internal static Neuron_SensorPosition[] CreateNeurons(ShipPartDNA dna, ItemOptions itemOptions, double neuronDensity, bool hasHoleInMiddle, bool ignoreSetValue)
{
var count = GetNeuronCount(dna.Scale, itemOptions.Sensor_NeuronGrowthExponent, neuronDensity);
Point3D[] staticPositions = null;
if (hasHoleInMiddle)
{
staticPositions = new Point3D[] { new Point3D(0, 0, 0) };
}
// Place them evenly within a circle.
// I don't want a neuron in the center, so placing a static point there to force the neurons away from the center
Vector3D[] positions = NeuralUtility.GetNeuronPositions_Circular_Even(dna.Neurons, staticPositions, 1d, count.neuronCount, count.radius);
return(positions.
Select(o => new Neuron_SensorPosition(o.ToPoint(), true, ignoreSetValue)).
ToArray());
}
private static NeuronLayer[] CreateNeurons_Layers(SensorVisionDNA dna, ItemOptionsArco itemOptions)
{
if (dna.Filters == null || dna.Filters.Length == 0)
{
throw new ApplicationException("This method requires filters to be populated");
}
var count = GetNeuronCount(dna.Scale, itemOptions.Sensor_NeuronGrowthExponent, itemOptions.VisionSensor_NeuronDensity);
//TODO: SensorVisionFilterType.Bot will need two layers
var neuronZs = GetNeuron_Zs(dna.Filters.Length, count.radius);
Point3D[] staticPositions = new Point3D[] { new Point3D(0, 0, 0) };
Point3D[] singlePlate = null;
if (dna.Neurons != null && dna.Neurons.Length > 0)
{
var byLayer = NeuralUtility.DivideNeuronLayersIntoSheets(dna.Neurons, neuronZs.z, count.neuronCount);
// Just use one of the layer's positions to re evenly distribute (first layer with the correct number of neurons - or closest number).
// Could try to find the nearest between each layer and use the average of each set, but that's a lot of expense with very little payoff
singlePlate = GetBestNeuronSheet(byLayer, count.neuronCount);
}
// Make sure there is a correct number of neurons and apply an even distribution
singlePlate = NeuralUtility.GetNeuronPositions_Circular_Even(singlePlate, staticPositions, 1d, count.neuronCount, count.radius).
Select(o => o.ToPoint()).
ToArray();
// Use the location of neurons in this single layer for all layers. By making sure that each neuron represents the same point in each
// layer (and the index of that neuron is the same in each layer), position logic in each tick can be optimized
return(Enumerable.Range(0, dna.Filters.Length).
Select(o => new NeuronLayer()
{
FilterType = dna.Filters[o],
Z = neuronZs.z[o],
Neurons = singlePlate.
Select(p => new Neuron_SensorPosition(new Point3D(p.X, p.Y, neuronZs.z[o]), true, true)).
ToArray(),
}).
ToArray());
}
private static Neuron_SensorPosition[] CreateNeurons(ShipPartDNA dna, ItemOptions itemOptions, double neuronDensity, bool ignoreSetValue)
{
#region calculate counts
// Figure out how many to make
//NOTE: This radius isn't taking SCALE into account. The other neural parts do this as well, so the neural density properties can be more consistent
double radius = Math1D.Avg(dna.Scale.X, dna.Scale.Y, dna.Scale.Z) / 2d; // dividing by 2, because radius is wanted, not diameter
double area = Math.Pow(radius, itemOptions.Sensor_NeuronGrowthExponent);
int neuronCount = (neuronDensity * area).ToInt_Ceiling();
neuronCount = Math.Max(neuronCount, 30);
#endregion
// Place them evenly on the surface of a sphere
Vector3D[] positions = NeuralUtility.GetNeuronPositions_Circular_Even(dna.Neurons, neuronCount, radius);
return(positions.
Select(o => new Neuron_SensorPosition(o.ToPoint(), true, ignoreSetValue)).
ToArray());
}
