// method to export true vs estimated (NN) output for a particular output index
public void TestNetworkOutputValidationTimeDomain(int outputIndex)
{
double[,] trueOutput = new double[Inputs.Count() - 1, 2];
double[,] nnOutput = new double[Inputs.Count() - 1, 2];
double[,] bestNNOutput = new double[Inputs.Count() - 1, 2];
double[] tempInput1 = new double[NumInputs];
double[] tempInput2 = new double[NumInputs];
double[] tempOutput1;
double[] tempOutput2;
for (int i = 0; i < Inputs.Count() - 1; i++)
{
if (i == 0)
{
for (int j = 0; j < NumInputs; j++)
{
tempInput1[j] = Inputs[0][j];
tempInput2[j] = Inputs[0][j];
}
}
trueOutput[i, 0] = Convert.ToDouble(i);
trueOutput[i, 1] = Outputs[i][outputIndex];
nnOutput[i, 0] = Convert.ToDouble(i);
tempOutput1 = NeuralNet.ForwardPass(tempInput1);
nnOutput[i, 1] = tempOutput1[outputIndex];
for (int j = 0; j < NumOutputs; j++)
{
tempInput1[j] = tempOutput1[j];
}
for (int j = NumOutputs; j < NumInputs; j++)
{
tempInput1[j] = Inputs[i][j];
}
bestNNOutput[i, 0] = Convert.ToDouble(i);
tempOutput2 = BestNetwork.ForwardPass(tempInput2);
bestNNOutput[i, 1] = tempOutput2[outputIndex];
for (int j = 0; j < NumOutputs; j++)
{
tempInput2[j] = tempOutput2[j];
}
for (int j = NumOutputs; j < NumInputs; j++)
{
tempInput2[j] = Inputs[i][j];
}
}
DE.Export2DArray(trueOutput, "true_data_validation");
DE.Export2DArray(nnOutput, "nn_output_validation");
DE.Export2DArray(bestNNOutput, "best_nn_output_validation");
}
// method to export true vs estimated (NN) output for a particular output index
public void TestNetworkOutputTraining(int inputIndex, int outputIndex)
{
double[,] trueOutput = new double[Inputs.Count(), 2];
double[,] nnOutput = new double[Inputs.Count(), 2];
double[,] bestNNOutput = new double[Inputs.Count(), 2];
for (int i = 0; i < Inputs.Count(); i++)
{
trueOutput[i, 0] = Inputs[i][inputIndex];
trueOutput[i, 1] = Outputs[i][outputIndex];
nnOutput[i, 0] = Inputs[i][inputIndex];
nnOutput[i, 1] = NeuralNet.ForwardPass(Inputs[i])[outputIndex];
bestNNOutput[i, 0] = Inputs[i][inputIndex];
bestNNOutput[i, 1] = BestNetwork.ForwardPass(Inputs[i])[outputIndex];
}
DE.Export2DArray(trueOutput, "true_data_training");
DE.Export2DArray(nnOutput, "nn_output_training");
DE.Export2DArray(bestNNOutput, "best_nn_output_training");
}
// method to export true vs estimated (NN) output for a particular output index
public void TestNetworkOutputValidation(int inputIndex, int outputIndex)
{
double[,] trueOutput = new double[ValidationInputs.Count(), 2];
double[,] nnOutput = new double[ValidationInputs.Count(), 2];
double[,] bestNNOutput = new double[ValidationInputs.Count(), 2];
for (int i = 0; i < ValidationInputs.Count(); i++)
{
trueOutput[i, 0] = ValidationInputs[i][inputIndex];
trueOutput[i, 1] = ValidationOutputs[i][outputIndex];
nnOutput[i, 0] = ValidationInputs[i][inputIndex];
nnOutput[i, 1] = NeuralNet.ForwardPass(ValidationInputs[i])[outputIndex];
bestNNOutput[i, 0] = ValidationInputs[i][inputIndex];
bestNNOutput[i, 1] = BestNetwork.ForwardPass(ValidationInputs[i])[outputIndex];
}
DE.Export2DArray(trueOutput, "true_data_validation");
DE.Export2DArray(nnOutput, "nn_output_validation");
DE.Export2DArray(bestNNOutput, "best_nn_output_validation");
}