Fixes for #20 and improvements like suggested in #22

R-package/R/outer_procedure.r

@@ -366,6 +366,7 @@ multi_Class_Sampling <- function(in.raster,
       g = RGB[2],
       b = RGB[3],
       num_models = num_models,
+      nb_models = nb_models,
       acc = acc,
       color = color,
       outPath = outPath,
@@ -473,6 +474,7 @@ multi_Class_Sampling <- function(in.raster,
         g = RGB[2],
         b = RGB[3],
         num_models = num_models,
+        nb_models = nb_models,
         acc = acc,
         color = color,
         outPath = outPath,
@@ -519,9 +521,7 @@ multi_Class_Sampling <- function(in.raster,
     raster::KML(kml, paste(outPath, paste("step_", ni, sep = ""), sep = ""), overwrite = overwrite)
 
     thres <- as.numeric(decision)
-    dummy <- maFo_rf@layer[[1]]
-    max_prob <- raster::cellStats(dummy, "max")
-    thres <- (max_prob/100) * thres
+    thres <- thres + num_models
     dummy[dummy < thres] <- 1
     dummy[dummy >= thres] <- NA
     in.raster <- in.raster * dummy
@@ -532,7 +532,7 @@ multi_Class_Sampling <- function(in.raster,
     out.raster <<- in.raster
     remove(dummy)
     remove(maFo_rf)
-
+    
     print(paste(paste("Habitat", i), "Done"))
 
     colnames(reference)  <-  names(in.raster)

R-package/R/plot_interactive.r

@@ -10,6 +10,7 @@
 #' @param b blue channel (integer)
 #' @param acc predictive accuracy (integer)
 #' @param num_models number of selected models
+#' @param nb_models number of models (independent classifiers) for the specification in the legend
 #' @param color color pallet
 #' @param outPath file path for '.html export (character)
 #' @param plot_on_browser plot on the browser or inline in a notebook (default TRUE)
@@ -24,6 +25,7 @@ iplot <- function(x,
                   b,
                   acc,
                   num_models,
+                  nb_models,
                   color,
                   outPath,
                   plot_on_browser = TRUE) {
@@ -125,11 +127,10 @@ iplot <- function(x,
   ######
   rr <- x
   raster::values(rr) <- 1:raster::ncell(rr)
-  max <- raster::cellStats(x, "max") - num_models
   x <- raster::calc(
       x,
       fun = function(y)
-        round(((y - num_models) / max) * 100, digits = 2)
+        (y - num_models)
     )
   ##############################################################################
   ##[2] Create Leaflet Html output for Webbrowser
@@ -166,16 +167,28 @@ iplot <- function(x,
     layerId = HaTy,
     prefix = "Habitat Type"
   )
-
-  mv <- leaflet::addLegend(
-    map = mv,
-    "bottomright",
-    pal = pal,
-    labFormat = leaflet::labelFormat(  suffix = "%",),
-    values =  c(0, 100),
-    title = paste("Habitat Type Probability<br>(for ", num_models, " models)", sep = ""),
-    opacity = 1
-  )
+  if (num_models<11) {
+    mv <- leaflet::addLegend(
+      map = mv,
+      "bottomright",
+      pal = pal,
+      labFormat = leaflet::labelFormat(suffix = " Models",
+                                       transform = function(x) seq(0, num_models, 1)),
+      values =  c(0, num_models),
+      title = paste("Number of models (", num_models," out of ", nb_models,")<br>predicting class ", HaTy, sep = ""),
+      opacity = 1
+    )
+  } else {
+    mv <- leaflet::addLegend(
+      map = mv,
+      "bottomright",
+      pal = pal,
+      labFormat = leaflet::labelFormat(suffix = " Models"),
+      values =  c(0, num_models),
+      title = paste("Number of models (", num_models," out of ", nb_models,")<br>predicting class ", HaTy, sep = ""),
+      opacity = 1
+    )
+  }
   mv <- leaflet::addLayersControl(map = mv,
                                   overlayGroups = c("RGB Composite", HaTy))
 

R-package/man/iplot.Rd

@@ -13,6 +13,7 @@ iplot(
   b,
   acc,
   num_models,
+  nb_models,
   color,
   outPath,
   plot_on_browser = TRUE
@@ -35,6 +36,8 @@ iplot(
 
 \item{num_models}{number of selected models}
 
+\item{nb_models}{number of models (independent classifiers) for the specification in the legend}
+
 \item{color}{color pallet}
 
 \item{outPath}{file path for '.html export (character)}