Github user MechCoder commented on a diff in the pull request:
https://github.com/apache/spark/pull/7554#discussion_r35239673
--- 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))
--- End diff --
I've started using Python3 and long(x) raises a NameError, for the reason
mentioned above.
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