public int GetWinner(NormalizeInput input)
{
var winner = 0;
if (Output == null)
{
Output = new double[OutputNeuronCount];
}
var biggest = Double.MinValue;
for (var i = 0; i < OutputNeuronCount; i++)
{
Output[i] = MatrixMath.DotProduct(input.InputMatrix, OutputWeights.GetRow(i)) * input.NormalizationFactor;
Output[i] = (Output[i] + 1.0) / 2.0;
if (Output[i] > biggest)
{
biggest = Output[i];
winner = i;
}
if (Output[i] < 0)
{
Output[i] = 0;
}
if (Output[i] > 1)
{
Output[i] = 1;
}
}
return(winner);
}
public override XElement ToXElement(string name)
{
var element = base.ToXElement(name);
element.Add(InputWeights.ToXElement("inputWeights"));
element.Add(OutputWeights.ToXElement("outputWeights"));
return(element);
}
/// <summary>
/// Used after all values have been loaded to link that value to all outputs
/// </summary>
/// <param name="values">The list of all values</param>
internal void LinkTo(List <IndexedValue> values)
{
foreach (string s in tmpOutputs.Keys)
{
IndexedValue output = values.Exists(v => v.Name.Equals(s))?values.Find(v => v.Name.Equals(s)):null;
if (output != null)
{
OutputWeights.Add(output, tmpOutputs[s]);
}
}
}
public Vector <float> FeedForward(Vector <float> inputs)
{
Vector <float> hiddenActivations = HiddenWeights
.Multiply(inputs)
.Add(HiddenBiases)
.Map(NetworkParameters.HiddenLayerActivationFunction);
return(OutputWeights
.Multiply(hiddenActivations)
.Add(OutputBiases)
.Map(NetworkParameters.OutputLayerActivationFunction));
}
public string ToFullParamsString()
{
return($"{NetworkParameters.InputCount}-{NetworkParameters.HiddenLayerNeuronCount}\n" +
$"{HiddenWeights.ToMatrixString()}\n{HiddenBiases.ToVectorString()}\n{OutputWeights.ToMatrixString()}\n{OutputBiases.ToVectorString()}");
}
public override string ToString()
{
return($"{NetworkParameters.InputCount}-{NetworkParameters.HiddenLayerNeuronCount} " +
$"{HiddenWeights.GetHashCode()},{HiddenBiases.GetHashCode()},{OutputWeights.GetHashCode()},{OutputBiases.GetHashCode()}");
}
public NeuralNetwork Clone()
{
return(new NeuralNetwork(HiddenWeights.Clone(), HiddenBiases.Clone(), OutputWeights.Clone(),
OutputBiases.Clone(), NetworkParameters.HiddenLayerActivationFunction, NetworkParameters.OutputLayerActivationFunction));
}
