Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/7963#discussion_r82472791
--- 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.
+ :param computeU: Whether or 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 = [(3, 1, 1), (-1, 3, 1)]
+ >>> rm = RowMatrix(sc.parallelize(data))
+ >>> svd_model = rm.computeSVD(2, True)
+ >>> svd_model.U.rows.collect()
+ [DenseVector([-0.7071, 0.7071]), DenseVector([-0.7071, -0.7071])]
+ >>> 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), bool(computeU), float(rCond))
+ return SingularValueDecomposition(j_model)
+
+ def computePrincipalComponents(self, k):
+ """
+ Computes the k principal components of the given row matrix
+
+ :param k: Number of principal components to keep.
+ :returns: DenseMatrix
+
+ >>> data = sc.parallelize([[1, 2, 3], [2, 4, 5], [3, 6, 1]])
+ >>> rm = RowMatrix(data)
+
+ >>> # Returns the two principal components of rm
+ >>> pca = rm.computePrincipalComponents(2)
+ >>> pca
+ DenseMatrix(3, 2, [-0.349, -0.6981, 0.6252, -0.2796, -0.5592,
-0.7805], 0)
+
+ >>> # Transform into new dimensions with the greatest variance.
+ >>> rm.multiply(pca).rows.collect() # doctest:
+NORMALIZE_WHITESPACE
+ [DenseVector([0.1305, -3.7394]), DenseVector([-0.3642, -6.6983]), \
+ DenseVector([-4.6102, -4.9745])]
+ """
+ return
self._java_matrix_wrapper.call("computePrincipalComponents", k)
+
+ def multiply(self, matrix):
+ """
+ Multiplies the given RowMatrix with another matrix.
+
+ :param matrix: Matrix to multiply with.
+ :returns: RowMatrix
+
+ >>> rm = RowMatrix(sc.parallelize([[0, 1], [2, 3]]))
+ >>> rm.multiply(DenseMatrix(2, 2, [0, 2, 1, 3])).rows.collect()
+ [DenseVector([2.0, 3.0]), DenseVector([6.0, 11.0])]
+ """
+ if not isinstance(matrix, DenseMatrix):
+ raise ValueError("Only multiplication with DenseMatrix "
+ "is supported.")
+ j_model = self._java_matrix_wrapper.call("multiply", matrix)
+ return RowMatrix(j_model)
+
+
+class SingularValueDecomposition(JavaModelWrapper):
+ """Wrapper around the SingularValueDecomposition scala case class"""
--- End diff --
Probably add a versionAdded
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