spark TallSkinnySVD 源码
spark TallSkinnySVD 代码
文件路径：/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnySVD.scala
/*
* 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}
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.linalg.distributed.RowMatrix
/**
* Compute the singular value decomposition (SVD) of a tall-and-skinny matrix.
*
* The input matrix must be stored in row-oriented dense format, one line per row with its entries
* separated by space. For example,
* {{{
* 0.5 1.0
* 2.0 3.0
* 4.0 5.0
* }}}
* represents a 3-by-2 matrix, whose first row is (0.5, 1.0).
*/
object TallSkinnySVD {
def main(args: Array[String]): Unit = {
if (args.length != 1) {
System.err.println("Usage: TallSkinnySVD <input>")
System.exit(1)
}
val conf = new SparkConf().setAppName("TallSkinnySVD")
val sc = new SparkContext(conf)
// Load and parse the data file.
val rows = sc.textFile(args(0)).map { line =>
val values = line.split(' ').map(_.toDouble)
Vectors.dense(values)
}
val mat = new RowMatrix(rows)
// Compute SVD.
val svd = mat.computeSVD(mat.numCols().toInt)
println(s"Singular values are ${svd.s}")
sc.stop()
}
}
// scalastyle:on println
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