<p>In <a href="https://github.com/mlpack/mlpack/pull/749#discussion_r74688842">src/mlpack/methods/lsh_model/lshmodel_impl.hpp</a>:</p>
<pre style='color:#555'>&gt; +  sampleSet = arma::shuffle(sampleSet);
&gt; +  const size_t numSamples = sampleSet.n_cols; // Points in sampled set.
&gt; +
&gt; +  Log::Info &lt;&lt; &quot;Training model with &quot; &lt;&lt; numSamples &lt;&lt; &quot; points in sample set.&quot;
&gt; +    &lt;&lt; std::endl;
&gt; +
&gt; +  // Step 2. Compute all-vs-all distances of points in the sample.
&gt; +  // The distance matrix is symmetric, so we only compute elements above the
&gt; +  // diagonal. There are (N * (N - 1)) / 2 such elements.
&gt; +  Timer::Start(&quot;pairwise_distances&quot;);
&gt; +  arma::vec distances(numSamples * (numSamples - 1) / 2);
&gt; +  size_t d = 0; // Index of where to store next.
&gt; +  for (size_t i = 0; i &lt; numSamples; ++i)
&gt; +    for (size_t j = i + 1; j &lt; numSamples; ++j)
&gt; +      distances(d++) = metric::EuclideanDistance::Evaluate(
&gt; +          sampleSet.unsafe_col(i), sampleSet.unsafe_col(j));
</pre>
<p>Nice, I like the optimization to only compute less than half the elements in the distance matrix here.</p>

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