public void LoadCSVFileTrainingClass()
{
var path = parameters.TrainFile;
var csvRead = new ReadCSV(new FileStream(path, FileMode.Open), true, CSVFormat.DecimalPoint);
var values = new List <double[]>();
var classes = new List <double[]>();
while (csvRead.Next())
{
values.Add(new double[2] {
csvRead.GetDouble(0), csvRead.GetDouble(1)
});
classes.Add(new double[1] {
csvRead.GetDouble(2)
});
}
csvRead.Close();
var min = parameters.FunctionType == FunctionTypeEnum.Unipolar ? 0d : -1d;
ArgNormalize = new NormalizedField[2] {
new NormalizedField(NormalizationAction.Normalize, "X", values.Max(v => v[0]), values.Min(v => v[0]), 1.0, min),
new NormalizedField(NormalizationAction.Normalize, "Y", values.Max(v => v[1]), values.Min(v => v[1]), 1.0, min)
};
for (int i = 0; i < values.Count; i++)
{
for (int argc = 0; argc < values[i].Length; argc++)
{
values[i][argc] = ArgNormalize[argc].Normalize(values[i][argc]);
}
}
//Normalization(values);
classCount = classes.Select(c => c[0]).Distinct().Count();
var normalizeClasses = new List <double[]>();
for (int i = 0; i < classes.Count; ++i)
{
var newClasses = new double[classCount];
for (int j = 0; j < newClasses.Length; j++)
{
newClasses[j] = min;
}
newClasses[(int)classes[i][0] - 1] = 1;// dodoac normalizacje na -1
normalizeClasses.Add(newClasses);
}
var trainSetCount = (int)((double)values.Count * ((100.0 - 15) / 100));
values.Shuffle();
normalizeClasses.Shuffle();
MyExtensions.ResetStableShuffle();
TrainSet = new BasicNeuralDataSet(values.Take(trainSetCount).ToArray(),
normalizeClasses.Take(trainSetCount).ToArray());
ValidationSet = new BasicNeuralDataSet(values.Skip(trainSetCount).ToArray(),
normalizeClasses.Skip(trainSetCount).ToArray());
}
public void LoadCSVFileTrainingRegg()
{
var path = parameters.TrainFile;
var csvRead = new ReadCSV(new FileStream(path, FileMode.Open), true, CSVFormat.DecimalPoint);
var valuesX = new List <double[]>();
var valuesY = new List <double[]>();
while (csvRead.Next())
{
valuesX.Add(new double[1] {
csvRead.GetDouble(0)
});
valuesY.Add(new double[1] {
csvRead.GetDouble(1)
});
}
csvRead.Close();
var min = parameters.FunctionType == FunctionTypeEnum.Unipolar ? 0d : -1d;
ArgNormalize = new NormalizedField[] { new NormalizedField(NormalizationAction.Normalize, "X", valuesX.Max(v => v[0]), valuesX.Min(v => v[0]), 1.0, min) };
YNormalize = new NormalizedField(NormalizationAction.Normalize, "Y", valuesY.Max(v => v[0]), valuesY.Min(v => v[0]), 1.0, min);
for (int i = 0; i < valuesX.Count; i++)
{
valuesX[i][0] = ArgNormalize[0].Normalize(valuesX[i][0]);
valuesY[i][0] = YNormalize.Normalize(valuesY[i][0]);
}
//if(parameters.FunctionType==FunctionTypeEnum.Bipolar)
// ansMeans = means[0];
//ansfactor = factor[0];
//ansMIn = Min[0];
var trainSetCount = (int)((double)valuesX.Count * ((100.0 - 15) / 100));
valuesX.Shuffle();
valuesY.Shuffle();
MyExtensions.ResetStableShuffle();
TrainSet = new BasicNeuralDataSet(valuesX.Take(trainSetCount).ToArray(),
valuesY.Take(trainSetCount).ToArray());
ValidationSet = new BasicNeuralDataSet(valuesX.Skip(trainSetCount).ToArray(),
valuesY.Skip(trainSetCount).ToArray());
}
