Commit directly from the sky, #567

Working SVM and CEC standard structure-like autocompletion
Workaround for RStudio autocompl bug

NAMESPACE

@@ -1,7 +1,5 @@
 # Generated by roxygen2 (4.1.1): do not edit by hand
 
-S3method(SVM,default)
-S3method(SVM,formula)
 S3method(clustering,Rcpp_CecModel)
 S3method(clustering,Rcpp_GNGServer)
 S3method(node,Rcpp_GNGServer)
@@ -28,6 +26,8 @@ export(CEC)
 export(GNG)
 export(OptimizedGNG)
 export(SVM)
+export(SVM.default)
+export(SVM.formula)
 export(calculateCentroids)
 export(caret.gmumSvmLinear)
 export(caret.gmumSvmPoly)
@@ -50,9 +50,9 @@ export(gngLoad)
 export(gngSave)
 export(insertExamples)
 export(isRunning)
+export(iterations)
 export(logClusters)
 export(logEnergy)
-export(logIterations)
 export(meanError)
 export(node)
 export(numberNodes)

R/R_scripts/test_cec_energy_func.R

@@ -13,16 +13,16 @@ test_that("EllipseGauss: energy is correct", {
     for(i in 1:t)
     {
         c <- CEC(k=nclusters, x=dataset_points, method.init='random', method.type='standard')
-        energy_func_value <- cec_energy(dataset = dataset_points, clustering = c$clustering(), entropy_func = standard_entropy)
-        expect_equal(expected=c$energy(),  object=energy_func_value, tolerance=.000001)        
+        energy_func_value <- cec_energy(dataset = dataset_points, clustering = c$clustering, entropy_func = standard_entropy)
+        expect_equal(expected=c$energy,  object=energy_func_value, tolerance=.000001)        
 
         c <- CEC(k=nclusters, x=dataset_points, method.init='random', method.type='spherical')
-        energy_func_value <- cec_energy(dataset = dataset_points, clustering = c$clustering(), entropy_func = sphere_entropy)
-        expect_equal(expected=c$energy(),  object=energy_func_value, tolerance=.000001)
+        energy_func_value <- cec_energy(dataset = dataset_points, clustering = c$clustering, entropy_func = sphere_entropy)
+        expect_equal(expected=c$energy,  object=energy_func_value, tolerance=.000001)
 
         c <- CEC(k=nclusters, x=dataset_points, method.init='random', method.type='diagonal')
-        energy_func_value <- cec_energy(dataset = dataset_points, clustering = c$clustering(), entropy_func = diagonal_entropy)
-        expect_equal(expected=c$energy(),  object=energy_func_value, tolerance=.000001)
+        energy_func_value <- cec_energy(dataset = dataset_points, clustering = c$clustering, entropy_func = diagonal_entropy)
+        expect_equal(expected=c$energy,  object=energy_func_value, tolerance=.000001)
     }
     print("EllipseGauss: energy is correct")
 })
@@ -37,16 +37,16 @@ test_that("mouse_1: energy is correct", {
     for(i in 1:t)
     {
         c <- CEC(k=nclusters, x=dataset_points, method.init='random', method.type='standard')
-        energy_func_value <- cec_energy(dataset = dataset_points, clustering = c$clustering(), entropy_func = standard_entropy)
-        expect_equal(expected=c$energy(),  object=energy_func_value, tolerance=.000001)
+        energy_func_value <- cec_energy(dataset = dataset_points, clustering = c$clustering, entropy_func = standard_entropy)
+        expect_equal(expected=c$energy,  object=energy_func_value, tolerance=.000001)
 
         c <- CEC(k=nclusters, x=dataset_points, method.init='random', method.type='spherical')
-        energy_func_value <- cec_energy(dataset = dataset_points, clustering = c$clustering(), entropy_func = sphere_entropy)
-        expect_equal(expected=c$energy(),  object=energy_func_value, tolerance=.000001)
+        energy_func_value <- cec_energy(dataset = dataset_points, clustering = c$clustering, entropy_func = sphere_entropy)
+        expect_equal(expected=c$energy,  object=energy_func_value, tolerance=.000001)
 
         c <- CEC(k=nclusters, x=dataset_points, method.init='random', method.type='diagonal')
-        energy_func_value <- cec_energy(dataset = dataset_points, clustering = c$clustering(), entropy_func = diagonal_entropy)
-        expect_equal(expected=c$energy(),  object=energy_func_value, tolerance=.000001)
+        energy_func_value <- cec_energy(dataset = dataset_points, clustering = c$clustering, entropy_func = diagonal_entropy)
+        expect_equal(expected=c$energy,  object=energy_func_value, tolerance=.000001)
     }
     print("mouse_1: energy is correct")
 })
@@ -62,16 +62,16 @@ test_that("mouse_1_spherical: energy is correct", {
     for(i in 1:t)
     {
         c <- CEC(k=nclusters, x=dataset_points, method.init='random', method.type='standard')
-        energy_func_value <- cec_energy(dataset = dataset_points, clustering = c$clustering(), entropy_func = standard_entropy)
-        expect_equal(expected=c$energy(),  object=energy_func_value, tolerance=.000001)
+        energy_func_value <- cec_energy(dataset = dataset_points, clustering = c$clustering, entropy_func = standard_entropy)
+        expect_equal(expected=c$energy,  object=energy_func_value, tolerance=.000001)
 
         c <- CEC(k=nclusters, x=dataset_points, method.init='random', method.type='spherical')
-        energy_func_value <- cec_energy(dataset = dataset_points, clustering = c$clustering(), entropy_func = sphere_entropy)
-        expect_equal(expected=c$energy(),  object=energy_func_value, tolerance=.000001)
+        energy_func_value <- cec_energy(dataset = dataset_points, clustering = c$clustering, entropy_func = sphere_entropy)
+        expect_equal(expected=c$energy,  object=energy_func_value, tolerance=.000001)
 
         c <- CEC(k=nclusters, x=dataset_points, method.init='random', method.type='diagonal')
-        energy_func_value <- cec_energy(dataset = dataset_points, clustering = c$clustering(), entropy_func = diagonal_entropy)
-        expect_equal(expected=c$energy(),  object=energy_func_value, tolerance=.000001)
+        energy_func_value <- cec_energy(dataset = dataset_points, clustering = c$clustering, entropy_func = diagonal_entropy)
+        expect_equal(expected=c$energy,  object=energy_func_value, tolerance=.000001)
     }
     print("mouse_1_spherical: energy is correct")
 })

R/cec.R

@@ -240,22 +240,22 @@ logClusters <- NULL
 #' @export
 logEnergy <- NULL
 
-#' @name logIterations
-#' @title logIterations
+#' @name iterations
+#' @title iterations
 #' 
-#' @aliases logIterations,Rcpp_CecModel-method
+#' @aliases iterations,Rcpp_CecModel-method
 #'
-#' @title logIterations
+#' @title iterations
 #' 
 #' @description Print how many iterations it took to learn CEC model
 #'
 #' @param c object Trained CEC model object.
 #' @examples
 #' \dontrun{
-#' logIterations(c) 
+#' iterations(c) 
 #' }
 #' @export
-logIterations <- NULL
+iterations <- NULL
 
 loadModule('cec', TRUE)
 
@@ -332,7 +332,21 @@ CEC <- function(x = NULL,
 
   model <- new(CecModel, config)
 
+
+
   assign("call", call, model)
+  assign("energy", model$.getEnergy(), model)
+  assign("clustering", model$.getClustering(), model)
+  assign("centers", model$.getCenters(), model)
+  assign("covMatrix", model$.getCovMatrix(), model)
+  assign("logEnergy", model$.getLogEnergy(), model)
+  assign("logNumberOfClusters", model$.getLogNumberOfClusters(), model)
+
+  assign("iterations", model$.getIterations(), model)
+
+  assign(".staticFields", c("call", "energy", "clustering", "centers", "covMatrix", 
+                            "logNumberOfClusters", "logEnergy", "iterations"), model)
+
   model
 }
 
@@ -345,35 +359,36 @@ runOneIteration <- function(c) {
 }
 
 energy <- function(c) {
-  c$energy()
+  c$.getEnergy()
 }
 
 clustering.Rcpp_CecModel <- function(c) {
-  c$clustering()
+  c$.getClustering()
 }
 
 getDataset <- function(c) {
-  c$getDataset()
+  c$.getDataset()
 }
 
 centers <- function(c) {
-  c$centers()
+  c$.getCenters()
 }
 
 covMatrix <- function(c) {
-  c$covMatrix()
+  c$.getCovMatrix()
 }
 
-logClusters <- function(c) {
-  c$log.ncluster()
+logEnergy <- function(c) {
+  c$.getLogEnergy()
 }
 
-logEnergy <- function(c) {
-  c$log.energy()
+logNumberOfClusters <- function(c) {
+  c$.getLogNumberOfClusters()
 }
 
-logIterations <- function(c) {
-  c$log.iters()
+
+iterations <- function(c) {
+  c$.getIterations()
 }
 
 predict.Rcpp_CecModel <- function(object, x, ...) {
@@ -388,7 +403,7 @@ predict.Rcpp_CecModel <- function(object, x, ...) {
     x = data.matrix(x)
   }
 
-  if(dim(object$getDataset())[2] != dim(x)[2]){
+  if(dim(object$.getDataset())[2] != dim(x)[2]){
     stop("Incompatible dimension!")
   }
 

R/cec.plot.R

@@ -27,7 +27,7 @@
 #' }
 plot.Rcpp_CecModel <- function(x, slice = c(), pca=FALSE, ellipses = FALSE, centers = FALSE, ...) {
 
-  d <- x$getDataset()
+  d <- x$.getDataset()
   if(pca){
     if(ncol(d) <= 2){
       stop("CEC dataset should have dimension > 2 to use PCA")
@@ -44,17 +44,17 @@ plot.Rcpp_CecModel <- function(x, slice = c(), pca=FALSE, ellipses = FALSE, cent
     } else {
       slice <- c(1:(dim(d)[2]))        
     }
-    plot(d[,slice], col = (x$clustering() + 1), pch=20)
+    plot(d[,slice], col = (x$clustering + 1), pch=20)
   }
   else if (length(slice) == 1 || length(slice) == 2) {
-    plot(d[,slice], col = (x$clustering() + 1), pch=20)
+    plot(d[,slice], col = (x$clustering + 1), pch=20)
   }
   else{
-    pairs(d[,slice], col = (x$clustering() + 1))
+    pairs(d[,slice], col = (x$clustering + 1))
   }
 
   if (ellipses || centers) {
-    cen <- x$centers()
+    cen <- x$centers
     n <- length(cen)
     if(pca){
       for (i in 1:n) {
@@ -65,7 +65,7 @@ plot.Rcpp_CecModel <- function(x, slice = c(), pca=FALSE, ellipses = FALSE, cent
     }
     if (ellipses && length(slice) <= 2){
       #library("car")
-      cov <- x$covMatrix()        
+      cov <- x$covMatrix     
       for (i in 1:n) {
         data <- unlist(cov[i])
         covMat <- matrix(data,ncol=sqrt(length(data)))

R/cec.print.R

@@ -15,11 +15,11 @@
 #' 
 print.Rcpp_CecModel <- function(x, ...) {
     print(sprintf("CEC clustering; %d clusters with energy = %f",
-                  length(x$centers()), x$energy()))
+                  length(x$centers), x$energy))
     print("Centers: ")
-    print(x$centers())
+    print(x$centers)
     print("Covariances: ")
-    print(x$covMatrix())
+    print(x$covMatrix)
 }
 
 show.Rcpp_CecModel <- function(object){

R/cec.summary.R

@@ -15,17 +15,17 @@
 summary.Rcpp_CecModel <- function(object, ...) {
   print(object)
 
-  if(isParameterOn(object$log.iters())){
+  if(isParameterOn(object$iterations)){
     print("Iterations: ")
-    print(object$log.iters())
+    print(object$iterations)
   }
-  if(isParameterOn(object$log.energy())){
+  if(isParameterOn(object$logEnergy)){
     print("Energy for every iteration: ")
-    print(object$log.energy())
+    print(object$logEnergy)
   }
-  if(isParameterOn(object$log.ncluster())){
+  if(isParameterOn(object$logNumberOfClusters)){
     print("Number of clusters for every iteration: ")
-    print(object$log.ncluster())
+    print(object$logNumberOfClusters)
   }
 }
 

R/gmum.R

@@ -1,8 +1,19 @@
 # Lazy loading to allow for discovery of all files
 evalqOnLoad( {
-  # Autocompletion fix
-  .GlobalEnv$`.DollarNames.C++Object` <- function( x, pattern ){
+  # Autocompletion override
+  autocompl <- function(x, pattern="") {
     targets <- c(asNamespace("Rcpp")$complete(x), x[['.staticFields']])
     grep(pattern, targets, value = TRUE)[! (substr(grep(pattern, targets, value = TRUE),1,1)==".")]
   }
+
+  `.DollarNames.Rcpp_C++Object` <<- autocompl
+  .DollarNames.Rcpp_SVMClient <<- autocompl
+  .DollarNames.Rcpp_GNGServer <<- autocompl
+  .DollarNames.Rcpp_CecModel <<- autocompl
+
+  # Workaround RStudio bug
+  if(exists(".rs.getAnywhere")) {
+    .rs.getAnywhere.original <<- .rs.getAnywhere
+    .rs.getAnywhere <<- function(a, envir=.GlobalEnv){ .rs.getAnywhere.original(a, .GlobalEnv) }
+  }
 })

R/svm.R

@@ -279,11 +279,11 @@ caret.gmumSvmLinear <- NULL
 caret.gmumSvmPoly <- NULL
 
 #' @rdname svm
-#' @export
+#' @export SVM.formula
 SVM.formula <- NULL
 
 #' @rdname svm
-#' @export
+#' @export SVM.default
 SVM.default <- NULL
 
 loadModule('svm_wrapper', TRUE)
@@ -317,7 +317,7 @@ SVM.formula <- function(formula, data, ...) {
     x <- data.matrix(x)
 
   ret <- SVM.default(x, y, ...)
-  assign("call", as.name("SVM"), ret) # SVM.formula call is different so overwriting
+  assign("call", call, ret) 
   return(ret)
 }
 
@@ -365,8 +365,7 @@ SVM.formula <- function(formula, data, ...) {
   }
 
   models <- list()
-  call[[1]] <- as.name("SVM")
-
+
   # Fit one model after another
   for (j in J) {
     x.model <- NULL
@@ -482,8 +481,7 @@ SVM.default <-
       core <- "svmlight"
 
     call <- match.call(expand.dots = TRUE)
-    call[[1]] <- as.name("SVM")
-
+
     # check for errors
     if (core != "libsvm" && core != "svmlight") {
       stop(sprintf("bad core: %s, available are: libsvm, svmlight", core))
@@ -697,8 +695,6 @@ SVM.default <-
     client <- new(SVMClient, config)
     client$.train()
 
-    call[[1]] <- as.name("SVM")
-
     # R object often have fields that don't change accessible through $ notation
     assign("call", call, client)
     assign(".levels", levels, client)

R/svm.utils.R

@@ -73,9 +73,11 @@ svm.dataset.circles <- function() {
 #' 
 #' @description Calculates accuracy of a prediction, returns precent of correctly predicted examples 
 #' over all test examples.
-#' @export
+#' @export svm.accuracy
 #' @rdname svm.accuracy
 #' 
+#' @usage svm.accuracy(prediction, target)
+#' 
 #' @param prediction factor or 1 dim vector with predicted classes
 #' @param target  factor or 1 dim vector with true classes
 #'