@@ -10,6 +10,6 @@ kNNinterface <- function(measurementsTrain, classesTrain, measurementsTest, ...,

   if(verbose == 3)

     message("Fitting k Nearest Neighbours classifier to data and predicting classes.")

-  class::knn(as.matrix(measurementsTrain), as.matrix(measurementsTest), classesTrain, ...)

+  setNames(class::knn(as.matrix(measurementsTrain), as.matrix(measurementsTest), classesTrain, ...), rownames(measurementsTest))

 }

 attr(kNNinterface, "name") <- "kNNinterface"

@@ -149,12 +149,14 @@ mixModelsPredict <- function(models, measurementsTest, difference = c("unweighte

       classScores <- classScores / sum(classScores) # Make different feature selection sizes comparable.

       classPredicted <- names(classScores)[which.max(classScores)]

     }

-

     data.frame(class = factor(classPredicted, levels = classesNames), t(classScores), check.names = FALSE)

   }))

-  switch(returnType, class = predictions[, "class"],

-         score = predictions[, colnames(predictions) %in% classesNames],

+  classPredictions <- predictions[, "class"]

+  classScores <- predictions[, colnames(predictions) %in% classesNames]

+  rownames(classScores) <- names(classPredictions) <- rownames(measurementsTest)

+  switch(returnType, class = classPredictions,

+         score = classScores,

          both = data.frame(class = predictions[, "class"], predictions[, colnames(predictions) %in% classesNames, drop = FALSE], check.names = FALSE)

   )

 }

@@ -115,9 +115,12 @@ naiveBayesKernel <- function(measurementsTrain, classesTrain, measurementsTest,

     data.frame(class = factor(classPredicted, levels = levels(classesTrain)), t(classScores), check.names = FALSE)

   }))

-

-  switch(returnType, class = predictions[, "class"],

-         score = predictions[, 2:ncol(predictions)],

+  

+  classPredictions <- predictions[, "class"]

+  classScores <- predictions[, 2:ncol(predictions)]

+  rownames(classScores) <- names(classPredictions) <- rownames(measurementsTest)

+  switch(returnType, class = classPredictions,

+         score = classScores,

          both = predictions)

 }

 attr(naiveBayesKernel, "name") <- "naiveBayesKernel"

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