Github user yinxusen commented on a diff in the pull request: https://github.com/apache/spark/pull/14229#discussion_r74851793 --- 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 -- Here I want to say the features column should be String format, e.g. column_str "this is the first document" "this is another one" or libSVM format, which represents in ml.Vector-format, e.g. column_vec ml.Vector(...) ml.Vector(...)
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