Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/5173#discussion_r27076053
--- Diff: python/pyspark/mllib/clustering.py ---
@@ -168,8 +168,8 @@ def predictSoft(self, x):
if isinstance(x, RDD):
means, sigmas = zip(*[(g.mu, g.sigma) for g in self.gaussians])
membership_matrix = callMLlibFunc("predictSoftGMM",
x.map(_convert_to_vector),
- self.weights, means, sigmas)
- return membership_matrix
+
_convert_to_vector(self.weights), means, sigmas)
+ return membership_matrix.map(lambda x: x.toArray())
--- End diff --
@mengxr Should we use Vector or numpy.ndarray ?
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