/// <summary>
/// Before we start GP prepare all neccessery information
/// </summary>
/// <param name="termSet"></param>
/// <param name="funSet"></param>
/// <param name="gpParams"></param>
public void PrepareAlgorithm(GPTerminalSet termSet, GPFunctionSet funSet, GPParameters gpParams = null)
{
m_IterationCounter = 0;
if (Population == null)
{
Population = new CHPopulation();
}
Population.InitPopulation(termSet, funSet, gpParams);
Population.CalculatePopulation();
IsAlgorthmPrepared = true;
StopIteration = false;
//calculate model and prediction
GPChromosome ch = Population.bestChromosome as GPChromosome;
double[][] model = CalculateModel(ch);
double[][] prediction = CalculateModel(ch, false);
//Report the evolution has been started
if (ReportEvolution != null)
{
ReportEvolution(this,
new ProgressIndicatorEventArgs()
{
ReportType = ProgramState.Started,
AverageFitness = Population.fitnessAvg,
BestChromosome = Population.bestChromosome,
CurrentIteration = 0, LearnOutput = model, PredicOutput = prediction
});
}
}
private void GenerateTerminals(IForecastingDataSets datasets, double[] gpConstants)
{
if (terminalSet == null)
{
terminalSet = new GPTerminalSet();
}
if (gpConstants == null)
{
gpConstants = GenerateConstants(mGPModelParameter.ConstantsIntervalFrom, mGPModelParameter.ConstantsIntervalTo, mGPModelParameter.ConstantsNumber);
}
//Kada znamo broj konstanti i podatke o experimenti sada mozemo popuniti podatke
terminalSet.NumConstants = mGPModelParameter.ConstantsNumber;
terminalSet.NumVariables = NumberOfVariables;
terminalSet.RowCount = NumberOfSamples;
terminalSet.TrainingData = new double[terminalSet.RowCount][];
int numOfVariables = terminalSet.NumVariables + terminalSet.NumConstants + 1 /*Output Value of experiment*/;
for (int i = 0; i < terminalSet.RowCount; i++)
{
terminalSet.TrainingData[i] = new double[numOfVariables];
for (int j = 0; j < numOfVariables; j++)
{
if (j < terminalSet.NumVariables)
{
terminalSet.TrainingData[i][j] = datasets.InputData[i][j];
}
else if (j >= terminalSet.NumVariables && j < numOfVariables - 1)
{
terminalSet.TrainingData[i][j] = gpConstants[j - terminalSet.NumVariables];
}
else
{
terminalSet.TrainingData[i][j] = datasets.OutputData[i][0];
}
}
}
terminalSet.CalculateStat();
TerminateExperiments();
}
/// <summary>
///
/// </summary>
/// <param name="gpTrainigData"></param>
/// <param name="consts"></param>
/// <param name="gpTestingData"></param>
/// <returns></returns>
public static GPTerminalSet GenerateTRANSPORTTerminalSet(double[][] gpTrainigData)
{
GPTerminalSet terminalSet = new GPTerminalSet();
//First define the initial properties of the terminalset
terminalSet.NumConstants = 0;
terminalSet.NumVariables = gpTrainigData[0].Length - 1;
terminalSet.RowCount = gpTrainigData.Length - 1;
//Generate training data
terminalSet.TrainingData = new double[terminalSet.RowCount][];
//
terminalSet.Destinations = new int[terminalSet.NumVariables];
terminalSet.Sources = new int[terminalSet.RowCount];
for (int i = 0; i < gpTrainigData.Length; i++)
{
if (i == 0)
{
for (int j = 1; j < gpTrainigData[i].Length; j++)
{
terminalSet.Destinations[j - 1] = (int)gpTrainigData[i][j];
}
}
else
{
terminalSet.TrainingData[i - 1] = new double[terminalSet.NumVariables];
for (int j = 0; j < gpTrainigData[i].Length; j++)
{
if (j == 0)
{
terminalSet.Sources[i - 1] = (int)gpTrainigData[i][j];
}
else
{
terminalSet.TrainingData[i - 1][j - 1] = gpTrainigData[i][j];
}
}
}
}
return(terminalSet);
}
/// <summary>
///
/// </summary>
/// <param name="gpTrainigData"></param>
/// <param name="consts"></param>
/// <param name="gpTestingData"></param>
/// <returns></returns>
public static GPTerminalSet GenerateALOCTerminalSet(double[][] gridLocation)
{
GPTerminalSet terminalSet = new GPTerminalSet();
//First define the initial properties of the terminalset
terminalSet.NumConstants = 0;
terminalSet.NumVariables = gridLocation[0].Length;
terminalSet.RowCount = gridLocation.Length;
//Generate training data
terminalSet.TrainingData = new double[terminalSet.RowCount][];
for (int j = 0; j < terminalSet.RowCount; j++)
{
terminalSet.TrainingData[j] = new double[terminalSet.NumVariables];
for (int i = 0; i < terminalSet.NumVariables; i++)
{
terminalSet.TrainingData[j][i] = gridLocation[j][i];
}
}
return(terminalSet);
}
/// <summary>
///
/// </summary>
/// <param name="gpTrainigData"></param>
/// <param name="consts"></param>
/// <param name="gpTestingData"></param>
/// <returns></returns>
public static GPTerminalSet GenerateTSPTerminalSet(double[][] tspMapCity)
{
GPTerminalSet terminalSet = new GPTerminalSet();
//First define the initial properties of the terminalset
terminalSet.NumConstants = 0;
terminalSet.NumVariables = tspMapCity.Length;
terminalSet.RowCount = tspMapCity.Length;
//Generate training data
terminalSet.TrainingData = new double[terminalSet.RowCount][];
for (int j = 0; j < terminalSet.RowCount; j++)
{
terminalSet.TrainingData[j] = new double[2];
for (int i = 0; i < 2; i++)
{
terminalSet.TrainingData[j][i] = tspMapCity[j][i];
}
}
return(terminalSet);
}
/// <summary>
/// Initialisation of chromosomes in Genetic programming
/// </summary>
/// <param name="size">Size of the chromosomes, default value is 1000</param>
/// <param name="initMethod">Initializastion method, default HalfandHalf</param>
public void InitPopulation(
GPTerminalSet termSet,
GPFunctionSet funSet,
GPParameters gpParams = null
)
{
if (gpParams == null)
{
gpParameters = new GPParameters();
}
else
{
gpParameters = gpParams;
}
fitnessFunction = gpParameters.GPFitness;
if (funSet == null)
{
throw new Exception("FunctionSet is not defined, and cannot be null!");
}
{
functionSet = funSet;
Globals.functions = functionSet;
}
if (funSet == null)
{
throw new Exception("TerminalSet is not defined, and cannot be null!");
}
Globals.terminals = termSet;
GeneratePopulation(gpParameters.popSize);
}
public void SetTerminalSet(GPTerminalSet gpTSet)
{
Population.SetTerminalSet(gpTSet);
}
/// <summary>
///
/// </summary>
/// <param name="gpTrainigData"></param>
/// <param name="consts"></param>
/// <param name="gpTestingData"></param>
/// <returns></returns>
public static GPTerminalSet GenerateTerminalSet(double[][] gpTrainigData, double[] consts, double[][] gpTestingData)
{
int numConst = 0;
if (consts != null)
{
numConst = consts.Length;
}
if (gpTrainigData == null)
{
throw new Exception("Training data cannot be null!");
}
GPTerminalSet terminalSet = new GPTerminalSet();
//First define the initial properties of the terminalset
terminalSet.NumConstants = numConst;
terminalSet.NumVariables = (gpTrainigData[0].Length - 1);
terminalSet.RowCount = gpTrainigData.Length;
int numOfVariables = terminalSet.NumVariables + terminalSet.NumConstants + 1 /*Output Value of experiment*/;
//Generate training data
terminalSet.TrainingData = new double[terminalSet.RowCount][];
for (int j = 0; j < terminalSet.RowCount; j++)
{
terminalSet.TrainingData[j] = new double[numOfVariables];
for (int i = 0; i < numOfVariables; i++)
{
if (i < terminalSet.NumVariables)//input variable
{
terminalSet.TrainingData[j][i] = gpTrainigData[j][i];
}
else if (i >= terminalSet.NumVariables && i < numOfVariables - 1)//constants
{
terminalSet.TrainingData[j][i] = consts[i - terminalSet.NumVariables];
}
else
{
terminalSet.TrainingData[j][i] = gpTrainigData[j][i - terminalSet.NumConstants];//output variable
}
}
}
//
// terminalSet.CalculateStat();
//Generate training data if exist
if (gpTestingData != null)
{
//Generate training data
terminalSet.TestingData = new double[gpTestingData.Length][];
for (int j = 0; j < gpTestingData.Length; j++)
{
terminalSet.TestingData[j] = new double[numOfVariables];
for (int i = 0; i < numOfVariables; i++)
{
if (i < terminalSet.NumVariables)//input variable
{
terminalSet.TestingData[j][i] = gpTestingData[j][i];
}
else if (i >= terminalSet.NumVariables && i < numOfVariables - 1)//constants
{
terminalSet.TestingData[j][i] = consts[i - terminalSet.NumVariables];
}
else
{
terminalSet.TestingData[j][i] = gpTestingData[j][i - terminalSet.NumConstants];
}
}
}
}
return(terminalSet);
}
/// <summary>
///
/// </summary>
/// <param name="tSet"></param>
internal void SetTerminalSet(GPTerminalSet tSet)
{
Globals.gpterminals = tSet;
}
/// <summary>
/// Initialisation of chromosomes in Genetic programming
/// </summary>
/// <param name="size">Size of the chromosomes, default value is 1000</param>
/// <param name="initMethod">Initializastion method, default HalfandHalf</param>
public void InitPopulation(
GPTerminalSet termSet,
IFunctionSet funSet,
GPParameters gpParams = null
)
{
if (gpParams == null)
{
Globals.gpparameters = new GPParameters();
}
else
{
Globals.gpparameters = gpParams;
}
if (Globals.gpparameters.algorithmType == Algorithm.GA)
{
if (Globals.gpparameters.chromosomeKind == GAChromosome.Continue)
{
GANumChromosome.functionSet = funSet;
}
else if (Globals.gpparameters.chromosomeKind == GAChromosome.TSP)
{
GAVChromosome.functionSet = funSet;
}
else if (Globals.gpparameters.chromosomeKind == GAChromosome.ALOC)
{
GAVChromosome.functionSet = funSet;
}
else if (Globals.gpparameters.chromosomeKind == GAChromosome.TP)
{
GAMChromosome.terminalSet = termSet;
GAMChromosome.functionSet = funSet;
}
else
{
GABinChromosome.functionSet = funSet;
}
}
else
{
GPChromosome.MaxOperationLevel = Globals.gpparameters.maxOperationLevel;
}
//init default fitness type
if (Globals.gpparameters.GPFitness == null)
{
Globals.gpparameters.GPFitness = new RMSEFitness();
}
fitnessFunction = Globals.gpparameters.GPFitness;
if (funSet == null)
{
throw new Exception("FunctionSet is not defined, and cannot be null!");
}
else
{
functionSet = funSet;
Globals.functions = functionSet;
}
if (termSet == null)
{
throw new Exception("TerminalSet is not defined, and cannot be null!");
}
else
{
Globals.gpterminals = termSet;
}
var siz = Globals.gpparameters.popSize - chromosomes.Count;
if (siz > 0)
{
GeneratePopulation(siz);
}
}
