| public GetLayerTotalNeuronCount ( int l ) : int | ||
| l | int | The layer. |
| Результат | int | |
public AnalyzeNetwork(BasicNetwork network)
{
int num3;
int num4;
int layerTotalNeuronCount;
int layerNeuronCount;
int num7;
int num8;
double num9;
int num10;
int num11;
double num12;
int num = 0;
int num2 = 0;
IList<double> list = new List<double>();
IList<double> list2 = new List<double>();
if (0 != 0)
{
goto Label_0115;
}
IList<double> values = new List<double>();
if ((((uint) num2) | 1) != 0)
{
num3 = 0;
goto Label_00C9;
}
Label_000B:
this._xd16d54155d6ebc35 = new NumericRange(values);
this._x8158512e31b17fc4 = EngineArray.ListToDouble(list2);
this._x7cd672b98e9d2817 = EngineArray.ListToDouble(values);
this._x5933bfade0487265 = EngineArray.ListToDouble(list);
Label_003D:
if (((uint) layerNeuronCount) >= 0)
{
}
return;
Label_0057:
if ((((uint) num2) + ((uint) num2)) < 0)
{
goto Label_0317;
}
this._x465229d781237721 = num2;
if (((uint) layerTotalNeuronCount) > uint.MaxValue)
{
goto Label_003D;
}
this._x2f33d779e5a20b28 = new NumericRange(list2);
if ((((uint) num3) + ((uint) layerTotalNeuronCount)) > uint.MaxValue)
{
goto Label_01E8;
}
this._x232c44e69c86297f = new NumericRange(list);
if ((((uint) num11) + ((uint) layerTotalNeuronCount)) <= uint.MaxValue)
{
goto Label_000B;
}
goto Label_01F2;
Label_00C3:
num3++;
Label_00C9:
if (num3 < (network.LayerCount - 1))
{
goto Label_0317;
}
this._x0dcd8230e4ec0670 = num;
goto Label_0057;
Label_00FF:
if (num11 < layerNeuronCount)
{
num12 = network.GetWeight(num3, num10, num11);
goto Label_0127;
}
goto Label_00C3;
Label_0115:
values.Add(num12);
num2++;
if (0 == 0)
{
num11++;
if (((uint) num9) < 0)
{
goto Label_02BF;
}
goto Label_00FF;
}
goto Label_0184;
Label_0127:
if (!network.Structure.ConnectionLimited)
{
goto Label_014B;
}
Label_0134:
if (Math.Abs(num12) < network.Structure.ConnectionLimit)
{
num++;
if ((((uint) num8) - ((uint) num12)) >= 0)
{
goto Label_0167;
}
goto Label_000B;
}
Label_014B:
list.Add(num12);
if ((((uint) num7) & 0) == 0)
{
goto Label_0115;
}
Label_0167:
if ((((uint) num2) & 0) == 0)
{
goto Label_014B;
}
goto Label_0127;
Label_0184:
if (4 != 0)
{
goto Label_00C3;
}
goto Label_0057;
Label_01E8:
num11 = 0;
goto Label_00FF;
Label_01F2:
num8++;
Label_01F8:
if (num8 < layerNeuronCount)
{
goto Label_02BF;
}
num7++;
Label_0207:
if (num7 < num4)
{
num8 = 0;
goto Label_01F8;
}
if (((((uint) num8) | 1) != 0) && (num4 == layerTotalNeuronCount))
{
goto Label_0184;
}
num10 = num4;
goto Label_01E8;
Label_02BF:
num9 = network.GetWeight(num3, num7, num8);
if (0 == 0)
{
if (network.Structure.ConnectionLimited && (((((uint) num10) + ((uint) num)) < 0) || (Math.Abs(num9) < network.Structure.ConnectionLimit)))
{
num++;
}
list2.Add(num9);
}
values.Add(num9);
num2++;
goto Label_01F2;
Label_0317:
num4 = network.GetLayerNeuronCount(num3);
layerTotalNeuronCount = network.GetLayerTotalNeuronCount(num3);
if (((uint) num7) > uint.MaxValue)
{
goto Label_0134;
}
layerNeuronCount = network.GetLayerNeuronCount(num3 + 1);
num7 = 0;
if ((((uint) layerTotalNeuronCount) + ((uint) layerTotalNeuronCount)) >= 0)
{
}
goto Label_0207;
}
/// <summary>
/// Randomize the connections between two layers.
/// </summary>
/// <param name="network">The network to randomize.</param>
/// <param name="fromLayer">The starting layer.</param>
private void RandomizeSynapse(BasicNetwork network, int fromLayer)
{
int toLayer = fromLayer + 1;
int toCount = network.GetLayerNeuronCount(toLayer);
int fromCount = network.GetLayerNeuronCount(fromLayer);
int fromCountTotalCount = network.GetLayerTotalNeuronCount(fromLayer);
IActivationFunction af = network.GetActivation(toLayer);
double low = CalculateRange(af, Double.NegativeInfinity);
double high = CalculateRange(af, Double.PositiveInfinity);
double b = 0.7d * Math.Pow(toCount, (1d / fromCount)) / (high - low);
for (int toNeuron = 0; toNeuron < toCount; toNeuron++)
{
if (fromCount != fromCountTotalCount)
{
double w = RangeRandomizer.Randomize(-b, b);
network.SetWeight(fromLayer, fromCount, toNeuron, w);
}
for (int fromNeuron = 0; fromNeuron < fromCount; fromNeuron++)
{
double w = RangeRandomizer.Randomize(0, b);
network.SetWeight(fromLayer, fromNeuron, toNeuron, w);
}
}
}
/// <summary>
/// Randomize one level of a neural network.
/// </summary>
///
/// <param name="network">The network to randomize</param>
/// <param name="fromLayer">The from level to randomize.</param>
public override void Randomize(BasicNetwork network, int fromLayer)
{
int fromCount = network.GetLayerTotalNeuronCount(fromLayer);
int toCount = network.GetLayerNeuronCount(fromLayer + 1);
for (int fromNeuron = 0; fromNeuron < fromCount; fromNeuron++)
{
for (int toNeuron = 0; toNeuron < toCount; toNeuron++)
{
double v = CalculateValue(toCount);
network.SetWeight(fromLayer, fromNeuron, toNeuron, v);
}
}
}
/// <summary>
/// Randomize one level of a neural network.
/// </summary>
///
/// <param name="network">The network to randomize</param>
/// <param name="fromLayer">The from level to randomize.</param>
public virtual void Randomize(BasicNetwork network, int fromLayer)
{
int fromCount = network.GetLayerTotalNeuronCount(fromLayer);
int toCount = network.GetLayerNeuronCount(fromLayer + 1);
for (int fromNeuron = 0; fromNeuron < fromCount; fromNeuron++)
{
for (int toNeuron = 0; toNeuron < toCount; toNeuron++)
{
double v = network.GetWeight(fromLayer, fromNeuron, toNeuron);
v = Randomize(v);
network.SetWeight(fromLayer, fromNeuron, toNeuron, v);
}
}
}
/// <summary>
/// Randomize one level of a neural network.
/// </summary>
///
/// <param name="network">The network to randomize</param>
/// <param name="fromLayer">The from level to randomize.</param>
public override void Randomize(BasicNetwork network, int fromLayer)
{
int fromCount = network.GetLayerTotalNeuronCount(fromLayer);
int toCount = network.GetLayerNeuronCount(fromLayer + 1);
for (int toNeuron = 0; toNeuron < toCount; toNeuron++)
{
double n = 0.0;
for (int fromNeuron = 0; fromNeuron < fromCount; fromNeuron++)
{
double w = network.GetWeight(fromLayer, fromNeuron, toNeuron);
n += w * w;
}
n = Math.Sqrt(n);
for (int fromNeuron = 0; fromNeuron < fromCount; fromNeuron++)
{
double w = network.GetWeight(fromLayer, fromNeuron, toNeuron);
w = _beta * w / n;
network.SetWeight(fromLayer, fromNeuron, toNeuron, w);
}
}
}
public override void Randomize(BasicNetwork network, int fromLayer)
{
int num2;
int num3;
double num4;
int num5;
double num6;
int num7;
double num8;
int layerTotalNeuronCount = network.GetLayerTotalNeuronCount(fromLayer);
goto Label_00DF;
Label_0011:
if (num3 < num2)
{
num4 = 0.0;
num5 = 0;
}
else if ((((uint) num8) - ((uint) layerTotalNeuronCount)) >= 0)
{
return;
}
while (true)
{
if (num5 >= layerTotalNeuronCount)
{
num4 = Math.Sqrt(num4);
num7 = 0;
if ((((uint) num4) + ((uint) num2)) < 0)
{
break;
}
goto Label_0065;
}
num6 = network.GetWeight(fromLayer, num5, num3);
num4 += num6 * num6;
num5++;
}
Label_0044:
if ((((uint) fromLayer) + ((uint) num6)) > uint.MaxValue)
{
goto Label_00DF;
}
num7++;
Label_0065:
if (num7 < layerTotalNeuronCount)
{
num8 = network.GetWeight(fromLayer, num7, num3);
}
else
{
num3++;
goto Label_0011;
}
Label_009C:
num8 = (this._xd7d571ecee49d1e4 * num8) / num4;
network.SetWeight(fromLayer, num7, num3, num8);
goto Label_0044;
Label_00DF:
num2 = network.GetLayerNeuronCount(fromLayer + 1);
if (((uint) num8) > uint.MaxValue)
{
goto Label_009C;
}
num3 = 0;
goto Label_0011;
}
public override void Randomize(BasicNetwork network, int fromLayer)
{
int num4;
double num5;
int layerTotalNeuronCount = network.GetLayerTotalNeuronCount(fromLayer);
int layerNeuronCount = network.GetLayerNeuronCount(fromLayer + 1);
int fromNeuron = 0;
if (((uint) fromLayer) < 0)
{
goto Label_0012;
}
Label_000E:
if (fromNeuron < layerTotalNeuronCount)
{
num4 = 0;
goto Label_0054;
}
Label_0012:
if (((uint) num5) > uint.MaxValue)
{
goto Label_0054;
}
if ((((uint) fromNeuron) + ((uint) fromNeuron)) >= 0)
{
return;
}
Label_003C:
num5 = this.x7417261f548b2c9b(layerNeuronCount);
network.SetWeight(fromLayer, fromNeuron, num4, num5);
num4++;
Label_0054:
if (num4 < layerNeuronCount)
{
goto Label_003C;
}
fromNeuron++;
goto Label_000E;
}
public virtual void Randomize(BasicNetwork network, int fromLayer)
{
int num4;
double num5;
int layerTotalNeuronCount = network.GetLayerTotalNeuronCount(fromLayer);
int layerNeuronCount = network.GetLayerNeuronCount(fromLayer + 1);
int fromNeuron = 0;
goto Label_002C;
Label_000D:
fromNeuron++;
if ((((uint) fromNeuron) + ((uint) fromNeuron)) > uint.MaxValue)
{
goto Label_004B;
}
if (0 != 0)
{
goto Label_003C;
}
Label_002C:
if (fromNeuron < layerTotalNeuronCount)
{
goto Label_0067;
}
return;
Label_003C:
network.SetWeight(fromLayer, fromNeuron, num4, num5);
num4++;
Label_004B:
if (num4 < layerNeuronCount)
{
num5 = network.GetWeight(fromLayer, fromNeuron, num4);
if (((uint) num5) >= 0)
{
num5 = this.Randomize(num5);
goto Label_003C;
}
goto Label_000D;
}
if ((((uint) fromLayer) + ((uint) fromLayer)) >= 0)
{
goto Label_000D;
}
Label_0067:
num4 = 0;
goto Label_004B;
}
