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 # Licensed to the Apache Software Foundation (ASF) under one or more
 # contributor license agreements.  See the NOTICE file distributed with
 # this work for additional information regarding copyright ownership.
 # The ASF licenses this file to You under the Apache License, Version 2.0
 # (the "License"); you may not use this file except in compliance with
 # the License.  You may obtain a copy of the License at
 #
 #    http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
 
 # mllib_stat.R: Provides methods for MLlib statistics algorithms integration
 
 #' S4 class that represents an KSTest
 #'
 #' @param jobj a Java object reference to the backing Scala KSTestWrapper
 #' @note KSTest since 2.1.0
 setClass("KSTest", representation(jobj = "jobj"))
 
 #' (One-Sample) Kolmogorov-Smirnov Test
 #'
 #' @description
 #' \code{spark.kstest} Conduct the two-sided Kolmogorov-Smirnov (KS) test for data sampled from a
 #' continuous distribution.
 #'
 #' By comparing the largest difference between the empirical cumulative
 #' distribution of the sample data and the theoretical distribution we can provide a test for the
 #' the null hypothesis that the sample data comes from that theoretical distribution.
 #'
 #' Users can call \code{summary} to obtain a summary of the test, and \code{print.summary.KSTest}
 #' to print out a summary result.
 #'
 #' @param data a SparkDataFrame of user data.
 #' @param testCol column name where the test data is from. It should be a column of double type.
 #' @param nullHypothesis name of the theoretical distribution tested against. Currently only
 #'                       \code{"norm"} for normal distribution is supported.
 #' @param distParams parameters(s) of the distribution. For \code{nullHypothesis = "norm"},
 #'                   we can provide as a vector the mean and standard deviation of
 #'                   the distribution. If none is provided, then standard normal will be used.
 #'                   If only one is provided, then the standard deviation will be set to be one.
 #' @param ... additional argument(s) passed to the method.
 #' @return \code{spark.kstest} returns a test result object.
 #' @rdname spark.kstest
 #' @aliases spark.kstest,SparkDataFrame-method
 #' @name spark.kstest
 #' @seealso \href{http://spark.apache.org/docs/latest/mllib-statistics.html#hypothesis-testing}{
 #'          MLlib: Hypothesis Testing}
 #' @examples
 #' \dontrun{
 #' data <- data.frame(test = c(0.1, 0.15, 0.2, 0.3, 0.25))
 #' df <- createDataFrame(data)
 #' test <- spark.kstest(df, "test", "norm", c(0, 1))
 #'
 #' # get a summary of the test result
 #' testSummary <- summary(test)
 #' testSummary
 #'
 #' # print out the summary in an organized way
 #' print.summary.KSTest(testSummary)
 #' }
 #' @note spark.kstest since 2.1.0
 setMethod("spark.kstest", signature(data = "SparkDataFrame"),
           function(data, testCol = "test", nullHypothesis = c("norm"), distParams = c(0, 1)) {
             tryCatch(match.arg(nullHypothesis),
                      error = function(e) {
                        stop("Distribution ", nullHypothesis, " is not supported.")
                      })
             if (nullHypothesis == "norm") {
               distParams <- as.numeric(distParams)
               mu <- ifelse(length(distParams) < 1, 0, distParams[1])
               sigma <- ifelse(length(distParams) < 2, 1, distParams[2])
               jobj <- callJStatic("org.apache.spark.ml.r.KSTestWrapper",
                                   "test", data@sdf, testCol, nullHypothesis,
                                   as.array(c(mu, sigma)))
               new("KSTest", jobj = jobj)
             }
 })
 
 #  Get the summary of Kolmogorov-Smirnov (KS) Test.
 
 #' @param object test result object of KSTest by \code{spark.kstest}.
 #' @return \code{summary} returns summary information of KSTest object, which is a list.
 #'         The list includes the \code{p.value} (p-value), \code{statistic} (test statistic
 #'         computed for the test), \code{nullHypothesis} (the null hypothesis with its
 #'         parameters tested against) and \code{degreesOfFreedom} (degrees of freedom of the test).
 #' @rdname spark.kstest
 #' @aliases summary,KSTest-method
 #' @note summary(KSTest) since 2.1.0
 setMethod("summary", signature(object = "KSTest"),
           function(object) {
             jobj <- object@jobj
             pValue <- callJMethod(jobj, "pValue")
             statistic <- callJMethod(jobj, "statistic")
             nullHypothesis <- callJMethod(jobj, "nullHypothesis")
             distName <- callJMethod(jobj, "distName")
             distParams <- unlist(callJMethod(jobj, "distParams"))
             degreesOfFreedom <- callJMethod(jobj, "degreesOfFreedom")
 
             ans <- list(p.value = pValue, statistic = statistic, nullHypothesis = nullHypothesis,
                         nullHypothesis.name = distName, nullHypothesis.parameters = distParams,
                         degreesOfFreedom = degreesOfFreedom, jobj = jobj)
             class(ans) <- "summary.KSTest"
             ans
           })
 
 #  Prints the summary of KSTest
 
 #' @rdname spark.kstest
 #' @param x summary object of KSTest returned by \code{summary}.
 #' @note print.summary.KSTest since 2.1.0
 print.summary.KSTest <- function(x, ...) {
   jobj <- x$jobj
   summaryStr <- callJMethod(jobj, "summary")
   cat(summaryStr, "\n")
   invisible(x)
 }

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