/*
 * 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 scala.collection.immutable

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
// $example on$
import org.apache.spark.mllib.linalg.Matrix
import org.apache.spark.mllib.linalg.SingularValueDecomposition
import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.linalg.distributed.RowMatrix
// $example off$

/**
 * Example for SingularValueDecomposition.
 */
object SVDExample {

  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setAppName("SVDExample")
    val sc = new SparkContext(conf)

    // $example on$
    val data = Array(
      Vectors.sparse(5, Seq((1, 1.0), (3, 7.0))),
      Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0),
      Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0))

    val rows = sc.parallelize(immutable.ArraySeq.unsafeWrapArray(data))

    val mat: RowMatrix = new RowMatrix(rows)

    // Compute the top 5 singular values and corresponding singular vectors.
    val svd: SingularValueDecomposition[RowMatrix, Matrix] = mat.computeSVD(5, computeU = true)
    val U: RowMatrix = svd.U  // The U factor is a RowMatrix.
    val s: Vector = svd.s     // The singular values are stored in a local dense vector.
    val V: Matrix = svd.V     // The V factor is a local dense matrix.
    // $example off$
    val collect = U.rows.collect()
    println("U factor is:")
    collect.foreach { vector => println(vector) }
    println(s"Singular values are: $s")
    println(s"V factor is:\n$V")

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