public void Constructor_WeightInitialization_RandomWeightCreated()
{
var toNeruonMock = new Mock <INeuron>();
var connection = new InputSynapse(toNeruonMock.Object);
Assert.AreEqual(1, connection.Weight);
}
public void GetOutput_ProperlyInitialized_SameAsFromNeuronOutput()
{
var toNeruonMock = new Mock <INeuron>();
var connection = new InputSynapse(toNeruonMock.Object, 1);
Assert.AreEqual(1, connection.GetOutput());
}
// подключаем пирамиду к рецептивному полю колонки.
// N - общее число битов в поле
// r - требуемое число присоединяемых входов
public void AttachToInputs( int N, int R, int S )
{
c = 1.0 / S;
r = R;
HashSet<int> used_inputs = new HashSet<int>();
double R0 = 1.0 / S; // одинаковый вес афферентов по умолчанию
afferents = new List<InputSynapse>();
while( afferents.Count < r )
{
int index = (int)( Init.rnd.NextDouble() * N );
if( !used_inputs.Contains( index ) )
{
used_inputs.Add( index );
InputSynapse synapse = new InputSynapse( index, R0 );
afferents.Add( synapse );
}
}
// --- отладка
double sumR = 0.0;
for( int i = 0; i < afferents.Count; ++i )
sumR += afferents[i].R;
double X = sumR / c;
bool is_overload = X > r;
return;
}
/// <summary>
/// Input Layer neurons just receive input values.
/// For this they need to have connections.
/// This function adds this kind of connection to the neuron.
/// </summary>
/// <param name="inputValue">
/// Initial value that will be "pushed" as an input to connection.
/// </param>
public void AddInputSynapse(double inputValue)
{
var inputSynapse = new InputSynapse(this, inputValue);
Inputs.Add(inputSynapse);
}
public void AddOutputSynapse(double?outputWeight = null)
{
var inputSynapse = new InputSynapse(this, outputWeight);
Outputs.Add(inputSynapse);
}
