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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.
*/
// scalastyle:off println
package org.apache.spark.examples.mllib
import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.fpm.PrefixSpan
// $example off$
object PrefixSpanExample {
def main(args: Array[String]) {
val conf = new SparkConf().setAppName("PrefixSpanExample")
val sc = new SparkContext(conf)
// $example on$
val sequences = sc.parallelize(Seq(
Array(Array(1, 2), Array(3)),
Array(Array(1), Array(3, 2), Array(1, 2)),
Array(Array(1, 2), Array(5)),
Array(Array(6))
), 2).cache()
val prefixSpan = new PrefixSpan()
.setMinSupport(0.5)
.setMaxPatternLength(5)
val model = prefixSpan.run(sequences)
model.freqSequences.collect().foreach { freqSequence =>
println(
freqSequence.sequence.map(_.mkString("[", ", ", "]")).mkString("[", ", ", "]") +
", " + freqSequence.freq)
}
// $example off$
}
}
// scalastyle:off println
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