/// <summary>
/// Get the hidden layer features from a visible layer
/// </summary>
/// <param name="dataArray"></param>
/// <returns></returns>
public double[][] GetHiddenLayer(double[][] dataArray)
{
var num_examples = dataArray.Length;
// Create a matrix, where each row is to be the hidden units (plus a bias unit)
// sampled from a training example.
var hidden_states = RealMatrix.Ones(num_examples, m_numHiddenElements + 1);
var data = new RealMatrix(dataArray);
// Insert bias units of 1 into the first column of data.
data = data.InsertCol(1); // np.insert(data, 0, 1, axis = 1)
// Calculate the activations of the hidden units.
var hiddenActivations = data * m_weights;
// Calculate the probabilities of turning the hidden units on.
var hiddenProbs = ActivationFunctions.Logistic(hiddenActivations);
// Turn the hidden units on with their specified probabilities.
hidden_states = hiddenProbs > Distributions.UniformRandromMatrix(num_examples, m_numHiddenElements + 1);
// Ignore the bias units.
hidden_states = hidden_states.RemoveFirstCol(); // hidden_states[:,1:]
return(hidden_states);
}
/// <summary>
/// Day dream - Reconstruct a randrom matrix (An interesting way of seeing strong features the machine has learnt)
/// </summary>
/// <param name="numberOfSamples">How many images/dreams</param>
/// <returns>Array of Reconstructed dreams</returns>
public double[][] DayDream(int numberOfSamples)
{
var data = RealMatrix.Ones(numberOfSamples, m_numVisibleElements + 1);
data.Update(0, 1, Distributions.UniformRandromMatrixBool(1, m_numVisibleElements), 1);
for (int i = 0; i < numberOfSamples; i++)
{
var visible = data.Submatrix(i, 0, 1).ToVector();
var hidden_activations = (visible * m_weights).ToVector();
var hidden_probs = ActivationFunctions.Logistic(hidden_activations);
var hidden_states = hidden_probs > RVector.Random(m_numHiddenElements + 1);
hidden_states[0] = 1;
var visible_activations = (hidden_states * m_weights.Transpose).ToVector();
var visible_probs = ActivationFunctions.Logistic(visible_activations);
var visible_states = visible_probs > RVector.Random(m_numVisibleElements + 1);
data.Update(visible_states, 0, false, i, 0);
}
return(data.Submatrix(0, 1).ToArray());
}
