu = "ones()")
+#' model$add_inputs(Ta = "Ta", mu = "one()")
 #' model$kseq <- 1:6
 #'
 #' # Here the expression which sets the parameters is defined

R/rls_summary.R

@@ -47,7 +47,7 @@
 #' # Define a model
 #' model <- forecastmodel$new()
 #' model$add_inputs(Ta = "Ta",
-#'                  mu = "ones()")
+#'                  mu = "one()")
 #' model$add_regprm("rls_prm(lambda=0.99)")
 #' model$kseq <- 1:6
 #' # Fit it

misc-R/temp.R

@@ -8,7 +8,7 @@ D <- subset(Dbuilding, c("2010-12-15", "2011-01-01"))
 D$y <- D$heatload
 # Define a model 
 model <- forecastmodel$new()
-model$add_inputs(Ta = "lp(Ta, a1=0.7)", mu = "ones()")
+model$add_inputs(Ta = "lp(Ta, a1=0.7)", mu = "one()")
 
 # Before fitting the model, define which points to include in the evaluation of the score function
 D$scoreperiod <- in_range("2010-12-20", D$t)

tests/testthat/test-rls-heat-load.R

@@ -23,7 +23,7 @@ test_that("run", {
     model$add_inputs(Ta = "lp(Ta, a1=0.9)", 
                      I = "lp(I, a1=0.7)", 
                      mu_tday = "fs(tday/24, nharmonics=10)",
-                     mu = "ones()")
+                     mu = "one()")
     model$add_regprm("rls_prm(lambda=0.9)")
 
     ## ------------------------------------------------------------------------

vignettes/forecast-evaluation.Rmd

@@ -45,9 +45,9 @@ knitr::opts_chunk$set(
   comment = "##    ",
   prompt = FALSE,
   cache = TRUE,
-  cache.path = paste0("tmp-output/tmp-",vignettename,"/"),
+  cache.path = paste0("../tmp/vignettes/tmp-",vignettename,"/"),
   fig.align="center",
-  fig.path = paste0("tmp-output/tmp-",vignettename,"/"),
+  fig.path = paste0("../tmp/vignettes/tmp-",vignettename,"/"),
   fig.height = figheight,
   fig.width = figwidth,
   out.width = "100%"
@@ -166,7 +166,7 @@ model <- forecastmodel$new()
 model$output = "y"
 model$add_inputs(Ta = "lp(Ta, a1=0.9)",
                  I = "lp(I, a1=0.9)",
-                 mu = "ones()")
+                 mu = "one()")
 model$add_prmbounds(Ta__a1 = c(0.8, 0.9, 0.99),
              I__a1 = c(0.6, 0.9, 0.99),
              lambda = c(0.9, 0.99, 0.9999))

vignettes/make.R

@@ -4,8 +4,11 @@ library(knitr)
 library(rmarkdown)
 
 # Put the files in this dir (ignored in the git)
-dirnam <- "tmp-output/"
+dirnam <- "../tmp/vignettes/"
+dir.create("../tmp")
 dir.create(dirnam)
+file.remove(dir("cache", full.names=TRUE))
+file.remove("cache")
 
 makeit <- function(nam, openit=FALSE, clean=TRUE){
     namrmd <- paste0(nam,".Rmd")
@@ -15,19 +18,18 @@ makeit <- function(nam, openit=FALSE, clean=TRUE){
     if(openit){ system(paste0("chromium-browser ",dirnam,nam,".html &")) }
 }
 
-file.remove(dir("tmp-output/tmp-setup-data/", full.names=TRUE))
+#
+file.remove(dir(paste0(dirnam,"tmp-setup-data/"), full.names=TRUE))
 makeit("setup-data", openit=FALSE)
 
 #
-file.remove(dir("cache", full.names=TRUE))
-file.remove("cache")
-file.remove(dir("tmp-output/tmp-setup-and-use-model/", full.names=TRUE))
+file.remove(dir(paste0(dirnam,"tmp-setup-and-use-model/"), full.names=TRUE))
 makeit("setup-and-use-model", openit=FALSE, clean=TRUE)
 
 #
-file.remove(dir("tmp-output/tmp-forecast-evaluation/", full.names=TRUE))
+file.remove(dir(paste0(dirnam,"tmp-output/tmp-forecast-evaluation/"), full.names=TRUE))
 makeit("forecast-evaluation", openit=FALSE)
 
 # Finish and include it!!
-## file.remove(dir("tmp-output/tmp-online-updating/", full.names=TRUE))
+## file.remove(dir(paste0(dirnam,"tmp-output/tmp-online-updating/"), full.names=TRUE))
 ## makeit("online-updating", openit=FALSE)