public virtual void StudyByGroups(int group)
{
NeuronGroup currGroup = NeuronGroups[group];
float dE_dOUT = float.MinValue;
float dOUT_dNET;
float p;
for (int i = 0; i < currGroup.Neurons.Count; i++ /*, dE_dOUT = float.MinValue*/)
{
if (currGroup == LastNeuronGroup)
{
dE_dOUT = currGroup.Neurons[i].OUT - currGroup.Neurons[i].studyValue;
}
else
{
dE_dOUT = currGroup.Neurons[i].dE_dX();
}
dOUT_dNET = currGroup.Neurons[i].dOUT_dNET();
for (int j = 0; j < currGroup.Neurons[i].sinapses.Count; j++)
{
currGroup.Neurons[i].sinapses[j].dE_dx = dE_dOUT * dOUT_dNET * currGroup.Neurons[i].sinapses[j].value;
p = -E *dE_dOUT *dNETdw(currGroup.Neurons[i].sinapses[j]) * dOUT_dNET;
currGroup.Neurons[i].sinapses[j].value += p + moment * currGroup.Neurons[i].sinapses[j].priv_direction;
currGroup.Neurons[i].sinapses[j].priv_direction = p;
}
p = -E * dE_dOUT * dOUT_dNET;
currGroup.Neurons[i].Shift += p + moment * currGroup.Neurons[i].priv_direction;
currGroup.Neurons[i].priv_direction = p;
}
}
public override void StudyByGroups(int group)
{
float Ew, Ec, Er;
Ew = 0.8f;
Ec = 0.0001f;
Er = E;
float dE_dOut;
NeuronGroup currGroup = NeuronGroups[group];
if (currGroup == LastNeuronGroup)
{
for (int i = 0; i < currGroup.Neurons.Count; i++)
{
dE_dOut = currGroup.Neurons[i].OUT - currGroup.Neurons[i].studyValue;
for (int j = 0; j < currGroup.Neurons[i].sinapses.Count; j++)
{
currGroup.Neurons[i].sinapses[j].dE_dx = dE_dOut;
}
}
}
else
{
float[] dc = new float[InputsCount];
float da, dw;
Neuron n;
for (int i = 0; i < currGroup.Neurons.Count; i++)
{
n = currGroup.Neurons[i];
dE_dOut = -n.dE_dX();
dw = n.OUT;
da = -n.aksons[0].value * n.OUT * n.NET;
for (int j = 0; j < InputsCount; j++)
{
dc[j] = 2 * n.aksons[0].value * n.OUT * n.A * (Inputs[j].value - n.Centers[j]);
}
n.A += Er * da * dE_dOut;
n.A = Math.Abs(n.A);
for (int j = 0; j < InputsCount; j++)
{
n.Centers[j] += Ec * dc[j] * dE_dOut;
if (n.Centers[j] > 1)
{
n.Centers[j] += 1.2f * (1 - n.Centers[j]);
}
if (n.Centers[j] < 0)
{
n.Centers[j] += -1.2f * n.Centers[j];
}
}
n.aksons[0].value += Ew * dw * dE_dOut;
}
}
}
public Neuron(Neuron obj, NeuronGroup group)
{
positionChanged = obj.positionChanged;
Shift = (float)rand.Next(-500, 500) / 1000;
activationFunction = obj.activationFunction;
A = obj.A;
active = obj.active;
wasPainted = obj.wasPainted;
NeuronGroup = group;
}
public void SetSelection(NeuronGroup group)
{
for (int i = 0; i < currentSelection.Neurons.Count; i++)
{
currentSelection.Neurons[i].active = false;
}
currentSelection = group;
for (int i = 0; i < group.Neurons.Count; i++)
{
group.Neurons[i].active = true;
}
}
public NeuronGroup GetNeuronsByRectangle(Rectangle rect)
{
NeuronGroup group = new NeuronGroup(0);
for (int i = 0; i < NeuronGroups.Count; i++)
{
for (int j = 0; j < NeuronGroups[i].Neurons.Count; j++)
{
if (rect.Contains(new Point((int)NeuronGroups[i].Neurons[j].Position.X, (int)NeuronGroups[i].Neurons[j].Position.Y)))
{
group.Neurons.Add(NeuronGroups[i].Neurons[j]);
}
}
}
return(group);
}
public Neuron(Neuron obj, NeuronGroup group)
{
if (obj != null)
{
positionChanged = obj.positionChanged;
activationFunction = obj.activationFunction;
A = obj.A;
active = obj.active;
wasPainted = obj.wasPainted;
}
else
{
activationFunction = DefaultActivationFunction;
}
Shift = (float)rand.Next(-500, 500) / 1000;
NeuronGroup = group;
}
public Neuron(NeuronGroup group)
{
NeuronGroup = group;
activationFunction = DefaultActivationFunction;
}
private NeuronNet AcceptJson(Root2 root2)
{
NeuronNet net = new NeuronNet();
for (int i = 0; i < root2.inputss.Count; i++)
{
PointF pointF = new PointF();
pointF.X = (float)root2.inputss[i].Position.X;
pointF.Y = (float)root2.inputss[i].Position.Y;
NeuronInput neuron = new NeuronInput((float)root2.inputss[i].value, root2.inputss[i].Name, pointF);
neuron.positionChanged = root2.inputss[i].positionChanged;
neuron.wasPainted = root2.inputss[i].wasPainted;
net.inputss.Add(neuron);
}
for (int i = 0; i < root2.NeuronGroups.Count; i++)
{
NeuronGroup neurongroup = new NeuronGroup();
neurongroup.Neurons = root2.NeuronGroups[i].Neurons;
neurongroup.SecondActivate = root2.NeuronGroups[i].SecondActivate;
neurongroup.SumForSoftMax = (float)root2.NeuronGroups[i].SumForSoftMax;
neurongroup.allNeuronsWasPainted = root2.NeuronGroups[i].allNeuronsWasPainted;
net.NeuronGroups.Add(neurongroup);
}
for (int i = 0; i < root2.studyPairss.Count; i++)
{
StudyPair studyPair = new StudyPair();
for (int j = 0; j < root2.studyPairss[i].inputs.Count; j++)
{
studyPair.inputs.Add((float)root2.studyPairss[i].inputs[j]);
}
for (int j = 0; j < root2.studyPairss[i].quits.Count; j++)
{
studyPair.quits.Add((float)root2.studyPairss[i].quits[j]);
}
for (int j = 0; j < root2.studyPairss[i].realQuits.Count; j++)
{
studyPair.realQuits.Add((float)root2.studyPairss[i].realQuits[j]);
}
net.studyPairss.Add(studyPair);
}
net.E = (float)root2.E;
net.moment = (float)root2.moment;
for (int i = 0; i < root2.errors.Count; i++)
{
PointF pointF = new PointF();
pointF.X = (float)root2.errors[i].X;
pointF.Y = (float)root2.errors[i].Y;
net.errors.Add(pointF);
}
for (int i = 0; i < root2.normalizedErrors.Count; i++)
{
PointF pointF = new PointF();
pointF.X = (float)root2.normalizedErrors[i].X;
pointF.Y = (float)root2.normalizedErrors[i].Y;
net.normalizedErrors.Add(pointF);
}
net.EraCount = root2.EraCount;
net.currentSelection = new NeuronGroup(0);
net.recognitionResults = new List <RecognitionResult>();
net.StudyPairsLoaded = root2.StudyPairsLoaded;
net.InputsSum = (float)root2.InputsSum;
net.allInputsWasPainted = root2.allInputsWasPainted;
net.minError = (float)root2.minError;
net.NormalizeOutputValue = (float)root2.NormalizeOutputValue;
net.biasX = (float[])root2.biasX;
net.biasY = (float[])root2.biasY;
net.scaleX = (float[])root2.scaleX;
net.scaleY = (float[])root2.scaleY;
net.StudyLimit = (float)root2.StudyLimit;
net.AccessChangeNet = root2.AccessChangeNet;
return(net);
}
