private static TrainedRecognizer Train(TrainerInput input, ConvolutionBase2D convolution, bool isColor, int finalResolution)
{
if (input == null || input.ImportantEvents == null || input.ImportantEvents.Length == 0)
{
return null;
}
TrainerInput normalized = GetTrainingInput(input, convolution, isColor, finalResolution);
List<double[]> inputs = new List<double[]>();
List<double[]> outputs = new List<double[]>();
// Important Events
inputs.AddRange(normalized.ImportantEvents.Select(o => o.Item2));
outputs.AddRange(normalized.ImportantEvents.Select(o => o.Item1.Vector));
// Unimportant Events (sensor input that should output zeros)
if (normalized.UnimportantEvents != null && normalized.UnimportantEvents.Length > 0)
{
int outputVectorLength = outputs[0].Length;
inputs.AddRange(normalized.UnimportantEvents);
outputs.AddRange(Enumerable.Range(0, normalized.UnimportantEvents.Length).
Select(o => new double[outputVectorLength]));
}
//NOTE: If there is an exception, the network couldn't be trained
BasicNetwork network = null;
try
{
//network = UtilityEncog.GetTrainedNetwork(inputs.ToArray(), outputs.ToArray(), UtilityEncog.ERROR, 15, 45).NetworkOrNull;
network = UtilityEncog.GetTrainedNetwork(inputs.ToArray(), outputs.ToArray(), UtilityEncog.ERROR, 5, 15).NetworkOrNull;
}
catch (Exception) { }
if (network == null)
{
return null;
}
return new TrainedRecognizer()
{
Network = network,
InputRaw = input,
InputNormalized = normalized,
};
}
/// <summary>
/// This runs the input through NormalizeInput, and returns an object with those results
/// </summary>
private static TrainerInput GetTrainingInput(TrainerInput raw, ConvolutionBase2D convolution, bool isColor, int finalResolution)
{
var importantEvents = raw.ImportantEvents.
Select(o =>
{
double[] normalized = NormalizeInput(o.Item2, raw.Width, raw.Height, finalResolution, convolution, isColor);
return Tuple.Create(o.Item1, normalized);
}).
ToArray();
var unimportantEvents = raw.UnimportantEvents.
Select(o => NormalizeInput(o, raw.Width, raw.Height, finalResolution, convolution, isColor)).
ToArray();
var awayPoints = GetAwayPoints(importantEvents.Select(o => o.Item2).ToArray());
//VectorInt reduction;
//if (convolution == null)
//{
// reduction = new VectorInt(0, 0);
//}
//else
//{
// reduction = convolution.GetReduction();
//}
return new TrainerInput()
{
//Width = raw.Width - reduction.X,
//Height = raw.Height - reduction.Y,
Width = finalResolution,
Height = finalResolution,
ImportantEvents = importantEvents,
UnimportantEvents = unimportantEvents.Concat(awayPoints).ToArray(),
IsColor = isColor,
};
}
private static void SaveImages(string parentFolder, string childFolder, TrainerInput trainerInput)
{
if (trainerInput == null)
{
return;
}
foreach (var set in trainerInput.ImportantEvents.ToLookup(o => o.Item1))
{
SaveImages(parentFolder, childFolder, trainerInput.Width, trainerInput.Height, set.Select(o => o.Item2).ToArray(), trainerInput.IsColor, set.Key.ToString());
}
SaveImages(parentFolder, childFolder, trainerInput.Width, trainerInput.Height, trainerInput.UnimportantEvents, trainerInput.IsColor, "none");
}
/// <summary>
/// This returns raw input (still need to call NormalizeInput)
/// </summary>
private TrainerInput GetTrainingInput()
{
var camera = _camera;
if (camera == null)
{
return null;
}
double numCategories = _importantEvents.
Distinct(o => o.Item1.Type).
Count();
int unimportantCount = Math.Min(20, (_importantEvents.Count / numCategories).ToInt_Round());
TrainerInput trainerInput = new TrainerInput()
{
ImportantEvents = _importantEvents.ToArray(),
UnimportantEvents = _nonLifeEventSnapshots.GetSamples(unimportantCount),
Width = camera.PixelWidthHeight,
Height = camera.PixelWidthHeight,
IsColor = _isColor,
};
return trainerInput;
}
