Github user dusenberrymw commented on a diff in the pull request:
https://github.com/apache/spark/pull/7554#discussion_r35248880
--- Diff: python/pyspark/mllib/linalg.py ---
@@ -1152,9 +1156,385 @@ def sparse(numRows, numCols, colPtrs, rowIndices,
values):
return SparseMatrix(numRows, numCols, colPtrs, rowIndices, values)
+class DistributedMatrix(object):
+ """Represents a distributively stored matrix backed by one or more
RDDs."""
+ def numRows(self):
+ """Get or compute the number of rows."""
+ raise NotImplementedError
+
+ def numCols(self):
+ """Get or compute the number of cols."""
+ raise NotImplementedError
+
+
+class DistributedMatrices(object):
+ """Factory methods for distributed matrices."""
+ @staticmethod
+ def rowMatrix(rows, numRows=0, numCols=0):
+ """
+ Create a RowMatrix.
+
+ :param rows: An RDD of Vectors.
+ """
+ javaRowMatrix = callMLlibFunc("createRowMatrix", rows,
long(numRows), int(numCols))
+ jrm = JavaModelWrapper(javaRowMatrix)
+ return RowMatrix(jrm)
+
+ @staticmethod
+ def indexedRowMatrix(rows, numRows=0, numCols=0):
+ """
+ Create an IndexedRowMatrix.
+
+ :param rows: An RDD of IndexedRows or (long, Vector) tuples.
+ """
+ # We use DataFrames for serialization of IndexedRows from Python,
so convert the RDD to a
+ # DataFrame. This will convert each IndexedRow to a Row containing
the 'index' and 'vector'
+ # values, which can both be easily serialized. We will convert
back to IndexedRows on the
+ # Scala side.
+ javaIndexedRowMatrix = callMLlibFunc("createIndexedRowMatrix",
rows.toDF(),
+ long(numRows), int(numCols))
+ jirm = JavaModelWrapper(javaIndexedRowMatrix)
+ return IndexedRowMatrix(jirm)
+
+ @staticmethod
+ def coordinateMatrix(entries, numRows=0, numCols=0):
+ """
+ Create a CoordinateMatrix.
+
+ :param entries: An RDD of MatrixEntry inputs or (long, long,
float) tuples.
+ """
+ # We use DataFrames for serialization of MatrixEntry inputs from
Python, so convert the RDD
+ # to a DataFrame. This will convert each MatrixEntry to a Row
containing the 'i', 'j', and
+ # 'value' values, which can each be easily serialized. We will
convert back to MatrixEntry
+ # inputs on the Scala side.
+ javaCoordinateMatrix = callMLlibFunc("createCoordinateMatrix",
entries.toDF(),
+ long(numRows), long(numCols))
+ jcm = JavaModelWrapper(javaCoordinateMatrix)
+ return CoordinateMatrix(jcm)
+
+
+class RowMatrix(DistributedMatrix):
+ """
+ Represents a row-oriented distributed Matrix with no meaningful row
indices.
+
+ .. note:: Experimental
+ """
+ def __init__(self, jrm):
+ """ Create a wrapper over a Java RowMatrix. """
+ self._jrm = jrm
+ self.rows = self._rows()
+
+ def _rows(self):
+ """
+ Get the rows of the RowMatrix as a RDD of Vectors.
+
+ >>> rows = sc.parallelize([Vectors.dense([1, 2, 3]),
Vectors.dense([4, 5, 6]),
--- End diff --
Yeah, thanks for sharing it! :)
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