public static double ComputePassRate(string[] lines, CrossroadControllerReinforcement crossroadController)
{
var passRate = 0.0;
foreach (var line in lines)
{
var input = new double[crossroadController.NofTrafficLights];
var output = new double[crossroadController.NofStates];
var values = line.Split(' ');
for (var i = 0; i < crossroadController.NofTrafficLights; i++)
{
input[i] = double.Parse(values[i], CultureInfo.InvariantCulture);
}
for (var i = 0; i < crossroadController.NofStates; i++)
{
output[i] = double.Parse(values[crossroadController.NofTrafficLights + i]);
}
var isPassed = true;
for (var i = 0; i < crossroadController.NofStates; i++)
{
for (var j = 0; j < crossroadController.NofTrafficLights; j++)
{
crossroadController.RNeurons[i].dendrits[j] = input[j] * crossroadController.W1[j, i];
}
crossroadController.RNeurons[i].Activation();
if (Math.Abs(crossroadController.RNeurons[i].axon - output[i]) > 0.1)
{
isPassed = false;
break;
}
}
if (isPassed)
{
passRate += 1.0;
}
}
return(passRate / lines.Length);
}
public static void TeachW1(CrossroadControllerReinforcement crossroadController, string pathToEducationExample)
{
var lines = File.ReadAllLines(pathToEducationExample);
double bestPassRate = ComputePassRate(lines, crossroadController);
var bestMatrix = new double[crossroadController.NofTrafficLights, crossroadController.NofStates];
CopyMatrix(crossroadController.W1, bestMatrix, crossroadController.NofTrafficLights, crossroadController.NofStates);
foreach (var line in lines)
{
var input = new double[crossroadController.NofTrafficLights];
var output = new double[crossroadController.NofStates];
var values = line.Split(' ');
for (var i = 0; i < crossroadController.NofTrafficLights; i++)
{
input[i] = double.Parse(values[i], CultureInfo.InvariantCulture);
}
for (var i = 0; i < crossroadController.NofStates; i++)
{
output[i] = double.Parse(values[crossroadController.NofTrafficLights + i]);
}
var nofRepeats = 0;
for (var i = 0; i < crossroadController.NofStates; i++)
{
if (nofRepeats >= 100)
{
nofRepeats = 0;
continue;
}
for (var j = 0; j < crossroadController.NofTrafficLights; j++)
{
crossroadController.RNeurons[i].dendrits[j] = input[j] * crossroadController.W1[j, i];
}
crossroadController.RNeurons[i].Activation();
if (Math.Abs(crossroadController.RNeurons[i].axon - output[i]) > 0.1)
{
// double e = (output[i] - crossroadController.RNeurons[i].axon) *
// crossroadController.RNeurons[i].axon * (1 - crossroadController.RNeurons[i].axon);
for (var j = 0; j < crossroadController.NofTrafficLights; j++)
{
//crossroadController.W1[j, i] += 0.01 * (input[j] - crossroadController.W1[j, i]);
crossroadController.W1[j, i] = crossroadController.W1[j, i] +
(output[i] - crossroadController.RNeurons[i].axon) * input[j];
// crossroadController.W1[j, i] = crossroadController.W1[j, i] + 0.025*e*input[j];
}
double newPassRate = ComputePassRate(lines, crossroadController);
if (newPassRate > bestPassRate)
{
bestPassRate = newPassRate;
CopyMatrix(crossroadController.W1, bestMatrix, crossroadController.NofTrafficLights, crossroadController.NofStates);
}
i--;
nofRepeats++;
}
else
{
nofRepeats = 0;
}
/* if (Math.Abs(crossroadController.RNeurons[i].axon - output[i] - (-1)) < double.Epsilon)
* {
* double e = (output[i] - crossroadController.RNeurons[i].axon)*
* crossroadController.RNeurons[i].axon*(1 - crossroadController.RNeurons[i].axon);
*
* for (var j = 0; j < crossroadController.NofTrafficLights; j++)
* {
* //crossroadController.W1[j, i] += 0.01*(input[j] - crossroadController.W1[j, i]);
* crossroadController.W1[j, i] = crossroadController.W1[j, i] + 0.025*e*input[j];
* }
*
* double newPassRate = ComputePassRate(lines, crossroadController);
* if (newPassRate > bestPassRate)
* {
* bestPassRate = newPassRate;
* CopyMatrix(crossroadController.W1, bestMatrix, crossroadController.NofTrafficLights, crossroadController.NofStates);
* }
*
* i--;
* nofRepeats++;
* }
* else if (Math.Abs(crossroadController.RNeurons[i].axon - output[i] - 1) < double.Epsilon)
* {
* double e = (output[i] - crossroadController.RNeurons[i].axon) *
* crossroadController.RNeurons[i].axon * (1 - crossroadController.RNeurons[i].axon);
*
* for (var j = 0; j < crossroadController.NofTrafficLights; j++)
* {
* //crossroadController.W1[j, i] -= 0.01*(input[j] - crossroadController.W1[j, i]);
* crossroadController.W1[j, i] = crossroadController.W1[j, i] + 0.025 * e * input[j];
* }
*
* double newPassRate = ComputePassRate(lines, crossroadController);
* if (newPassRate > bestPassRate)
* {
* bestPassRate = newPassRate;
* CopyMatrix(crossroadController.W1, bestMatrix, crossroadController.NofTrafficLights, crossroadController.NofStates);
* }
*
* i--;
* nofRepeats++;
* }
* else
* {
* nofRepeats = 0;
* }*/
}
}
}
