1 files changed, 0 insertions, 93 deletions
diff --git a/examples/src/main/r/kmeans.R b/examples/src/main/r/kmeans.R
deleted file mode 100644
index 6e6b5cb937..0000000000
--- a/examples/src/main/r/kmeans.R
+++ /dev/null
@@ -1,93 +0,0 @@
-#
-# 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.
-#
-
-library(SparkR)
-
-# Logistic regression in Spark.
-# Note: unlike the example in Scala, a point here is represented as a vector of
-# doubles.
-
-parseVectors <-  function(lines) {
-  lines <- strsplit(as.character(lines) , " ", fixed = TRUE)
-  list(matrix(as.numeric(unlist(lines)), ncol = length(lines[[1]])))
-}
-
-dist.fun <- function(P, C) {
-  apply(
-    C,
-    1, 
-    function(x) { 
-      colSums((t(P) - x)^2)
-    }
-  )
-}
-
-closestPoint <-  function(P, C) {
-  max.col(-dist.fun(P, C))
-}
-# Main program
-
-args <- commandArgs(trailing = TRUE) 
-
-if (length(args) != 3) {
-  print("Usage: kmeans <file> <K> <convergeDist>")
-  q("no")
-}
-
-sc <- sparkR.init(appName = "RKMeans")
-K <- as.integer(args[[2]])
-convergeDist <- as.double(args[[3]])
-
-lines <- textFile(sc, args[[1]])
-points <- cache(lapplyPartition(lines, parseVectors))
-# kPoints <- take(points, K)
-kPoints <- do.call(rbind, takeSample(points, FALSE, K, 16189L))
-tempDist <- 1.0
-
-while (tempDist > convergeDist) {
-  closest <- lapplyPartition(
-    lapply(points,
-           function(p) {
-             cp <- closestPoint(p, kPoints); 
-             mapply(list, unique(cp), split.data.frame(cbind(1, p), cp), SIMPLIFY=FALSE)
-           }),
-    function(x) {do.call(c, x)
-    })
-  
-  pointStats <- reduceByKey(closest,
-                            function(p1, p2) {
-                              t(colSums(rbind(p1, p2)))
-                            },
-                            2L)
-  
-  newPoints <- do.call(
-    rbind,
-    collect(lapply(pointStats,
-                   function(tup) {
-                     point.sum <- tup[[2]][, -1]
-                     point.count <- tup[[2]][, 1]
-                     point.sum/point.count
-                   })))
-  
-  D <- dist.fun(kPoints, newPoints)
-  tempDist <- sum(D[cbind(1:3, max.col(-D))])
-  kPoints <- newPoints
-  cat("Finished iteration (delta = ", tempDist, ")\n")
-}
-
-cat("Final centers:\n")
-writeLines(unlist(lapply(kPoints, paste, collapse = " ")))