update parameter description, replace velox with fasterize in documentation

R-package/R/inner_procedure.r

@@ -11,6 +11,8 @@
 #' @param reference reference spectra either SpatialPointsDataFrame (shape file) or data.frame with lines = classes, column = predictors]
 #' @param model which machine learning classifier to use c("rf", "svm") for random forest or suppurt vector machine implementation
 #' @param mtry number of predictor used at random forest splitting nodes (mtry << n predictors)
+#' @param mod.error threshold for model error until which iteration is being executed
+#' @param in.memory boolean for raster processing (memory = "TRUE", from disk = "FALSE")
 #' @param last only true for one class classifier c("FALSE", TRUE")
 #' @param seed set seed for reproducable results
 #' @param init.seed "sample" for new or use run1@seeds to reproduce previous steps
@@ -65,7 +67,6 @@ sample_nb <- function(raster,
   ###
   l <- 1        ###6. opt=260
   pbtn1 <- matrix(1, nrow = 1, ncol = 1)
-  pbtn2 <- matrix(2, nrow = 1, ncol = 1)
   m <- vector("numeric", length = length(nb_samples))
   layer <- list()
   for (r in nb_samples) {
@@ -116,7 +117,6 @@ sample_nb <- function(raster,
         mtry = mtry,
         mod.error = mod.error,
         pbtn1 = pbtn1,
-        pbtn2 = pbtn2,
         rast = rast,
         max_samples_per_class = max_samples_per_class,
         mc.cores = cores,
@@ -144,7 +144,6 @@ sample_nb <- function(raster,
           mtry = mtry,
           mod.error = mod.error,
           pbtn1 = pbtn1,
-          pbtn2 = pbtn2,
           rast = rast,
           max_samples_per_class = max_samples_per_class
         )

R-package/R/model_opt.r

@@ -13,9 +13,9 @@
 #' @param n_channel number of channels
 #' @param seed2 spatial points sample
 #' @param mtry number of predictor used at random forest splitting nodes (mtry << n predictors)
+#' @param mod.error threshold for model error until which iteration is being executed
 #' @param pbtn1 matrix for points
-#' @param pbtn2 matrix for points
-#' @param ras_vx velox raster
+#' @param rast raster
 #' @param max_samples_per_class maximum number of samples per class
 #' 
 #' @return a list with 4 elements:
@@ -37,7 +37,6 @@ model_opt_r <- function(k,
                         mtry,
                         mod.error,
                         pbtn1,
-                        pbtn2,
                         rast,
                         max_samples_per_class) {
   points <- NULL

R-package/R/outer_procedure.r

@@ -11,6 +11,7 @@
 #' @param reference reference spectra as a data.frame with (lines = classes, column = predictors)
 #' @param model which machine learning classifier to use c("rf", "svm") for random forest or support vector machine implementation
 #' @param mtry number of predictor used at random forest splitting nodes (mtry << n predictors)
+#' @param mod.error threshold for model error until which iteration is being executed
 #' @param last only true for one class classifier c("FALSE", TRUE")
 #' @param seed set seed for reproducible results
 #' @param init.seed "sample" for new or use run1@seeds to reproduce previous steps
@@ -20,7 +21,8 @@
 #' @param n_classes total number of classes (habitat types) to be separated
 #' @param multiTest number of test runs to compare different probability outputs
 #' @param RGB rgb channel numbers for image plot
-#' @param color color pallet
+#' @param in.memory boolean for raster processing (memory = "TRUE", from disk = "FALSE")
+#' @param color single colors for continuous color palette interpolation
 #' @param overwrite overwrite the KML and raster files from previous runs (default TRUE)
 #' @param save_runs an Habitat object is saved into disk for each run (default TRUE)
 #' @param parallel_mode run loops using all available cores (default FALSE)
@@ -30,7 +32,7 @@
 #' @return 4 files per step:
 #' 1) Habitat type probability map as geocoded *.kmz file (with a *.kml layer and *.png image output), and *.tif raster file 
 #' 2) A Habitat object (only if save_runs is set to TRUE) consisting of 7 slots: \cr
-#' run1@models - list of selcted classifiers \cr
+#' run1@models - list of selected classifiers \cr
 #' run1@ref_samples - list of SpatialPointsDataFrames with same length as run1@models holding reference labels [1,2] for each selected model \cr
 #' run1@switch - vector of lenght run1@models indicating if target class equals 2, if not NA the labels need to be switched \cr
 #' run1@layer - raster map of habitat type probability \cr

demo/HabitatSampler.r

@@ -43,7 +43,7 @@ remotes::install_git(
 )
 
 ##0.2##
-libraries <- c("rgdal","raster","maptools","spatialEco","randomForest","e1071","devtools","velox","rgeos","leaflet","htmlwidgets", "HaSa")
+libraries <- c("rgdal","raster","maptools","spatialEco","randomForest","e1071","devtools", "fasterize","rgeos","leaflet","htmlwidgets", "HaSa")
 lapply(libraries, library, character.only = TRUE)
 
 

docker/README.md

@@ -13,9 +13,8 @@ The dependency list is defined in the docker file, `context/hasa.docker`
 
 The image uses the latest version of `R` available for `Ubuntu 20.04`.
 
-### HaSa
+### HaSa Dependencies
 
-The `HaSa` R package depends on `velox-v0.2.0`. To install `velox-v0.2.0` it is necessary to pin the version of some R packages: `BH`, `sp`, `sf`, and `rgdal`.
 All the dependencies and installation command for `HaSa` are defined in `context/install.R`.
 
 ## Start a docker container

docs/HabitatSampler.Rmd

@@ -65,14 +65,7 @@ The point shapefile contains a point location per class and is used to extract t
 The following procedure will lead you through the preliminary steps required to setup the HaSa tool.
 
 ## 2.1 HaSa dependencies
-HaSa uses the latest version of the `velox` library (`v0.2.0`) which does not compile with the latest version the interface to the C++ Boost library `BH`. Hence, it is necessary to pin the `BH` version.
-
-The installation of `BH` is possible with the following commands:
-```{r install dependencies, eval = FALSE}
-install.packages("remotes")
-install.packages("https://cran.r-project.org/src/contrib/Archive/BH/BH_1.69.0-1.tar.
-                 gz", repos = NULL, type = "source")
-```
+All the dependencies and installation command for `HaSa` are defined in `context/install.R`.
 
 ## 2.2 Install HaSa
 HaSa is not yet available as a CRAN package. The user needs to install it directly from its repository. Since versions of certain packages were pinned (c.f., Section 2.1), the user should use `upgrade = FALSE` when calling `remotes::install_git`. In the following example the user can install the `HaSa` R package, build its manual and its vignettes.
@@ -94,7 +87,7 @@ Before the user starts using `HaSa` it is necessary to load the library and some
 ```{r load libraries, echo = TRUE, results = 'hide', message = FALSE, warning = FALSE}
 options("rgdal_show_exportToProj4_warnings" = "none")
 libraries <- c("rgdal","raster","maptools","spatialEco","randomForest","e1071",
-                   "devtools","velox","rgeos","leaflet","htmlwidgets", "IRdisplay", "HaSa")
+                   "devtools","fasterize","rgeos","leaflet","htmlwidgets", "IRdisplay", "HaSa")
 lapply(libraries, library, character.only = TRUE)
 ```
 

docs/README.md

@@ -110,18 +110,7 @@ required to setup the HaSa tool.
 
 ## 2.1 HaSa dependencies
 
-HaSa uses the latest version of the `velox` library (`v0.2.0`) which
-does not compile with the latest version the interface to the C++ Boost
-library `BH`. Hence, it is necessary to pin the `BH` version.
-
-The installation of `BH` is possible with the following commands:
-
-``` r
-install.packages("remotes")
-install.packages("https://cran.r-project.org/src/contrib/Archive/BH/BH_1.69.0-1.tar.
-                 gz", 
-    repos = NULL, type = "source")
-```
+All the dependencies and installation command for `HaSa` are defined in `context/install.R`.
 
 ## 2.2 Install HaSa
 
@@ -149,7 +138,7 @@ warning messages related with the latest changes in `gdal` and `PROJ6`.
 ``` r
 options(rgdal_show_exportToProj4_warnings = "none")
 libraries <- c("rgdal", "raster", "maptools", "spatialEco", "randomForest", 
-    "e1071", "devtools", "velox", "rgeos", "leaflet", "htmlwidgets", "IRdisplay", 
+    "e1071", "devtools", "fasterize", "rgeos", "leaflet", "htmlwidgets", "IRdisplay", 
     "HaSa")
 lapply(libraries, library, character.only = TRUE)
 ```

requirements.txt

@@ -14,7 +14,7 @@ htmlwidgets_1.2
 leafem_0.0.1
 leaflet_2.0.1      
 rgeos_0.3-26
-velox_0.2.0
+fasterize_1.0.3
 e1071_1.6-8        
 randomForest_4.6-14
 spatialEco_1.1-0