Github user MechCoder commented on a diff in the pull request:
https://github.com/apache/spark/pull/7554#discussion_r35570323
--- 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 RowMatrix(DistributedMatrix):
+ """
+ Represents a row-oriented distributed Matrix with no meaningful
+ row indices.
+
+ .. note:: Experimental
+ """
+ def __init__(self, rows, numRows=0, numCols=0):
+ """
+ Create a wrapper over a Java RowMatrix.
+
+ :param rows: An RDD of Vectors.
+ """
+ if not isinstance(rows, RDD):
+ raise TypeError("rows should be an RDD object, got %s" %
type(rows))
+ first = rows.first()
+ if not isinstance(first, Vector):
+ raise TypeError("rows should be an RDD of Vectors, got an RDD
of %s" % type(first))
+
+ javaRowMatrix = callMLlibFunc("createRowMatrix", rows,
long(numRows), int(numCols))
+ self._jrm = JavaModelWrapper(javaRowMatrix)
+ self.rows = rows
+
+ @staticmethod
+ def _from_java(javaRowMatrix):
--- End diff --
For conversion, it seems you are constructing the java_model on the scala
side, deconstructing it into rows and constructing the model again in the scala
side again. This might be expensive.
I defer to @mengxr for decision regarding this.
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