diff options
| -rw-r--r-- | examples/src/main/scala/org/apache/spark/examples/SparkSVD.scala | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkSVD.scala b/examples/src/main/scala/org/apache/spark/examples/SparkSVD.scala index d9c672f140..ce7c1c48b5 100644 --- a/examples/src/main/scala/org/apache/spark/examples/SparkSVD.scala +++ b/examples/src/main/scala/org/apache/spark/examples/SparkSVD.scala @@ -29,12 +29,12 @@ import org.apache.spark.mllib.linalg.SparseMatrix * Where i is the column, j the row, and value is the matrix entry * * For example input file, see: - * mllib/data/als/test.data + * mllib/data/als/test.data (example is 4 x 4) */ object SparkSVD { def main(args: Array[String]) { - if (args.length != 2) { - System.err.println("Usage: SparkSVD <master> <file>") + if (args.length != 4) { + System.err.println("Usage: SparkSVD <master> <file> m n") System.exit(1) } val sc = new SparkContext(args(0), "SVD", @@ -45,8 +45,8 @@ object SparkSVD { val parts = line.split(',') MatrixEntry(parts(0).toInt, parts(1).toInt, parts(2).toDouble) } - val m = 4 - val n = 4 + val m = args(2).toInt + val n = args(3).toInt // recover largest singular vector val decomposed = SVD.sparseSVD(SparseMatrix(data, m, n), 1) |