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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.
*/
package org.apache.spark.examples.mllib
import org.apache.spark.SparkContext
import org.apache.spark.mllib.linalg.SVD
import org.apache.spark.mllib.linalg.MatrixEntry
import org.apache.spark.mllib.linalg.SparseMatrix
/**
* Compute SVD of an example matrix
* Input file should be comma separated, 1 indexed of the form
* i,j,value
* Where i is the column, j the row, and value is the matrix entry
*
* For example input file, see:
* mllib/data/als/test.data (example is 4 x 4)
*/
object SparkSVD {
def main(args: Array[String]) {
if (args.length != 4) {
System.err.println("Usage: SparkSVD <master> <file> m n")
System.exit(1)
}
val sc = new SparkContext(args(0), "SVD",
System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
// Load and parse the data file
val data = sc.textFile(args(1)).map { line =>
val parts = line.split(',')
MatrixEntry(parts(0).toInt - 1, parts(1).toInt - 1, parts(2).toDouble)
}
val m = args(2).toInt
val n = args(3).toInt
// recover largest singular vector
val decomposed = SVD.sparseSVD(SparseMatrix(data, m, n), 1)
val u = decomposed.U.data
val s = decomposed.S.data
val v = decomposed.V.data
println("singular values = " + s.collect().mkString)
}
}
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