# # 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. # # To run this example use # ./bin/spark-submit examples/src/main/r/ml/kmeans.R # Load SparkR library into your R session library(SparkR) # Initialize SparkSession sparkR.session(appName = "SparkR-ML-kmeans-example") # $example on$ # Fit a k-means model with spark.kmeans t <- as.data.frame(Titanic) training <- createDataFrame(t) df_list <- randomSplit(training, c(7,3), 2) kmeansDF <- df_list[[1]] kmeansTestDF <- df_list[[2]] kmeansModel <- spark.kmeans(kmeansDF, ~ Class + Sex + Age + Freq, k = 3) # Model summary summary(kmeansModel) # Get fitted result from the k-means model head(fitted(kmeansModel)) # Prediction kmeansPredictions <- predict(kmeansModel, kmeansTestDF) head(kmeansPredictions) # $example off$ sparkR.session.stop()