some clean-up

R/DataSource.R

@@ -48,6 +48,7 @@ setClass("DataSource",
 #'               it is ignored.
 #' @param index1 Whether the user indices and item indices start with 1
 #'               (\code{index1 = TRUE}) or 0 (\code{index1 = FALSE}).
+#' @param \dots Currently unused.
 #' @return An object of class "DataSource" as required by
 #' \code{$\link{tune}()}, \code{$\link{train}()}, and \code{$\link{predict}()}.
 #' 
@@ -88,8 +89,3 @@ data_memory = function(user_index, item_index, rating = NULL, index1 = FALSE, ..
     new("DataSource", source = list(user_index, item_index, rating),
                       index1 = index1, type = "memory")
 }
-
-data_rmm = function(rmm, ...)
-{
-
-}

R/Output.R

@@ -14,6 +14,7 @@ setClass("Output",
 #' the result is not needed and will not be returned.
 #' 
 #' @param path Path to the output file.
+#' @param \dots Currently unused.
 #' @return An object of class "Output" as required by
 #' \code{$\link{output}()} and \code{$\link{predict}()}.
 #' 

inst/NEWS.Rd

@@ -10,7 +10,7 @@
                   has been expanded to and replaced by \code{costp_l1},
                   \code{costp_l2}, \code{costq_l1}, and \code{costq_l2}, to
                   allow for more flexibility of the model.
-            \item Added a \code{loss} parameter in \code{$train()} and
+            \item A new \code{loss} parameter in \code{$train()} and
                   \code{$tune()} to specify loss function.
           }
     \item Other API change:

src/mf.cpp

@@ -1019,8 +1019,12 @@ mf_model* Utility::init_model(mf_int fun,
     }
     catch(bad_alloc const &e)
     {
-        cerr << e.what() << endl;
+        // cerr << e.what() << endl;
+        // mf_destroy_model(&model);
+        // throw;
+
         mf_destroy_model(&model);
+        Rcpp::stop(e.what());
         throw;
     }
 
@@ -3544,8 +3548,12 @@ mf_model* mf_load_model(char const *path)
     }
     catch(bad_alloc const &e)
     {
-        cerr << e.what() << endl;
+        // cerr << e.what() << endl;
+        // mf_destroy_model(&model);
+        // return nullptr;
+
         mf_destroy_model(&model);
+        Rcpp::stop(e.what());
         return nullptr;
     }
 

vignettes/introduction.Rmd

@@ -226,7 +226,7 @@ $P$ and $Q$ into files or return them as R objects.
 
 Below is an example on some simulated data:
 
-```{r cache=TRUE}
+```{r}
 library(recosystem)
 set.seed(123) # This is a randomized algorithm
 train_set = data_file(system.file("dat", "smalltrain.txt", package = "recosystem"))