<p>In <a href="https://github.com/mlpack/mlpack/pull/749#discussion_r76482680">src/mlpack/methods/lsh/lshmodel_impl.hpp</a>:</p>
<pre style='color:#555'>&gt; +
&gt; +    // Reference set for kNN
&gt; +    arma::mat refMat = sampleSet.cols(refSetStart, refSetEnd);
&gt; +    referenceSizes(i) = refMat.n_cols;
&gt; +
&gt; +    arma::Mat&lt;size_t&gt; neighbors; // Not going to be used but required.
&gt; +    arma::mat kNNDistances; // What we need.
&gt; +    KNN naive(refMat, true); // true: train and use naive kNN.
&gt; +    naive.Search(queryMat, k, neighbors, kNNDistances);
&gt; +
&gt; +    // Store the squared distances (what we need).
&gt; +    kNNDistances = arma::pow(kNNDistances, 2);
&gt; +
&gt; +    // Compute Arithmetic and Geometric mean of the distances.
&gt; +    Ek.row(i) = arma::mean(kNNDistances.t());
&gt; +    Gk.row(i) = arma::exp(arma::mean(arma::log(kNNDistances.t()), 0));
</pre>
<p>Shouldn't <code>Gk</code> be the geometric mean?  What's implemented here doesn't appear to be that.  I think you can calculate the geometric mean as</p>

<pre><code>Gk.row(i) = arma::pow(arma::prod(kNNDistances.t()), 1.0 / kNNDistances.n_cols);
</code></pre>

<p>After making that change, L-BFGS to fit the geometric distribution converges, instead of resulting in a NaN.</p>

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