fix default value, test and order

apache · Feb 15, 2017 · 9b2147f · 9b2147f
1 parent 2e7cec8
commit 9b2147f
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Showing 3 changed files with 6 additions and 6 deletions.
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
@@ -1380,10 +1380,6 @@ setGeneric("spark.kstest", function(data, ...) { standardGeneric("spark.kstest")
 #' @export
 setGeneric("spark.lda", function(data, ...) { standardGeneric("spark.lda") })
 
-#' @rdname spark.svmLinear
-#' @export
-setGeneric("spark.svmLinear", function(data, formula, ...) { standardGeneric("spark.svmLinear") })
-
 #' @rdname spark.logit
 #' @export
 setGeneric("spark.logit", function(data, formula, ...) { standardGeneric("spark.logit") })
@@ -1405,6 +1401,10 @@ setGeneric("spark.randomForest",
 #' @export
 setGeneric("spark.survreg", function(data, formula) { standardGeneric("spark.survreg") })
 
+#' @rdname spark.svmLinear
+#' @export
+setGeneric("spark.svmLinear", function(data, formula, ...) { standardGeneric("spark.svmLinear") })
+
 #' @rdname spark.lda
 #' @export
 setGeneric("spark.posterior", function(object, newData) { standardGeneric("spark.posterior") })

diff --git a/R/pkg/R/mllib_classification.R b/R/pkg/R/mllib_classification.R
@@ -95,7 +95,7 @@ setClass("NaiveBayesModel", representation(jobj = "jobj"))
 #' @note spark.svmLinear since 2.2.0
 setMethod("spark.svmLinear", signature(data = "SparkDataFrame", formula = "formula"),
           function(data, formula, regParam = 0.0, maxIter = 100, tol = 1E-6, standardization = TRUE,
-                   threshold = 0.5, weightCol = NULL, aggregationDepth = 2) {
+                   threshold = 0.0, weightCol = NULL, aggregationDepth = 2) {
             formula <- paste(deparse(formula), collapse = "")
 
             if (!is.null(weightCol) && weightCol == "") {

diff --git a/R/pkg/inst/tests/testthat/test_mllib_classification.R b/R/pkg/inst/tests/testthat/test_mllib_classification.R
@@ -30,7 +30,7 @@ absoluteSparkPath <- function(x) {
 test_that("spark.svmLinear", {
   df <- suppressWarnings(createDataFrame(iris))
   training <- df[df$Species %in% c("versicolor", "virginica"), ]
-  model <- model <- spark.svmLinear(training,  Species ~ ., regParam = 0.01, maxIter = 10)
+  model <- spark.svmLinear(training,  Species ~ ., regParam = 0.01, maxIter = 10)
   summary <- summary(model)
 
   # test summary coefficients return matrix type