private static RecognitionResults RecognizeImage(double[] input, long inputToken, int width, int height, int finalResolution, TrainedRecognizer[] recognizers, LifeEventToVector lifeEvents, ConvolutionBase2D convolution, bool isColor)
{
if (recognizers == null)
{
return new RecognitionResults(inputToken, new double[lifeEvents.Types.Length]);
}
double[] normalized = NormalizeInput(input, width, height, finalResolution, convolution, isColor);
foreach (var recognizer in recognizers)
{
double[] output = recognizer.Network.Compute(normalized);
//TODO: Analyze outputs of all the recognizers to come up with a final result. Can't just take the average -- if they all
//agree, that's great. But disagreement should have a zero output (or at least a very weak output)
return new RecognitionResults(inputToken, output);
}
throw new ApplicationException("finish this");
}
//TODO: This should store results in dna
public void AssignOutputs(LifeEventToVector lifeEvents)
{
if (_lifeEvents != null)
{
throw new InvalidOperationException("LifeEvents can only be assigned once");
}
_lifeEvents = lifeEvents;
lifeEvents.EventOccurred += LifeEvents_EventOccurred;
// Build the neurons
_outputNeurons = CreateNeurons(this.DNA, _itemOptions, lifeEvents);
}
private static Neuron_SensorPosition[] CreateNeurons(ShipPartDNA dna, ItemOptions itemOptions, LifeEventToVector lifeEvents)
{
//TODO: Instead of just having a single output neuron for each life event type, report the type at location?
//For example: the camera's input is a square, so have a grid of blocks. The output grid doesn't need to be very high
//resolution, maybe 3x3 up to 5x5. Each block would be a line of neurons perpendicular to the square
double radius = (dna.Scale.X + dna.Scale.Y + dna.Scale.Z) / (3d * 2d); // xyz should all be the same anyway
Vector3D[] positions = Brain.GetNeuronPositions_Line2D(null, lifeEvents.Types.Length, radius);
return positions.
Select(o => new Neuron_SensorPosition(o.ToPoint(), true)).
ToArray();
}
