public void ConstructorTest()
{
// Create a Gaussian function with slope alpha = 4.2
GaussianFunction function = new GaussianFunction(4.2);
// Computes the function output (linear, y = alpha * x)
double y = function.Function(x: 0.2); // 4.2 * 2 = 0.48
// Draws a sample from a Gaussian distribution with
// mean given by the function output y (previously 0.48)
double z = function.Generate(x: 0.4); // (random, between 0 and 1)
// Please note that the above is completely equivalent
// to computing the line below (remember, 0.48 == y)
double w = function.Generate2(y: 0.48); // (random, between 0 and 1)
// We can also compute the derivative of the sigmoid function
double d = function.Derivative(x: 0.2); // 4.2 (the slope)
// Or compute the derivative given the functions' output y
double e = function.Derivative2(y: 0.2); // 4.2 (the slope)
Assert.AreEqual(0.84, y, 1e-7);
Assert.AreEqual(4.2, d, 1e-7);
Assert.AreEqual(4.2, e, 1e-7);
}
/// <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;
}