public NetworkTestingViewModel(NeuralNetwork network, PatternContainer patternContainer)
{
_network = network;
_patternContainer = patternContainer;
PredictCommand = new AsyncRelayCommand(async() => await Task.Run(() => Predict()));
AddToTrainingSetCommand = new RelayCommand(() =>
{
var dialog = new Views.SavePatternAsDialog
{
ResponseText = Predictions?.FirstOrDefault(p => p.IsHighest)?.Name ?? string.Empty
};
if (dialog.ShowDialog() != true)
{
return;
}
var pattern = new Pattern
{
Name = dialog.ResponseText,
Rows = 15,
Columns = 10
};
// 0 - black, 1 - white
pattern.FillUsing(Pixels.Cast <double>().Select(p => p < 0.5).ToArray());
_patternContainer.Add(pattern);
});
}
public void Predict()
{
if (Pixels == null)
{
return;
}
var output = _network.Run(Pixels.Cast <double>().Select(x => x >= 0.5 ? -1.0 : 1.0).ToArray());
var maxIndex = output
.Select((value, index) => new { Index = index, Value = value })
.OrderByDescending(x => x.Value)
.Select(x => x.Index)
.First();
Predictions = _patternContainer.Patterns
.GroupBy(pattern => pattern.Name)
.OrderBy(x => x.Key)
.Select((group, index) => new Prediction
{
Name = group.Key,
Value = output[index],
IsHighest = index == maxIndex
}).ToArray();
}
private void AddToTrainingSet()
{
if (string.IsNullOrEmpty(SaveAsName))
{
return;
}
var pattern = new Pattern
{
Name = SaveAsName,
Rows = 15,
Columns = 10
};
// 0 - black, 1 - white
pattern.FillUsing(Pixels.Cast <double>().Select(x => x < 0.5).ToArray());
_patternContainer.Add(pattern);
}
public void Normalize()
{
double sum = Pixels.Cast <double>().Sum();
Pipeline(pixel => pixel / sum);
}