<p>In <a href="https://github.com/mlpack/mlpack/pull/749#discussion_r76755488">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>Yes, I'm using an alternative way to compute it:</p>
<pre><code>pow(prod(x_i)), 1/N) =
exp(log(pow(prod(x_i)), 1/N))) =
exp(1/N * [ log(prod(x_i))]) =
exp(1/N * sum( log(x_i))) =
exp(mean(log(x_i)))
</code></pre>
<p>Computing the geometric mean this way allows me to use arma::mean(), which can calculate the row-wise, column-wise or overall mean. In this case we have a <code>queryMat.n_rows</code> x <code>k</code> matrix, and we want the average of each column - or at least, that's my understanding of the paper.</p>
<p style="font-size:small;-webkit-text-size-adjust:none;color:#666;">—<br />You are receiving this because you are subscribed to this thread.<br />Reply to this email directly, <a href="https://github.com/mlpack/mlpack/pull/749/files/57c9d5e634d7d3d7e2ca1618353fe37d9e23b34a#r76755488">view it on GitHub</a>, or <a href="https://github.com/notifications/unsubscribe-auth/AJ4bFNzcNFUUUcXkOVr9JKNPNYk0SEjiks5qk_DAgaJpZM4JczVR">mute the thread</a>.<img alt="" height="1" src="https://github.com/notifications/beacon/AJ4bFM4PldDQObyxlKjGqO0g2cMGzzL_ks5qk_DAgaJpZM4JczVR.gif" width="1" /></p>
<div itemscope itemtype="http://schema.org/EmailMessage">
<div itemprop="action" itemscope itemtype="http://schema.org/ViewAction">
<link itemprop="url" href="https://github.com/mlpack/mlpack/pull/749/files/57c9d5e634d7d3d7e2ca1618353fe37d9e23b34a#r76755488"></link>
<meta itemprop="name" content="View Pull Request"></meta>
</div>
<meta itemprop="description" content="View this Pull Request on GitHub"></meta>
</div>
<script type="application/json" data-scope="inboxmarkup">{"api_version":"1.0","publisher":{"api_key":"05dde50f1d1a384dd78767c55493e4bb","name":"GitHub"},"entity":{"external_key":"github/mlpack/mlpack","title":"mlpack/mlpack","subtitle":"GitHub repository","main_image_url":"https://cloud.githubusercontent.com/assets/143418/17495839/a5054eac-5d88-11e6-95fc-7290892c7bb5.png","avatar_image_url":"https://cloud.githubusercontent.com/assets/143418/15842166/7c72db34-2c0b-11e6-9aed-b52498112777.png","action":{"name":"Open in GitHub","url":"https://github.com/mlpack/mlpack"}},"updates":{"snippets":[{"icon":"PERSON","message":"@mentekid in #749: Yes, I'm using an alternative way to compute it:\r\n```\r\npow(prod(x_i)), 1/N) = \r\nexp(log(pow(prod(x_i)), 1/N))) = \r\nexp(1/N * [ log(prod(x_i))]) = \r\nexp(1/N * sum( log(x_i))) = \r\nexp(mean(log(x_i)))\r\n```\r\n\r\nComputing the geometric mean this way allows me to use arma::mean(), which can calculate the row-wise, column-wise or overall mean. In this case we have a `queryMat.n_rows` x `k` matrix, and we want the average of each column - or at least, that's my understanding of the paper."}],"action":{"name":"View Pull Request","url":"https://github.com/mlpack/mlpack/pull/749/files/57c9d5e634d7d3d7e2ca1618353fe37d9e23b34a#r76755488"}}}</script>