From 33ed4ec000d426601818db66cf5b7c0f8e026989 Mon Sep 17 00:00:00 2001
From: Peder <pbac@dtu.dk>
Date: Fri, 10 Jul 2020 10:28:08 +0200
Subject: [PATCH] Changed ones() to one()

---
 .Rbuildignore                       |  2 +-
 .gitignore                          | 12 +-----------
 R/bspline.R                         |  2 +-
 R/forecastmodel.R                   |  2 +-
 R/input_class.R                     |  2 +-
 R/lm_fit.R                          |  4 ++--
 R/lm_optim.R                        |  2 +-
 R/lm_predict.R                      |  2 +-
 R/long_format.R                     |  2 +-
 R/ones.R                            | 12 ++++++------
 R/plot_ts.R                         |  2 +-
 R/rls_fit.R                         |  2 +-
 R/rls_optim.R                       |  2 +-
 R/rls_predict.R                     |  2 +-
 R/rls_prm.R                         |  2 +-
 R/rls_summary.R                     |  2 +-
 misc-R/temp.R                       |  2 +-
 tests/testthat/test-rls-heat-load.R |  2 +-
 vignettes/forecast-evaluation.Rmd   |  6 +++---
 vignettes/make.R                    | 16 +++++++++-------
 vignettes/online-updating.Rmd       |  6 +++---
 vignettes/setup-and-use-model.Rmd   | 24 ++++++++++++------------
 vignettes/setup-data.Rmd            |  4 ++--
 23 files changed, 53 insertions(+), 61 deletions(-)

diff --git a/.Rbuildignore b/.Rbuildignore
index 4a7a006..7a419b7 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -2,7 +2,7 @@
 ^\.Rproj\.user$
 ^make.R
 ^data/all
-^vignettes/tmp-output
+^tmp
 ^vignettes/make.R
 ^vignettes/shared-init.Rmd
 ^vignettes/cache
diff --git a/.gitignore b/.gitignore
index 64b396e..8c94d92 100644
--- a/.gitignore
+++ b/.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/
diff --git a/R/bspline.R b/R/bspline.R
index 56eeb30..0fe03e3 100644
--- a/R/bspline.R
+++ b/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}
diff --git a/R/forecastmodel.R b/R/forecastmodel.R
index e33b37e..562058a 100644
--- a/R/forecastmodel.R
+++ b/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) {
diff --git a/R/input_class.R b/R/input_class.R
index 3c54a81..3d86867 100644
--- a/R/input_class.R
+++ b/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
diff --git a/R/lm_fit.R b/R/lm_fit.R
index 5996426..33adff3 100644
--- a/R/lm_fit.R
+++ b/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")
diff --git a/R/lm_optim.R b/R/lm_optim.R
index 2b7486a..ad6a796 100644
--- a/R/lm_optim.R
+++ b/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
diff --git a/R/lm_predict.R b/R/lm_predict.R
index 8e5531c..6cef48d 100644
--- a/R/lm_predict.R
+++ b/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$t)
diff --git a/R/long_format.R b/R/long_format.R
index 838c50b..3cf381c 100644
--- a/R/long_format.R
+++ b/R/long_format.R
@@ -21,7 +21,7 @@
 #' # Define a model
 #' model <- forecastmodel$new()
 #' model$add_inputs(Ta = "Ta",
-#'                  mu = "ones()")
+#'                  mu = "one()")
 #' model$add_regprm("rls_prm(lambda=0.99)")
 #' model$kseq <- 1:6
 #' # Fit it
diff --git a/R/ones.R b/R/ones.R
index e079be5..b2353b0 100644
--- a/R/ones.R
+++ b/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$kseq))))
 }
diff --git a/R/plot_ts.R b/R/plot_ts.R
index c030cbc..97d059b 100644
--- a/R/plot_ts.R
+++ b/R/plot_ts.R
@@ -466,7 +466,7 @@ plot_ts_series <- function(data, pattern, iplot = 1,
 #' model <- forecastmodel$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)
diff --git a/R/rls_fit.R b/R/rls_fit.R
index 79805ed..ed9d62d 100644
--- a/R/rls_fit.R
+++ b/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
diff --git a/R/rls_optim.R b/R/rls_optim.R
index 09f2cec..f8ba8af 100644
--- a/R/rls_optim.R
+++ b/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
diff --git a/R/rls_predict.R b/R/rls_predict.R
index 4f3855d..4726cef 100644
--- a/R/rls_predict.R
+++ b/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
diff --git a/R/rls_prm.R b/R/rls_prm.R
index 389c58e..4c9347c 100644
--- a/R/rls_prm.R
+++ b/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
diff --git a/R/rls_summary.R b/R/rls_summary.R
index 65f81e5..414e4e1 100644
--- a/R/rls_summary.R
+++ b/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
diff --git a/misc-R/temp.R b/misc-R/temp.R
index e13dd69..4f0ba53 100644
--- a/misc-R/temp.R
+++ b/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)
diff --git a/tests/testthat/test-rls-heat-load.R b/tests/testthat/test-rls-heat-load.R
index fdf9835..054105f 100644
--- a/tests/testthat/test-rls-heat-load.R
+++ b/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)")
 
     ## ------------------------------------------------------------------------
diff --git a/vignettes/forecast-evaluation.Rmd b/vignettes/forecast-evaluation.Rmd
index b8483cd..33bf041 100644
--- a/vignettes/forecast-evaluation.Rmd
+++ b/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))
diff --git a/vignettes/make.R b/vignettes/make.R
index 62a89ce..698cef4 100644
--- a/vignettes/make.R
+++ b/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)
diff --git a/vignettes/online-updating.Rmd b/vignettes/online-updating.Rmd
index 6011a3e..3f5a3f1 100644
--- a/vignettes/online-updating.Rmd
+++ b/vignettes/online-updating.Rmd
@@ -49,9 +49,9 @@ knitr::opts_chunk$set(
   comment = "##    ",
   prompt = FALSE,
   cache = TRUE,
-  cache.path = paste0("tmp-output/tmp-",vignettename,"/"),
+  cache.path = paste0("../tmp/vignettes/tmp-",vignettename,"/"),
   fig.align="center",
-  fig.path = paste0("tmp-output/tmp-",vignettename,"/"),
+  fig.path = paste0("../tmp/vignettes/tmp-",vignettename,"/"),
   fig.height = figheight,
   fig.width = figwidth,
   out.width = "100%"
@@ -110,7 +110,7 @@ D$trainperiod <- in_range(D$t[1], D$t, "2011-02-01")
 model <- forecastmodel$new()
 model$output = "heatload"
 model$add_inputs(Ta = "lp(Ta, a1=0.9)",
-                 mu = "ones()")
+                 mu = "one()")
 model$add_regprm("rls_prm(lambda=0.9)")
 model$add_prmbounds(Ta__a1 = c(0.5, 0.9, 0.9999),
                     lambda = c(0.9, 0.99, 0.9999))
diff --git a/vignettes/setup-and-use-model.Rmd b/vignettes/setup-and-use-model.Rmd
index 890123b..035a912 100644
--- a/vignettes/setup-and-use-model.Rmd
+++ b/vignettes/setup-and-use-model.Rmd
@@ -49,9 +49,9 @@ knitr::opts_chunk$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%"
@@ -142,7 +142,7 @@ model <- forecastmodel$new()
 model$output = "heatload"
 # Inputs (transformation step)
 model$add_inputs(Ta = "Ta",
-                 mu = "ones()")
+                 mu = "one()")
 # Regression step parameters
 model$add_regprm("rls_prm(lambda=0.9)")
 # Optimization bounds for parameters
@@ -170,7 +170,7 @@ The model inputs are defined by:
 ```{r}
 # Inputs (transformation step)
 model$add_inputs(Ta = "Ta",
-                 mu = "ones()")
+                 mu = "one()")
 ```
 So this is really where the structure of the model is specified. The inputs are
 given a name (`Ta` and `mu`), which each are set as an R expression (given as a
@@ -279,11 +279,11 @@ legend("topright", c("Observations",pst("Predictions (",min(model$kseq)," to ",m
 ## Input transformations
 
 The inputs can be transformations of the variables in the data, i.e. `D` in this
-example. The function `ones()` generate a forecast matrix of 1 for the needed
+example. The function `one()` generate a forecast matrix of 1 for the needed
 horizons. It cannot be called directly:
 ```{r, eval=FALSE}
 # This will give error
-ones()
+one()
 ```
 (the code above was not executed)
 
@@ -293,18 +293,18 @@ however we can see the result of the evaluation by:
 datatr <- model$transform_data(D)
 # See what came out
 summary(datatr)
-# In particular for the mu = "ones()"
+# In particular for the mu = "one()"
 head(datatr$mu)
 ```
 
 If we wanted to debug we could:
 ```{r, eval=FALSE}
 # Set to debug
-#debug(ones)
-# Run the input transformation now and it will stop in ones()
+#debug(one)
+# Run the input transformation now and it will stop in one()
 datatr <- model$transform_data(D)
 # Set to undebug
-#undebug(ones)
+#undebug(one)
 ```
 (the code above was not executed).
 
@@ -323,7 +323,7 @@ the new transformation:
 model <- forecastmodel$new()
 model$output = "heatload"
 model$add_inputs(Ta = "lp(Ta, a1=0.9)",
-                 mu = "ones()")
+                 mu = "one()")
 model$add_regprm("rls_prm(lambda=0.9)")
 model$add_prmbounds(Ta__a1 = c(0.5, 0.9, 0.9999),
                     lambda = c(0.9, 0.99, 0.9999))
@@ -359,7 +359,7 @@ Ta forecast is really smoothed, which models a system with a time constant
 
 There are quite a few functions available for input transformations:
 
-- `ones()` generates an matrix of ones (for including an intercept).
+- `one()` generates an matrix of ones (for including an intercept).
 - `fs()` generate Fourier series for modelling harmonic functions.
 - `bspline()` wraps the `bs()` function for generating base splines.
 - `AR()` generates auto-regressive model inputs.
diff --git a/vignettes/setup-data.Rmd b/vignettes/setup-data.Rmd
index 3629605..c8950db 100644
--- a/vignettes/setup-data.Rmd
+++ b/vignettes/setup-data.Rmd
@@ -50,9 +50,9 @@ 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%"
-- 
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