public void Load(string fileName, TransferFunction transFunc)
{
int iWeight = 0;
int iThresh = 0;
StateParse curState = StateParse.Begin;
StreamReader modelFile = new StreamReader(fileName);
while (!modelFile.EndOfStream)
{
string line = modelFile.ReadLine();
if (StateParse.Begin == curState)
{
if (line.Equals("nStartNodes", StringComparison.OrdinalIgnoreCase))
{
curState = StateParse.StartNodes;
}
}
else if (StateParse.StartNodes == curState)
{
if (line.Equals("nEndNodes", StringComparison.OrdinalIgnoreCase))
{
curState = StateParse.EndNodes;
}
else
{
nInputs = int.Parse(line);
}
}
else if (StateParse.EndNodes == curState)
{
if (line.Equals("nWeights", StringComparison.OrdinalIgnoreCase))
{
curState = StateParse.NWeights;
}
else
{
this.nOutputs = int.Parse(line);
this.outputs = new float[nOutputs];
this.weights = new float[nOutputs][];
this.thresholds = new float[nOutputs];
for (int i = 0; i < nOutputs; i++)
{
weights[i] = new float[nInputs];
}
}
}
else if (StateParse.NWeights == curState)
{
int.Parse(line); // == nOutputs * nInputs
curState = StateParse.Weights;
}
else if (StateParse.Weights == curState)
{
if (line.Equals("thresholds", StringComparison.OrdinalIgnoreCase))
{
curState = StateParse.Thresholds;
}
else
{
float w = float.Parse(line);
int iOut = iWeight / nInputs;
int iIn = iWeight % nInputs;
weights[iOut][iIn] = w;
iWeight++;
}
}
else if (StateParse.Thresholds == curState)
{
float t = float.Parse(line);
this.thresholds[iThresh++] = t;
}
}
modelFile.Close();
this.transFunc = transFunc;
}
//construct a node for multiclass (> 2) classification - there are cClass nodes for cClass classification model
//where each node corresponding to each class; i.e., the (iClass) node sums all and only the regression trees for that (iClass) class
public Node(int layerID, int nodeID, int cInputs, int cClass, int iClass)
{
this.layerID = layerID;
this.nodeID = nodeID;
this.Weights = new float[cInputs + 1];
this.Weights[0] = 0.0F;
for (int i = 1; i < cInputs + 1; i++)
{
this.Weights[i] = 0.0F;
if ((i-1) % cClass == iClass) //iClass is zero based but i is one based
{
this.Weights[i] = 1.0F;
}
}
this.transFunc = TransferFunction.Linear;
}
//construct a new node based on corresponding node in the sub-model to handle the additional boosted regression trees
public Node(int layerID, int nodeID, int cInputs, Node curNode, bool fKeepCur, bool fKeepNew)
{
this.layerID = layerID;
this.nodeID = nodeID;
this.Weights = new float[cInputs+1];
for (int i = 0; i < cInputs+1; i++)
{
this.Weights[i] = 0.0F;
if (i < curNode.Weights.Length)
{
if (fKeepCur)
{
this.Weights[i] = curNode.Weights[i];
}
}
else
{
if (fKeepNew)
{
this.Weights[i] = 1.0F;
}
}
}
if (fKeepCur)
{
this.transFunc = curNode.transFunc;
}
else
{
this.transFunc = TransferFunction.Linear;
}
}
private float[] Weights; //Weights[0] is the bias
#endregion Fields
#region Constructors
//construct node from .ini configuration/description
public Node(DataSection dataSec)
{
string[] fields = dataSec.Name.Split(':');
this.layerID = int.Parse(fields[1]);
this.nodeID = int.Parse(fields[2]);
List<float> weightList = new List<float>();
List<int> idxList = new List<int>();
for (int i = 0; i < dataSec.Data.Length; i++)
{
if (dataSec.Data[i].StartsWith("Weight:"))
{
fields = dataSec.Data[i].Split(':');
fields = fields[1].Split('=');
idxList.Add(int.Parse(fields[0]));
weightList.Add(float.Parse(fields[1]));
}
else if (dataSec.Data[i].StartsWith("Type="))
{
fields = dataSec.Data[i].Split('=');
if (string.Compare(fields[1], "Tanh", StringComparison.CurrentCultureIgnoreCase) == 0)
{
this.transFunc = TransferFunction.Tanh;
}
else if (string.Compare(fields[1], "Sigmoid", StringComparison.CurrentCultureIgnoreCase) == 0)
{
this.transFunc = TransferFunction.Sigmoid;
}
else if (string.Compare(fields[1], "Linear", StringComparison.CurrentCultureIgnoreCase) == 0)
{
this.transFunc = TransferFunction.Linear;
}
else if (string.Compare(fields[1], "Logistic", StringComparison.CurrentCultureIgnoreCase) == 0)
{
this.IsLogistic = true;
}
}
}
this.Weights = new float[weightList.Count];
for (int i = 0; i < this.Weights.Length; i++)
{
this.Weights[idxList[i]] = weightList[i];
}
}
