<p><b>@lozhnikov</b> commented on this pull request.</p>
<p>I've taken a quick look at the code. The code looks fine except two very minor issues. I'll look more thoroughly later.</p><hr>
<p>In <a href="https://github.com/mlpack/mlpack/pull/790#pullrequestreview-1455123">src/mlpack/core/tree/octree/octree_impl.hpp</a>:</p>
<pre style='color:#555'>> + const VecType& point,
+ typename boost::enable_if<IsVector<VecType>>::type*) const
+{
+ return bound.RangeDistance(point);
+}
+
+//! Serialize the tree.
+template<typename MetricType, typename StatisticType, typename MatType>
+template<typename Archive>
+void Octree<MetricType, StatisticType, MatType>::Serialize(
+ Archive& ar,
+ const unsigned int /* version */)
+{
+ using data::CreateNVP;
+
+ //
</pre>
<p>It seems you have forgotten to remove <code>//</code>:)</p>
<hr>
<p>In <a href="https://github.com/mlpack/mlpack/pull/790#pullrequestreview-1455123">src/mlpack/core/tree/octree/octree_impl.hpp</a>:</p>
<pre style='color:#555'>> +template<typename MetricType, typename StatisticType, typename MatType>
+void Octree<MetricType, StatisticType, MatType>::SplitNode(
+ const arma::vec& center,
+ const double width,
+ const size_t maxLeafSize)
+{
+ // No need to split if we have fewer than the maximum number of points in this
+ // node.
+ if (count <= maxLeafSize)
+ return;
+
+ // We must split the dataset by sequentially creating each of the children.
+ // We do this in two steps: first we make a pass to count the number of points
+ // that will fall into each child; then in the second pass we rearrange the
+ // points and create the children.
+ arma::Col<size_t> childCounts(std::pow(2, dataset->n_rows),
</pre>
<p>I think that <code>((size_t) 2) << dataset->n_rows</code> is better than <code>std::pow()</code>, how do you think?</p>
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