public List <double> Predict(DiscreteTendon tendon, bool normalizeInputs = true)
{
double[] inputs = new double[5];
if (tendon.IsFullTendon())
{
FullTendon fullTendon = tendon.GetFullTendon();
inputs[0] = fullTendon.PointA.Z;
inputs[1] = fullTendon.PointB.X;
inputs[2] = fullTendon.PointB.Z;
inputs[3] = fullTendon.PointC.Z;
inputs[4] = fullTendon.SupportRadius;
}
else if (tendon.IsPartialTendon())
{
PartialTendon partialTendon = tendon.GetPartialTendon();
inputs[0] = partialTendon.PointA.Z;
inputs[1] = partialTendon.PointB.X;
inputs[2] = partialTendon.PointB.Z;
inputs[3] = partialTendon.PointC.Z;
inputs[4] = partialTendon.SupportRadius;
}
else
{
return(new List <double>());
}
return(Predict(inputs, normalizeInputs));
}
public TendonNeuralNetwork(AnalyticalBridge analyticalBridge)
{
foreach (PrestressingCaseResult result in analyticalBridge.PrestressingCaseResults)
{
if (result.DiscreteTendon.IsFullTendon())
{
FullTendon fullTendon = result.DiscreteTendon.GetFullTendon();
double[] tendonOutputs = new double[result.MY.Count()];
foreach (double my in result.MY)
{
tendonOutputs[result.MY.IndexOf(my)] = my;
}
AddMember(new double[5] {
fullTendon.PointA.Z, fullTendon.PointB.X, fullTendon.PointB.Z, fullTendon.PointC.Z, fullTendon.SupportRadius
}, tendonOutputs);
}
else if (result.DiscreteTendon.IsPartialTendon())
{
PartialTendon partialTendon = result.DiscreteTendon.GetPartialTendon();
double[] tendonOutputs = new double[result.MY.Count()];
foreach (double my in result.MY)
{
tendonOutputs[result.MY.IndexOf(my)] = my;
}
AddMember(new double[5] {
partialTendon.PointA.Z, partialTendon.PointB.X, partialTendon.PointB.Z, partialTendon.PointC.Z, partialTendon.SupportRadius
}, tendonOutputs);
}
}
NumberOfHiddenUnits = int.Parse(analyticalBridge.PhysicalBridge.DataForm.DataFormHiddenUnits.Text);
MaxNumberOfEpochs = int.Parse(analyticalBridge.PhysicalBridge.DataForm.DataFormMaxEpochs.Text);
LearnRate = double.Parse(analyticalBridge.PhysicalBridge.DataForm.DataFormLearnRate.Text);
double errorTreshold = double.Parse(analyticalBridge.PhysicalBridge.DataForm.DataFormErrorTreshold.Text);
double error = errorTreshold + 1.000000;
TimeElapsed = new Stopwatch();
TimeElapsed.Start();
Logs = analyticalBridge.PhysicalBridge.Directory + Globals.TextFiles.NeuralNetworkLogs;
Initialize(int.Parse(analyticalBridge.PhysicalBridge.DataForm.DataFormTestSetSize.Text));
int epochs = 100;
for (int epoch = 0; epoch < MaxNumberOfEpochs; epoch += epochs)
{
if (error <= errorTreshold)
{
break;
}
if (TotalEpochs >= MaxNumberOfEpochs)
{
break;
}
error = Train(epochs, errorTreshold);
}
PrintSummary();
}
