Merge branch 'master' of github.com:0xdata/h2o into mm_fix_prediction

R/.gitignore

@@ -1,4 +1,5 @@
 *.pdf
 h2o-package/DESCRIPTION
+h2o-package/man/h2o-package.Rd
 h2o-package/h2o_package.pdf
 h2o-package/inst/java

R/h2o-DESCRIPTION.template

@@ -10,5 +10,5 @@ License: Apache License (== 2.0)
 Depends: R (>= 2.13.0), RCurl, rjson, statmod, tools, methods, utils
 Collate: Wrapper.R Internal.R Classes.R ParseImport.R Algorithms.R
 NeedsCompilation: no
-SystemRequirements: java
+SystemRequirements: java 6 or newer
 URL: http://www.0xdata.com

R/h2o-package.template

@@ -6,7 +6,7 @@
 H2O R Interface
 }
 \description{
-This is a package for running H2O via its REST API from within R. To communicate with a H2O instance, the version of the R package must match the version of H2O, so when connecting to a new H2O cluster, it is necessary to re-run the initializer.
+This is a package for running H2O via its REST API from within R. To communicate with a H2O instance, the version of the R package must match the version of H2O. When connecting to a new H2O cluster, it is necessary to re-run the initializer.
 }
 \details{
 \tabular{ll}{
@@ -18,11 +18,11 @@ License: \tab Apache License (== 2.0)\cr
 Depends: \tab R (>= 2.13.0), RCurl, rjson, statmod, tools, methods, utils\cr
 }
 
-This package allows the user to run basic H2O commands using R commands. In order to use it, you must first have H2O running (See \href{http://0xdata.com/h2o/docs/}{How to Start H2O}). To run H2O on your local machine, call \code{h2o.init} without any arguments, and H2O will be automatically launched on \url{http://127.0.0.1:54321}, where the IP is "127.0.0.1" and the port is 54321. If H2O is running on a cluster, you must provide the IP and port of the remote machine. 
+This package allows the user to run basic H2O commands using R commands. In order to use it, you must first have H2O running (See \href{http://docs.0xdata.com/newuser/quickstart_jar.html}{How to Start H2O}). To run H2O on your local machine, call \code{h2o.init} without any arguments, and H2O will be automatically launched on \url{http://127.0.0.1:54321}, where the IP is "127.0.0.1" and the port is 54321. If H2O is running on a cluster, you must provide the IP and port of the remote machine as arguments to the h2o.init() call. 
 
-H2O supports a number of standard statistical models, such as GLM, K-means, and random forest classification. For example, to run GLM, call \code{\link{h2o.glm}} with the parsed data and parameters (response variable, error distribution) as arguments. (The operation will be done on the server associated with the data object). 
+H2O supports a number of standard statistical models, such as GLM, K-means, and Random Forest classification. For example, to run GLM, call \code{\link{h2o.glm}} with the H2O parsed data and parameters (response variable, error distribution, etc...) as arguments. (The operation will be done on the server associated with the data object where H2O is running, not within the R environment). 
 
-Note that no actual data is stored in the workspace - R only saves the hex keys, which uniquely identify the data set, model, etc on the server. When the user makes a request, R queries the server via the REST API, which returns a JSON file with the relevant information that R then displays in the console.
+Note that no actual data is stored in the R workspace; and no actual work is carried out by R. R only saves the named objects, which uniquely identify the data set, model, etc on the server. When the user makes a request, R queries the server via the REST API, which returns a JSON file with the relevant information that R then displays in the console.
 }
 \author{
 Anqi Fu, Tom Kraljevic and Petr Maj, with contributions from the 0xdata team
@@ -45,7 +45,7 @@ library(h2o)
 localH2O = h2o.init(ip = "127.0.0.1", port = 54321, startH2O = TRUE)
 
 # Import iris dataset into H2O and print summary
-irisPath = system.file("extdata", "iris.csv", package="h2o")
+irisPath = system.file("extdata", "iris.csv", package = "h2o")
 iris.hex = h2o.importFile(localH2O, path = irisPath, key = "iris.hex")
 summary(iris.hex)
 

R/h2o-package/R/Wrapper.R

@@ -22,6 +22,7 @@ h2o.init <- function(ip = "127.0.0.1", port = 54321, startH2O = TRUE, Xmx = "1g"
   }
   cat("Successfully connected to", myURL, "\n")
   H2Oserver = new("H2OClient", ip = ip, port = port)
+  Sys.sleep(1)    # Give cluster time to come up
   h2o.clusterInfo(H2Oserver); cat("\n")
 
   if((verH2O = .h2o.__version(H2Oserver)) != (verPkg = packageVersion("h2o")))
@@ -49,6 +50,13 @@ h2o.shutdown <- function(client, prompt = TRUE) {
 }
 
 # ----------------------- Diagnostics ----------------------- #
+
+
+#****TODO: This isn't really a cluster status... it's a node status check for the node we're connected to.
+#This is possibly confusing because this can come back without warning,
+#but if a user tries to do any remoteSend, they will get a "cloud sick warning"
+#Suggest cribbing the code from Internal.R that checks cloud status (or just call it here?)
+
 h2o.clusterStatus <- function(client) {
   if(missing(client) || class(client) != "H2OClient") stop("client must be a H2OClient object")
   myURL = paste("http://", client@ip, ":", client@port, "/", .h2o.__PAGE_CLOUD, sep = "")