public ANNParameters()
{
//
m_LearningAlgo = LearningAlgoritm.BP;
m_PSOParameters = new PSOParameters();
m_IsClasificationProblem = false;
m_Momentum = 0.4;
m_inputNormalization = NormalizationType.MinMax;
m_outputNormalization = NormalizationType.MinMax;
m_ActFunction = new Sigmoid(1.0);
m_ActFuncParam1 = 1.0;
m_LearningRate = 0.1;
m_NumHiddenLayers = 1;
m_NeuronsInHiddenLayer = 50;
}
/// <summary>
/// Before we start solver prepare all neccessery information
/// </summary>
/// <param name="termSet"></param>
/// <param name="funSet"></param>
/// <param name="annParams"></param>
public override void PrepareAlgorithm(Experiment expData, ANNParameters annParams = null)
{
if (annParams == null || expData == null)
{
throw new Exception("Argument value cannot be null");
}
//reset iteration and network
if (m_Network == null)
{
m_IterationCounter = 0;
//depending on the type of the colum create adequate neural network
var colType = expData.GetOutputColumnType();
if (colType == ColumnDataType.Binary)//Binary Clasification
{
m_Network = new BCNeuralNetwork(annParams, expData.GetColumnInputCount_FromNormalizedValue(), expData.GetColumnOutputCount_FromNormalizedValue());
}
else//multiclass classification
{
m_Network = new MCNeuralNetwork(annParams, expData.GetColumnInputCount_FromNormalizedValue(), expData.GetColumnOutputCount_FromNormalizedValue());
}
m_Network.InitializeNetwork();
}
//
m_Experiment = expData;
PSOParameters swarm = null;
if (m_Parameters != null)
{
swarm = m_Parameters.m_PSOParameters;
}
else
{
m_psoAlgorithm = null;
swarm = annParams.m_PSOParameters;
}
//
m_Parameters = annParams;
m_Parameters.m_PSOParameters = swarm;
m_expRowCount = m_Experiment.GetRowCount();
IsAlgorthmPrepared = true;
StopIteration = false;
float newfitness = 0;
if (m_psoAlgorithm == null)
{
//initilaize swarm
m_Parameters.m_PSOParameters.m_Dimension = m_Network.GetWeightsAndBiasCout();
m_psoAlgorithm = new ParticleSwarm(m_Parameters.m_PSOParameters, CrossEntropy);
//init
newfitness = m_psoAlgorithm.InitSwarm();
}
// else
newfitness = m_psoAlgorithm.RunSwarm();
var model = CalculateModel(false);
//Send report for iteration
var rp = new ProgressIndicatorEventArgs()
{
ReportType = ProgramState.Started,
LearningError = newfitness,
CurrentIteration = 0,
LearnOutput = model,
};
ReportProgress(rp);
}
