/// <summary>
/// Creates a Mixed-Bernoulli network.
/// </summary>
///
/// <param name="visible">The <see cref="IStochasticFunction"/> to be used in the first visible layer.</param>
/// <param name="hidden">The <see cref="IStochasticFunction"/> to be used in all other layers.</param>
///
/// <param name="inputsCount">The number of inputs for the network.</param>
/// <param name="hiddenNeurons">The number of hidden neurons in each layer.</param>
///
public static DeepBeliefNetwork CreateMixedNetwork(IStochasticFunction visible,
IStochasticFunction hidden, int inputsCount, params int[] hiddenNeurons)
{
DeepBeliefNetwork network = new DeepBeliefNetwork(hidden, inputsCount, hiddenNeurons);
foreach (StochasticNeuron neuron in network.machines[0].Visible.Neurons)
{
neuron.ActivationFunction = visible;
}
return(network);
}
/// <summary>
/// Creates a Gaussian-Bernoulli network.
/// </summary>
///
/// <param name="inputsCount">The number of inputs for the network.</param>
/// <param name="hiddenNeurons">The number of hidden neurons in each layer.</param>
///
public static DeepBeliefNetwork CreateGaussianBernoulli(int inputsCount, params int[] hiddenNeurons)
{
DeepBeliefNetwork network = new DeepBeliefNetwork(inputsCount, hiddenNeurons);
GaussianFunction gaussian = new GaussianFunction();
foreach (StochasticNeuron neuron in network.machines[0].Visible.Neurons)
{
neuron.ActivationFunction = gaussian;
}
return(network);
}
