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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.
#
# 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()