Commit 50d3244f authored by Daniela Rabe's avatar Daniela Rabe
Browse files

update parameter description, replace velox with fasterize in documentation

parent 321adc56
......@@ -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
)
......
......@@ -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
......
......@@ -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
......
......@@ -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)
......
......@@ -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
......
......@@ -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)
```
......
......@@ -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)
```
......
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