/// <summary>
/// Compute the outputs for this layer given the input pattern. The output is
/// also stored in the fire instance variable.
/// </summary>
/// <param name="pattern">The input pattern.</param>
/// <returns>The output from this layer.</returns>
public virtual INeuralData Compute(INeuralData pattern)
{
INeuralData result = (INeuralData)pattern.Clone();
if (this.HasBias)
{
// apply the bias values
for (int i = 0; i < this.biasWeights.Length; i++)
{
result[i] = result[i] + this.biasWeights[i];
}
}
// apply the activation function
ActivationFunction.ActivationFunction(result.Data, 0, result.Data.Length);
return(result);
}
/// <summary>
/// Compute the output for this layer given the input pattern.
/// The output is also stored in the fire instance variable.
/// </summary>
/// <param name="pattern">The input pattern</param>
/// <returns>The output from this layer.</returns>
public double[] ComputeOutputs(double[] pattern)
{
if (pattern != null)
{
for (int i = 0; i < NeuronCount; i++)
{
SetFire(i, pattern[i]);
}
}
Matrix inputMatrix = CreateInputMatrix(_fire);
for (int i = 0; i < _next.NeuronCount; i++)
{
Matrix col = _matrix.GetColumn(i);
double sum = MatrixMath.DotProduct(col, inputMatrix);
_next.SetFire(i, _activationFunction.ActivationFunction(sum));
}
return(_fire);
}
/// <inheritdoc/>
public double Calculate()
{
var sumArray = new double[1];
_sum = 0;
// sum the input connections
foreach (IFreeformConnection connection in _inputs)
{
connection.Source.PerformCalculation();
_sum += connection.Weight
* connection.Source.Activation;
}
// perform the activation function
sumArray[0] = _sum;
ActivationFunction
.ActivationFunction(sumArray, 0, sumArray.Count());
return(sumArray[0]);
}