public static void ShowPercent(NeuralNetwork network, IUnorderedNeuralVectorFlowProvider provider)
{
Contract.Requires(network != null);
Contract.Requires(provider != null);
if (!Console.KeyAvailable) return;
Console.ReadKey();
var prov = provider as LeCunSampleProvider;
if (prov == null) return;
if (validationVectorFlows == null) validationVectorFlows = prov.GetAll().ToList();
var clone = network; // TODO: Clone
double count = 0;
double okCount = 0;
foreach (var vectorFlow in validationVectorFlows)
{
clone.Reset();
var entry = vectorFlow.Entries[0];
for (int idx = 0; idx < entry.InputVector.Length; idx++)
{
clone.InputInterface[idx] = entry.InputVector[idx].Value;
}
for (int i = 0; i < entry.NumberOfIterations; i++)
{
clone.Iteration();
}
int desiredNumber = GetNumber(entry.DesiredOutputVector);
int detectedNumber = GetNumber(clone.OutputInterface);
if (desiredNumber == detectedNumber) okCount++;
count++;
}
double current = 100.0 - (okCount / count) * 100.0;
Console.WriteLine("Current precent: " + current.ToString("0.0000") + "%");
GC.Collect();
}
public static void TestNetwork(NeuralNetwork network, IUnorderedNeuralVectorFlowProvider validationProvider)
{
Contract.Requires(network != null);
if (!Console.KeyAvailable) return;
Console.ReadKey();
var prov = (FullSampleProvider)validationProvider;
if (validationVectorFlows == null) validationVectorFlows = prov.GetAllVectors().ToList();
var clone = network; //TODO: Clone
//Task.Factory.StartNew(() =>
//{
int fnCount = 0;
int fpCount = 0;
int okCount = 0;
int count = 0;
foreach (var vectorFlow in validationVectorFlows)
{
bool isDetectable = false;
clone.Reset();
foreach (var vector in vectorFlow.Entries)
{
for (int idx = 0; idx < vector.InputVector.Length; idx++)
{
clone.InputInterface[idx] = vector.InputVector[idx].Value;
}
for (int i = 0; i < vector.NumberOfIterations; i++)
{
clone.Iteration();
}
if (vector.DesiredOutputVector[0] != null)
{
isDetectable = vector.DesiredOutputVector[0].Value > 0.0;
}
}
double detectedOutput = clone.OutputInterface[0];
bool isDetectedByNetwork = detectedOutput > 0.0;
if (isDetectedByNetwork == isDetectable)
{
okCount++;
}
else
{
if (isDetectedByNetwork)
{
fpCount++;
}
else
{
fnCount++;
}
}
count++;
}
double percent = ((double)okCount / count) * 100.0;
var result = new TestResult(percent, fnCount, fpCount);
Console.WriteLine(result);
//});
}
public static void TestNetwork(NeuralNetwork network, IUnorderedNeuralVectorFlowProvider validationProvider)
{
//Contract.Requires(network != null);
//if (!Console.KeyAvailable) return;
//Console.ReadKey();
//var prov = (FullSampleProvider)validationProvider;
//if (validationVectorFlows == null) validationVectorFlows = prov.GetAllVectors().ToList();
//var clone = network; //TODO: Clone
////Task.Factory.StartNew(() =>
////{
// int fnCount = 0;
// int fpCount = 0;
// int okCount = 0;
// int count = 0;
// foreach (var vectorFlow in validationVectorFlows)
// {
// bool isDetectable = false;
// clone.Reset(NeuralNetworkResetTarget.Outputs);
// foreach (var vector in vectorFlow.Entries)
// {
// clone.WriteInput(vector.InputVector);
// for (int i = 0; i < vector.NumberOfIterations; i++)
// {
// clone.Iteration();
// }
// if (vector.DesiredOutputVector[0] != null)
// {
// isDetectable = vector.DesiredOutputVector[0].Value > 0.0;
// }
// }
// float[] outputVector = new float[1];
// clone.ReadOutput(outputVector);
// float detectedOutput = outputVector[0];
// bool isDetectedByNetwork = detectedOutput > 0.0;
// if (isDetectedByNetwork == isDetectable)
// {
// okCount++;
// }
// else
// {
// if (isDetectedByNetwork)
// {
// fpCount++;
// }
// else
// {
// fnCount++;
// }
// }
// count++;
// }
// float percent = ((float)okCount / (float)count) * 100.0f;
// var result = new TestResult(percent, fnCount, fpCount);
// Console.WriteLine(result);
////});
}
