public double GradeResults(ITrainedNeuralNetwork network, List <TrainingData> testData)
{
int numberCorrect = 0;
foreach (TrainingData t in testData)
{
double[] outputs = network.Execute(t.X);
if (EquivalentOutputs(outputs, t.Y))
{
numberCorrect++;
}
}
return(numberCorrect / (testData.Count * 1.0));
}
public double GradeResults(ITrainedNeuralNetwork network, List <TrainingData> dataToGrade)
{
int correct = 0;
foreach (TrainingData testDataInstance in dataToGrade)
{
double[] result = network.Execute(testDataInstance.X);
if ((result[0] >= .5 && testDataInstance.Y[0] == 1) ||
(result[0] < .5 && testDataInstance.Y[0] == 0))
{
correct++;
}
}
return(correct / (dataToGrade.Count * 1.0));
}