public IForecastingModel TrainNewModel(double[][] iInput, double[][] iOutput)
{
int inputSize = iInput[0].Length, samplesNum = iOutput.Length;
if (samplesNum != iInput.Length)
{
throw new ArgumentException();
}
for (int i = 0; i < samplesNum; ++i)
{
if (iInput[i].Length != inputSize || iOutput[i].Length != 1) //iInput isn't a square matrix or iOutput isn't a vector
{
throw new ArgumentException();
}
}
int[] neuronsCount = (int[])ModelParametersDict[NeuronsInLayersKey];
string activationFunction = (string)ModelParametersDict[ActivationFunctionKey];
long maxIterNum = (long)ModelParametersDict[MaxIterationsNumberKey];
double stopError = (double)ModelParametersDict[StopErrorKey];
ActivationNetwork netToTrain = new ActivationNetwork(ActivationFunctionsDict[activationFunction], inputSize, neuronsCount);
DataNormalizer normalizer = new DataNormalizer(iInput.Concat(iOutput).ToArray());
IForecastingModel aModel = new ANNforecastingModel(netToTrain, normalizer);
ISupervisedLearning teacher = new ResilientBackpropagationLearning(netToTrain);
double[][] trainInputSet, trainOutputSet;
TrainingSubsetGenerator.GenerateRandomly(iInput, iOutput, out trainInputSet, out trainOutputSet, iMultiplier: TrainSubsetMultiplier);
trainInputSet = normalizer.Normalize(trainInputSet); trainOutputSet = normalizer.Normalize(trainOutputSet);
long epochsCount = 0;
double nextError = ErrorCalculator.CalculateMSE(aModel, iInput, iOutput), prevError;
do
{
prevError = nextError;
teacher.RunEpoch(trainInputSet, trainOutputSet);
nextError = ErrorCalculator.CalculateMSE(aModel, iInput, iOutput);
}while (epochsCount++ <= maxIterNum && Math.Abs(prevError - nextError) >= stopError);
return(aModel);
}
public IForecastingModel TrainNewModel(double[][] iInput, double[][] iOutput)
{
int inputSize = iInput[0].Length, samplesNum = iOutput.Length;
if (samplesNum != iInput.Length)
throw new ArgumentException();
for (int i = 0; i < samplesNum;++i)
if (iInput[i].Length != inputSize || iOutput[i].Length != 1) //iInput isn't a square matrix or iOutput isn't a vector
throw new ArgumentException();
int[] neuronsCount = (int[]) ModelParametersDict[NeuronsInLayersKey];
string activationFunction = (string) ModelParametersDict[ActivationFunctionKey];
long maxIterNum = (long) ModelParametersDict[MaxIterationsNumberKey];
double stopError = (double)ModelParametersDict[StopErrorKey];
ActivationNetwork netToTrain = new ActivationNetwork(ActivationFunctionsDict[activationFunction], inputSize, neuronsCount);
DataNormalizer normalizer = new DataNormalizer(iInput.Concat(iOutput).ToArray());
IForecastingModel aModel = new ANNforecastingModel(netToTrain, normalizer);
ISupervisedLearning teacher = new ResilientBackpropagationLearning(netToTrain);
double[][] trainInputSet, trainOutputSet;
TrainingSubsetGenerator.GenerateRandomly(iInput, iOutput, out trainInputSet, out trainOutputSet, iMultiplier: TrainSubsetMultiplier);
trainInputSet = normalizer.Normalize(trainInputSet); trainOutputSet = normalizer.Normalize(trainOutputSet);
long epochsCount = 0;
double nextError = ErrorCalculator.CalculateMSE(aModel, iInput, iOutput), prevError;
do
{
prevError = nextError;
teacher.RunEpoch(trainInputSet, trainOutputSet);
nextError = ErrorCalculator.CalculateMSE(aModel, iInput, iOutput);
}
while (epochsCount++ <= maxIterNum && Math.Abs(prevError - nextError) >= stopError);
return aModel;
}
