Github user yinxusen commented on a diff in the pull request:
https://github.com/apache/spark/pull/14229#discussion_r75041418
--- Diff: R/pkg/R/mllib.R ---
@@ -605,6 +701,69 @@ setMethod("spark.survreg", signature(data =
"SparkDataFrame", formula = "formula
return(new("AFTSurvivalRegressionModel", jobj = jobj))
})
+#' Latent Dirichlet Allocation
+#'
+#' \code{spark.lda} fits a Latent Dirichlet Allocation model on a
SparkDataFrame. Users can call
+#' \code{summary} to get a summary of the fitted LDA model,
\code{spark.posterior} to compute
+#' posterior probabilities on new data, \code{spark.perplexity} to compute
log perplexity on new
+#' data and \code{write.ml}/\code{read.ml} to save/load fitted models.
+#'
+#' @param data A SparkDataFrame for training
+#' @param features Features column name, default "features". Either Vector
format column or String
--- End diff --
SparkR doesn't provide the type explicitly. However, you may load the
libSVM file through `text <- read.df("data/mllib/sample_lda_libsvm_data.txt",
source = "libsvm")`.
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