/// <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);
}
/// <summary>
/// Randomize a synapse, only randomize those connections that are actually connected.
/// </summary>
/// <param name="network">The network the synapse belongs to.</param>
/// <param name="synapse">The synapse to randomize.</param>
public virtual void Randomize(BasicNetwork network, ISynapse synapse)
{
if (synapse.WeightMatrix != null)
{
bool limited = network.Structure.IsConnectionLimited;
double[][] d = synapse.WeightMatrix.Data;
for (int fromNeuron = 0; fromNeuron < synapse.WeightMatrix.Rows; fromNeuron++)
{
for (int toNeuron = 0; toNeuron < synapse.WeightMatrix.Cols; toNeuron++)
{
if (!limited || network.IsConnected(synapse, fromNeuron, toNeuron))
{
d[fromNeuron][toNeuron] = Randomize(d[fromNeuron][toNeuron]);
}
}
}
}
}
/// <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);
}