Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/7963#discussion_r82472498
--- Diff: python/pyspark/mllib/linalg/distributed.py ---
@@ -303,6 +303,121 @@ def tallSkinnyQR(self, computeQ=False):
R = decomp.call("R")
return QRDecomposition(Q, R)
+ def computeSVD(self, k, computeU=False, rCond=1e-9):
+ """
+ Computes the singular value decomposition of the RowMatrix.
+
+ The given row matrix A of dimension (m X n) is decomposed into
+ U * s * V'T where
+
+ * U: (m X k) (left singular vectors) is a RowMatrix whose
+ columns are the eigenvectors of (A X A')
+ * s: DenseVector consisting of square root of the eigenvalues
+ (singular values) in descending order.
+ * v: (n X k) (right singular vectors) is a Matrix whose columns
+ are the eigenvectors of (A' X A)
+
+ For more specific details on implementation, please refer
+ the scala documentation.
+
+ :param k: Set the number of singular values to keep.
--- End diff --
It might be good to copy the longer description from RowMatrix for the k
param
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