/*
 * 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)
    }
    // $example off$

    sc.stop()
  }
}
// scalastyle:off println
