public double[] CalculateOutput(double x, double y, double[] parameters)
{
var paramIdx = 0;
var outputs = new double[_layers.Length][];
const int likenessLayerIdx = 1;
// 1 - likeness neuron layer
outputs[likenessLayerIdx] = new double[_layers[likenessLayerIdx]];
// Activate the likeness layer
for (var likenessNeuronIdx = 0; likenessNeuronIdx < _layers[likenessLayerIdx]; likenessNeuronIdx++)
{
var weightX = parameters[paramIdx++];
var varianceX = parameters[paramIdx++];
var weightY = parameters[paramIdx++];
var varianceY = parameters[paramIdx++];
var xy = new[] { x, y };
var w = new[] { weightX, weightY };
var s = new[] { varianceX, varianceY };
outputs[likenessLayerIdx][likenessNeuronIdx] = ActivationFunctions.Likeness(xy, w, s);
}
// Pass through the hidden layer
for (var hiddenLayerIdx = 2; hiddenLayerIdx < _layers.Length; hiddenLayerIdx++)
{
var hidden = _layers[hiddenLayerIdx];
outputs[hiddenLayerIdx] = new double[hidden];
var previousOutput = outputs[hiddenLayerIdx - 1];
// Pass through every neuron
for (var hiddenNeuronIdx = 0; hiddenNeuronIdx < hidden; hiddenNeuronIdx++)
{
// Collect previous outputs
// Bias + previous output
var output = parameters[paramIdx++] + previousOutput.Sum(o => o * parameters[paramIdx++]);
outputs[hiddenLayerIdx][hiddenNeuronIdx] = ActivationFunctions.Sigmoid(output);
}
}
return(outputs[_layers.Length - 1]);
}
private static void Task1()
{
var path = Root + "Files/Data/Task1/";
var y = new List <double>();
var x = new List <double>();
const double w = 2.0;
var ss = new[] { 1.0, 0.25, 4.0 };
foreach (var s in ss)
{
for (var i = -8; i <= 10; i++)
{
x.Add(i);
y.Add(ActivationFunctions.Likeness(new double[] { i }, new[] { w }, new[] { s }));
}
path = $"{path}/s{s.ToString(CultureInfo.InvariantCulture).Replace('.', '_')}.txt";
FileHelper.WriteToFile(x, y, path);
y.Clear();
x.Clear();
}
}
