<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'>> +
> + // Reference set for kNN
> + arma::mat refMat = sampleSet.cols(refSetStart, refSetEnd);
> + referenceSizes(i) = refMat.n_cols;
> +
> + arma::Mat<size_t> neighbors; // Not going to be used but required.
> + arma::mat kNNDistances; // What we need.
> + KNN naive(refMat, true); // true: train and use naive kNN.
> + naive.Search(queryMat, k, neighbors, kNNDistances);
> +
> + // Store the squared distances (what we need).
> + kNNDistances = arma::pow(kNNDistances, 2);
> +
> + // Compute Arithmetic and Geometric mean of the distances.
> + Ek.row(i) = arma::mean(kNNDistances.t());
> + 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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