<p>In <a href="https://github.com/mlpack/mlpack/pull/749#discussion_r75926330">src/mlpack/methods/lsh/lshmodel_impl.hpp</a>:</p>
<pre style='color:#555'>> + maxKValue = k;
> +
> + // Save pointer to training set.
> + this->referenceSet = &referenceSet;
> +
> + // Step 1. Select a random sample of the dataset. We will work with only that
> + // sample.
> + arma::vec sampleHelper(referenceSet.n_cols, arma::fill::randu);
> +
> + // Keep a sample of the dataset: We have uniformly random numbers in [0, 1],
> + // so we expect about N*sampleRate of them to be in [0, sampleRate).
> + arma::mat sampleSet = referenceSet.cols(
> + arma::find(sampleHelper < sampleRate));
> + // Shuffle to be impartial (in case dataset is sorted in some way).
> + sampleSet = arma::shuffle(sampleSet);
> + const size_t numSamples = sampleSet.n_cols; // Points in sampled set.
</pre>
<p>Are you sampling with or without replacement? If you're sampling without replacement (I don't think that's the case based on the code here) you can use <code>math::ObtainDistinctSamples()</code> from somewhere in <code>core/math/</code>. Otherwise it might be better to simply keep a list of indices of samples, and don't actually extract it from the original matrix. Then later you can use that vector of indices to create a non-contiguous matrix subview, like this:</p>
<pre><code>extern arma::uvec indices; // This has already been filled with stuff.
extern arma::mat dataset; // This is our dataset.
dataset.cols(indices); // Returns all the columns we're interested in.
</code></pre>
<p>This is a pretty low-priority comment, though, so don't worry too heavily about it, only if you want to. I'd say testing is higher priority. :)</p>
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