Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/6342#discussion_r32780890
--- Diff: python/pyspark/mllib/linalg.py ---
@@ -821,6 +822,36 @@ def __reduce__(self):
self.numRows, self.numCols, self.values.tostring(),
int(self.isTransposed))
+ def __str__(self):
+ """
+ Pretty printing of a DenseMatrix
+
+ >>> dm = DenseMatrix(2, 2, range(4))
+ >>> print(dm)
+ 0.0 2.0
+ 1.0 3.0
+ >>> dm = DenseMatrix(2, 2, range(4), isTransposed=True)
+ >>> print(dm)
+ 0.0 1.0
+ 2.0 3.0
+ """
+ ds = []
+ for row in self.toArray():
+ ds.append(" ".join([str(col) for col in row]))
+ return "\n".join(ds)
+
+ def __repr__(self):
+ """
+ Representation of a DenseMatrix
+
+ >>> dm = DenseMatrix(2, 2, range(4))
+ >>> dm
+ DenseMatrix(2, 2, [0.0, 1.0, 2.0, 3.0], False)
+ """
+ entries = ', '.join([_format_float(val) for val in self.values])
--- End diff --
We shouldn't always display all elements in case there are millions of
them. Again, it would be nice to reuse numpy's implementation.
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