diff options
| -rw-r--r-- | docs/mllib-guide.md | 5 | ||||
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/linalg/SVD.scala | 24 | ||||
| -rw-r--r-- | mllib/src/test/scala/org/apache/spark/mllib/linalg/SVDSuite.scala | 3 |
3 files changed, 16 insertions, 16 deletions
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index 711187fbea..abeb55d081 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -251,9 +251,10 @@ val data = sc.textFile("mllib/data/als/test.data").map { line => } val m = 4 val n = 4 +val k = 1 -// recover singular vectors for singular values at or above 1e-5 -val (u, s, v) = SVD.sparseSVD(data, m, n, 1e-5) +// recover largest singular vector +val (u, s, v) = SVD.sparseSVD(data, m, n, 1) println("singular values = " + s.toArray.mkString) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SVD.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SVD.scala index ac9178e78c..465fc746ed 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SVD.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SVD.scala @@ -43,9 +43,8 @@ object SVD { * Then we compute U via easy matrix multiplication * as U = A * V * S^-1 * - * Only singular vectors associated with singular values - * greater or equal to MIN_SVALUE are recovered. If there are k - * such values, then the dimensions of the return will be: + * Only the k largest singular values and associated vectors are found. + * If there are k such values, then the dimensions of the return will be: * * S is k x k and diagonal, holding the singular values on diagonal * U is m x k and satisfies U'U = eye(k) @@ -57,22 +56,22 @@ object SVD { * @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 + * @param k Recover k singular values and vectors * @return Three sparse matrices: U, S, V such that A = USV^T */ def sparseSVD( data: RDD[MatrixEntry], m: Int, n: Int, - min_svalue: Double) + k: Int) : SVDecomposedMatrix = { if (m < n || m <= 0 || n <= 0) { throw new IllegalArgumentException("Expecting a tall and skinny matrix") } - if (min_svalue < 1.0e-8) { - throw new IllegalArgumentException("Minimum singular value requested is too small") + if (k < 1 || k > n) { + throw new IllegalArgumentException("Must request up to n singular values") } // Compute A^T A, assuming rows are sparse enough to fit in memory @@ -93,12 +92,13 @@ object SVD { // Since A^T A is small, we can compute its SVD directly val svd = Singular.sparseSVD(ata) val V = svd(0) - val sigma = MatrixFunctions.sqrt(svd(1)).toArray.filter(x => x >= min_svalue) + val sigmas = MatrixFunctions.sqrt(svd(1)).toArray.filter(x => x > 1e-9) - // threshold s values - if(sigma.isEmpty) { - throw new Exception("All singular values are smaller than min_svalue: " + min_svalue) - } + if(sigmas.size < k) { + throw new Exception("Not enough singular values to return") + } + + val sigma = sigmas.take(k) val sc = data.sparkContext diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/SVDSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/SVDSuite.scala index 71749ff729..dc4e9239a2 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/SVDSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/SVDSuite.scala @@ -66,9 +66,8 @@ class SVDSuite extends FunSuite with BeforeAndAfterAll { val n = 3 val data = sc.makeRDD(Array.tabulate(m,n){ (a,b)=> MatrixEntry(a+1,b+1, (a+2).toDouble*(b+1)/(1+a+b)) }.flatten ) - val min_svalue = 1.0e-8 - val decomposed = SVD.sparseSVD(data, m, n, min_svalue) + val decomposed = SVD.sparseSVD(data, m, n, n) val u = decomposed.U val v = decomposed.V val s = decomposed.S |