public void GatherStatistics(Network network)
{
double averageError = 0;
foreach (var testElement in TestElements)
{
var output = network.ForwardPropagation(testElement.Input);
var error = Network.MeanSquaredError(output, testElement.DesiredOutput);
averageError += error;
}
averageError /= TestElements.Count;
TestErrors.Add(averageError);
}
public void GatherStatistics(Network network)
{
double averageError = 0;
double positiveV = 0;
double positiveT = 0;
//GET COUNT POSITIVES FOR TESTING SET
foreach (var testElement in TestElements)
{
var output = network.ForwardPropagation(testElement.Input);
int guessedClass = ConvertMatrixToClass(output);
int rightClass = ConvertMatrixToClass(testElement.DesiredOutput);
if (guessedClass == rightClass)
{
positiveV++;
}
var error = Network.MeanSquaredError(output, testElement.DesiredOutput);
averageError += error;
}
AccuracyListV.Add(positiveV / TestElements.Count);
averageError /= TestElements.Count;
TestErrors.Add(averageError);
//GET COUNT POSITIVES FOR TRAINING SET
foreach (var trainElement in TrainingElements)
{
var output = network.ForwardPropagation(trainElement.Input);
int guessedClass = ConvertMatrixToClass(output);
int rightClass = ConvertMatrixToClass(trainElement.DesiredOutput);
if (guessedClass == rightClass)
{
positiveT++;
}
}
AccuracyListT.Add(positiveT / TrainingElements.Count);
}