<p>In <a href="https://github.com/mlpack/mlpack/pull/686#discussion_r66149003">src/mlpack/methods/ne/genome.hpp</a>:</p>
<pre style='color:#555'>> +
> + // Loop neurons to calculate neurons' activation.
> + for (unsigned in j=aNumInput; j<aNeuronGenes.size(); ++j) {
> + double x = aNeuronGenes[j].aInput; // TODO: consider bias. Difference?
> + aNeuronGenes[j].aInput = 0;
> +
> + double y = 0;
> + switch (aNeuronGenes[j].Type()) { // TODO: revise the implementation.
> + case SIGMOID: // TODO: more cases.
> + y = sigmoid(x);
> + break;
> + case RELU:
> + y = relu(x);
> + break;
> + default:
> + y = sigmoid(x);
</pre>
<p>I think, since our network structure could be somewhat sparse:</p>
<pre><code>x0-----------------|
| |
|---h0^0-----|---h0^1---|
x1---- | |------o0
| |
x2----------------------------|
</code></pre>
<p>it isn't that easy to reuse the activation function of the sparse autoencoder. However, what we should do here is to reuse the ann activation functions: <a href="https://github.com/mlpack/mlpack/tree/master/src/mlpack/methods/ann/activation_functions">https://github.com/mlpack/mlpack/tree/master/src/mlpack/methods/ann/activation_functions</a></p>
<p>so instead of <code>y = sigmoid(x);</code> we can write <code>y = LogisticFunction::fn(x)</code><code>or</code><code>y = RectifierFunction::fn(x)</code></p>
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