Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/7222#discussion_r34008171
--- Diff: python/pyspark/mllib/linalg.py ---
@@ -590,18 +590,14 @@ def dot(self, other):
return np.dot(other.array[self.indices], self.values)
elif isinstance(other, SparseVector):
- result = 0.0
- i, j = 0, 0
- while i < len(self.indices) and j < len(other.indices):
- if self.indices[i] == other.indices[j]:
- result += self.values[i] * other.values[j]
- i += 1
- j += 1
- elif self.indices[i] < other.indices[j]:
- i += 1
- else:
- j += 1
- return result
+ # Find out common indices.
+ self_cmind = np.in1d(self.indices, other.indices)
--- End diff --
Could you try `in1d1 with option `assume_unique=True`? Based on the
documentation, it should be faster.
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