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>
/// Construct a network analyze class. Analyze the specified network.
/// </summary>
///
/// <param name="network">The network to analyze.</param>
public AnalyzeNetwork(BasicNetwork network)
{
IList<Double> biasList = new List<Double>();
IList<Double> weightList = new List<Double>();
IList<Double> allList = new List<Double>();
for (int layerNumber = 0; layerNumber < network.LayerCount - 1; layerNumber++)
{
int fromCount = network.GetLayerNeuronCount(layerNumber);
int fromBiasCount = network
.GetLayerTotalNeuronCount(layerNumber);
int toCount = network.GetLayerNeuronCount(layerNumber + 1);
// weights
for (int fromNeuron = 0; fromNeuron < fromCount; fromNeuron++)
{
for (int toNeuron = 0; toNeuron < toCount; toNeuron++)
{
double v = network.GetWeight(layerNumber, fromNeuron,
toNeuron);
weightList.Add(v);
allList.Add(v);
}
}
// bias
if (fromCount != fromBiasCount)
{
int biasNeuron = fromCount;
for (int toNeuron = 0; toNeuron < toCount; toNeuron++)
{
double v = network.GetWeight(layerNumber, biasNeuron,
toNeuron);
biasList.Add(v);
allList.Add(v);
}
}
}
_disabledConnections = 0;
_totalConnections = 0;
_weights = new NumericRange(weightList);
_bias = new NumericRange(biasList);
_weightsAndBias = new NumericRange(allList);
_weightValues = EngineArray.ListToDouble(weightList);
_allValues = EngineArray.ListToDouble(allList);
_biasValues = EngineArray.ListToDouble(biasList);
}
/// <summary>
/// Construct a network analyze class. Analyze the specified network.
/// </summary>
///
/// <param name="network">The network to analyze.</param>
public AnalyzeNetwork(BasicNetwork network)
{
int assignDisabled = 0;
int assignedTotal = 0;
IList<Double> biasList = new List<Double>();
IList<Double> weightList = new List<Double>();
IList<Double> allList = new List<Double>();
for (int layerNumber = 0; layerNumber < network.LayerCount - 1; layerNumber++)
{
int fromCount = network.GetLayerNeuronCount(layerNumber);
int fromBiasCount = network
.GetLayerTotalNeuronCount(layerNumber);
int toCount = network.GetLayerNeuronCount(layerNumber + 1);
// weights
for (int fromNeuron = 0; fromNeuron < fromCount; fromNeuron++)
{
for (int toNeuron = 0; toNeuron < toCount; toNeuron++)
{
double v = network.GetWeight(layerNumber, fromNeuron,
toNeuron);
if (network.Structure.ConnectionLimited )
{
if (Math.Abs(v) < network.Structure.ConnectionLimit )
{
assignDisabled++;
}
}
weightList.Add(v);
allList.Add(v);
assignedTotal++;
}
}
// bias
if (fromCount != fromBiasCount)
{
int biasNeuron = fromCount;
for (int toNeuron = 0; toNeuron < toCount; toNeuron++)
{
double v = network.GetWeight(layerNumber, biasNeuron,
toNeuron);
if (network.Structure.ConnectionLimited)
{
if (Math.Abs(v) < network.Structure.ConnectionLimit)
{
assignDisabled++;
}
}
biasList.Add(v);
allList.Add(v);
assignedTotal++;
}
}
}
_disabledConnections = assignDisabled;
_totalConnections = assignedTotal;
_weights = new NumericRange(weightList);
_bias = new NumericRange(biasList);
_weightsAndBias = new NumericRange(allList);
_weightValues = EngineArray.ListToDouble(weightList);
_allValues = EngineArray.ListToDouble(allList);
_biasValues = EngineArray.ListToDouble(biasList);
}
/// <summary>
/// Construct a network analyze class. Analyze the specified network.
/// </summary>
/// <param name="network">The network to analyze.</param>
public AnalyzeNetwork(BasicNetwork network)
{
int assignDisabled = 0;
int assignedTotal = 0;
IList<double> biasList = new List<double>();
IList<double> weightList = new List<double>();
IList<double> allList = new List<double>();
foreach (ILayer layer in network.Structure.Layers)
{
if (layer.HasBias)
{
for (int i = 0; i < layer.NeuronCount; i++)
{
biasList.Add(layer.BiasWeights[i]);
allList.Add(layer.BiasWeights[i]);
}
}
}
foreach (ISynapse synapse in network.Structure.Synapses)
{
if (synapse.MatrixSize > 0)
{
for (int from = 0; from < synapse.FromNeuronCount; from++)
{
for (int to = 0; to < synapse.ToNeuronCount; to++)
{
if (network.IsConnected(synapse, from, to))
{
double d = synapse.WeightMatrix[from, to];
weightList.Add(d);
allList.Add(d);
}
else
{
assignDisabled++;
}
assignedTotal++;
}
}
}
}
this.disabledConnections = assignDisabled;
this.totalConnections = assignedTotal;
this.weights = new NumericRange(weightList);
this.bias = new NumericRange(biasList);
this.weightsAndBias = new NumericRange(allList);
this.weightValues = EngineArray.ListToDouble(weightList);
this.allValues = EngineArray.ListToDouble(allList);
this.biasValues = EngineArray.ListToDouble(biasList);
}
