public bool Equals(DestinyCharacterCustomization input)
{
if (input == null)
{
return(false);
}
return
((
Personality == input.Personality ||
(Personality.Equals(input.Personality))
) &&
(
Face == input.Face ||
(Face.Equals(input.Face))
) &&
(
SkinColor == input.SkinColor ||
(SkinColor.Equals(input.SkinColor))
) &&
(
LipColor == input.LipColor ||
(LipColor.Equals(input.LipColor))
) &&
(
EyeColor == input.EyeColor ||
(EyeColor.Equals(input.EyeColor))
) &&
(
HairColors == input.HairColors ||
(HairColors != null && HairColors.SequenceEqual(input.HairColors))
) &&
(
FeatureColors == input.FeatureColors ||
(FeatureColors != null && FeatureColors.SequenceEqual(input.FeatureColors))
) &&
(
DecalColor == input.DecalColor ||
(DecalColor.Equals(input.DecalColor))
) &&
(
WearHelmet == input.WearHelmet ||
(WearHelmet != null && WearHelmet.Equals(input.WearHelmet))
) &&
(
HairIndex == input.HairIndex ||
(HairIndex.Equals(input.HairIndex))
) &&
(
FeatureIndex == input.FeatureIndex ||
(FeatureIndex.Equals(input.FeatureIndex))
) &&
(
DecalIndex == input.DecalIndex ||
(DecalIndex.Equals(input.DecalIndex))
));
}
FeatureIndex[] GetEdgeFacesIndices(int globalEdgeIndex)
{
FeatureIndex[] features = new FeatureIndex[2];
int currentFeature = 0;
for (int i = 0; i < faces.Count; i++)
{
if (currentFeature == 2)
{
break;
}
Face f = faces[i];
bool doneWithFace = false;
for (int u = 0; u < f.loops.Count; u++)
{
if (doneWithFace)
{
break;
}
List <OrderedEdge> faceEdges = f.loops[u].path;
for (int v = 0; v < faceEdges.Count; v++)
{
if (faceEdges[v].e == globalEdgeIndex)
{
features[currentFeature].face = i;
features[currentFeature].faceLoop = u;
features[currentFeature].loopEdge = v;
currentFeature++;
doneWithFace = true;
break;
}
}
}
}
if (currentFeature == 0)
{
throw new Exception("Degenerate mesh; An edge has no associated faces.");
}
else if (currentFeature == 1)
{
throw new Exception("Degenerate mesh; An edge only has one associated face.");
}
else
{
return(features);
}
}
public NeuralNetwork(int numOfInputs, int numOfOutputs,
int[] numOfHiddens) : base()
{
this.numOfHiddens = numOfHiddens;
this.numOfInputs = numOfInputs;
this.numOfOutputs = numOfOutputs;
numOfLayers = this.numOfHiddens.Length + 1;
layers = new PreceptorLayer[numOfLayers];
int prevNumOfPreceptors = this.numOfInputs;
for (int i = 0; i < numOfLayers; i++)
{
int numOfPreceptors = i < (numOfLayers - 1) ? this.numOfHiddens[i] : this.numOfOutputs;
Neuron.NormalizedMethod method = i == (numOfLayers - 1) ? Neuron.NormalizedMethod.SigmoidZeroTo1 : Neuron.NormalizedMethod.HyperTan;
layers[i] = new PreceptorLayer(numOfPreceptors, prevNumOfPreceptors, method);
numOfFeatures += numOfPreceptors;
numOfConnections += prevNumOfPreceptors * numOfPreceptors;
prevNumOfPreceptors = numOfPreceptors;
}
int idx = 0;
featureIndex = new FeatureIndex[numOfFeatures];
for (int i = 0; i < numOfLayers; i++)
{
for (int j = 0; j < layers[i].numOfPreceptors; j++)
{
featureIndex[idx] = new FeatureIndex(i, j);
idx += 1;
}
}
idx = 0;
connectionIndex = new ConnectionIndex[numOfConnections];
for (int i = 0; i < numOfLayers; i++)
{
for (int j = 0; j < layers[i].numOfPreceptors; j++)
{
for (int w = 0; w < layers[i].preceptors[j].GetNumWeights(); w++)
{
connectionIndex[idx] = new ConnectionIndex(i, j, w);
idx += 1;
}
}
}
}
