From 915d53f8acb1f7ab14894b1255eb334b0812d9d3 Mon Sep 17 00:00:00 2001 From: Reza Zadeh Date: Wed, 1 Jan 2014 20:20:16 -0800 Subject: javadoc for sparsesvd --- .../main/scala/org/apache/spark/mllib/linalg/sparsesvd.scala | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) (limited to 'mllib') diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/sparsesvd.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/sparsesvd.scala index 83b2178c09..19173fd26a 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/sparsesvd.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/sparsesvd.scala @@ -24,6 +24,7 @@ import org.apache.spark.rdd.RDD import org.jblas.{DoubleMatrix, Singular, MatrixFunctions} +object SVD { /** * Singular Value Decomposition for Tall and Skinny matrices. * Given an m x n matrix A, this will compute matrices U, S, V such that @@ -48,10 +49,13 @@ import org.jblas.{DoubleMatrix, Singular, MatrixFunctions} * * All input and output is expected in sparse matrix format, 1-indexed * as tuples of the form ((i,j),value) all in RDDs + * + * @param data RDD Matrix in sparse 1-index format ((int, int), value) + * @param m number of rows + * @param n number of columns + * @param min_svalue Recover singular values greater or equal to min_svalue + * @return Three sparse matrices: U, S, V such that A = USV^T */ - - -object SVD { def sparseSVD( data: RDD[((Int, Int), Double)], m: Int, -- cgit v1.2.3