Github user dusenberrymw commented on a diff in the pull request:
https://github.com/apache/spark/pull/7963#discussion_r36552903
 Diff: python/pyspark/mllib/linalg/distributed.py 
@@ 352,6 +458,56 @@ def toBlockMatrix(self, rowsPerBlock=1024, colsPerBlock=1024):
colsPerBlock)
return BlockMatrix(java_block_matrix, rowsPerBlock, colsPerBlock)
+ def computeSVD(self, k, computeU=False, rCond=1e9):
+ """
+ Computes the singular value decomposition of the IndexedRowMatrix.
+
+ 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 IndexedRowMatrix 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.
+ :param computeU: Whether of not to compute U. If set to be True, then U is computed
+ by A * V * s^1
+ :param rCond: Reciprocal condition number. All singular values smaller than
+ rCond * s[0] are treated as zero, where s[0] is the largest
+ singular value.
+ :returns: SingularValueDecomposition object
+
+ >>> data = [(0, (3, 1, 1)), (1, (1, 3, 1))]
+ >>> irm = IndexedRowMatrix(sc.parallelize(data))
+ >>> svd_model = irm.computeSVD(2, True)
+ >>> svd_model.U.rows.collect() # doctest: +NORMALIZE_WHITESPACE
+ [IndexedRow(0, [0.707106781187,0.707106781187]),\
+ IndexedRow(1, [0.707106781187,0.707106781187])]
+ >>> svd_model.s
+ DenseVector([3.4641, 3.1623])
+ >>> svd_model.V
+ DenseMatrix(3, 2, [0.4082, 0.8165, 0.4082, 0.8944, 0.4472, 0.0], 0)
+ """
+ j_model = self._java_matrix_wrapper.call("computeSVD", int(k), computeU, float(rCond))
 End diff 
Same as above with `computeU`.

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