Github user dusenberrymw commented on a diff in the pull request:
https://github.com/apache/spark/pull/7554#discussion_r35873295
--- Diff: python/pyspark/mllib/linalg.py ---
@@ -1152,9 +1156,416 @@ 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):
+ """
+ .. note:: Experimental
+
+ Represents a row-oriented distributed Matrix with no meaningful
+ row indices.
+
+ :param rows: An RDD of vectors.
+ :param numRows: Number of rows in the matrix. A non-positive
+ value means unknown, at which point the number
+ of rows will be determined by the number of
+ records in the `rows` RDD.
+ :param numCols: Number of columns in the matrix. A non-positive
+ value means unknown, at which point the number
+ of columns will be determined by the size of
+ the first row.
+ """
+ def __init__(self, rows, numRows=0, numCols=0):
+ """Create a wrapper over a Java RowMatrix."""
+ if not isinstance(rows, RDD):
+ raise TypeError("rows should be an RDD of vectors, got %s" %
type(rows))
--- End diff --
Yeah the argument doesn't have to be an RDD of actual `Vector` objects, but
it should still be an RDD of _vectors_, which could be NumPy arrays, Python
lists, `Vector`s, etc. for PySpark. The Spark MLlib Data Types guide makes this
distinction for the end-user, so I think it is helpful to use it in the error
message as well.
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