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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
import org.apache.spark.SparkContext
// $example on$
import org.apache.spark.mllib.linalg.Matrix
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.linalg.distributed.RowMatrix
// $example off$
object PCAOnRowMatrixExample {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("PCAOnRowMatrixExample")
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 dataRDD = sc.parallelize(data, 2)
val mat: RowMatrix = new RowMatrix(dataRDD)
// Compute the top 4 principal components.
// Principal components are stored in a local dense matrix.
val pc: Matrix = mat.computePrincipalComponents(4)
// Project the rows to the linear space spanned by the top 4 principal components.
val projected: RowMatrix = mat.multiply(pc)
// $example off$
val collect = projected.rows.collect()
println("Projected Row Matrix of principal component:")
collect.foreach { vector => println(vector) }
}
}
// scalastyle:on println
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