Fixes

R/svm.R

@@ -592,6 +592,7 @@ evalqOnLoad({
 
     }else{
       t <- predict(x, X)
+      true_target <- NULL
     }
     labels <- levels(as.factor(t))
 
@@ -615,8 +616,11 @@ evalqOnLoad({
     }
     colnames(df) <- c("X1", "X2") # This is even worse
     df['prediction'] <- as.factor(t)
-    levels(true_target) <- x$levels
-    df['label'] <- true_target
+
+    if(!is.null(true_target)){
+      levels(true_target) <- x$levels
+      df['label'] <- true_target
+    }
 
     #4. Prepare data for plotting
     if (obj$areExamplesWeighted()) {
@@ -671,23 +675,39 @@ evalqOnLoad({
       grid['prediction'] <- prediction
 
 
-
-      pl <- ggplot()+ 
-        geom_tile(data=grid, aes(x=x,y=y, fill=prediction, alpha=.5)) + 
-        theme(legend.position="none") + 
-        scale_fill_brewer(palette="Set1") + 
-        scale_alpha_identity() + 
-        geom_point(data=df, aes(X1, X2, size=sizes, colour=prediction, shape=label)) + 
-        scale_colour_brewer(palette="Set1") + 
-        scale_size
-
+      if(!is.null(true_target)){
+        pl <- ggplot()+ 
+          geom_tile(data=grid, aes(x=x,y=y, fill=prediction, alpha=.5)) + 
+          theme(legend.position="none") + 
+          scale_fill_brewer(palette="Set1") + 
+          scale_alpha_identity() + 
+          geom_point(data=df, aes(X1, X2, size=sizes, colour=prediction, shape=label)) + 
+          scale_colour_brewer(palette="Set1") + 
+          scale_size
+      }
+      else{
+        pl <- ggplot()+ 
+          geom_tile(data=grid, aes(x=x,y=y, fill=prediction, alpha=.5)) + 
+          theme(legend.position="none") + 
+          scale_fill_brewer(palette="Set1") + 
+          scale_alpha_identity() + 
+          geom_point(data=df, aes(X1, X2, size=sizes, colour=prediction)) + 
+          scale_colour_brewer(palette="Set1") + 
+          scale_size
+      }  
     }else{
       warning("Only limited plotting is currently supported for multidimensional data")
-
-      pl <- ggplot()+ 
-        geom_point(data=df, aes(X1, X2, size=sizes, colour=prediction, shape=label)) + 
-        scale_colour_brewer(palette="Set1") + 
-        scale_size
+      if(!is.null(true_target)){
+        pl <- ggplot()+ 
+          geom_point(data=df, aes(X1, X2, size=sizes, colour=prediction, shape=label)) + 
+          scale_colour_brewer(palette="Set1") + 
+          scale_size
+      }else{
+        pl <- ggplot()+ 
+          geom_point(data=df, aes(X1, X2, size=sizes, colour=prediction)) + 
+          scale_colour_brewer(palette="Set1") + 
+          scale_size
+      }
     }
 
     # Add line

demo/samples/compile.R

@@ -18,10 +18,17 @@ render("gng.wine.R", output_options=c(highlight=highlighting))
 render("gng.optimized.R", output_options=c(highlight=highlighting))
 render("gng.mouse.R", output_options=c(highlight=highlighting))
 
+
+render("cec.basic.R", output_options=c(highlight=highlighting))
+render("cec.ellipse.R", output_options=c(highlight=highlighting))
+render("cec.T.dataset.R", output_options=c(highlight=highlighting))
+
 # THIS IS REALLY TRICKY - we generate not self contained files
 # and then link them to gmum.r FTP server 
 
 scripts <- list.files(".", pattern = glob2rx("*.R"), full.names = FALSE)
+# NOTE: Run once for each script online!
+
 # TO RUN FOR PART REPLACE SCRIPTS WITH LIST OF FILES YOU WISH TO UPDATE
 run.cmds <- function(script.name){
   file.folder <- paste(substr(script.name, 1, nchar(script.name)-2), "_files", sep="")

demo/samples/gng.mouse.R

@@ -12,21 +12,20 @@ library(ggplot2)
 
 # Load our library dataset
 data(cec_mouse_1_spherical)
-cec.mouse.1.spherical <- input
 
 # Train GNG model and find centroids of the resulting graph
-g <- GNG(cec.mouse.1.spherical, max.nodes=50)
+g <- GNG(cec.mouse1spherical, max.nodes=50)
 
 # GNG aims at making it easy to work with its graph.
 # Here we will predict closest centroid 
 mouse.centr <- centroids(g)
 
 # Now we can plot results to see decision boundary for assigning node to centroid
-m = as.data.frame(cec.mouse.1.spherical)
+m = as.data.frame(cec.mouse1spherical)
 colnames(m) = c("x", "y")
 
-x_col <- cec.mouse.1.spherical[,1]
-y_col <- cec.mouse.1.spherical[,2]
+x_col <- cec.mouse1spherical[,1]
+y_col <- cec.mouse1spherical[,2]
 
 x_max <- max(x_col)
 x_min <- min(x_col) 

demo/samples/gng.online.R

@@ -18,6 +18,7 @@ labels <- round(runif(10000)*3)
 # Insert dataset with labels
 insertExamples(gng, ex, labels)
 
+
 # Run algorithm in parallel 
 # (GNG will be running in separate thread!)
 run(gng)