Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/7222#discussion_r34008168
--- Diff: python/pyspark/mllib/linalg.py ---
@@ -649,27 +645,20 @@ def squared_distance(self, other):
return result
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]:
- diff = self.values[i] - other.values[j]
- result += diff * diff
- i += 1
- j += 1
- elif self.indices[i] < other.indices[j]:
- result += self.values[i] * self.values[i]
- i += 1
- else:
- result += other.values[j] * other.values[j]
- j += 1
- while i < len(self.indices):
- result += self.values[i] * self.values[i]
- i += 1
- while j < len(other.indices):
- result += other.values[j] * other.values[j]
- j += 1
- return result
+ self_cmind = np.in1d(self.indices, other.indices)
--- End diff --
Could we simply do the following?
~~~python
return math.sqrt(np.dot(self.values, self.values) + np.dot(other.values,
other.values) - 2.0 * self.dot(other))
~~~
It might generate some numeric errors when both vectors are long and very
close to each other. It should be sufficient for normal use cases.
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